A control method for a saline-alkali soil improvement water quality desalination system

Abstract

The present application relates to the technical field of saline-alkali soil improvement, and particularly relates to a control method for a water quality desalination system for saline-alkali soil improvement. The method comprises the following steps: accurately determining the degree of soil salinization by comprehensively analyzing the multi-point sampling data of the saline-alkali soil and the ion concentration of the soil; comprehensively determining the linkage degradation characteristics and the ecological imbalance of the saline-alkali soil based on the evaluation of the microstructure disintegration trend and the limited plant metabolism according to the degree of soil salinization; further determining the degree of degradation of the saline-alkali groundwater, and implementing targeted saline-alkali soil improvement treatment through the water quality desalination system according to the degree of degradation; finally, evaluating the system defects based on the improvement treatment effect, and optimizing the control of the water quality desalination system according to the defect data; the present application realizes more efficient and accurate saline-alkali soil water quality regulation through the optimized control of the water quality desalination system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of saline-alkali soil improvement, and particularly relates to a control method for a saline-alkali soil improvement water quality desalination system. BACKGROUND

[0002] The main characteristics of saline-alkali soil are high soil salt content, poor water permeability, shallow groundwater level, and severe soil acid-base degree fluctuation. In the prior art, in order to reduce the salt concentration of the surface soil of the saline-alkali soil, means such as large-scale flooding irrigation, deep ploughing and mixing, laying open channel and buried pipe for salt removal, and applying chemical improvement agents are commonly used, and water sources are processed by means of local water desalination or groundwater regulation to support irrigation. However, in long-term application, it is found that the traditional water desalination or regulation system relies on a single desalination equipment working process, lacks a data regulation mechanism based on real-time ion concentration dynamic feedback of the soil, and cannot automatically adjust the irrigation amount, desalinated water injection concentration, or irrigation cycle according to the position and concentration gradient of the salt-enriched layer, resulting in unstable desalination efficiency, low water quality utilization rate, and even secondary salt return in some areas. However, the traditional saline-alkali soil improvement water quality has the problems of inaccurate detection of the linkage degradation characteristics of the saline-alkali soil and inaccurate detection of the degradation degree of the saline-alkali groundwater source. SUMMARY

[0003] Therefore, it is necessary to provide a control method for a saline-alkali soil improvement water quality desalination system to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a control method for a saline-alkali soil improvement water quality desalination system comprises the following steps:

[0005] Step S1: acquiring saline-alkali soil multi-point sampling data; statistically analyzing saline-alkali soil ion concentration composition data according to the saline-alkali soil multi-point sampling data; and determining the soil salinization degree based on the saline-alkali soil multi-point sampling data and the saline-alkali soil ion concentration composition data;

[0006] Step S2: detecting the soil microstructure synergistic disintegration trend according to the soil salinization degree; determining the plant metabolism limitation trend according to the soil microstructure synergistic disintegration trend; and determining the saline-alkali soil linkage degradation characteristics according to the soil microstructure synergistic disintegration trend and the plant metabolism limitation trend;

[0007] Step S3: determining the saline-alkali soil ecological vitality imbalance condition based on the saline-alkali soil linkage degradation characteristics; determining the saline-alkali groundwater source degradation degree according to the saline-alkali soil ecological vitality imbalance condition and the saline-alkali soil linkage degradation characteristics; and performing saline-alkali soil improvement treatment on the saline-alkali groundwater source degradation degree based on the saline-alkali soil improvement water quality desalination system to obtain the saline-alkali soil improvement treatment condition;

[0008] Step S4: Based on the improvement of saline-alkali land treatment, the defect data of the improved water quality desalination system is obtained by evaluating the defect of the improved water quality desalination system. According to the defect data of the improved water quality desalination system, the improved water quality desalination system is controlled and optimized, and the improved water quality desalination optimization system is obtained.

[0009] The present application realizes accurate determination of the degree of salinization of saline-alkali soil by multi-point sampling and ion concentration composition statistics, and ensures that the improvement measures are based on real and detailed soil conditions. Based on the microstructure and disintegration trend detection of the degree of salinization, combined with the limited plant metabolism, the internal degradation mechanism of saline-alkali soil is fully revealed, which promotes the in-depth understanding of the degradation process. Therefore, the present application solves the problems of inaccurate detection of the linkage degradation characteristics of saline-alkali land and inaccurate detection of the degradation degree of saline-alkali groundwater in the traditional saline-alkali land improvement water quality desalination system. The accuracy of the linkage degradation characteristics detection of saline-alkali land and the accuracy of the degradation degree detection of saline-alkali groundwater are improved. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 It is a step flow chart for a control method for a saline-alkali land improvement water quality desalination system.

[0011] Figure 2 It is a microstructure and metabolic degradation trend diagram.

[0012] Figure 3 It is a main ion concentration distribution diagram.

[0013] Figure 4 It is a scanning electron microscope and X-ray diffractometer diagram.

[0014] Figure 5 It is a schematic diagram of the original spectrum of soil extract solution ion concentration.

[0015] The implementation, functional characteristics and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0016] To achieve the above-mentioned purpose, please refer to Figures 1 to 5 A control method for a saline-alkali land improvement water quality desalination system, comprising the following steps:

[0017] Step S1: Obtain multi-point sampling data of saline-alkali land; According to the multi-point sampling data of saline-alkali land, the ion concentration composition data of saline-alkali land soil is counted; Based on the multi-point sampling data of saline-alkali land and the ion concentration composition data of saline-alkali land soil, the degree of soil salinization is determined;

[0018] In the embodiment of the present application, the acquisition of multi-point sampling data of saline-alkali soil adopts a distributed sampling grid method. First, the target saline-alkali soil is divided into a plurality of uniform grid units, the unit area is determined according to the terrain and soil type, and the spatial heterogeneity of the entire region is ensured. A sampling point is set at the center of each grid unit, and a soil drilling equipment is used to vertically collect soil profile samples. The sampling depth is set in layers, usually three levels of 0-20 cm, 20-40 cm and 40-60 cm. After sampling is completed, a physical and chemical analyzer is used to detect the main ion concentrations in the sampling samples, including sodium ions ( ), potassium ions ( ), calcium ions ( ), magnesium ions ( ), chloride ions ( ), and sulfate ions ( ). Ion concentration detection uses ion chromatography or atomic absorption spectrometry to ensure that the data accuracy meets the laboratory detection standard. The ion concentration data of all sampling points are summarized and statistically analyzed to form a soil ion concentration composition data matrix of saline-alkali soil. Based on the statistical results, the total salt content and effective salt content are calculated using the soil salinization evaluation standard, and the soil salinization degree index is comprehensively generated in combination with the soil electrical conductivity (EC) and pH value determination results. The index is calculated by a multi-parameter weighted algorithm to ensure accurate reflection of the salinization distribution characteristics of different sampling layers and spatial positions. The output of step S1 is a soil salinization degree data set, which is used as the basis for subsequent step analysis.

[0019] It should be noted that, as shown in Figure 3 , the groundwater in the same salinization area is selected as raw water, and ten sampling points P1 to P10 are set along the processing flow, wherein P1 is the water inlet and P10 is the final water outlet. Index detection and data acquisition: at each sampling point P1 to P10, water samples are collected synchronously, and the concentrations (unit: mmol / kg) of three main salt-forming ions ( , , ) in the water are detected using an ion chromatograph (model: Dionex ICS-6000).

[0020] As shown in the main ion concentration distribution diagram, the original experimental data of ion concentration sampling is shown in Table 1:

[0021]

[0022] Table 1

[0023] The experimental conclusions are as follows: the ion concentration along the process (P1-P10) does not show a monotonic decrease, and multiple concentration peaks appear at P3, P6, P7 and P8 (for example At point P8, the concentration reached as high as 24.8 mmol / kg, indicating severe back-salinization. Unstable desalination efficiency and secondary back-salinization led to drastic fluctuations in effluent quality, highlighting the necessity of evaluating and optimizing the desalination system. Although the ion concentration at point P10 was slightly lower than at P1, the repeated fluctuations throughout the process made the removal effect unreliable and energy efficiency low. In contrast, the desalination process of this invention exhibits a stable and continuous downward trend in ion concentration, with all ion concentrations in the effluent (P10) consistently dropping below 1.0 mmol / kg, a desalination rate consistently above 95%, and energy consumption reduced by more than 30%.

[0024] Step S2: Detect the synergistic disintegration trend of soil microstructure based on the degree of soil salinization; determine the trend of restricted plant metabolism based on the synergistic disintegration trend of soil microstructure; determine the linked degradation characteristics of saline-alkali land based on the synergistic disintegration trend of soil microstructure and the trend of restricted plant metabolism.

[0025] In this embodiment of the invention, soil salinization data obtained in step S1 is used to perform microstructure analysis on representative soil samples using scanning electron microscopy (SEM) and X-ray diffraction (XRD). The changes in soil particle size distribution, pore structure, and mineral composition are quantitatively described. Combined with salt distribution information, the microscopic disintegration characteristics of soil particle aggregates and pore collapse are identified. Based on continuous time-series sampling, image analysis software is used to perform time-series fitting on the trend of soil microstructure changes, identifying the dynamic characteristics of coordinated disintegration. Correlation analysis is performed between soil microstructure changes and soil moisture retention capacity and gas exchange capacity to infer the physical limitations of the soil environment on plant root growth and nutrient absorption. Furthermore, combined with the soil microstructure disintegration trend, chlorophyll content meters and plant respiration rate measuring devices are used to detect the physiological status of major surface vegetation, quantifying indicators of plant metabolic limitations, such as decreased photosynthetic efficiency and abnormal respiration metabolism. Through statistical multivariate correlation analysis, soil microstructure synergistic disintegration indices and plant metabolic restriction data were fused to extract comprehensive characteristic parameters of the linked degradation of saline-alkali land ecosystems, forming a dataset of linked degradation characteristics of saline-alkali land. This dataset reflects the degradation coupling mechanism between the soil and plant systems in saline-alkali land, and can be used for further analysis in step S3.

[0026] It should be added that, such as Figure 4 The diagram shows a schematic of scanning electron microscopy (SEM) and X-ray diffraction (XRD) images. The SEM image, with its high resolution at the micrometer level, reveals the microscopic morphology of the saline-alkali soil sample. The image clearly shows the morphology of soil particle aggregates, rough surfaces, irregular pore structures, and the regular pores and attached crystals on the surface of the spherical particles in the lower left corner. The horizontal axis of the XRD pattern represents the diffraction angle (…). ), the ordinate represents the diffraction intensity. The multiple characteristic diffraction peaks (i.e. obvious wave peaks) appearing in the spectrum and their corresponding specific positions, intensities and widths can accurately determine the phase composition of the soil sample.

