Salt-tolerant crop and microorganism synergistic saline-alkali soil conditioning and remediation system and method

Abstract

The application discloses a salt-tolerant crop and microorganism synergistic saline-alkali soil clearing and repairing system and method, and relates to the technical field of saline-alkali soil improvement.The application comprises the following steps: collecting soil samples of a target saline-alkali soil, performing soil physical and chemical property determination, and obtaining soil physical and chemical parameters; performing saline-alkali degree grading according to initial pH value and salt content, calculating the amount of salt-tolerant microorganisms to be put according to the saline-alkali grade and salt content, performing salt clearing treatment, and obtaining salt clearing state parameters; determining salt-tolerant microorganism putting control parameters, obtaining pH values multiple times and updating the salt-tolerant microorganism putting control parameters until the pH value reaches a preset target interval; outputting the matching parameters of organic fertilizer and original microbial flora according to the organic matter content and nutrient content, and performing soil microbial flora transplantation; collecting real-time monitoring parameters and crop growth state parameters, and adjusting the salt-tolerant microorganism putting control parameters.The application realizes multi-stage cooperative repair of saline-alkali soil based on soil state information driving.

Description

Technical Field

[0001] This invention relates to the field of saline-alkali land improvement technology, specifically to a saline-alkali soil cleanup and remediation system and method that combines salt-tolerant crops and microorganisms. Background Technology

[0002] Currently, saline-alkali land remediation mainly includes several approaches: physical improvement, chemical improvement, water conservancy projects, and bioremediation. Physical improvement alters the soil's physical structure through methods such as topsoil import and deep tillage; chemical improvement neutralizes alkalinity and replaces sodium ions by applying amendments such as gypsum; water conservancy projects flush out salt through irrigation; and bioremediation improves the soil environment by planting salt-tolerant plants or applying microbial agents. In practical applications, these methods are often used in simple combinations.

[0003] However, existing remediation methods are mostly implemented independently as single measures or simply superimposed in sequence, lacking organic synergy and dynamic adjustment capabilities based on real-time soil conditions. Microbial agents are often applied in a one-time application or supplemented at a fixed schedule, failing to coordinate different functional strains in an orderly manner according to the remediation process; the switching between stages of various improvement methods relies on preset procedures and cannot adaptively adjust according to actual changes in soil salinity and alkalinity, resulting in open-loop operation and limited efficiency of the remediation process. Summary of the Invention

[0004] This invention provides a saline-alkali soil cleanup and remediation system and method that combines salt-tolerant crops and microorganisms, aiming to solve the technical problem that the application strategies at each stage of the remediation process in the prior art cannot be adaptively adjusted according to real-time soil condition information.

[0005] In view of the above problems, the present invention provides a saline-alkali soil cleanup and remediation system and method that combines salt-tolerant crops and microorganisms.

[0006] In a first aspect, the present invention provides a saline-alkali soil cleanup and remediation system that combines salt-tolerant crops and microorganisms, comprising:

[0007] The soil baseline diagnostic module is used to collect soil samples from the target saline-alkali land, measure the soil physicochemical properties, and obtain soil physicochemical parameters, which include at least the initial pH value, salinity, organic matter content, soil bulk density, and nutrient content.

[0008] The halophilic cleansing control module is used to classify the degree of salinization based on the initial pH value and salt content, obtain the salinity level, calculate the amount of halophilic microorganisms to be added based on the salinity level and salt content, perform cleansing treatment, and obtain cleansing status parameters.

[0009] The alkali-loving regulation module is used to determine the control parameters for the addition of alkali-loving microorganisms based on the salinity parameters. During the alkali adjustment process, the pH value is acquired multiple times and the control parameters for the addition of alkali-loving microorganisms are updated until the pH value reaches the preset target range. The control parameters for the addition of alkali-loving microorganisms include the amount of alkali-loving microorganisms added and the time interval between additions.

[0010] The microbial community transplantation and cultivation module is used to output the ratio parameters of organic fertilizer and native microbial community based on the organic matter content and nutrient content, perform soil microbial community transplantation, and plant salt-tolerant crops.

[0011] The collaborative feedback control module is used to collect real-time soil monitoring parameters and crop growth status parameters, and adjust the control parameters for the release of alkaliphilic microorganisms based on these parameters.

[0012] Secondly, this invention provides a method for the synergistic remediation of saline-alkali soil by salt-tolerant crops and microorganisms, including:

[0013] Soil samples were collected from the target saline-alkali land, and the soil physicochemical properties were measured to obtain soil physicochemical parameters. The soil physicochemical parameters include at least the initial pH value, salt content, organic matter content, soil bulk density, and nutrient content.

[0014] The degree of salinization is classified according to the initial pH value and salinity to obtain the salinity level. The amount of halophilic microorganisms to be added is calculated based on the salinity level and salinity. The salt removal treatment is then performed, and the salt removal status parameters are obtained.

[0015] The control parameters for the addition of alkaliphilic microorganisms are determined based on the salinity parameters. During the alkali adjustment process, the pH value is obtained multiple times and the control parameters for the addition of alkaliphilic microorganisms are updated until the pH value reaches the preset target range. The control parameters for the addition of alkaliphilic microorganisms include the amount of alkaliphilic microorganisms added and the time interval between additions.

[0016] Based on the organic matter and nutrient content, output the ratio parameters of organic fertilizer and native microbial community, carry out soil microbial community transplantation, and plant salt-tolerant crops;

[0017] Collect real-time soil monitoring parameters and crop growth status parameters, and adjust the control parameters for the release of alkaliphilic microorganisms based on these parameters.

[0018] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0019] This invention provides a system and method for the synergistic remediation of saline-alkali soil using salt-tolerant crops and microorganisms. The system collects soil physicochemical parameters of the target saline-alkali land through a soil baseline diagnosis module to determine the starting point for remediation. A halophilic cleansing and regulation module calculates the amount of halophilic microorganisms to be deployed based on the initial pH value and salinity, performs cleansing treatment, and obtains cleansing state parameters characterizing soil salt release and alkali reduction effects. An alkali-loving alkali-regulating module uses the cleansing state parameters as decision input to determine the control parameters for alkali-loving microorganism deployment, allowing the deployment amount and time interval during the alkali-regulating stage to be adaptively adjusted based on the actual cleansing effect. To address the lag in microbial response, the alkali-loving microorganism deployment control parameters are dynamically updated by repeatedly acquiring pH values. A microbial community transplantation and cultivation module optimizes the microbial fertilizer ratio based on soil organic matter and nutrient content, performs soil microbial community transplantation, and plants salt-tolerant crops. A collaborative feedback control module forms a feedback loop by real-time monitoring of soil parameters and crop growth status, adjusting the amount of halophilic microorganisms and the alkali-loving microorganism deployment control parameters when conditions are abnormal, achieving precise, intelligent, and efficient saline-alkali land remediation. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the structure of the saline-alkali soil cleanup and remediation system that combines salt-tolerant crops and microorganisms, as provided in an embodiment of the present invention.

[0021] Figure 2 This is a schematic flowchart of the method for cleaning and remediating saline-alkali soil through the synergistic effect of salt-tolerant crops and microorganisms provided in an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of the overall logic of the method for cleaning and remediating saline-alkali soil through the synergistic interaction of salt-tolerant crops and microorganisms provided in an embodiment of the present invention.

[0023] The components represented by each number in the attached diagram are explained below:

[0024] Soil background diagnosis module 11, halophilic salt removal regulation module 12, alkaliophilic alkali regulation module 13, microbial community transplantation and cultivation module 14, and collaborative feedback control module 15. Detailed Implementation

[0025] This invention provides a saline-alkali soil cleanup and remediation system and method that combines salt-tolerant crops and microorganisms, addressing the technical problem that existing technologies cannot adaptively adjust the application strategies at each stage of the remediation process based on real-time soil condition information.

[0026] Example 1, as Figure 1 As shown, this invention provides a saline-alkali soil cleanup and remediation system that combines salt-tolerant crops and microorganisms. The system includes:

[0027] The soil background diagnosis module 11 is used to collect soil samples from the target saline-alkali land, measure the soil physicochemical properties, and obtain soil physicochemical parameters, which include at least the initial pH value, salinity, organic matter content, soil bulk density, and nutrient content.