[0027] Step S3: determining the imbalance condition of the saline-alkali land ecological vitality based on the linkage degradation characteristics of the saline-alkali land; determining the degradation degree of the saline-alkali land groundwater source according to the imbalance condition of the saline-alkali land ecological vitality and the linkage degradation characteristics of the saline-alkali land; performing saline-alkali land improvement treatment on the degradation degree of the saline-alkali land groundwater source based on the saline-alkali land improvement water quality desalination system, to obtain the saline-alkali land improvement treatment condition;

[0028] In the embodiment of the application, the linkage degradation characteristic data of the saline-alkali land obtained in step S2 is used to calculate the imbalance condition of the saline-alkali land ecological vitality by using an ecosystem function evaluation index system. The calculation is based on multiple ecological function parameters such as net primary productivity (NPP), soil microbial diversity index and root respiration intensity, all of which are obtained by field sampling and laboratory determination. The ecological vitality imbalance index is calculated by the weighted arithmetic mean method, reflecting the change trend of the stability and self-recovery ability of the ecological system. Combined with the ecological vitality imbalance index of the saline-alkali land and the linkage degradation characteristics, the underground water level and water quality monitoring data, including salt concentration, conductivity, heavy metal content, etc., are collected by using underground water level monitoring instruments, to quantitatively analyze the degradation degree of the underground water. The saline-alkali land groundwater source degradation index is calculated by the multi-factor comprehensive scoring method, and the specific numerical value is taken as an input parameter. The degradation index is input into the saline-alkali land improvement water quality desalination system, which includes a reverse osmosis desalination module, an ion exchange module and a water quality adjustment module. According to the numerical range of the degradation index, the system automatically adjusts the operating parameters, such as the reverse osmosis membrane pressure, the ion exchange agent dosage and the pH adjustment liquid ratio, to realize the targeted water quality improvement treatment. The treatment process includes four stages of raw water pretreatment, desalination filtration, ion exchange adsorption and secondary filtration, and each stage is equipped with real-time water quality monitoring equipment to ensure that the treated water quality meets the design indicators. After the treatment is completed, the system automatically records the saline-alkali land improvement treatment condition data, including the treatment time, the treatment amount, the water quality indicators of the effluent and the system operation state, to form a complete treatment condition file. The file provides a data basis for step S4.

[0029] Step S4: performing defect evaluation of the improvement water quality desalination system based on the improvement treatment condition of the saline-alkali land, to obtain defect data of the improvement water quality desalination system; performing control optimization treatment of the improvement water quality desalination system based on the defect data of the improvement water quality desalination system, to obtain an improved water quality desalination optimization system.

[0030] In the embodiment of the present application, the defect evaluation of the improved water quality desalination system is performed by using the saline-alkali soil improvement treatment file data in step S3, combining system operation logs and real-time sensor collected equipment state information. The defect evaluation adopts multi-dimensional indexes, including membrane pollution rate, ion exchanger adsorption saturation, water pump operation efficiency and abnormal power consumption, etc. All indexes are collected in real time by field detection instruments and system built-in monitoring modules. Threshold comparison method is adopted to compare and analyze historical operation data to determine the performance deviation degree of each link of the equipment. Combined with fault tree analysis technology, the defect root cause is identified to generate the defect data set of the improved water quality desalination system, including defect type, severity and location distribution. Based on the defect data set, control optimization processing is implemented. The optimization processing includes adjusting the reverse osmosis membrane cleaning cycle and the amount of chemical cleaning agent, resetting the regeneration scheme of the ion exchange module, optimizing the water pump operation curve and the logic of the electric control system. The control strategy is implemented through the rule-based automatic control system, which automatically adjusts the parameters according to the defect data to ensure that the system operation parameters are in the best state. After the optimization processing, the system operation indexes are continuously collected by the field monitoring equipment to verify the adjustment effect of the control strategy, form the improved water quality desalination optimization system, and record the optimized control parameters, operation curve and system state data. The optimization system operation data serves as the basis for subsequent maintenance and improvement.

[0031] Preferably, step S1 comprises the following steps:

[0032] Step S11: Obtain saline-alkali soil distribution sampling data;

[0033] In the embodiment of the present application, a pentagonal multi-edge grid sampling area is arranged on the target saline-alkali land, and GNSS precise positioning technology is used in combination with unmanned aerial survey data to calibrate the accurate geographic coordinate points of the sampling area. Sampling points are uniformly arranged at an interval of 20 meters along the longitudinal and transverse directions of the sampling area. A depth-adjustable electric spiral drill is used for layer-by-layer sampling operation at each sampling point. The specific layering is three depth levels of 0-20 cm, 20-40 cm and 40-60 cm. A spiral drill bit is used to collect undisturbed soil samples at each layer, and the sampling amount is controlled at about 500g each time. The samples are numbered and placed in vacuum sealed aluminum foil bags. During the sampling process, a data recording terminal records the number, GPS coordinates, elevation information, sampling depth, sampling time and soil appearance characteristics (such as color, crystallization phenomenon, moisture) of each sampling point, and synchronously stores them into the ground station central platform. After all the soil samples are collected, they are transported to the laboratory and placed for 12 hours under the condition that the environmental temperature is kept at and the humidity is kept at After air drying, standardized sample data is provided for subsequent analysis. In this step, soil distribution sampling data sets covering different spatial areas and different soil layer depths of the saline-alkali land are obtained, including data fields such as sampling point number, latitude and longitude, elevation, sampling level, and preliminary soil appearance characteristics.

[0034] Step S12: test the saline-alkali soil ion concentration composition data based on the saline-alkali soil distribution sampling data;

[0035] In the embodiment of the present application, the numbered soil samples after air drying in step S11 are uniformly sieved through a 1mm sieve, and soil extraction solution is prepared by mixing deionized water and the soil samples at a mass ratio of 10:1 The constant temperature magnetic stirrer is continuously stirred at 25℃ for 40 minutes, and the supernatant is taken after 10 minutes of sedimentation. Ion chromatography (model Dionex ICS-5000+) is used to determine the cation and anion concentrations of the extraction solution. The cations include 、 、 、 The anions include 、 、 , and the pH value and electrical conductivity (EC value) are simultaneously detected. The working voltage of the chromatograph is set to 300V, the sample injection amount is , the chromatographic column flow rate is kept at 0.25mL / min, and IonPac CS16 and AS19 are used for cation and anion separation operations, respectively. Before detecting each batch of samples, a calibration standard liquid is injected to correct the instrument, ensuring the accuracy of the detection value. After each sample detection is completed, the data is automatically generated by the built-in controller of the instrument to generate a result report, extract the ion concentration (unit: mmol / L) data, and uniformly archive it to the laboratory database according to the sample number. This step forms the 、 、 、 、 、 、 , etc. concentration composition data of each sampling point soil sample, which serves as the basic data set for subsequent statistical analysis.

[0036] It should be noted that, for example Figure 5 , the original graph of the ion concentration of the soil extraction solution is a typical chromatogram directly generated by the ion chromatograph after analyzing a specific saline-alkali soil extraction solution sample. It records the target cations ( , , , ) and anions ( , , The separation and detection process of the sample (e.g., a sample of a mixture of ions) is shown in the figure, in which a plurality of chromatographic peaks are shown, the X-axis coordinate (retention time) of which is a qualitative basis for identifying different ion species, because the retention time of each ion under specific chromatographic conditions is fixed; the Y-axis coordinate (signal intensity) is directly proportional to the concentration of the corresponding ion in the sample, and is the direct basis for quantitative analysis. The picture clearly distinguishes the cation spectrum part separated by the IonPac CS16 column and the anion spectrum part separated by the IonPac AS19 column.

[0037] Step S13: According to the saline soil distribution sampling data and the saline soil ion concentration composition data, the salt concentration gradient distribution information is counted.

[0038] In the embodiment of the application, by matching the sampling point number in step S11 with the detection data number in step S12, a unified mapping relationship between the soil ion concentration composition data and the spatial position data is constructed. Taking each sampling depth level as a unit, a three-dimensional space interpolation algorithm (Kriging interpolation) is used to construct the distribution grid of the salt ion concentration in the horizontal and vertical directions. Each type of ion is coded in the form of an independent layer, and a spatial distribution heat map is generated for each layer, and the terrain relief information is superimposed to correct the ion distribution deviation error caused by the surface slope. In order to determine the overall salt concentration gradient, the total salt concentration value of each sampling point is calculated by summing the concentrations of the main ions, and a distribution contour map is drawn; at the same time, according to the difference between the total salt values of adjacent layers in the vertical section, the longitudinal gradient change rate (unit: ) of the soil is calculated. A three-dimensional grid data structure is formed, including latitude, longitude, elevation, total salt concentration, main ion concentration, vertical gradient, etc. The output data of this step is a set of salt concentration gradient distribution information, which provides an accurate basis for subsequent determination of the degree of salinization.

[0039] Step S14: Determine the degree of soil salinization based on the saline soil ion concentration composition data and the salt concentration gradient distribution information.

[0040] In the embodiment of the application, the generated total salt concentration and ion concentration values are mapped to the salinization grade determination threshold. Among them, the electrical conductivity is defined as the starting point of moderate salinization, the concentration > 25mmol / L and The concentration > 20 mmol / L is defined as a strong alkalization reference index. The salt index of each sampling point is compared, the salt-alkalization degree of each point is divided into four levels of mild, moderate, severe and extremely severe, and the level code (L1 to L4) is marked and added to the corresponding spatial database. To further determine the regional salt-alkalization trend, the point level is processed by the Thiessen polygon interpolation in the GIS platform to form a planar salt-alkalization level distribution map. At the same time, the vertical salt concentration gradient is combined to analyze the salt accumulation rate within the range of 40 cm layer from the ground and the ion migration ability of the upper and lower layers, and the salt rising rate index (unit: ) is calculated. The soil salt-alkalization degree evaluation data table is output, including fields: sampling point number, total salt concentration, main ion composition, EC value, pH value, salt-alkalization level code, vertical salt gradient change rate, salt rising rate index. The data is used as the direct input data source for detecting the soil microstructure and the synergistic disintegration trend in the subsequent steps.

[0041] Preferably, step S13 comprises the following steps:

[0042] Step S131: performing multi-point distributed vertical profile sampling processing on the saline-alkali soil distribution sampling data to obtain saline-alkali soil multi-point vertical profile data;

[0043] In the embodiment of the application, a measurement point grid is arranged in the saline-alkali soil treatment area, 100 measurement points are uniformly arranged in the treatment range at a resolution of 10m x 10m, and are respectively identified as P1 to P100. The GPS-RTK system is used to accurately position each measurement point and record its longitude and latitude coordinates. The electric hydraulic soil drilling equipment (such as TSP series hydraulic multi-stage sampling drill) with multi-layer sampling function is used to perform vertical soil profile sampling at each measurement point position, the sampling depth is controlled to be 2 meters, each 0.2 meters is taken as a layering unit, a total of 10 layers are formed to form a vertical profile. Each layer of soil sample is placed in a sealed sampling bag, a unique number label is attached, and the sampling is immediately sent to the laboratory for physical and chemical analysis after completion. The analysis contents include but are not limited to conductivity (EC), pH value, sodium ion ( ), calcium ion ( ), magnesium ion ( ), chloride ion ( ) and bicarbonate ion ( ) and the like. The analysis adopts inductively coupled plasma mass spectrometer (ICP-MS) and ion chromatograph (IC) combined detection, all detection values are calculated by dry soil quality, and are uniformly recorded in structured CSV format to form multi-point vertical profile data of each measurement point.

[0044] Step S132: determining the saline-alkali soil sampling geographic location information according to the saline-alkali soil distribution sampling data and the saline-alkali soil multi-point vertical profile data; ​

[0045] In the embodiment of the present application, based on the GPS coordinates and vertical profile data of 100 measuring points collected in step S131, the measuring point number is one-to-one corresponding to the spatial coordinates, and a geographic information system (GIS) database is constructed. Using ArcGIS Pro software, all measuring points are spatially registered, and topographic maps, land use maps and groundwater depth distribution maps are imported as basic layers. Through spatial overlay analysis, the regions to which each measuring point belongs (such as low-lying areas, highlands, near-water areas, etc.) are marked. Further, the above profile data and GIS coordinates are bound to construct a geographic data table with spatial attributes, which is standardized to a GeoJSON structure with fields of measuring point number, longitude, latitude, profile level, and ion concentration data, and stored in a PostGIS spatial database to form complete saline-alkali soil sampling geographic location information.

[0046] Step S133: Based on the saline-alkali soil sampling geographic location information and the saline-alkali soil ion concentration data, a soil ion corresponding mapping process is performed to obtain soil ion corresponding mapping data.

[0047] In the embodiment of the present application, the profile level ion concentration field and the spatial coordinate field in the PostGIS spatial database are called, and data processing is performed through GeoPandas and Pandas libraries in Python language. Grouping is performed according to the measuring point number and the layer height, and a three-dimensional correlation table of "ion concentration value-layer depth coordinate-longitude and latitude" is established for each group of data. On this basis, using the "value rasterization" plug-in in QGIS, taking each ion (such as Na+, K+, Ca2+, Mg2+, Cl-, SO42-, HCO3-) as the theme layer, the distribution range of each ion in space is converted into a two-dimensional visual grid, and the ion concentration value is taken as the Z-axis mapping content. All ion mappings are constructed in the same resolution (10 meters) to ensure data splicing, and the results are output in TIFF format with geographic reference information to form the soil ion corresponding mapping data.

[0048] Step S134: Constructing a saline-alkali soil ion concentration spatial distribution matrix according to the soil ion corresponding mapping data.

[0049] In the embodiment of the present application, the multiple ion concentration mapping layers in TIFF format obtained in step S133 are read, and pixel-level analysis is performed using a matrix processing module in Matlab. Each layer is divided into a two-dimensional matrix according to a 10-meter resolution, and the matrix element value is the ion concentration value of the corresponding cell. The matrix dimension is determined according to the area of the region. A three-dimensional array is constructed with each measuring point and all ion concentrations as a unit, and the array dimension is (ion species number x X grid number x Y grid number). For example, a 6-channel matrix is constructed for 6 main ions to form a spatial distribution data matrix. The data structure is uniformly encoded into NetCDF format to obtain the saline-alkali soil ion concentration spatial distribution matrix.​

[0050] Step S135: Calculate the horizontal and vertical ion concentration change rate based on the spatial distribution matrix of ion concentration in saline-alkali land;

[0051] In this embodiment of the invention, the spatial distribution matrix in NetCDF format constructed in step S134 is extracted. , A two-dimensional concentration matrix of major ions was generated, and the concentration difference between adjacent grids along the X-axis (horizontal) and Y-axis (vertical) was numerically calculated. The concentration change rate per unit distance was obtained by dividing the numerical difference by the physical distance between adjacent grids (fixed at 10 meters). This processing was implemented using the difference operator "diff" in Matlab, generating a difference matrix for each direction and then normalizing it to the unit "mmol / kg / m". To ensure that the quantified change rate was averaged in both the horizontal and vertical directions, change rate matrices in the dC / dx and dC / dy directions were output separately, named "Horizontal Ion Concentration Change Rate Matrix" and "Vertical Ion Concentration Change Rate Matrix", respectively, both output in GeoTIFF format with georeferenced data.

[0052] Step S136: Determine the soil salt enrichment layer information using multi-point vertical profile data of saline-alkali land when the horizontal-vertical ion concentration change rate exceeds 1.1 mmol / kg / m;

[0053] In this embodiment of the invention, the horizontal-vertical ion concentration change rate matrix obtained in step S135 is used as input, and the NumPy library of Python is called for traversal and judgment. A judgment threshold is set as follows. When the dC / dx or dC / dy value at any measuring point exceeds the threshold, it is marked as an ion migration anomaly. All anomalies are clustered according to their respective profile strata, using a fixed-depth clustering method: if anomalies occur consecutively in 2 to 4 layers at a given measuring point, it is considered a salt enrichment layer. The location, stratum, ion species, and rate of change of all measuring points identified as enrichment layers are recorded in a structured table to form soil salt enrichment layer information, and the output is a CSV file and KML map data.

[0054] Step S137: Calculate the distribution information of salt concentration gradient based on the information of soil salt enrichment layer and the horizontal-vertical ion concentration change rate.

[0055] In the embodiment of the present application, the enriched layer information table generated in step S136 is called to generate a horizontal and vertical change rate matrix data, and a distribution statistical system is constructed. Taking 10m*10m as a basic grid unit, the depth of each grid is analyzed, and the concentration change rate of the depth center value, upper boundary and lower boundary is calculated. The ion concentration gradient diagram of each measuring point is drawn by using the Matplotlib visualization library, in which the X axis represents the depth and the Y axis represents the concentration, and the concentration gradient from the surface to the bottom is drawn. After the concentration gradient data of all measuring points are collected, the concentration gradient spatial distribution diagram is generated by using the "raster interpolation" module in ArcGIS, and the output is a GeoTIFF file, and the coordinates of each gradient peak position and the corresponding ion concentration peak are attached, so as to form the "salt concentration gradient distribution information", which provides basic data for subsequent steps of water regulation and desalination path planning based on the enrichment area.

[0056] Preferably, step S14 comprises the following steps:

[0057] Step S141: setting the pH value measurement range of the saline-alkali soil monitoring sensor to 0ph-14ph, and the water quality sampling frequency to 5kHz;

[0058] In the embodiment of the present application, before the saline-alkali soil improvement water quality desalination system is deployed, the accuracy of the online saline-alkali soil monitoring sensor used is set and the function is calibrated. The selected sensor is an integrated soil-water quality multi-parameter online monitoring module, which uses a potential method glass electrode combined with a reference electrode to measure the pH value, and the sensor measurement circuit is composed of a high-impedance amplifier and a precision ADC module, which has a full-range response capability of 0pH to 14pH, and has a resolution of The data acquisition control unit adopts an embedded STM32F407 processing chip, the ADC sampling period is set to 0.2ms, the water quality sampling frequency is set to 5kHz, and 5000 valid water quality signal points can be collected per second. The sampling process adopts a double-buffered DMA mechanism for real-time sampling and processing, and after the signal acquisition and pH value conversion are completed, the original data is uploaded to the host system through the SPI bus. The setting of this step ensures the data integrity and accurate capture of transient response of the subsequent soil pH value acquisition, and provides a high time resolution data basis for the detection of excessive ion-induced pH value changes.

[0059] Step S142: identifying the excessive degree of ion concentration in the saline-alkali soil based on the ion concentration composition data of the saline-alkali soil;

[0060] In the embodiment of the present application, after the soil sample collection, ion species identification and quantitative analysis are completed, the ion concentration composition data of the saline-alkali soil obtained in the foregoing steps is used as an input data source. The data contains the sodium ion (Na+), potassium ion (K+), calcium ion (Ca2+), magnesium ion (Mg2+) and chloride ion (Cl-) of each sampling point on the five vertical profiles (20cm per layer). ), calcium ion ( ), magnesium ion ( ), chloride ion ( ), sulfate ion ( ) and bicarbonate ion ( ) and the like, and upper limit thresholds of the concentrations of the ions are respectively set, such as that the upper limit of the concentration of sodium ion is 15 mmol / kg and the upper limit of the concentration of chloride ion is 10 mmol / kg. The data is traversed ion by ion, and comparison is made between the concentration of each sampling point and the threshold. If the concentration of a certain ion exceeds the set threshold, the sampling point is marked as being out of standard, and the type of the ion out of standard and the exceeding degree are recorded. An index table of the ions out of standard is constructed in a relational database by using a structured query language (SQL), and four-tuple data of "sampling number-depth-ion type-exceeding degree" is output, which is used as index information of target points for subsequent acid-base value detection.

[0061] Step S143: The degree of the ion concentration out of standard of the saline-alkali soil is tested by using the saline-alkali soil monitoring sensor, and the acid-base value of the saline-alkali soil water quality is obtained.

[0062] In the embodiment of the present application, based on the four-tuple index data of the sampling points of the ions out of standard generated in step S142, the spatial position information and the profile depth information are combined to drive the automatic monitoring device of the saline-alkali soil to complete the extraction of the corresponding soil solution and the pH value test operation. The front end of the monitoring device is connected by a negative pressure water collection unit, a micro liquid pump, a liquid pretreatment module and a pH monitoring probe. The extraction depth is accurately positioned at the required detection horizon by using an electric spiral catheter system. The volume of the liquid sample is set to 30 mL each time, and after pre-filtering (using a 10 μm filter) and degassing, the liquid sample is sent into a pH measurement cell. After the stable electrode response potential is 3 seconds, the pH value is detected by the foregoing configuration, and the detection result is written into the real-time monitoring database in the form of three fields of time stamp, sampling number and pH value. After the acid-base values of all the sampling points out of standard are detected, a complete "pH value list of the points out of standard" is obtained, which provides direct data support for analyzing the saline-alkali abnormality and the acid-base response mechanism.

[0063] Step S144: The salt migration trend of the soil profile is evaluated according to the salt concentration gradient distribution information.

[0064] In the embodiment of the present application, the salt concentration gradient distribution data generated in step S137 is taken as input, and the longitudinal concentration variation trend of the main ions (such as , ) in each profile depth horizon is extracted. The profile data is aggregated according to the plot number, and a longitudinal difference sequence is formed for the five profiles of each sampling point, for example, the concentrations of C1-C5 of a certain sampling point are respectively C1, C2, C3, C4 and C5, and the difference sequence C1-C2, C2-C3, C3-C4 and C4-C5 is constructed. ​={C2–C1,C3–C2,...,C5–C4}. If the difference value is consistently positive, it indicates that salt is migrating to deeper layers; if the difference is consistently negative, it indicates that salt is enriched in shallow layers or migrating upwards. Directionality is determined by longitudinal difference analysis at each sampling point, while the degree of difference between adjacent lateral points is statistically analyzed. A three-dimensional migration trend map is constructed by combining the vertical and horizontal differences. The illustration uses a heat map to display the migration path density. Raster interpolation is performed using spatial analysis tools such as ArcGIS or QGIS to obtain a complete "salt migration trend layer." Each pixel in the layer is labeled with both vertical and horizontal migration trends, serving as a basis for identifying anti-salinity areas.

[0065] Step S145: Identify soil salinity risk areas based on soil profile salinity migration trends;

[0066] In this embodiment of the invention, based on the salt migration trend layer constructed in step S144, regions exhibiting a clear "upward migration" trend within the profile depth are identified. The specific identification process is as follows: Taking each sampling point as the center, determine its... Whether the ion concentration sequence forms a "negative gradient continuous chain," i.e., the concentration difference of three consecutive layers shows a negative increase, is judged as upward migration. Simultaneously, considering the sudden increase in ion concentration between the surface layer (0-20cm) and the subsurface layer (20-40cm), if the surface layer concentration is more than 30% higher than the subsurface layer concentration, it is considered that anti-salinization has occurred. Areas meeting the above two conditions are marked as "anti-salinization risk areas" on the layer. To further enhance the accuracy of the results, the surface salt patch image retrieved by remote sensing is compared and analyzed with the vegetation index NDVI. If the gray value of the salt patch image is greater than 220 and the NDVI is less than 0.2, the area overlapping with the risk area is identified as a "high-risk anti-salinization plot." All results are summarized into an "anti-salinization risk area distribution layer," output in GeoTIFF format, for subsequent conductivity trend extrapolation.