[0028] Saline-alkali land exhibits high spatial heterogeneity; pH ​​values, salinity, organic matter content, soil bulk density, and nutrient content can vary significantly across different locations within the same plot. Directly remediating the entire plot without a unified approach fails to accurately assess the actual soil conditions and provides unreliable data for subsequent steps such as calculating halophilic microorganism deployment and optimizing microbial community composition. Furthermore, accurate measurement of soil physicochemical parameters is a prerequisite for subsequent salinity classification, and the accuracy of the classification results directly determines the precision of halophilic microorganism deployment calculations. Therefore, before implementing a remediation plan, representative soil samples must be collected, and various soil physicochemical parameters must be obtained using standardized measurement methods to serve as the data foundation for subsequent remediation stages.

[0029] In one embodiment, the soil background diagnosis module 11 is further configured to:

[0030] The target saline-alkali land was divided into multiple grid units. Sampling points were set in each grid unit to collect surface soil samples. The surface soil samples were mixed evenly to obtain representative soil samples.

[0031] Take a representative soil sample and mix it with distilled water in a certain proportion. After stirring, let it stand and insert a pH electrode into the supernatant to measure the initial pH value.

[0032] Take a representative soil sample and mix it with distilled water. After extraction by shaking, filter the sample, evaporate the filtrate to dryness and dry it to constant weight. Weigh the residue and calculate the salt content.

[0033] A representative soil sample was added to potassium dichromate solution and sulfuric acid, heated to react, and then titrated with ferrous sulfate standard solution. The organic matter content was calculated based on the amount consumed.

[0034] Press the ring cutter into the soil and remove it. After drying, weigh the dry soil and calculate the mass of dry soil per unit volume to obtain the soil bulk density.

[0035] Nutrient content includes total nitrogen content, available phosphorus content, and available potassium content. Total nitrogen content is determined by titration after digestion and distillation of a representative soil sample. Available phosphorus content is determined by colorimetric analysis after extraction. Available potassium content is determined by flame photometry after extraction.

[0036] First, the target saline-alkali land was divided into multiple grid units. Sampling points were set up within each grid unit to collect surface soil samples. These samples were then thoroughly mixed to obtain representative soil samples. The target saline-alkali land was further divided into multiple grid units according to a pre-set grid spacing. Sampling points were set up at the intersections or center of each grid unit. At each sampling point, after clearing surface weeds, dead branches, and fallen leaves, surface soil samples were collected up to a depth of 20 cm. The surface soil samples collected from each sampling point were placed in clean, sealed polyethylene bags, labeled with the sampling point number and location coordinates, and then transported back to the laboratory.

[0037] In the laboratory, surface soil samples from each sampling point were spread on clean kraft paper and allowed to air dry in a well-ventilated, shady place, turning the soil samples daily during the drying process. After drying to constant weight, the surface soil samples from each sampling point were crushed and sieved to remove visible plant roots, gravel, and animal remains. Equal amounts of the sieved soil samples from each sampling point were weighed and thoroughly mixed in the same clean container to obtain a representative soil sample.

[0038] For example, taking a coastal saline-alkali land demonstration area as an example, the demonstration area covers 50 mu (approximately 3.3 hectares). The demonstration area was divided into multiple grid units with a grid spacing of 20m × 20m, and 20 sampling points were selected from the grid intersections. At each sampling point, approximately 1 kg of topsoil from the surface to a depth of 20 cm was collected. After the samples from each sampling point were transported back to the laboratory, they were spread out on kraft paper and air-dried naturally in a well-ventilated and shady place for 7 days, turning them over twice a day. After air-drying, the soil was crushed and passed through a 2mm nylon sieve. 100g of the sieved sample from each sampling point was taken and thoroughly mixed in the same large container to obtain approximately 2 kg of representative soil sample.

[0039] Next, a representative soil sample was mixed with distilled water in a specific ratio, stirred, and allowed to stand. A pH electrode was then inserted into the supernatant to measure the initial pH value. pH is an important indicator of soil acidity and alkalinity, used for subsequent salinization classification. During the measurement, a sieved representative soil sample was weighed and placed in a clean beaker. Distilled water (with carbon dioxide removed) was added to mix the soil and water at the set water-to-soil ratio. The mixture was stirred with a glass rod to ensure thorough mixing, and then allowed to stand. After standing, the pH electrode was inserted into the supernatant in the beaker, and the pH value was recorded after the pH meter reading stabilized.

[0040] For example, weigh 10g of a representative soil sample that has passed through a 2mm nylon sieve and place it in a 100mL beaker. Add 25mL of distilled water (with carbon dioxide removed), resulting in a soil-to-water ratio of 2.5:1. Stir with a glass rod for 2 minutes to thoroughly mix the soil and distilled water, and let it stand for 30 minutes. Insert a pH electrode into the supernatant and, after the pH meter reading stabilizes, record the initial pH value as 9.8.

[0041] Next, a representative soil sample is mixed with distilled water, extracted by shaking, filtered, and the filtrate is evaporated to dryness and dried to constant weight. The mass of the residue is weighed, and the salt content is calculated. Salt content refers to the mass percentage of soluble salts in the soil, which is one of the core indicators for determining the degree of salinization and a key input parameter for calculating the amount of halophilic microorganisms required. During the determination, a sieved representative soil sample is weighed and placed in an Erlenmeyer flask, distilled water is added, and the sample is shaken on a shaker to fully dissolve the soluble salts. After shaking, the suspension is filtered, the initial filtrate is discarded, and subsequent filtrates are collected. The filtrate is placed in a pre-weighed evaporating dish, evaporated to dryness in a water bath, and then dried in an oven to constant weight. After cooling in a desiccator, the total mass of the evaporating dish and residue is weighed. The salt content is calculated using the following formula: .in, Salt content, The total mass of the evaporating dish and residue. The initial mass of the evaporating dish is denoted as . The mass of the representative soil sample taken.

[0042] For example, 50g of a representative soil sample passed through a 2mm nylon sieve was weighed and placed in a 250mL Erlenmeyer flask. 250mL of distilled water was added, and the sample was extracted by shaking on a shaker for 3 minutes. After shaking, the suspension was filtered through slow-speed quantitative filter paper, discarding the first 5mL of filtrate and collecting the subsequent filtrate. 50mL of the filtrate was placed in a pre-weighed evaporating dish, evaporated to dryness in a water bath, and then dried in a 105℃ oven until constant weight. After cooling in a desiccator for 30 minutes, it was weighed. The total mass of the evaporating dish and residue was 56.1234g, the initial mass of the evaporating dish was 55.7634g, and the mass of the representative soil sample was 50g. (Salt content...) .

[0043] Next, a representative soil sample was added to potassium dichromate solution and sulfuric acid, heated to react, and then titrated with ferrous sulfate standard solution. The organic matter content was calculated based on the amount consumed. Organic matter content is an important basis for assessing soil fertility and determining the organic fertilizer ratio parameters during the microbial colony transplantation stage. During the determination, a sieved representative soil sample was weighed and placed in a hard glass test tube. Potassium dichromate standard solution was added, followed by concentrated sulfuric acid. After thorough mixing, the sample was heated in an oil bath to the specified temperature and maintained for the set time. After heating, the reaction solution in the test tube was transferred to an Erlenmeyer flask, diluted with distilled water, and an o-phenanthroline indicator was added. Titration with ferrous sulfate standard solution continued until the solution changed from orange-yellow to blue-green and then to reddish-brown, marking the endpoint.

[0044] Specifically, the organic matter content is calculated using the following formula: .in, Organic matter content, This represents the volume of ferrous sulfate standard solution consumed in the blank titration. This refers to the volume of ferrous sulfate standard solution consumed in the sample titration. 0.003 represents the concentration of the ferrous sulfate standard solution, 1.724 represents the mass of carbon per millimole, 1.1 represents the coefficient for converting organic carbon to organic matter, and 1.1 represents the correction factor. The mass of the representative soil sample taken.

[0045] For example, 0.5 g of a representative soil sample passed through a 0.25 mm nylon sieve was weighed and placed in a hard glass test tube. 5 mL of 0.8 mol / L potassium dichromate standard solution was added, followed by 5 mL of concentrated sulfuric acid. The mixture was shaken thoroughly and heated in an oil bath at 185°C for 5 minutes. After heating, the reaction solution was transferred to a 250 mL Erlenmeyer flask, diluted with distilled water to approximately 60 mL, and 3 drops of o-phenanthroline indicator were added. Titration was performed with 0.2 mol / L ferrous sulfate standard solution until the solution changed from orange-yellow to blue-green and then to reddish-brown. The sample titration consumed 24.3 mL of ferrous sulfate standard solution, and the blank titration consumed 25.6 mL. Organic matter content... .

[0046] Subsequently, the ring sampler was pressed into the soil and removed. After drying, the mass of the dry soil was measured, and the mass of the dry soil per unit volume was calculated to obtain the soil bulk density. Soil bulk density refers to the mass of dry soil per unit volume and is used to correct the soil bulk density correction factor in the calculation of halophilic microorganism dosage. During the test, the ring sampler was vertically pressed into the soil at the sampling point until the top of the ring sampler was level with the ground surface. The ring sampler and soil were then excavated from the bottom with a shovel, and excess soil at both ends of the ring sampler was scraped off with a scraper. The ring sampler containing soil was brought back to the laboratory, dried in an oven to constant weight, and then the total mass of the ring sampler and dry soil was measured. Soil bulk density is calculated using the following formula: .in, For soil bulk density, The total mass of the cutter head and dry soil. Let be the initial mass of the ring cutter. This refers to the volume of the ring cutter.

[0047] For example, three bulk density measurement points corresponding to the soil sampling points were selected in the demonstration area, and a volume of 100 cm³ was measured. 3 The stainless steel ring cutter was vertically pressed into the soil and then removed. The ring cutter, containing soil, was brought back to the laboratory and dried in a 105℃ oven until constant weight, then weighed. The total mass of the ring cutter and dry soil was 213g, the initial mass of the ring cutter was 83g, and the volume of the ring cutter was 100cm³. 3 Soil bulk density BD = (213-83) / 100 = 1.3 g / cm³ 3 .

[0048] In addition, nutrient content includes total nitrogen content, available phosphorus content, and available potassium content. Total nitrogen content is determined by titration after digestion and distillation of a representative soil sample; available phosphorus content is determined by colorimetric analysis after extraction; and available potassium content is determined by flame photometry after extraction. Nutrient content, including total nitrogen content, available phosphorus content, and available potassium content, serves as auxiliary input parameters for calculating the ratio of organic fertilizer to native microbial communities during the microbial community transplantation stage.

[0049] The total nitrogen content was determined using the Kjeldahl method. The principle is to digest the soil sample with concentrated sulfuric acid and a catalyst to convert the organic nitrogen in the soil into ammonium nitrogen. Then, an alkaline solution is added for distillation, and the ammonium nitrogen is distilled off in the form of ammonia gas. After absorption with boric acid solution, it is titrated with a standard acid, and the total nitrogen content is calculated based on the amount of standard acid consumed.

[0050] The available phosphorus content was determined using the sodium bicarbonate extraction-molybdenum antimony colorimetric method. The principle is to extract available phosphorus from the soil using sodium bicarbonate solution. The phosphate ions in the extract react with ammonium molybdate and potassium antimony tartrate under acidic conditions to form a phosphomolybdate blue complex. The absorbance of this complex at a specific wavelength is proportional to the phosphorus content. The available phosphorus content is calculated by colorimetrically measuring the absorbance and substituting it into the phosphorus standard curve.

[0051] The available potassium content was determined using the ammonium acetate extraction-flame photometry method. The principle is to extract available potassium from the soil using an ammonium acetate solution, atomize the extract and spray it into a flame photometer. Potassium atoms are excited in the flame and emit light of a specific wavelength. The intensity of the emitted light is proportional to the potassium concentration. The intensity of the emitted light is measured by the flame photometer and substituted into the potassium standard curve to calculate the available potassium content.

[0052] For example, the total nitrogen content was measured to be 0.18 g / kg, the available phosphorus content was measured to be 2.3 mg / kg, and the available potassium content was measured to be 45 mg / kg.

[0053] In this embodiment of the invention, by acquiring key soil physicochemical parameters such as initial pH, salinity, organic matter content, soil bulk density, total nitrogen content, available phosphorus content, and available potassium content of the target saline-alkali land, an accurate data foundation is provided for subsequent salinization degree classification, calculation of halophilic microorganism application rates, and calculation of the ratio of organic fertilizer to native microorganisms during the microbial community transplantation stage. The grid sampling method ensures the representativeness of soil samples, and the use of standard measurement methods for each parameter guarantees the accuracy and repeatability of the data, providing a reliable basis for decision-making in subsequent remediation stages.

[0054] The halophilic cleansing control module 12 is used to classify the degree of salinization based on the initial pH value and salt content, obtain the salinity level, calculate the amount of halophilic microorganisms to be added based on the salinity level and salt content, perform cleansing treatment, and obtain cleansing status parameters.

[0055] In this embodiment, saline-alkali land exhibits significant differences in salinization degree and soil compaction. It is necessary to classify the land based on initial pH and salinity, and set differentiated application coefficients for different levels. Simultaneously, a soil bulk density correction coefficient is introduced to accurately adjust the application amount of halophilic microorganisms, ensuring that the application amount matches the actual soil conditions. More importantly, the effectiveness of the desalination stage is not only reflected in the residual salinity but also in the rate of salinity reduction, accompanying pH changes, and the ebb and flow of halophilic microorganism activity. These process characteristics have a crucial impact on optimizing the application strategy in the subsequent alkali adjustment stage. Therefore, it is necessary to simultaneously monitor these indicators and extract characteristic parameters during the desalination process, generating desalination state parameters to be transmitted to the alkali adjustment stage as decision input.

[0056] In one embodiment, the halophilic salt control module 12 is further configured to:

[0057] The degree of salinization is classified according to the initial pH value and salinity to obtain the salinity level, which includes mild salinity, moderate salinity and severe salinity. Different dosage coefficients are set according to the salinity level.

[0058] Obtain the standard soil bulk density, and determine the soil bulk density correction factor based on the ratio of the soil bulk density to the standard soil bulk density;

[0059] Based on the application coefficient, salt content and soil bulk density correction coefficient corresponding to the salinity level, calculate the application amount of halophilic microorganisms and apply the halophilic microorganism agent into the soil to carry out salt removal treatment.

[0060] During the desalination process, soil salinity, pH value and halophilic microbial activity indicators were measured multiple times at preset time intervals to obtain the salinity decrease curve, pH change curve and microbial activity change curve.

[0061] The slope of salt decrease was extracted from the salt content decrease curve; the cumulative pH change at the end of salt removal was extracted from the pH change curve; and the peak activity time was extracted from the bacterial activity change curve.

[0062] To obtain the residual salt content after the salt removal process;

[0063] The slope of salt concentration decrease, cumulative pH change, peak activity time, and residual salt content were used as parameters for the salt removal state.

[0064] First, the degree of salinization is classified based on the initial pH value and salinity to obtain a salinity level, which includes mild, moderate, and severe salinity. Different application coefficients are set according to the salinity level. Based on the initial pH value and salinity S measured by the soil baseline diagnosis module 11, the salinity level of the target saline-alkali land is determined according to the following classification standards: The salinity levels include three grades: mild, moderate, and severe. Mild salinity: 7.5 ≤ pH < 8.5, 0.1% ≤ S < 0.3%; Moderate salinity: 8.5 ≤ pH < 9.5, 0.3% ≤ S < 0.6%; Severe salinity: pH ≥ 9.5, S ≥ 0.6%. The determination of mild, moderate, and severe salinity requires that both pH and salinity conditions be met simultaneously. If the pH classification and salinity classification are inconsistent, the higher level is taken as the final classification result. Different application coefficients are set according to the salinity level. The dosage coefficient is 0.5 for mild salinity, 1.0 for moderate salinity, and 1.5 for severe salinity. The higher the degree of salinization, the larger the dosage coefficient, in order to enhance the initial colonization ability and salt adsorption effect of halophilic microorganisms in harsh environments.

[0065] For example, the soil background diagnosis module 11 measured an initial pH value of 9.8 and a salinity of 0.72%. According to the grading standards, an initial pH value of 9.8 and a salinity of 0.72% both indicate severe salinity; therefore, the demonstration area is classified as severely salinized. The application coefficient corresponding to severe salinity is... It is 1.5.

[0066] Secondly, obtain the standard soil bulk density, and determine the soil bulk density correction factor based on the ratio of the original soil bulk density to the standard soil bulk density. The soil bulk density correction factor is used to correct for the impact of varying soil compaction on the diffusion efficiency of the bacterial solution in the calculation of halophilic microorganism dosage. Determined based on the ratio of soil bulk density to standard soil bulk density: .in, This is the soil bulk density correction factor. The soil bulk density measured by the soil background diagnosis module 11 This refers to the standard soil bulk density. For example, the soil bulk density in this demonstration area... 1.3 g / cm 3 Standard soil bulk density 1.3 g / cm 3 Soil bulk density correction factor .