[0067] Step S146: Predict the trend of soil electrical conductivity change based on soil profile salt migration trend and soil anti-salinization risk area;

[0068] In this embodiment of the invention, the migration trend layer and risk zone layer generated in steps S144 and S145 are used as input to construct a spatial weighted analysis model. The following weight logic is set in each grid cell: if the region belongs to a high-risk area for anti-salt migration, a risk weight of 0.8 is assigned; if an upward migration trend exists simultaneously, the migration trend weight is 0.7; if both are superimposed, the total weight of the region is 1.5. Using the measured conductivity values ​​from historical sampling data, the sum of current ion concentrations in the same region is weighted and converted. The following steps are used: multiply the concentration of each major ion by its conductivity coefficient (e.g., ...). Approximately ), and the theoretical conductivity estimate is obtained by summing all ions. By comparing the historical value with the current calculated value, the change trend is obtained to construct the soil conductivity change trend layer, and the area where the conductivity increases or decreases is marked to predict the future formation of high conductivity hotspot area.

[0069] Step S147: Determine the degree of soil salinization according to the soil conductivity change trend and the soil water quality acid-base value of the saline-alkali soil.

[0070] In the embodiment of the present application, the pH value list obtained in step S143 is spatially superimposed with the conductivity change trend layer calculated in step S146 to match the pH-conductivity combination state of each sampling point or grid cell, and the following discrimination logic is established: if the pH is greater than 8.5 and the conductivity is greater than 4 dS / m, it is defined as strong salinization; if the pH is between 7.5 and 8.5 and the conductivity is between 2 and 4 dS / m, it is defined as moderate salinization; and the rest is mild salinization. All calculation processes are performed in the database using pre-set rules, and each point generates a clear "salinization grade" label to export the "salinization degree distribution map" in the ESRI Shapefile format, and the attribute table records the salinization grade, pH value, conductivity and spatial position information, providing accurate input parameters for the water quality desalination control system.

[0071] Preferably, the soil microstructure synergistic disintegration trend detection in step S2 comprises:

[0072] According to the degree of soil salinization, the accumulation degree of soil salt in the saline-alkali soil is measured;

[0073] In the embodiment of the present application, the degree of soil salinization is determined by the soil conductivity (EC value), the total salt concentration in the soil solution, and the ion composition of the soil water solution. First, a soil conductivity sensor array is deployed at different depth layers in the saline-alkali soil for high-frequency sampling, and the sampling frequency is set to 10 times per minute to ensure data timeliness. The conductivity sensor is designed using the four-electrode method to avoid electrode polarization affecting measurement accuracy. Through the collected data, combined with the on-site collected soil water solution samples, ion chromatography is used to quantitatively analyze the main salt ions (such as Na+, K+, Ca2+, Mg2+, Cl-, SO42-, HCO3-, NO3-), and the total amount of ion concentration of salt accumulation is obtained. The salt concentration data of all sampling points are processed by a spatial interpolation algorithm (such as Kriging interpolation) to construct a salt accumulation spatial distribution map to obtain quantitative data of the accumulation degree of soil salt in the saline-alkali soil as the basis for subsequent salinization analysis. 、 、

[0074] According to the accumulation degree of soil salt in the saline-alkali soil and the degree of soil salinization, the alkalinity concentration growth trend of the saline-alkali soil is determined;

[0075] ​In the embodiment of the present application, the obtained salt accumulation data and soil water quality pH value data are used to detect the change trend of soil alkalinity concentration by time series analysis method (such as moving average and linear regression analysis based on historical data). The pH value data is collected by the pH sensor installed on the surface layer and different soil depths of the saline-alkali soil in real time, and the sampling frequency is once per hour. Through statistical processing of pH value data at different time periods, the alkalinity concentration growth rate is extracted. Combined with the spatial distribution change of salt accumulation, the growth trend of soil alkalinity concentration in different regions and depths is comprehensively evaluated. The results are reflected in the form of pH value growth curve and salt accumulation growth curve, which provide data support for further detection of mineral structure damage.

[0076] Based on the growth trend of the alkalinity concentration of the saline-alkali soil, the damage of the clay mineral structure of the saline-alkali soil is detected;

[0077] In the embodiment of the present application, the damage of the clay mineral structure mainly manifests as expansion, contraction and loosening of the mineral particles. The X-ray diffractometer (XRD) is used to analyze the mineral phase of the saline-alkali soil sample, and the growth trend of the alkalinity concentration obtained in step 2 is combined to analyze the change of the mineral structure. The soil sample is taken from different depths, and the physical characteristics reflecting the structural change include particle size distribution, mineral crystallinity and proportion change of mineral types. By comparing the mineral composition data at different time points before and after, the damage degree of the clay mineral caused by the increase of the alkalinity concentration is determined. At the same time, the scanning electron microscope (SEM) is used to observe the microstructure of the soil particle surface, and the energy spectrum analysis (EDS) is used to identify the change of the mineral composition, so as to further verify the specific form of the mineral structure damage. The quantitative index of the clay mineral structure damage is obtained, which is used for soil structure stability evaluation.

[0078] According to the damage of the clay mineral structure of the saline-alkali soil and the growth trend of the alkalinity concentration of the saline-alkali soil, the reduction of the soil aggregate structure stability is determined;

[0079] In the embodiment of the present application, the stability of the soil aggregate structure is directly affected by the change of the clay mineral structure and the alkaline environment. The soil aggregate stability tester is used to test the aggregate breakage of the soil sample. The sample is taken from different soil layers, and the anti-breakage index of the soil aggregate is measured by simulating rainwater flushing or mechanical vibration during the test. Combined with the obtained mineral structure damage degree index and the pH growth trend of step 2, the aggregate structure stability evaluation model is established. The evaluation model reflects the change trend of the soil aggregate structure stability by comparing the aggregate size distribution and breakage rate before and after the experiment. The output of the model includes the reduction proportion and time process parameters of the aggregate structure stability, which are important basis for the next step of soil hardening state judgment.

[0080] According to the salt accumulation degree of the saline-alkali soil, the disintegration condition of the soil aggregate is detected;

[0081] In the embodiment of the present application, the determination of soil aggregate disintegration is based on the change of the physical parameters of soil structure. The soil porosity instrument and mercury injection apparatus are used to analyze the pore structure of the collected soil samples, and the porosity and pore size distribution between aggregates are measured. By comparing the degree of soil salt accumulation, the disintegration of aggregates in high salt environment is analyzed. By measuring the soil water retention capacity and permeability under different salt accumulation levels, the degree of aggregate disintegration is further verified. Combined with the continuous time series of salt accumulation data, the evolution trend of pore structure is compared and analyzed, and the spatial distribution and degree quantization data of aggregate disintegration are obtained.

[0082] According to the detection of the decrease of the stability of the soil aggregate structure, the soil hardening condition of the saline-alkali soil is detected.

[0083] In the embodiment of the present application, the soil hardening is determined by soil hardness and compaction degree detection. The portable soil compaction degree detector is used to measure the soil compressive strength along the different depths of the sampling area, and the data acquisition frequency is once per meter depth point. Combined with the aggregate structure stability index, the soil hardness change trend is analyzed. The degree of hardening is represented by the proportion of soil compressive strength exceeding the critical value. Further, combined with the soil moisture content and soil density data, the development stage and severity of soil hardening are comprehensively evaluated. The spatial distribution map of soil hardening and the corresponding quantitative index are output.

[0084] Based on the detection of the degree of salt accumulation and the hardening condition of the saline-alkali soil, the local dense and uneven condition of the soil structure is detected.

[0085] In the embodiment of the present application, combined with the spatial distribution data of medium salt accumulation and the soil hardening condition data, the uniformity of the soil structure is evaluated by using the geostatistics method. The spatial difference of soil density is calculated by using the variation function analysis and spatial autocorrelation coefficient, and the local area with abnormally high or low density is identified. Through the data of high-density soil density and conductivity sensor, the multi-level soil structure density is formed. The range and intensity of local dense and uneven are displayed, which assists the comprehensive judgment of the subsequent microstructure synergistic disintegration trend.

[0086] According to the detection of the local dense and uneven condition of the soil structure and the hardening condition of the saline-alkali soil, the synergistic disintegration trend of the soil microstructure is detected.

[0087] In the embodiment of the present application, the local dense uneven data and the soil compaction degree obtained in this step are comprehensively evaluated by using time series aggregation analysis method to evaluate the microstructure synergistic disintegration trend. By comparing the density change and compaction development of the same region at different time points, the disintegration rate and synergistic effect of soil microstructure in saline-alkaline environment are identified. The multi-sensor data fusion technology is introduced to unify the parameters such as conductivity, porosity, hardness and salt concentration for processing, and the key characteristic parameters of soil microstructure destruction are extracted. The output results include the microstructure disintegration trend curve and the regional disintegration risk level, which provide accurate data support for saline-alkaline land improvement measures.

[0088] Preferably, the plant metabolism limitation trend determination in step S2 comprises:

[0089] According to the soil microstructure synergistic disintegration trend, the soil pore connectivity attenuation is detected;

[0090] In the embodiment of the present application, first, according to the obtained soil microstructure synergistic disintegration trend data, three-dimensional imaging of the saline-alkaline soil sample is performed by using high-resolution X-ray computed tomography (X-ray CT) technology, and the pore structure inside the soil is captured. The three-dimensional image is segmented by using image processing software to extract the soil pore space distribution and pore connectivity characteristics. Then, by using digital pore network analysis method, the connection degree, porosity and connected path length of the pores are calculated. The pore connectivity attenuation degree is determined by comparing the pore connectivity parameter changes at different time points or under different treatment conditions, and the pore connectivity attenuation index data is formed to reflect the water and gas flow blocking condition inside the saline-alkaline soil. The attenuation index provides a quantitative basis for the subsequent steps, realizing the front-end data of soil water vapor exchange capacity evaluation.

[0091] According to the soil microstructure synergistic disintegration trend, the soil capillary continuity damage degree is detected;

[0092] In the embodiment of the present application, based on the soil physical changes revealed by the soil microstructure synergistic disintegration trend, the soil capillary continuity damage degree is detected by combining the capillary water permeation experiment and the capillary structure observation under the microscope. The saline-alkaline soil sample is collected, and the standard capillary permeation test is performed on the soil sample by using a capillary permeameter, and the permeation rate of water through the capillary system and the capillary water pressure response curve are recorded. Then, the same batch of soil samples are observed under a scanning electron microscope (SEM) at high magnification, and the integrity and fracture of the capillary network are analyzed. The permeation rate data, the number of micro-fracture points of the capillary structure, the crack width and distribution are combined to quantify the capillary continuity damage degree. This data directly reflects the saline-alkaline soil water migration obstruction condition, and provides a basis for subsequent capillary water permeation difficulty trend evaluation.

[0093] Determine the growth trend of soil capillary water infiltration difficulty based on the degree of soil capillary continuity destruction;

[0094] In the embodiment of the application, according to the determined soil capillary continuity destruction index, combined with the periodically collected soil water infiltration rate data, the time series analysis technology is used to identify the trend of water infiltration rate. The specific operation includes normalizing the infiltration rates of different time periods, and then determining the trend of the capillary water infiltration difficulty with time by statistical methods such as linear regression or nonlinear curve fitting. The increase of the infiltration difficulty is manifested as a significant decrease in the infiltration rate and an increase in the infiltration resistance. The trend data is output in numerical form, which clearly reflects the negative impact of capillary structure destruction on the water infiltration function, and the data is used to support the subsequent comprehensive evaluation of the decline degree of soil water vapor exchange capacity.