[0067] The standard soil bulk density is taken as the typical bulk density reference value for loamy soils recognized in the field for soil fertility evaluation. This value is used as a benchmark reference point to measure the degree of soil compaction, so as to calculate the deviation of the measured soil bulk density from this reference value, thereby correcting the amount of halophilic microorganisms to be added. In this embodiment, the standard soil bulk density is taken as 1.3 g / cm³. 3 When the measured soil bulk density is greater than the standard soil bulk density, the soil bulk density correction factor is greater than 1, indicating that the dosage needs to be increased to overcome the obstruction of bacterial diffusion by compacted soil; when the measured soil bulk density is less than the standard soil bulk density, the soil bulk density correction factor is less than 1, indicating that the dosage can be appropriately reduced.

[0068] Next, based on the application coefficient corresponding to the salinity level, salt content, and soil bulk density correction coefficient, the application rate of halophilic microorganisms is calculated, and the halophilic microbial agent is applied to the soil for salinization treatment. The application rate of halophilic microorganisms is calculated according to the following formula: .in, This refers to the amount of halophilic microorganisms added. This refers to the application coefficient corresponding to the salinity level. Salt content, This is the soil bulk density correction coefficient. The halophilic microbial agent, calculated according to the above formula, is evenly applied to the target saline-alkali soil. Tillage is then performed to ensure thorough mixing of the agent with the soil, thus carrying out desalination treatment. The halophilic microbial agent is a liquid agent prepared by fermentation from indigenous halophilic microbial strains isolated and screened from the target saline-alkali soil, with a viable bacterial concentration greater than or equal to 1×10⁻⁶. 8 CFU / mL.

[0069] For example, the demonstration area is severely saline-alkali, and the application coefficient is... The salt content is 1.5. The soil bulk density correction factor is 0.72%. The value is 1.0. (This refers to the amount of halophilic microorganisms added.) The demonstration area covers 50 mu (approximately 3.3 hectares) and requires a total of 54 L of halophilic microbial inoculant. In this example, halophilic Bacillus subtilis is used as the halophilic microbial inoculant, with a viable cell concentration of 1 × 10⁻⁶. 9 CFU / mL. Dilute 54L of the microbial agent 30 times and spray it evenly on the soil surface of the demonstration area. Then, till the soil to a depth of 15 to 20cm to fully mix the microbial agent with the soil.

[0070] Furthermore, during the desalination process, soil salinity, pH value, and halophilic microbial activity indicators were measured multiple times at preset time intervals to obtain salinity decrease curves, pH change curves, and microbial activity change curves. The slope of salinity decrease was extracted from the salinity decrease curve, the cumulative pH change at the end of desalination was extracted from the pH change curve, and the peak activity time was extracted from the microbial activity change curve.

[0071] Specifically, after the desalination treatment is performed, soil samples are collected multiple times at preset time intervals to measure soil salinity, pH value, and halophilic microbial activity indicators. The measurement data at each time point are recorded for subsequent characteristic parameter extraction. The methods for measuring soil salinity and pH value are the same as those in Soil Background Diagnosis Module 11. The halophilic microbial activity indicator is the number of viable halophilic microorganisms, determined by the plate count method: a soil suspension is prepared from the soil sample, serially diluted, and spread onto a halophilic microbial selective culture medium plate. After incubation at a suitable temperature, the colony-forming units are counted, expressed as the number of colony-forming units per gram of soil (CFU / g).

[0072] During the desalination process, it is necessary to determine when to terminate the desalination treatment and enter the alkali adjustment phase. The determination of the desalination endpoint requires the simultaneous fulfillment of two conditions: first, the soil salinity must have decreased to within the optimal salinity window that alkali-loving microorganisms can tolerate and maintain high activity; second, the activity of halophilic microorganisms must have passed its peak and begun to decline, indicating that the optimal window for the desalination phase has ended. When both conditions are met simultaneously, continuing to wait will not significantly increase the desalination effect; on the contrary, it may cause the salinity to rebound due to the release of intracellular salts from dead microorganisms. Therefore, the current time point is determined as the desalination endpoint.

[0073] The optimal salinity window for alkaliphilic microorganisms was pre-determined as follows: Alkaliphilic microbial strains were isolated and screened from the target saline-alkali soil. The isolated and purified strains were inoculated into liquid culture medium, and multiple salinity gradients were established. Shake-flask culture was then conducted under the same temperature, pH, and culture time conditions. Samples were taken periodically during the culture period for analysis. Calculate absorbance values ​​and plot growth curves at various salinity gradients. Compare the maximum absorbance values ​​at each salinity gradient. Value and specific growth rate, The salinity range corresponding to the maximum value of the growth rate and the specific growth rate is the optimal salinity window for the growth of this strain. In this embodiment, the optimal salinity window for this alkaliphilic microbial strain was determined to be 0.20% to 0.35% through the above-mentioned gradient salinity acclimatization experiment.

[0074] For example, after the desalination treatment was carried out in the demonstration area, soil samples were collected on days 1, 3, 5, 7, 10, and 14, respectively, and the salinity, pH value, and viable number of halophilic microorganisms were measured at each time point. The monitoring data are shown in Table 1.

[0075] Table 1 Monitoring Data of Salt Treatment Process

[0076]

[0077] Based on the monitoring data above, the salinity decreased continuously from the initial 0.72%, dropping to 0.31% on the 7th day, falling within the optimal salinity window (0.20% to 0.35%) for this alkaliphilic microbial strain; simultaneously, the viable bacterial count reached a peak of 5.8 × 10⁻⁶ on the 5th day. 7 After CFU / g, it had decreased to 5.2×10 on day 7. 7 The CFU / g level showed a decreasing trend, indicating that the optimal window for the salinization phase had ended. Both conditions were met simultaneously on day 7, therefore day 7 was determined to be the salinization endpoint.

[0078] Secondly, the salinity decrease slope was extracted. The salinity decrease slope characterizes the release rate of soil salt in the initial stage of desalination treatment, reflecting soil permeability and sensitivity to microbial regulation. The salinity decrease slope was obtained by linearly fitting the salinity-time data of the initial segment of the salinity decrease curve.

[0079] Specifically, salinity data from the first three days after the start of the desalination treatment were used. A linear regression was performed with the number of days on the x-axis and salinity on the y-axis. The slope of the fitted line represents the salinity decrease slope. The initial stage of the desalination treatment (0 to 3 days) is the period of rapid adsorption of free salt ions by halophilic microorganisms, resulting in the fastest rate of salinity decrease. This stage best reflects the proportion of free salt in the soil and its permeability. Using a longer timeframe, the later data showing a slower rate of salinity decrease will lower the overall slope estimate, making the fitting results less accurate in representing the initial response characteristics of the soil and thus affecting the assessment of soil response sensitivity during subsequent alkali adjustment stages.

[0080] For example, the salinity data for the first three days after the start of the salt removal treatment in this demonstration area are: Day 0: 0.72%, Day 1: 0.55%, and Day 3: 0.42%. A linear fit was performed using the least squares method, with the number of days (x) as the x-axis and the salinity (y) as the y-axis. The three data points are (0, 0.72), (1, 0.55), and (3, 0.42). The slope of the fitted line was calculated using the least squares method. The absolute value of the slope reflects the average rate of decrease in the initial response phase and is used to determine the soil's sensitivity to subsequent alkali-regulating stages. A larger absolute value of the slope indicates that the soil salinity is predominantly in a free state, has good permeability, and is more sensitive to subsequent regulation by alkali-loving microorganisms. In this example, the average daily decrease was approximately 0.085 percentage points / day, indicating good soil permeability and high sensitivity to subsequent regulation by alkali-loving microorganisms.

[0081] Next, the cumulative pH change was extracted. The cumulative pH change characterizes the incidental neutralization effect on soil alkalinity produced by halophilic microorganisms during the salt removal process, while they adsorb salt ions. The cumulative pH change is the difference between the initial pH value and the pH value at the end of the salt removal process. .in, This represents the cumulative pH change at the end of the salt removal process. The initial pH value measured by the soil background diagnostic module 11. This is the pH value measured at the end of the salt removal process.

[0082] For example, the initial pH value of the demonstration area The value was 9.8, and it was at the end of the 7th day after the salt removal process. The cumulative change in pH is 8.7. =9.8-8.7=1.1. This cumulative pH change will be transmitted to the alkaliphilic alkali adjustment module 13 to correct the start threshold of the alkali adjustment stage.

[0083] Simultaneously, the peak activity time was extracted. The peak activity time characterizes the point at which halophilic microorganisms reach maximum activity during the salt removal process, reflecting the optimal window period for salt removal. The peak activity time is extracted from the microbial community activity change curve, that is, the number of days corresponding to when the viable number of halophilic microorganisms reaches its maximum value.

[0084] For example, the time series data of viable halophilic microorganisms in this demonstration area is as follows: Day 1: 2.1 × 10⁻⁶ 7 CFU / g, 4.5×10 on day 3 7 CFU / g, 5.8×10 on day 5 7 CFU / g, 5.2×10 on day 7 7 CFU / g, 4.0×10 on day 10 7 CFU / g, 3.1×10 on day 14 7 CFU / g. The viable count peaked at 5.8 × 10⁻⁶ on day 5. 7 CFU / g, peak activity time It is day 5.

[0085] Then, the residual salt content is obtained after the desalination process. The residual salt content is the salt content in the soil at the end of the desalination treatment. After the desalination treatment, the salt content is measured according to the method in Soil Background Diagnosis Module 11, and the measured value is taken as the residual salt content. For example, if the salt content is measured to be 0.31% at the end of the 7th day of desalination treatment, this value is the residual salt content. .

[0086] Finally, the salt concentration decrease slope, cumulative pH change, peak activity time, and residual salt content were used as parameters for the salt purification state. Salt purification state parameters = { , , , The salt clearing status parameters are stored in the form of a data set and transmitted to the alkali-loving alkali-adjusting control module 13 as the input basis for determining the control parameters for the alkali-adjusting stage.

[0087] In this embodiment of the invention, a complete process was established, from the classification of salinity levels to the calculation of halophilic microorganism dosage and the extraction of salinity-clearing state parameters. The classification of salinity levels allows the dosage coefficient to be adaptively adjusted according to the soil salinity level, and the soil bulk density correction coefficient allows the dosage to be precisely adjusted according to the soil compaction level, achieving quantification and precision in the dosage of halophilic microorganisms. During the salinity-clearing process, the temporal changes in salt content, pH value, and viable count of halophilic microorganisms are monitored simultaneously. Four salinity-clearing state parameters are extracted: the slope of salt decrease, the cumulative change in pH, the peak activity time, and the residual salt content. The process characteristic information of the salinity-clearing stage is quantified and transmitted to the alkali-loving regulation module 13, realizing information-driven regulation of the alkali-adjusting stage by the salinity-clearing stage, providing a data foundation for the adaptive correction of subsequent alkali-adjusting dosage strategies.

[0088] The alkali-loving regulation module 13 is used to determine the control parameters for the addition of alkali-loving microorganisms based on the clear salt state parameters. During the alkali adjustment process, the pH value is acquired multiple times and the control parameters for the addition of alkali-loving microorganisms are updated until the pH value reaches the preset target range. The control parameters for the addition of alkali-loving microorganisms include the amount of alkali-loving microorganisms added and the time interval between additions.

[0089] In this embodiment of the invention, the goal of the alkali-adjusting stage is to further neutralize soil alkalinity and replace sodium ions, based on the reduction of salt content in the salinity-clearing stage, so that the soil pH value is reduced to the target range suitable for crop growth. However, the response of alkali-loving microorganisms to the soil environment has a lag of 2 to 3 days. If decisions are made solely based on the current pH value, repeated application may occur before the lag effect is fully released, leading to over-correction of pH and waste of microbial agents. Furthermore, different soils exhibit varying sensitivities to microbial regulation, and the execution effect of the salinity-clearing stage directly affects the initial conditions and decision-making requirements of the alkali-adjusting stage. Therefore, the alkali-adjusting strategy must be adaptively modified based on the actual execution effect of the salinity-clearing stage. To this end, salinity-clearing state parameters are used as decision inputs to modify rule weights, application rates, activation thresholds, activity compensation coefficients, and intervention time windows. During execution, fuzzy logic is used to dynamically adjust the application rate and interval, and pH values ​​are obtained multiple times for iterative updates until the pH value reaches the modified activation threshold.

[0090] In one embodiment, the alkali-loving alkali-regulating module 13 is further configured to:

[0091] The activation threshold of the preset target range is adjusted based on the cumulative change in pH.

[0092] The activity compensation coefficient is determined based on the residual salt content;

[0093] The intervention time window for the alkali adjustment stage is determined based on the peak activity time.

[0094] Within the intervention time window, the current pH value and pH change rate are obtained. Based on the current pH value and pH change rate, the corresponding fuzzy rules are matched from the pre-constructed modified fuzzy rule library, and the first alkaliphilic microorganism dosage and the first dosage time interval are output.

[0095] Repeatedly obtain the current pH value and pH change rate, match the corresponding fuzzy rules from the modified fuzzy rule library, and output the amount of alkaliphilic microorganisms to be added and the time interval between additions, until the current pH value reaches the activation threshold;

[0096] The construction of a revised fuzzy rule base includes:

[0097] An initial fuzzy rule base is constructed, which contains multiple fuzzy rules. The fuzzy rules include the correspondence between pH value range, pH change rate range and alkaliphilic microorganism dosage level and dosage time interval level. Among them, the pH value range includes high value range, medium value range and low value range, and the pH change rate range includes positive value range, zero value range and negative value range.

[0098] When the slope of the salt decrease is greater than the first threshold, the weight of the fuzzy rules in the initial fuzzy rule base that have a high pH value range and a positive pH change rate range is reduced by the first correction amount.

[0099] When the slope of the salt decrease is less than the second threshold, the weight of the fuzzy rules in the initial fuzzy rule base that have a pH value range of median value and a pH change rate range of positive value is increased by the second correction amount.

[0100] When the slope of the salt concentration decrease is between the second threshold and the first threshold, the weights of the initial fuzzy rule base remain unchanged.

[0101] The modified fuzzy rule base is obtained by adjusting the fuzzy logic output of the alkaliphilic microorganism dosage level based on the slope of the salinity decrease.

[0102] First, an initial fuzzy rule base is constructed. The initial fuzzy rule base has multiple preset fuzzy rules. The fuzzy rules include the correspondence between pH value range, pH change rate range and alkaliphilic microorganism dosage level and dosage time interval level. Among them, the pH value range includes high value range, medium value range and low value range, and the pH change rate range includes positive value range, zero value range and negative value range.

[0103] Wherein, the pH value intervals include a high-value interval, a medium-value interval and a low-value interval. Each pH value interval, its interval width and boundary values are as follows: high-value interval: 8.5 < pH < 9.0; medium-value interval: 7.5 ≤ pH ≤ 8.5; low-value interval: 6.5 < pH < 7.5.

[0104] Wherein, the pH change rate intervals include a positive interval, a zero interval and a negative interval. Each pH change rate interval, its interval width and boundary values are as follows: positive interval: 0.05 < ΔpH < 0.15; zero interval: -0.05 ≤ ΔpH ≤ 0.05; negative interval: -0.15 < ΔpH < -0.05.

[0105] Wherein, the preset rules in the fuzzy rule base include the following multiple fuzzy rules, each of which corresponds to the mapping relationship between different combinations of pH value intervals and pH change rate intervals and the alkaliphilic microorganism dosage gear and the dosing time interval gear. The alkaliphilic microorganism dosage gears include low gear, medium gear and high gear, and the corresponding reference dosage values are 2 L / mu, 5 L / mu and 8 L / mu respectively. The dosing time interval gears include short interval gear, medium interval gear and long interval gear, and the corresponding reference time interval values are 5 days, 10 days and 15 days respectively.

[0106] Specifically, each fuzzy rule is as follows: Rule 1: When the pH value interval is the high-value interval and the pH change rate interval is the positive interval, the alkaliphilic microorganism dosage gear is the high gear and the dosing time interval gear is the short interval gear; Rule 2: When the pH value interval is the high-value interval and the pH change rate interval is the zero interval, the alkaliphilic microorganism dosage gear is the medium gear and the dosing time interval gear is the medium interval gear; Rule 3: When the pH value interval is the medium-value interval and the pH change rate interval is the positive interval, the alkaliphilic microorganism dosage gear is the medium gear and the dosing time interval gear is the medium interval gear; Rule 4: When the pH value interval is the medium-value interval and the pH change rate interval is the zero interval, the alkaliphilic microorganism dosage gear is the low gear and the dosing time interval gear is the long interval gear; Rule 5: When the pH value interval is the low-value interval or the pH change rate interval is the negative interval, no alkaliphilic microorganisms are dosed, that is, the dosage of alkaliphilic microorganisms is zero. The initial weight of each rule in the initial fuzzy rule base is 1.0, which means that each rule has the same basic influence in the uncorrected state.