[0095] Evaluate the decline degree of soil pore water vapor exchange capacity based on the attenuation of soil pore connectivity and the growth trend of soil capillary water infiltration difficulty;

[0096] In the embodiment of the application, the soil pore connectivity attenuation index and the soil capillary water infiltration difficulty trend data are comprehensively utilized to evaluate the decline degree of soil pore water vapor exchange capacity by constructing a process of physical experiment and data fusion. The specific steps include: using soil gas exchange measuring instruments (such as soil gas diffusion coefficient tester) to quantitatively detect the water vapor diffusion capacity of soil samples, combining the aforementioned pore structure and capillary infiltration characteristic data, and using the weighted average method to comprehensively evaluate the water vapor exchange capacity. The evaluation output is a specific numerical value, which reflects the attenuation of the internal gas-water dynamic exchange function of the saline-alkali soil. The evaluation result is directly used as a key parameter input for the restriction of plant root physiological activity, and provides a quantitative basis for the analysis of root expansion capacity.

[0097] Evaluate the decline of plant root expansion capacity based on the synergistic disintegration trend of soil microstructure;

[0098] In the embodiment of the application, according to the synergistic disintegration trend of soil microstructure and related soil physical parameters, root scanning technology and image analysis technology are used to quantitatively analyze the plant root expansion capacity. The specific method is: collecting saline-alkali soil plant root samples, using a high-resolution root scanner to obtain two-dimensional images of the roots, and then using root analysis software to calculate key indicators such as root length, root area and root distribution density. Combined with the soil microstructure parameters, the degree of root growth obstruction is determined through statistical analysis. The root expansion capacity decline data is presented in specific numerical values, which reflects the physical limitation of saline-alkali soil on plant root growth, and provides data support for the subsequent prediction of abnormal plant root nutrient absorption.

[0099] Predict the abnormal plant root nutrient absorption based on the decline of plant root expansion capacity;

[0100] In the embodiment of the present application, based on the plant root system expansion capacity attenuation data, combined with plant nutrient absorption monitoring technology, the abnormal condition of plant root system nutrient absorption is predicted. The operation process includes: using ion selective electrode or plant nutrient element analyzer to regularly measure the concentration change of key nutrients (such as nitrogen, phosphorus, potassium, etc.) in the rhizosphere soil solution of plants, at the same time, through collecting plant leaf or stem tissue samples, atomic absorption spectrometry or mass spectrometry analysis technology is used to determine the nutrient content in the plant body. Combined with the data of the growth of the root system being blocked, the change trend of the nutrient absorption efficiency of the root system is analyzed. The abnormal condition of nutrient absorption is quantitatively evaluated by comparing the nutrient accumulation rate of the normal growth plant and the dynamic change of the rhizosphere nutrient. The index provides the necessary physiological parameter basis for the rhizosphere metabolic response mapping.

[0101] According to the degree of decline of soil pore water vapor exchange capacity and the abnormal condition of plant root system nutrient absorption, multivariate regression mapping processing is carried out to obtain the correlation mapping information of plant rhizosphere metabolic response;

[0102] In the embodiment of the present application, based on the soil pore water vapor exchange capacity decline value and the plant root system nutrient absorption abnormal index, a multivariate statistical analysis method such as principal component analysis (PCA) and multiple regression analysis is used to construct the rhizosphere metabolic response correlation mapping. The specific steps include: first, the pore water vapor exchange capacity value and the nutrient absorption abnormal condition data are input into the statistical software in time sequence synchronization; then the correlation analysis between variables is performed to determine the mutual influence relationship between variables; then, the quantitative mapping relationship of rhizosphere metabolic response is generated through regression analysis, and the mapping information includes the correlation strength and trend parameters of rhizosphere environmental factors and plant metabolic changes. The mapping information is stored in the form of data table and graph, which provides a scientific basis for the quantitative determination of the limited trend of plant metabolism.

[0103] According to the plant rhizosphere metabolic response correlation mapping information, the limited trend of plant metabolism is determined.

[0104] In the embodiment of the present application, according to the obtained plant rhizosphere metabolic response correlation mapping information, combined with time sequence data and rhizosphere environmental change characteristics, threshold judgment method is used to quantitatively identify the limited trend of plant metabolism. The specific method is: setting the quantitative threshold range of metabolism limitation, through dynamic comparison of the mapping parameters of continuous monitoring, if the mapping parameters are continuously above the threshold, it is judged that the plant metabolism appears limited trend. The judgment process depends on the comprehensive performance of the key indexes such as water vapor exchange and nutrient absorption reflected in the rhizosphere metabolic response mapping, and the limited trend parameter output is used for the trend data generated by the control system for the optimization adjustment of the water quality and soil environment of saline-alkali soil to be transmitted to the control module in the form of digital signal, realizing the real-time monitoring and feedback adjustment of the plant metabolism state.

[0105] It should be noted that, for example, Figure 2The original experimental data of the microstructure and metabolic degradation trend diagram are as follows: In the experimental greenhouse, a gradient environment from mild to severe saline-alkali stress is configured, and ten levels of saline-alkali soil samples S1 to S10 are set. S1 is a mild stress control sample, and S10 is a severe stress sample. Different concentrations of 、 and mixed solution are added to simulate different degrees of salinization. In each stress level (S1-S10) sample, soil microstructure and plant physiological indicators are detected, soil microstructure and disintegration trend are detected, scanning electron microscope (SEM, model: Hitachi SU3500) and image analysis software are used to quantitatively analyze soil pore connectivity and aggregate breakage rate. Plant metabolism restriction trend determination, standard indicator plants (such as Suaeda salsa) are planted in each group of soil, root scanning system (WinRHIZO) is used to analyze root morphology, and root absorption rate is calculated; and plant physiological monitor (LI-6800) is used to measure leaf gas exchange parameters, and metabolic restriction index is calculated by specific algorithm. The experimental data is shown in Table 2:

[0106]

[0107] Table 2

[0108] The experimental conclusions are as follows: With the increase of saline-alkali stress (from S1 to S10), the pore connectivity decreases significantly from 85.1% to 44.6%, and the aggregate breakage rate increases sharply from 10.0% to 46.4%. This data directly and quantitatively supports the detection results of soil microstructure and disintegration trend, proves that salinization can lead to the disintegration of soil physical structure, and the root absorption rate decreases from 91.6% to 50.3%, which is the determination of plant metabolism restriction trend, indicating that plant physiological function is severely inhibited. By setting a gradient control, the salinization land linkage degradation characteristics are successfully quantified, which provides precise diagnostic basis and optimization target for subsequent determination of salinization land ecological imbalance and determination of salinization groundwater degradation degree. That is, it can be in a state similar to S10.

[0109] Preferably, the determination of the salinization land linkage degradation characteristics in step S2 comprises:

[0110] According to the soil microstructure and disintegration trend and the plant metabolism restriction trend, the salinity stress of the salinization land is determined;

[0111] In this embodiment of the invention, high-resolution scanning electron microscopy (SEM) and X-ray computed tomography (XCT) were used to detect the microstructure of soil samples collected from saline-alkali land. The collected soil samples were imaged at different time points to analyze the synergistic disintegration trend of the soil microstructure, focusing on extracting key parameters such as porosity, pore size distribution, and the degree of soil aggregate fragmentation. Data processing employed an image segmentation algorithm to quantify the trends of pore connectivity and structural integrity decay. Simultaneously, based on the plant metabolic restriction trend data obtained in the aforementioned steps, specifically including abnormal root nutrient absorption indicators and rhizosphere metabolic response intensity, combined with soil salinity measurement data (detected by ion chromatography in the soil solution),... , The soil microstructure degradation data and plant metabolic limitation data were subjected to multivariate cross-analysis (using plasma concentration data). By constructing a salt stress response index system, principal component analysis (PCA) was used to extract characterizing parameters of salt stress from multidimensional data, obtaining a quantitative description of salt stress in saline-alkali land. Output parameters include a salt stress intensity index, reflecting the degree of influence of soil salinity on plant root metabolic limitation, providing a foundation for further analysis of soil degradation variables.

[0112] The superposition of multiple variables of soil degradation was determined based on the salinity stress of saline-alkali land and the synergistic disintegration trend of soil microstructure.

[0113] In this embodiment of the invention, multi-sensor data fusion technology is employed to assess the degradation of saline-alkali soil through the superposition of multiple variables. Input data includes: the obtained salt stress intensity index; the degree of damage to soil porosity and capillary structure; soil chemical property parameters (such as pH and electrical conductivity); and soil physical property parameters (such as density and compaction). Using a multi-dimensional data fusion algorithm, various variables are converted into indicators of a unified scale, and hierarchical clustering is used to identify the correlation patterns of degradation characteristics. Specifically, each soil variable is normalized, and different degradation factors are weighted and accumulated using weight allocation rules to obtain a comprehensive parameter representing the superposition of multiple variables in soil degradation. This parameter accurately reflects the superposition of degradation in the physical structure, chemical properties, and biological activity of saline-alkali soil. The steps are closely interconnected; relying on the salt stress data from step 1 and combining it with multi-dimensional soil degradation indicators, the degree of degradation is quantified, laying the foundation for subsequent degradation factor extraction and response prediction.

[0114] Extracting the dominant influencing factors of abnormal soil degradation from the superposition of multidimensional variables of soil degradation;

[0115] In the embodiment of the application, for the obtained soil degradation multi-dimensional variable superposition parameters, the factor analysis technology is used to decompose the internal correlation structure in the multi-dimensional variable and identify the key influence factors of the dominant soil abnormal degradation. In implementation, the spatial distribution of the degradation variable data is analyzed by using the geostatistical method, and the indicators that have a significant impact on soil degradation, such as the salt content change rate, the porosity attenuation rate and the capillary structure damage intensity, are screened out by combining the variance contribution rate of the variable and the correlation matrix between variables. By constructing a principal factor extraction function, a plurality of dominant influence factors representing soil abnormal degradation are obtained, and the spatial distribution characteristics of these factors are visualized to determine the action strength and distribution trend of the factors in different regions of the saline-alkali soil. The result parameters are the weight of the dominant influence factors and the spatial coordinate distribution, which ensure the scientific support for subsequent prediction of the chain reaction growth of soil microstructure degradation.

[0116] Based on the soil abnormal degradation dominant influence factors, the soil microstructure collaborative disintegration trend is predicted, and soil degradation chain reaction growth data are obtained.

[0117] In the embodiment of the application, according to the extracted abnormal degradation dominant influence factors and combined with the continuous time sequence soil microstructure observation data, a recursive algorithm is used to predict the evolution of the soil microstructure disintegration trend. The time series analysis technology is used to implement the trend extrapolation of the multi-period microstructure detection data, which specifically includes the time sequence statistical analysis of the pore connectivity and the aggregate breakage rate, and then the chain reaction strength of soil degradation in a future period of time is calculated by combining the change rate of the dominant factors. The signal processing tool is used to filter the noise of the observation data and extract the effective signal, so as to ensure the accuracy of the prediction data. The output result is the soil degradation chain reaction growth curve and the corresponding numerical value, which reflects the change trend of the soil degradation reaction with time accumulation, and provides dynamic data support for the next step of soil collapse critical evolution path detection.

[0118] According to the soil degradation chain reaction growth data and the soil degradation comprehensive factor index, the soil collapse critical evolution path is detected.