[0107] Secondly, when the salinity decrease slope is greater than the first threshold, the weights of fuzzy rules in the initial fuzzy rule library that have high pH values ​​and positive pH change rates are reduced by the first correction amount. When the salinity decrease slope in the salinity status parameters is greater than the first threshold, it indicates that the target saline-alkali soil has good permeability and high sensitivity to microbial regulation. In this case, if the large dosage of Rule 1 is used, it is easy to cause pH overcorrection after the hysteresis effect is fully released. Therefore, the weights of fuzzy rules in the initial fuzzy rule library that have high pH values ​​and positive pH change rates are reduced, i.e., the weights of Rule 1, by the first correction amount.

[0108] The first threshold is the upper limit for judging the sensitivity of the salinity decrease slope. Its value is determined to distinguish between fast-responding soils with good permeability and a high proportion of free salt, and ordinary soils with moderate permeability. This value corresponds to the minimum response rate of fast-responding soils; soils exceeding this value are classified as highly sensitive. In this embodiment, the first threshold is 0.15 percentage points / day.

[0109] Specifically, the slope of the salt concentration decrease is first calculated. With the first threshold deviation ,when Time correction amount ,when Time correction amount ,when Time correction amount The coefficient 2.0 is a preset weight adjustment coefficient. The design logic is that when the deviation reaches 15%, the correction amount is 0.3, reducing the weight from 1.0 to 0.7. The maximum correction amount of 0.6 is a preset upper limit value. The design logic is that the weight should not be lower than 0.4, ensuring that Rule 1 retains a certain basic influence even in highly sensitive soil conditions. The corrected weights for Rule 1. .

[0110] For example, the slope of the salinity decrease in this demonstration area The first threshold is 0.17 percentage points per day. It is 0.15 percentage points / day, which meets the requirements. > Deviation ,because Correction amount ,Pick The revised weights of Rule 1 .

[0111] Secondly, when the salinity decrease slope is less than the second threshold, the weights of fuzzy rules in the initial fuzzy rule base that have a median pH range and a positive pH change rate range are increased by the second correction amount. When the salinity decrease slope in the salinity state parameters is less than the second threshold, it indicates that the target saline-alkali land soil has poor permeability and low sensitivity to microbial regulation. In this case, the weight of rule 3 needs to be increased to strengthen the medium-range application strategy to compensate for the sluggish response.

[0112] The second threshold is the lower limit for judging the sensitivity of the salinity decrease slope. Its value is determined to distinguish between sluggish-response soils with poor permeability and a high proportion of bound salts, and ordinary soils with moderate permeability. This value corresponds to the highest response rate of sluggish-response soils; soils below this value are judged as low-sensitivity soils. In this embodiment, the second threshold is 0.05 percentage points / day.

[0113] Specifically, the slope of the salt concentration decrease is first calculated. With the second threshold deviation ,when Time correction amount ,when Time correction amount ,when Time correction amount The coefficient 2.0 is the preset weight adjustment coefficient, and the maximum correction amount of 0.6 is the preset upper limit. The design logic is that the weight should not exceed 1.6, meaning the upward adjustment of Rule 3 should not exceed 0.6, to avoid Rule 3 becoming too dominant and causing decision imbalance. The corrected weights for Rule 3. .

[0114] Furthermore, the alkaliphilic microorganism dosage level output by the fuzzy logic is corrected based on the slope of the salt concentration decrease, thus obtaining the corrected fuzzy rule base.

[0115] First, when the slope of the salt drop Greater than the first threshold When the target saline-alkali soil has good permeability and high sensitivity to microbial regulation, it is necessary to adjust the reference values ​​of the dosage levels of all rules in the initial fuzzy rule base downward to avoid pH overcorrection due to excessive sensitivity.

[0116] The correction factor γ is determined as follows: when hour, ;when hour, The coefficient 1.5 is the preset adjustment coefficient, and the maximum downward adjustment of 0.2 is the preset upper limit. The design logic of this upper limit is to ensure that the dosage reduction does not exceed 20%, so as to avoid insufficient inoculant dosage due to excessive correction, which would prevent effective pH adjustment. The corrected dosage reference value is calculated according to the following formula: .in, This is the revised reference value for the amount of material distributed. This is a reference value for the amount of material released before the correction. This is a correction factor.

[0117] Secondly, when the slope of the salt drop Less than the second threshold This indicates that the target saline-alkali land has poor soil permeability and low sensitivity to microbial regulation. Therefore, it is necessary to adjust the reference values ​​of the deployment amount corresponding to the deployment amount level of all rules in the initial fuzzy rule base upward to strengthen the deployment intensity and compensate for the impact of sluggish response.

[0118] Among them, the correction factor Determined in the following manner: when hour, ;when hour, The coefficient 1.5 is a preset adjustment coefficient, and the maximum upward adjustment of 0.2 is a preset upper limit. The design logic of this upper limit is to ensure that the increase in the dosage does not exceed 20%, so as to avoid excessive dosage of microbial agents due to excessive adjustment. When the slope of the salt decrease is within the second threshold... and the first threshold When in between, the correction factor No adjustments will be made to the distribution volume tiers.

[0119] For example, the slope of the salinity decrease in this demonstration area It is 0.17 percentage points per day, which is greater than the first threshold. =0.15, the reference value for the amount of material to be distributed needs to be lowered. Deviation , Therefore, the reference values ​​for the application rate for each rule are reduced by 20% overall. Specifically: the small rate is revised from 2L / mu to 2×0.8=1.6L / mu, the medium rate from 5L / mu to 5×0.8=4.0L / mu, and the large rate from 8L / mu to 8×0.8=6.4L / mu. The reference value for the application time interval remains unchanged. For Rule 1, the reference value for the large rate application rate is revised from 8L / mu to 6.4L / mu, while the reference value for the application time interval remains unchanged at 5 days; for Rule 3, the reference value for the medium rate application rate is revised from 5L / mu to 4.0L / mu, while the reference value for the application time interval remains unchanged at 10 days. After the above revisions, the revised fuzzy rule base is obtained.

[0120] Then, the activation threshold of the preset target range is adjusted based on the cumulative pH change. The activation threshold of the preset target range refers to the critical pH value at which intervention begins in the alkali adjustment stage. When the soil pH value is higher than this activation threshold, the alkali-loving alkali adjustment module 13 begins to execute the application decision; when the soil pH value is lower than or equal to this activation threshold, no alkali adjustment application is required. The activation threshold before correction is equal to the upper limit of the preset target range, i.e., pH 8.5. The activation threshold is adjusted based on the cumulative pH change in the salinity removal state parameters. During the salinity removal stage, halophilic microorganisms will produce a certain alkali reduction effect during the adsorption of salt ions, and this effect will continue to some extent into the alkali adjustment stage after the salinity removal is completed. Therefore, the cumulative pH change during the salinity removal stage is multiplied by the retention coefficient λ to obtain the effective pre-alkali reduction amount, and then the activation threshold is adjusted upward by this effective pre-alkali reduction amount.

[0121] Specifically, the revised startup threshold Calculate using the following formula: Where 8.5 is the upper limit of the preset target range. This represents the cumulative change in pH value among the parameters of a saline state. The retention factor is... In this embodiment, the retention coefficient The value is 0.2. This indicates that of the pH reduction effect during the salinization stage, about 20% will remain in the alkali adjustment stage, while the remaining 80% will be offset by the soil's own buffering capacity. It is an empirical balance value based on the degree of confidence in the soil's buffering capacity.

[0122] For example, the cumulative change in pH value in the salinity parameters of this demonstration area. The effective pre-treatment alkali reduction amount is 1.1, and the retention coefficient λ is 0.2. The corrected initiation threshold is 1.1 × 0.2 = 0.22. =8.5 + 0.22 = 8.72. That is, the alkali adjustment stage starts at pH 8.72, instead of the original 8.5. The residual alkali reduction effect of the salt cleaning stage is used to delay the intervention time, saving an unnecessary early addition.

[0123] Subsequently, the activity compensation coefficient was determined based on the residual salt content. This coefficient was used to adjust the dosage of alkaliphilic microorganisms to compensate for the impact of residual salt content deviating from the optimal salinity window on their activity. When the residual salt content was higher than the upper limit of the optimal salinity window, the salt exerted osmotic pressure inhibition on the alkaliphilic microorganisms, requiring an increased dosage to ensure a sufficient number of viable bacteria. When the residual salt content was lower than the lower limit of the optimal salinity window, insufficient salt led to reduced metabolic activity of the alkaliphilic microorganisms, requiring a reduced dosage to avoid wasting the inoculant. When the residual salt content fell within the optimal salinity window, the activity compensation coefficient was 1.0, and the dosage was based on the baseline amount.

[0124] Specifically, based on the residual salt content in the clean salt state parameters Optimal salinity window with alkaliphilic microorganisms Determine the relationship between the activity compensation coefficient and the activity compensation coefficient. :when hour, ;when hour, ;when hour, .in, This is the lower limit of the optimal salinity window. This represents the upper limit of the optimal salinity window. The coefficients 5 and 10 above are preset adjustment slopes, corresponding to the activity compensation sensitivity under insufficient and excessive salinity conditions, respectively. The adjustment slope is larger under excessive salinity because the osmotic inhibition of alkaliphilic microorganisms by high salinity is more severe than the decrease in metabolic activity under low salinity, requiring a more sensitive compensation response. The determination of the above compensation direction is based on the gradient salinity activity test results of the alkaliphilic microorganism strains in this embodiment: when deviating from the optimal salinity window, the metabolic activity per unit cell shows a decreasing trend; therefore, compensation is achieved by increasing the dosage. For example, The concentration was 0.31%, falling within the optimal salinity window of 0.20% to 0.35%, with an activity compensation coefficient f = 1.0.

[0125] Furthermore, the intervention time window for the alkali-conditioning stage is determined based on the peak activity time. The intervention time window refers to the optimal time range for starting the alkali-conditioning stage. The peak activity time characterizes the point at which halophilic microorganisms reach maximum activity during salt removal treatment, reflecting the optimal window of action for the salt removal stage. The alkali-conditioning stage should be initiated within the time window after the salt removal effect has been fully released, but before the decline of halophilic bacteria leads to soil environmental deterioration.

[0126] Specifically, the starting point of the intervention time window is determined based on the peak activity time: Intervention starting point = +2 days. The 2-day value is based on the fact that after reaching peak activity, the halophilic microorganisms remain in a stable phase for two days, meaning the salt-clearing effect has been largely achieved, and they have not yet entered a large-scale decline phase, preventing the re-release of salt due to microbial death. This value is determined based on the population dynamics of the halophilic microorganisms in this embodiment and can be adjusted in practical applications according to the decline rate of different strains and soil conditions. In this embodiment, the peak activity time... The intervention window begins on day 5, which is day 5 + day 2 = day 7. That is, the alkali adjustment phase begins on day 7 after the salt removal treatment.

[0127] Furthermore, within the intervention time window, the current pH value and pH change rate are obtained. Based on the current pH value and current pH change rate, the corresponding fuzzy rules are matched from the modified fuzzy rule base, and the first alkaliphilic microorganism dosage and the first dosage time interval are output.

[0128] Within the intervention time window, the current pH value and pH change rate are obtained. Based on the current pH value and pH change rate, corresponding fuzzy rules are matched from the modified fuzzy rule base to output the first alkaliphilic microorganism dosage and the first dosage time interval, including:

[0129] Obtain the current pH value and the current pH change rate. Determine the bias weight of each fuzzy rule matching the current pH value based on the position of the current pH value in each pH value interval. Determine the bias weight of each fuzzy rule matching the current pH change rate based on the position of the current pH change rate in each change rate interval.

[0130] The bias weights of the fuzzy rules matching the current pH value and the fuzzy rules matching the current pH change rate are multiplied pairwise to obtain the combined bias weights of each rule combination.

[0131] Based on the comprehensive bias weight, the reference values ​​of the delivery volume in each combination rule are weighted and averaged to obtain the initial value of the first delivery volume. The reference values ​​of the delivery time interval in each combination rule are weighted and averaged to obtain the initial value of the first delivery time interval.

[0132] The initial value of the first dosage is corrected according to the activity compensation coefficient to obtain the first dosage of alkalophilic microorganisms, and the initial value of the first dosage time interval is taken as the first dosage time interval.

[0133] First, obtain the current pH value and the current pH change rate. Then, determine the bias weight of each fuzzy rule matching the current pH value based on the position of the current pH value in each pH value interval. Finally, determine the bias weight of each fuzzy rule matching the current pH change rate based on the position of the current pH change rate in each change rate interval.

[0134] Specifically, the bias weights of each fuzzy rule matching the current pH value are determined based on the position of the current pH value within each pH value interval, including:

[0135] Determine the current pH value range into which the current pH value falls, and determine the first common boundary value that the current pH value is closest to within the current pH value range. The first common boundary value is the boundary value corresponding to the smaller distance between the current pH value and each common boundary value of the current pH value range. Calculate the absolute value of the difference between the current pH value and the first common boundary value as the first deviation.

[0136] Calculate the ratio of the first deviation to the width of the current pH range, and use it as the first position ratio;

[0137] The bias weight of the fuzzy rule corresponding to the current pH value interval is a first bias weight, and the first bias weight is equal to 1 minus the first position ratio;

[0138] The bias weight of the fuzzy rule corresponding to the adjacent pH value interval corresponding to the first shared boundary value is a second bias weight, and the second bias weight is equal to the first position ratio.

[0139] First, determine the current pH value interval where the current pH value falls, determine the first shared boundary value that the current pH value approaches within the current pH value interval, wherein the first shared boundary value is the boundary value corresponding to the one with a smaller distance between the current pH value and each shared boundary value of the current pH value interval, calculate the absolute value of the difference between the current pH value and the first shared boundary value, and use it as the first deviation degree. For example, the current pH value obtained at the starting point (the 7th day) of the intervention time window is 8.7. In the pH value interval division, the high value interval is 8.5 < pH < 9.0, and the median value interval is 7.5 ≤ pH ≤ 8.5, so the current pH value 8.7 falls into the high value interval. The first shared boundary value approached by the current pH value 8.7 is 8.5, and the first deviation degree .

[0140] Secondly, calculate the ratio of the first deviation degree to the interval width of the current pH value interval, and use it as the first position ratio. The first position ratio is calculated according to the following formula: . Wherein, is the first position ratio, , is the first deviation degree, is the interval width of the current pH value interval. For example, the interval width of the high value interval is 0.5. The first position ratio .

[0141] Thirdly, the bias weight of the fuzzy rule corresponding to the current pH value interval is a first bias weight, and the first bias weight is equal to 1 minus the first position ratio; the bias weight of the fuzzy rule corresponding to the adjacent pH value interval corresponding to the first shared boundary value is a second bias weight, and the second bias weight is equal to the first position ratio. Wherein, when the first bias weight is less than 0, it is taken as 0, and when the second bias weight is greater than 1, it is taken as 1. For example, the first bias weight = 1 - 0.4 = 0.6, and the second bias weight = 0.4.

[0142] Wherein, determining the bias weight of each fuzzy rule matched by the current pH change rate according to the position of the current pH change rate in each change rate interval comprises:

[0143] Determine the current pH change rate within the current change rate interval, identify the second common boundary value that the current pH change rate is closest to within the current change rate interval, the second common boundary value is the boundary value corresponding to the smaller distance between the current pH change rate and each common boundary value of the current change rate interval, and calculate the absolute value of the difference between the current pH change rate and the second common boundary value as the second deviation.

[0144] Calculate the ratio of the second deviation to the width of the current rate of change interval, and use it as the second position ratio;

[0145] The bias weight of the fuzzy rule corresponding to the current rate of change interval is the third bias weight, which is equal to 1 minus the proportion of the second position.

[0146] The bias weight of the fuzzy rule corresponding to the adjacent rate of change intervals corresponding to the second shared boundary value is the fourth bias weight, which is equal to the second position ratio.

[0147] First, determine the current pH change rate within the current change rate interval. Then, identify the second common boundary value that the current pH change rate is closest to within this interval. The second common boundary value is the boundary value corresponding to the smallest distance between the current pH change rate and any other common boundary value within the current change rate interval. Calculate the absolute value of the difference between the current pH change rate and the second common boundary value, which is used as the second deviation. For example, the current pH change rate ΔpH obtained at the start of the intervention time window (day 7) is +0.1. In the change rate interval division, the positive range is 0.05 < ΔpH < 0.15, and the zero range is -0.05 ≤ ΔpH ≤ 0.05. The current pH change rate +0.1 falls into the positive range. The second common boundary value that the current pH change rate +0.1 is closest to is 0.05, which is the second deviation. .

[0148] Next, the ratio of the second deviation to the width of the current rate of change interval is calculated as the second position ratio. The second position ratio is calculated according to the following formula: .in, For the second position ratio, , The second degree of deviation, This represents the width of the current rate of change interval. The width of the interval for positive values. It is 0.10. The proportion of the second position. .