[0119] In the embodiment of the present application, based on the obtained soil degradation chain reaction growth data and comprehensive factor index, the critical path of soil collapse evolution is identified by a critical point detection method. The specific implementation includes critical point detection on the time series of soil degradation indicators, application of break point analysis (Break Point Analysis) technology, and determination of critical threshold value combined with soil mechanical property test data (such as soil bearing capacity, compression modulus). When the degradation chain reaction growth data reaches the threshold value, it is marked that the soil collapse risk significantly increases. The detection process collects soil mechanical parameters and degradation indicators in real time through an integrated sensor monitoring system, compares historical threshold data using a data processing unit, and outputs the time point and evolution trajectory of the collapse critical evolution path. The output result is a soil collapse critical path data set, which clearly defines the precursor conditions and process time nodes of soil collapse, and provides key path information for determining the characteristics of salinized land linkage degradation.

[0120] The characteristics of salinized land linkage degradation are determined according to the soil collapse critical evolution path and the soil degradation chain reaction growth data.

[0121] In the embodiment of the present application, the soil collapse critical evolution path detected and the soil degradation chain reaction growth data are integrated to analyze the characteristics of salinized land linkage degradation. The specific method includes spatiotemporal data fusion technology, which jointly maps soil structure changes and degradation reaction growth, uses multi-dimensional data correlation analysis to extract the interaction mode of various factors in the salinized land degradation process. The geographic information system (GIS) platform is used to perform spatial superposition and trend analysis on the above data to generate a salinized land linkage degradation characteristic map. The map reflects the coupling relationship and spatial distribution characteristics of multiple factors such as soil structure degradation, salt stress, and plant metabolic limitation. The salinized land linkage degradation characteristic parameter set is output, including a multi-factor coupling strength index and a key region identifier, which provides comprehensive technical support for subsequent control strategies of the salinized land improvement water quality desalination system.

[0122] Preferably, step S3 comprises the following steps:

[0123] Step S31: determining the imbalance of salinized land ecological vitality based on the characteristics of salinized land linkage degradation;

[0124] In the embodiment of the present application, the obtained salinized land linkage degradation characteristic data is used, which contains key parameters such as soil microstructure disintegration rate, salt stress intensity, and soil collapse critical evolution path. These data are used as basic inputs, and a plurality of environmental indicators related to the salinized land ecosystem are collected, including soil organic matter content, soil microbial diversity index, plant root activity index, and ground vegetation coverage. Sampling is completed by laying standard sampling points in a plurality of different areas of the salinized land, using high-precision soil analysis instruments (such as soil organic matter measuring instruments, microbial sequencing technology equipment), and vegetation index collection instruments (such as NDVI remote sensing equipment). The ecological imbalance condition is analyzed by comparing the salinized land linkage degradation characteristics with the collected ecological indicators, and the energy flow efficiency and material cycle integrity of the ecological system are analyzed. Specifically, the soil salt stress data, microbial diversity, and vegetation coverage are analyzed for correlation, and the ecological system vitality decline trend caused by salinized stress is identified. The data analysis tool uses statistical regression analysis and multi-factor cross-validation, and the output salinized land ecological imbalance index value reflects the current degradation degree of the ecological system. The index is used to guide the subsequent evaluation of the ecological regulation capacity.

[0125] Step S32: detecting the salinized land ecological regulation capacity attenuation condition according to the salinized land ecological imbalance condition and the salinized land linkage degradation characteristics;

[0126] In the embodiment of the present application, based on the ecological imbalance index determined in step S31 and the linkage degradation characteristics obtained in step S2, the quantitative detection of the ecological regulation capacity is further carried out. The key indicators of the ecological regulation capacity include soil water retention capacity, salt self-purification rate, microbial community stability, and plant community recovery speed. The water content is collected by a soil water sensor, and the salt dynamic change is monitored by a salt concentration measuring instrument at regular time intervals. The self-purification rate is determined by using a continuous sampling method, and the soil solution sample is collected at regular time intervals, and the salt ion concentration change is determined by an ion chromatograph to analyze the salt reduction rate. The microbial community stability is tracked by high-throughput sequencing technology to quantify the stability index of the key functional flora. The plant community recovery speed is obtained according to the long-term vegetation coverage change data and the plant growth rate measuring instrument. The above indicators are comprehensively analyzed in association with the ecological imbalance index and the linkage degradation characteristics, and a multi-dimensional variable fusion algorithm is used to realize the accurate determination of the ecological regulation capacity attenuation degree. The output ecological regulation capacity attenuation index clearly reflects the decline degree of the self-repairing and environmental change adapting ability of the salinized land ecosystem, and serves as the basis for the subsequent evaluation of the groundwater degradation degree.

[0127] Step S33: determining the salinized groundwater degradation degree according to the salinized land ecological regulation capacity attenuation condition and the salinized land linkage degradation characteristics;

[0128] In the embodiment of the present application, the degradation condition of saline-alkali groundwater source is analyzed by comprehensively considering the attenuation of ecological regulation capacity and the linkage degradation characteristics of saline-alkali land. The main detection indexes of groundwater degradation include the salt concentration, electrical conductivity, total dissolved solids (TDS) and water level change trend of groundwater. By setting groundwater monitoring wells at different depths in the saline-alkali land, the above indexes are monitored and data are collected in real time by using an automatic water quality analyzer. Combined with the soil salt stress intensity and the soil microstructure disintegration trend, the abnormal rising trend of the salt concentration and electrical conductivity of groundwater is analyzed in time sequence, and the spatial distribution and evolution law of the intensification of groundwater salinization are identified. The water level dynamics are recorded by using a water level monitoring instrument, and the effective utilization condition of groundwater resources is calculated in combination with the salt concentration data. The data processing uses multi-time sequence analysis method to determine the quantitative relationship between the salt concentration of groundwater and the attenuation of ecological regulation capacity. The degradation degree index of groundwater source is formed to reflect the degree of groundwater quality deterioration and resource exhaustion, which provides accurate basis for subsequent saline-alkali land improvement treatment.

[0129] Step S34: The saline-alkali land improvement treatment is performed on the saline-alkali groundwater source degradation degree based on the saline-alkali land improvement water quality desalination system, and the saline-alkali land improvement treatment condition is obtained.

[0130] In the embodiment of the present application, according to the saline-alkali groundwater source degradation degree determined in step S33, the saline-alkali land improvement water quality desalination system is started for targeted treatment. The system includes a multi-stage salt removal module, a soil wetting control module and a water quality recycling module. The reverse osmosis device is used for salt removal treatment of groundwater to reduce the sodium salt content in the water body, effectively reduce the salt concentration, and use the micro-sprinkling technology to uniformly irrigate the saline-alkali soil with the treated desalination water. The soil wetting control equipment is used to maintain the appropriate soil moisture to promote salt dissolution and leaching. The sensor array is used to monitor the soil salt concentration, moisture condition and groundwater level change in real time to ensure the dynamic adjustment and accurate control of the improvement process. The water quality recycling module is responsible for recycling and processing the irrigation drainage to ensure the recycling of water resources and the effective migration of salt. During the whole treatment process, the system continuously collects data such as soil salt content, groundwater salinity and plant growth condition before and after treatment to form an improvement treatment condition report. The report records in detail the saline-alkali water quality desalination effect and the soil improvement progress.

[0131] Especially important is that step S33 includes the following steps:

[0132] Step S331: predicting the failure of saline-alkali groundwater salt regulation balance according to the attenuation of saline-alkali land ecological regulation capacity and the linkage degradation characteristics of saline-alkali land;

[0133] In the embodiment of the present application, firstly, the salt-alkali ecological regulation ability attenuation condition parameter (hereinafter referred to as "regulation attenuation index") and the salt-alkali land linkage degradation characteristic parameter (hereinafter referred to as "linkage degradation factor") output in the previous step are acquired. The regulation attenuation index is calculated based on the soil microbial growth obstacle rate, the soil carbon cycle flow reduction amplitude and the plant photosynthetic rate interannual attenuation curve, and the unit is % / year. The linkage degradation factor is constructed into a three-dimensional array based on the dynamic change rate of soil conductivity (unit: dS / m), permeability coefficient (unit: cm / h) and soil water retention capacity (unit: %). The change trend of the linkage degradation factor in the past five years is integrated by taking the regulation attenuation index as the weight, and the multi-dimensional regulation failure index (hereinafter referred to as "TBI index") of the salt-alkali land is calculated. By establishing a linear correspondence table between the TBI index and the groundwater salt regulation response time (unit: month), when the TBI exceeds the set threshold (such as 65%), it is determined that the current regional salt-alkali groundwater salt regulation mechanism has failed. The determination logic is based on the superposition analysis of field monitoring results and long-term dynamic data, and the groundwater layer piezometric conductivity instrument, evaporation and infiltration measuring device and multi-point soil resistance tension meter are used as data acquisition tools. The matching regulation failure judgment program is completed by the embedded execution module in the PLC system control unit, so as to realize the accurate prediction of the imbalance state of the current regional groundwater salt regulation ability. The output result is "salt-alkali groundwater regulation failure state marker", which provides a basis for the subsequent steps.

[0134] Step S332: determining the salt-alkali groundwater composition change condition according to the salt-alkali groundwater salt regulation balance failure condition;

[0135] In the embodiment of the present application, based on the "salt-alkali groundwater regulation failure state marker" output in the previous step, the conductivity automatic recorder, groundwater sampling pump and water quality online analysis sensing unit are arranged at the groundwater well to sample and analyze the groundwater sample in real time. The sensing modules used in the analysis process include: conductivity sensor (measuring 、 content), ion selective electrode (ISE) sensor (used for detecting 、 、 concentration), and dissolved oxygen probe and pH sensor. The sampling period is set to 24 hours once, and the continuous execution is not less than 14 days, and the sample data is uniformly sent to the ground data processing unit for summary analysis. In the data processing process, the change trends of the following four types of indexes are calculated: (1) the variation rate of total dissolved solids TDS concentration, unit: mg / L; (2) the concentration change rate (%) of main ions such as 、 ; (3) the water alkalinity change trend, represented by the pH fluctuation range; (4) the change of cation exchange capacity, represented by / The ratio dynamic analysis reflects this. The above data is uniformly incorporated into the groundwater composition change record table, and the "Saline-Alkali Groundwater Composition Change Status" is recorded and output in the form of a multi-field dataset, which is classified and recorded according to dimensions such as aquifer depth, water sample collection time, ion concentration type and concentration value, etc. It includes parameters such as: time series number, sample number, sampling depth, aquifer conductivity, TDS value, change ratio of major ion content, pH value fluctuation range, cation exchange ratio index, etc., providing a data foundation for subsequent construction of water quality change curves.

[0136] Step S333: Construct a curve showing the change in groundwater quality parameters based on the changes in the composition of saline-alkali groundwater;

[0137] In this embodiment of the invention, based on the groundwater composition change record table generated in step S332, the water quality parameter curve generation module in the surface data analysis terminal is used for processing to analyze the TDS, etc. of groundwater samples from different water layers (0-5m, 5-10m, 10-20m). , , , Ratios and other parameters are archived in time series, and the fluctuation range and average rate of change of each parameter over a continuous 14 or 30-day period are calculated. During the processing, a piecewise fitting method is used to construct the change curve for each indicator. For example, Concentration change curves were fitted using a time-interval sampling method, with data collected every 24 hours as x-axis points and concentration values ​​as the y-axis, forming a line graph. For pH changes, a moving average was used before smoothing the curve to identify potential periodic trends. The plotting tool was a multi-parameter trend analysis module integrated into the ground data processing terminal, using a time-series data structure (e.g., TSV format). Each constructed groundwater quality parameter change curve was labeled with: parameter name, unit, curve generation date, sampling time span, maximum fluctuation range, and fitted trend slope, to accurately reflect the changing trends of key water quality indicators in the current groundwater source. Output results include: "Groundwater TDS Change Curve", "Groundwater..." Concentration change curve, groundwater pH value change trend graph, Four core trend graphs, including the "ratio fluctuation curve," provide intuitive quantitative basis for subsequent degradation assessment.

[0138] Step S334: Determine the degree of degradation of saline-alkali groundwater sources based on the change curves of groundwater quality parameters and the changes in the composition of saline-alkali groundwater.

[0139] In this embodiment of the invention, threshold values ​​for the changes in each parameter are first set, for example: the TDS increase rate exceeds 20 mg / L per month. Concentration fluctuation greater than 10%, pH value deviates from the neutral range greater than ± 1.2, The ratio continuously decreases by more than 25%; the point-by-point comparison is performed on each change curve, when the change amplitude of a certain parameter is detected to be greater than the threshold value, the index is determined to be in the "over-limit" state; the number of indexes in the same groundwater layer that simultaneously appear in the "over-limit" state is counted, if more than 3, the water layer is marked as a "degradation area"; the degradation area is further classified according to the cause, and the degradation driving source is identified according to the ion exchange ratio change direction and the TDS component proportion structure change trend (such as sodium chloride type and sodium sulfate type degradation); the output results include: groundwater degradation level (mild, moderate and severe), degradation driving type, water layer degradation depth range, degradation index detail table, etc. All analysis and judgment operations are performed in the PLC control system integrated data platform, and the standardized conclusion text and chart data are output by the groundwater degradation evaluation module according to the rule-based judgment logic, and are imported into the comprehensive control platform of the desalination system, to provide data support for water diversion, desalination and repair resource scheduling strategy.

[0140] Preferably, step S34 comprises the following steps:

[0141] Step S341: grouping processing of saline and alkaline groundwater degradation types is performed based on the degradation degree of the saline and alkaline groundwater source, to obtain saline and alkaline groundwater degradation type grouping data;

[0142] In the embodiment of the present application, the degradation degree index of the saline and alkaline groundwater source obtained in step S33 is used, which includes multiple parameters such as groundwater salinity concentration, conductivity, total dissolved solids content (TDS) and water level change. The degradation degree data collected by all monitoring points is summarized to form a database. According to the numerical range of the degradation degree, the database is classified and processed to determine the saline and alkaline groundwater degradation type. The classification operation is based on the quantile division method, and the groundwater degradation index is divided into several categories according to a certain numerical interval, such as mild degradation, moderate degradation and severe degradation. Statistical analysis tools are used to divide the salinity concentration and conductivity index of each monitoring point into intervals, and the change trend of the groundwater level is combined to confirm the degradation type attribution. The saline and alkaline groundwater degradation type grouping data obtained through this grouping processing clearly expresses the degradation type distribution of the groundwater in different regions, and provides basic data support for subsequent hierarchical structure collection. The grouping data includes degradation type number, corresponding geographical position, various monitoring indexes and their numerical range.

[0143] Step S342: saline and alkaline groundwater degradation hierarchical structure collection is performed based on the saline and alkaline groundwater degradation type grouping data, to obtain saline and alkaline groundwater degradation hierarchical structure;

[0144] In the embodiment of the present application, the saline-alkali groundwater degradation type grouping data obtained in step S341 is subjected to hierarchical structure collection to form a hierarchical degradation degree description system. The hierarchical structure collection process first determines the relationship between the degradation types, and establishes a multi-level structure by summarizing the characteristic differences of different degradation types. The degradation types are divided into multiple levels according to the severity, spatial distribution and influence range by using the hierarchical analysis method (Hierarchical Analysis), for example, the first level is the overall degradation state, the second level is the specific degradation type category, and the third level is the specific index subdivision of the monitoring point. The hierarchical structure data includes the node information of each level, the parent-child relationship between the nodes, the degradation index threshold range corresponding to the nodes and the spatial coordinate information. The data collection is ensured to be accurately mapped on the geographic space and stored in the system by integrating the geographic information system (GIS) technology and the database management system. The saline-alkali groundwater degradation hierarchical structure obtained by the system storage is a tree data structure, which supports subsequent clustering analysis and decision support.

[0145] Step S343: saline-alkali groundwater degradation hierarchical structure and saline-alkali groundwater degradation type grouping data are subjected to saline-alkali land degradation condition clustering analysis to obtain saline-alkali groundwater degradation condition clustering data;

[0146] In the embodiment of the present application, based on the saline-alkali groundwater degradation hierarchical structure of step S342 and the degradation type grouping data of step S341, clustering analysis is performed to further subdivide the saline-alkali groundwater degradation condition. The clustering analysis adopts an unsupervised clustering algorithm to realize the classification of the saline-alkali groundwater degradation condition by comprehensively considering the multi-dimensional data characteristics such as the groundwater salinity concentration, conductivity, TDS and spatial proximity relationship. In specific implementation, first, the multi-dimensional data is subjected to standardization processing to eliminate the influence of different dimensions. Subsequently, a distance-based clustering method such as hierarchical clustering or density clustering is adopted to group the data and demarcate the groundwater areas with similar degradation conditions. The clustering result is output as multiple clustering categories, each of which corresponds to different degradation characteristics and geographical ranges. The clustering analysis is completed by using professional data analysis software, and the output result includes the clustering category number, the category center value, the data point distribution within the category and the spatial position thereof. The clustering data serves as the basis for formulating the saline-alkali groundwater desalination strategy and assists in determining the regional differentiated treatment scheme.

[0147] Step S344: determining the saline-alkali groundwater desalination strategy according to the saline-alkali groundwater degradation condition clustering data;

[0148] In the embodiment of the present application, the saline groundwater degradation condition clustering data obtained in step S343 is used to determine the corresponding saline groundwater desalination strategy. The strategy is first determined by analyzing the main degradation characteristics of each cluster category, such as the salt concentration peak, the salt concentration change rate and the groundwater level change trend, and matching the corresponding desalination process parameters. Specifically, it includes the operating pressure of the reverse osmosis device, the selection of membrane material, the operating time and the salt leaching cycle, etc. According to the degradation conditions of different categories, the treatment intensity and frequency of the desalination system are determined. The strategy also includes the adjustment scheme of soil moisture and the water quality recycling method. The desalination strategy data forms a structured scheme file, which covers the cluster category number, the corresponding treatment process parameters and the equipment operation parameters. In the strategy development process, historical treatment data and on-site monitoring feedback are combined to ensure the scientificity and feasibility of the scheme, and to provide clear guidance for subsequent system operation.

[0149] Step S345: transmitting the saline groundwater desalination strategy to the saline land improvement water desalination system, and performing saline land improvement treatment to obtain the saline land improvement treatment condition.

[0150] In the embodiment of the present application, the saline groundwater desalination strategy developed in step S344 is transmitted to the control unit of the saline land improvement water desalination system through an industrial control network. The control unit automatically adjusts the reverse osmosis device parameters according to the received desalination strategy, including the operating pressure, the membrane filtration rate and the discharge control. At the same time, the control unit adjusts the irrigation frequency and water quantity of the micro-sprinkling irrigation system, so as to realize accurate irrigation for different degradation type regions. The system collects soil salt content, groundwater quality parameters and water level changes in real time, and feeds back the improvement effect data through the sensor network. During the improvement treatment, the system continuously records the water quality change, the salt removal efficiency and the plant growth condition during the treatment process, and forms a complete improvement treatment condition data set. The data set includes the treatment time node, the improvement region coordinate, the treatment intensity parameter and the treatment effect index. After the improvement treatment is completed, the control system automatically generates an improvement treatment report, which provides technical support for subsequent maintenance and system optimization. The whole treatment process ensures that the water desalination and soil improvement are carried out synchronously, and realizes systematic saline land ecological restoration control.

[0151] Preferably, step S1 comprises the following steps:

[0152] Step S41: determining the change condition of the saline land soil physical and chemical properties based on the saline land improvement treatment condition;

[0153] In the embodiment of the present application, based on the saline-alkali soil improvement treatment data obtained in step S345, the change condition of the saline-alkali soil physicochemical properties is analyzed and determined. The improvement treatment data includes improvement time, treatment area coordinates, soil salt concentration, electrical conductivity, pH value, organic matter content and soil moisture content before and after improvement, and other multi-dimensional indexes. Soil sampling equipment is used to collect soil samples in the improvement area according to the predetermined grid, and the sampling depth is matched with the soil layer structure. Then, the physicochemical parameters of the samples are determined by using field portable salt tester, conductivity tester and pH meter and other equipment. All sampling data are uploaded to the database through the wireless transmission system and compared with the baseline data before improvement. The change condition of physicochemical properties is reflected by the numerical change trend, including the salt concentration reduction amplitude, the electrical conductivity change and the pH value adjustment trend. In the analysis process, the time series data comparison method is used to ensure that the spatial and temporal distribution characteristics of the physicochemical parameters fully reflect the soil physicochemical property change condition report, including the change value of each index, regional distribution map and change trend graph, which are used as input data for subsequent soil community repair detection.

[0154] Step S42: detecting the saline-alkali soil community repair condition according to the saline-alkali soil improvement treatment and the saline-alkali soil physicochemical property change condition;

[0155] In the embodiment of the present application, when detecting the saline-alkali soil community repair condition, the improvement treatment of step S345 and the soil physicochemical property change condition of step S41 are used as core data. Using field vegetation investigation tools, a plurality of fixed quadrats are set in the improvement area, and the vegetation species composition, coverage, biomass and plant health status are recorded regularly. The vegetation data collection is combined with high-resolution unmanned aerial vehicle aerial image, and the spectral analysis technology is used to extract vegetation indexes such as NDVI (normalized difference vegetation index) to quantify the vegetation growth condition. The soil microbial community repair condition is analyzed by soil microbial community DNA extraction and high-throughput sequencing technology to reflect the community structure change. All community data are integrated with the improvement treatment time node and the soil physicochemical parameter change to form a soil community repair condition database. By comparing and analyzing the community diversity, biomass change and plant species richness before and after repair, the specific numerical index of community recovery is determined. The data provides biological and ecological basis for subsequent evaluation of saline-alkali soil water quality change, and the data content covers species statistics table, community structure diagram and biomass time series diagram.

[0156] Step S43: evaluating the saline-alkali soil water quality change condition based on the saline-alkali soil community repair condition and the saline-alkali soil physicochemical property change condition;

[0157] In the embodiment of the present application, the soil community restoration condition data obtained in step S42 is integrated with the soil physical and chemical property change condition data in step S41 to evaluate the change of water quality in saline-alkali soil. The change of water quality evaluation covers the change trend collection of salt concentration, pH, conductivity and other indicators in underground water and surface water. Underground water and surface water samples in the improvement area are collected by using automatic water quality samplers for regular sampling, and ion chromatograph, atomic absorption spectrometer and other equipment are used to detect the ion composition and concentration change of the water sample. Combined with the soil physical and chemical data, the influence of water-soil interaction on water quality is analyzed, and the correlation between water quality parameters and soil salt, conductivity and community restoration indicators is analyzed to evaluate the change trend of water quality. The data visualization technology is used to draw the spatial distribution map and time change curve of water quality change to form the water quality change evaluation report, which includes the numerical change of water quality parameters, the specific performance of regional water quality improvement or deterioration and the corresponding time node, providing data basis for subsequent defect evaluation of the improvement system.

[0158] Step S44: Defect evaluation of the improved water quality desalination system based on the change of water quality in saline-alkali soil, to obtain defect data of the improved water quality desalination system;

[0159] In the embodiment of the present application, the water quality change in step S43 is used as the basis for evaluating the defects existing in the improved water quality desalination system. The defect evaluation adopts system performance monitoring technology, and the monitoring equipment includes flow meter, pressure sensor, salt sensor and membrane pollution detector. According to the water quality change data, combined with the system operation log, the running efficiency and salt removal rate change of the desalination system in different processing stages are analyzed. The flux attenuation of the membrane material of the desalination equipment, the decline trend of the filtration effect and the physical defects such as pipeline scaling are detected. The expected processing parameters and the actual output data are compared to identify the abnormal index fluctuation points and fault frequency in the system. The defect data is stored in the form of table, and the content covers the defect type, occurrence time, influence range and severity. The defect evaluation process adopts multi-parameter comprehensive judgment method to ensure the accuracy of defect positioning and support subsequent system control optimization. The defect data provides clear technical basis and processing direction for system optimization.

[0160] Step S45: System control optimization processing according to the defect data of the improved water quality desalination system, to obtain the improved water quality desalination optimization system.

[0161] In the embodiments of the present application, the system control optimization process is performed by using the modified water quality desalination system defect data. The optimization process first adjusts the equipment operating parameters through an automatic control algorithm, including the reverse osmosis membrane operating pressure, pump speed, cleaning cycle and salt leaching time, fine control to improve the membrane flux and prolong the service life of the equipment. The optimization adjustment is based on real-time monitoring data of sensors, and the feedback control technology is used to correct the parameters in real time to ensure the stability of the system output water quality. After the system controller integrates the execution instructions, the monitoring equipment automatically performs the optimization operation, and records the changes of the key parameters in real time. The optimized system operation data is updated in real time through the database to form a dynamic operation state file. The performance of the optimized system is obtained by comparing the water quality indicators, system efficiency and fault rate changes before and after optimization, and a detailed optimization processing report is formed. The report content includes adjustment parameter details, running state change trend and equipment performance improvement data, to ensure that the modified water quality desalination system remains efficient and stable in subsequent operation.

[0162] The above description is merely a specific implementation of the present application, which enables those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method for a water quality improvement and desalination system in saline-alkali land, characterized in that, Includes the following steps: Step S1: Obtain multi-point sampling data of saline-alkali land; statistically analyze the composition data of soil ion concentration in saline-alkali land based on the multi-point sampling data of saline-alkali land; determine the degree of soil salinization based on the multi-point sampling data of saline-alkali land and the composition data of soil ion concentration in saline-alkali land. Step S2: Detect the synergistic disintegration trend of soil microstructure based on the degree of soil salinization. The detection of the synergistic disintegration trend of soil microstructure includes: The degree of salt accumulation in saline-alkali land is measured based on the degree of soil salinization. The increasing trend of soil alkalinity concentration in saline-alkali land was determined based on the degree of soil salt accumulation and soil salinization. Detecting the damage to the mineral structure of clay in saline-alkali land based on the increasing trend of soil alkalinity concentration; The degree of decrease in soil aggregate structure stability was determined based on the damage to the clay mineral structure of saline-alkali land and the increasing trend of soil alkalinity concentration. The disintegration status of soil aggregates was detected based on the degree of salt accumulation in saline-alkali soil. The soil compaction status in saline-alkali land was detected based on the decrease in soil aggregate structure stability. Based on the degree of soil salinity accumulation and soil compaction in saline-alkali land, the local density and unevenness of soil structure were detected. Based on the local compaction and unevenness of soil structure and the soil compaction in saline-alkali land, the trend of synergistic disintegration of soil microstructure was detected. Plant metabolic restriction trends were determined based on the synergistic disintegration trend of soil microstructure. The determination of plant metabolic restriction trends included: The decay of soil pore connectivity was detected based on the synergistic disintegration trend of soil microstructure. The degree of damage to soil capillary continuity was detected based on the synergistic disintegration trend of soil microstructure; The increasing trend of soil capillary water infiltration difficulty is determined based on the degree of damage to soil capillary continuity; The degree of decline in soil pore water vapor exchange capacity is assessed based on the decline in soil pore connectivity and the increasing trend of soil capillary water infiltration difficulty. Assessing the decline in plant root expansion capacity based on the synergistic disintegration trend of soil microstructure; Predicting abnormalities in plant root nutrient absorption based on the decline in plant root expansion capacity; Based on the degree of decline in soil pore water vapor exchange capacity and the abnormal status of plant root nutrient absorption, multivariate back mapping was performed to obtain the correlation mapping information of plant rhizosphere metabolic response. The trend of plant metabolic restriction was determined based on the correlation mapping information of plant rhizosphere metabolic response; the linkage degradation characteristics of saline-alkali land were determined based on the trend of synergistic disintegration of soil microstructure and the trend of plant metabolic restriction. Step S3: Determine the ecological vitality imbalance of saline-alkali land based on the interconnected degradation characteristics of saline-alkali land; determine the degree of degradation of saline-alkali groundwater sources based on the ecological vitality imbalance and interconnected degradation characteristics of saline-alkali land; carry out saline-alkali land improvement treatment based on the saline-alkali land water quality improvement and desalination system to obtain the saline-alkali land improvement treatment status. Step S4: Based on the saline-alkali land improvement treatment, conduct a defect assessment of the improved water desalination system to obtain defect data of the improved water desalination system; based on the defect data of the improved water desalination system, conduct control optimization treatment of the improved water desalination system to obtain an optimized improved water desalination system.

2. The control method for a saline-alkali land improvement and desalination system according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain soil distribution sampling data for saline-alkali land; Step S12: Test the composition data of ion concentration in saline-alkali soil based on soil distribution sampling data; Step S13: Based on the soil distribution sampling data and soil ion concentration composition data of saline-alkali land, statistical information on the salt concentration gradient distribution is obtained; Step S14: Determine the degree of soil salinization based on soil ion concentration composition data and salt concentration gradient distribution information.

3. The control method for a saline-alkali land improvement and desalination system according to claim 2, characterized in that, Step S13 includes the following steps: Step S131: Perform multi-point distributed vertical profile sampling processing on the soil distribution sampling data of saline-alkali land to obtain multi-point vertical profile data of saline-alkali land; Step S132: Determine the geographical location information of saline-alkali land sampling based on soil distribution sampling data and multi-point vertical profile data of saline-alkali land; Step S133: Based on the geographical location information of saline-alkali land sampling and the soil ion concentration composition data of saline-alkali land, perform soil ion correspondence mapping processing to obtain soil ion correspondence mapping data; Step S134: Construct a spatial distribution matrix of ion concentration in saline-alkali land based on soil ion mapping data; Step S135: Calculate the horizontal and vertical ion concentration change rate based on the spatial distribution matrix of ion concentration in saline-alkali land; Step S136: Determine the soil salt enrichment layer information using multi-point vertical profile data of saline-alkali land when the horizontal-vertical ion concentration change rate exceeds 1.1 mmol / kg / m; Step S137: Calculate the distribution information of salt concentration gradient based on the information of soil salt enrichment layer and the horizontal-vertical ion concentration change rate.

4. The control method for a saline-alkali land improvement and desalination system according to claim 2, characterized in that, Step S14 includes the following steps: Step S141: Set the pH measurement range of the saline-alkali land monitoring sensor to 0ph-14ph and the water quality sampling frequency to 5kHz; Step S142: Identify the degree of exceedance of ion concentration in saline-alkali land based on soil ion concentration composition data; Step S143: Use a saline-alkali land monitoring sensor to test the pH value of the saline-alkali land to determine the degree of ion concentration exceeding the standard, and obtain the pH value of the saline-alkali land soil water. Step S144: Assess the salt migration trend in the soil profile based on the salt concentration gradient distribution information; Step S145: Identify soil salinity risk areas based on soil profile salinity migration trends; Step S146: Predict the trend of soil electrical conductivity change based on soil profile salt migration trend and soil anti-salinization risk area; Step S147: Determine the degree of soil salinization based on the trend of soil electrical conductivity changes and the pH value of soil water in saline-alkali land.

5. The control method for a saline-alkali land improvement and desalination system according to claim 1, characterized in that, The determination of the linked degradation characteristics of saline-alkali land in step S2 includes: The salinity stress in saline-alkali land was determined based on the synergistic disintegration trend of soil microstructure and the restricted trend of plant metabolism. The superposition of multiple variables of soil degradation was determined based on the salinity stress of saline-alkali land and the synergistic disintegration trend of soil microstructure. Extracting the dominant influencing factors of abnormal soil degradation from the superposition of multidimensional variables of soil degradation; Based on the dominant influencing factors of abnormal soil degradation, the growth of the soil degradation chain reaction was predicted to be synergistically disintegrated in soil microstructure, and the growth data of the soil degradation chain reaction were obtained. The critical evolution path of soil collapse was detected based on the growth data of soil degradation chain reaction and the comprehensive factor index of soil degradation. Based on the critical evolution path of soil collapse and the growth data of the chain reaction of soil degradation, the characteristics of the linkage degradation of saline-alkali land were determined.

6. The control method for a saline-alkali land improvement and desalination system according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Determine the imbalance of ecological vitality of saline-alkali land based on the characteristics of its interconnected degradation. Step S32: Detect the decline in the ecological regulation capacity of saline-alkali land based on the imbalance of ecological vitality and the characteristics of the linkage degradation of saline-alkali land. Step S33: Determine the degree of degradation of saline-alkali groundwater sources based on the decline in the ecological regulation capacity of saline-alkali land and the characteristics of the linkage degradation of saline-alkali land; Step S34: Based on the saline-alkali land improvement and desalination system, the saline-alkali groundwater source degradation is improved by saline-alkali land improvement treatment to obtain the saline-alkali land improvement treatment status.

7. The control method for a saline-alkali land improvement and desalination system according to claim 6, characterized in that, Step S34 includes the following steps: Step S341: Based on the degree of degradation of saline-alkali groundwater sources, group the saline-alkali groundwater degradation types to obtain saline-alkali groundwater degradation type grouping data; Step S342: Collect the degradation hierarchy structure of saline-alkali groundwater based on the grouped data of saline-alkali groundwater degradation types to obtain the degradation hierarchy structure of saline-alkali groundwater; Step S343: Perform cluster analysis on the degradation status of saline-alkali land on the grouped data of saline-alkali groundwater degradation hierarchy structure and saline-alkali groundwater degradation type to obtain saline-alkali groundwater degradation status cluster data; Step S344: Determine the desalination strategy for saline-alkali groundwater based on the clustering data of saline-alkali groundwater degradation status; Step S345: Transmit the saline-alkali groundwater desalination strategy to the saline-alkali land improvement water quality desalination system, and carry out saline-alkali land improvement treatment to obtain the saline-alkali land improvement treatment status.

8. The control method for a water quality improvement and desalination system for saline-alkali land according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Determine the changes in the physical and chemical properties of saline-alkali soil based on the treatment of saline-alkali land improvement; Step S42: Detect the soil community restoration status of saline-alkali land based on the treatment of saline-alkali land improvement and the changes in the physical and chemical properties of saline-alkali land soil; Step S43: Assess the water quality changes in saline-alkali land based on the restoration status of the soil community and the changes in the physicochemical properties of the soil. Step S44: Based on the changes in water quality in saline-alkali land, conduct a defect assessment of the improved water quality desalination system to obtain defect data of the improved water quality desalination system; Step S45: Based on the defect data of the improved water desalination system, perform system control optimization processing to obtain the improved water desalination optimization system.

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