[0149] Secondly, the bias weight of the fuzzy rule corresponding to the current rate of change interval is the third bias weight, which is equal to 1 minus the second position ratio; the bias weight of the fuzzy rule corresponding to the adjacent rate of change interval corresponding to the second shared boundary value is the fourth bias weight, which is equal to the second position ratio. Specifically, the third bias weight is 0 when it is less than 0, and the fourth bias weight is 1 when it is greater than 1. The third bias weight = 1 - 0.5 = 0.5, and the fourth bias weight = 0.5.

[0150] Furthermore, the bias weights of the fuzzy rules matching the current pH value and the fuzzy rules matching the current pH change rate are multiplied pairwise to obtain the comprehensive bias weight of each combined rule. The number of fuzzy rules matching the current pH value and the number of fuzzy rules matching the current pH change rate may be different. The two are combined pairwise, and the comprehensive bias weight of each combined rule is equal to the product of the bias weight corresponding to the pH value and the bias weight corresponding to the change rate in that combination.

[0151] For example, the current pH value matches two fuzzy rules: the high-value interval rule has a bias weight of 0.6, and the median interval rule has a bias weight of 0.4; the current pH change rate matches two fuzzy rules: the positive-value interval rule has a bias weight of 0.5, and the zero-value interval rule has a bias weight of 0.5. Multiplying these pairs yields the combined bias weights of the four combined rules: Combined rule 1 (high-value interval + positive-value interval) has a combined bias weight of 0.6 × 0.5 = 0.3; Combined rule 2 (high-value interval + zero-value interval) has a combined bias weight of 0.6 × 0.5 = 0.3; Combined rule 3 (median interval + positive-value interval) has a combined bias weight of 0.4 × 0.5 = 0.2; Combined rule 4 (median interval + zero-value interval) has a combined bias weight of 0.4 × 0.5 = 0.2.

[0152] Subsequently, based on the overall bias weights, a weighted average is calculated on the reference values ​​for the delivery volume in each combination rule to obtain the first initial delivery volume value. Similarly, a weighted average is calculated on the reference values ​​for the delivery time interval in each combination rule to obtain the first initial delivery time interval value. The initial delivery volume value is equal to the sum of the overall bias weights of each combination rule multiplied by the corresponding delivery volume reference value, divided by the sum of all overall bias weights. The initial delivery time interval value is also equal to the sum of the overall bias weights of each combination rule multiplied by the corresponding delivery time interval reference value, divided by the sum of all overall bias weights.

[0153] Specifically, the weighted average is calculated using the following formula: , .in, This is the initial value for the first distribution. This is the initial value for the first delivery time interval. Let i be the overall bias weight of the i-th combination rule. This is the reference value for the delivery volume corresponding to the i-th combination rule. This is the reference value for the delivery time interval corresponding to the i-th combination rule. This is the sum of the combined bias weights of all combined rules. (Reference value for delivery volume) Reference value for delivery time interval It is determined by the reference values ​​corresponding to the delivery volume level and delivery time interval level of the combined rule in the modified fuzzy rule base.

[0154] For example, the four combined rules and their overall bias weights are as follows: Combined rule 1 has an overall weight of 0.3, a reference application rate of 6.4 L / mu, and a reference time interval of 5 days; Combined rule 2 has an overall weight of 0.3, a reference application rate of 5.0 L / mu, and a reference time interval of 10 days; Combined rule 3 has an overall weight of 0.2, a reference application rate of 4.0 L / mu, and a reference time interval of 10 days; Combined rule 4 has an overall weight of 0.2, a reference application rate of 2.0 L / mu, and a reference time interval of 15 days. The initial application rate is calculated as (0.3 × 6.4 + 0.3 × 5.0 + 0.2 × 4.0 + 0.2 × 2.0) / 1.0 = 4.62 L / mu. The initial time interval is calculated as (0.3 × 5 + 0.3 × 10 + 0.2 × 10 + 0.2 × 15) / 1.0 = 9.5 days, rounded down to 9 days.

[0155] Then, the initial value of the first dosage is corrected according to the activity compensation coefficient to obtain the dosage of the first alkalophilic microorganism. The initial value of the first dosage time interval is taken as the first dosage time interval. The initial value of the first dosage is multiplied by the activity compensation coefficient f, and the product is the dosage of the first alkalophilic microorganism. The initial value of the first dosage time interval is directly output as the first dosage time interval without correction by the activity compensation coefficient. The dosage of the first alkalophilic microorganism is calculated according to the following formula: .in, This is the dosage of the first alkalophilic microorganism. This is the initial value for the first distribution. This is the activity compensation coefficient.

[0156] For example, the activity compensation coefficient The initial application rate is 4.62 L / mu (approximately 0.067 hectares). The initial application rate of alkaliphilic microorganisms is 4.62 L × 1.0 = 4.62 L / mu. The first application interval is 9 days. That is, 4.62 L / mu of alkaliphilic microorganism inoculant is applied on the 7th day, and the next application will be 9 days later, on the 16th day. In one embodiment, the alkaliphilic microorganism inoculant is a compound inoculant of *Bacillus lindianifolia* and *Bacillus amyloliquefaciens*.

[0157] Finally, the current pH value and pH change rate are repeatedly acquired, the corresponding fuzzy rules are matched from the modified fuzzy rule base, and the dosage and time interval of alkaliphilic microorganisms are output until the current pH value reaches the activation threshold. Each time this process is repeated, the latest acquired current pH value and current pH change rate are used as input to recalculate and output the dosage and time interval of the alkaliphilic microorganisms for that administration. When the current pH value reaches the activation threshold, the alkali adjustment administration is stopped, and the alkali adjustment phase is complete.

[0158] For example, after the first addition on day 7, the current pH value and current pH change rate are re-acquired on day 16, following a 9-day addition interval. The current pH value obtained on day 16 is 8.2, and the current pH change rate is -0.1. Recalculated using the above method: the current pH value of 8.2 falls within the median range (7.5 ≤ pH ≤ 8.5), with the closest boundary value being 8.5, representing the first deviation. The width of the median interval The ratio is 1.0, the first position ratio. The first bias weight is 1 - 0.3 = 0.7, and the second bias weight is 0.3. The current pH change rate of -0.1 falls into the negative range (ΔpH < -0.05). According to rule 5 in the initial fuzzy rule base, the output for alkaliphilic microorganism deployment is zero. That is, no deployment will be carried out on day 16. Monitoring continues, and on day 20, the current pH value is 7.9, which is below the activation threshold of 8.72, indicating that the alkali adjustment phase is complete.

[0159] In this embodiment of the invention, the alkali-regulating strategy can be adaptively adjusted according to the soil's response sensitivity by using a dual correction of rule weights and dosage levels driven by the slope of salinity decrease; the cumulative pH change drives the correction of the initiation threshold, utilizing the residual alkali-reducing effect during the salinization stage to delay intervention and save on microbial agent dosage; residual salinity drives the determination of the activity compensation coefficient, enabling precise adjustment of the dosage based on the actual activity requirements of alkali-loving microorganisms; and the peak activity time drives the determination of the intervention time window, ensuring timely intervention after the salinization effect is fully released. The bias weight calculation and weighted averaging in the fuzzy rule matching process achieve a smooth rule transition, avoiding decision jumps caused by hard switching. By repeatedly acquiring pH values ​​and iteratively updating the dosage control parameters, a closed feedback adjustment loop is formed until the pH value reaches the corrected initiation threshold, achieving precise, adaptive, and closed-loop control of the alkali-regulating stage.

[0160] The microbial community transplantation and cultivation module 14 is used to output the ratio parameters of organic fertilizer and native microbial community based on the organic matter content and nutrient content, perform soil microbial community transplantation, and plant salt-tolerant crops.

[0161] In this embodiment of the invention, although the soil salinity has been reduced to a level suitable for crop growth after the desalination and alkali adjustment stages, the soil organic matter content and microbial diversity are often still at a low level. The soil's nutrient supply capacity and ecological function have not been fully restored, and it is necessary to replenish organic matter and beneficial microorganisms to rebuild a healthy soil ecological environment. At the same time, the organic matter content and nutrient status vary among different saline-alkali lands, requiring the determination of the optimal ratio of organic fertilizer to native microbial communities based on actual soil conditions. The residual salt content after the desalination stage also affects the decomposition efficiency of organic fertilizer and the colonization effect of microorganisms; therefore, it needs to be used as the basis for adjusting the ratio parameters.

[0162] In one embodiment, the microbial transplantation and replenishment module 14 is further configured to:

[0163] Based on the organic matter content, various organic fertilizer ratio schemes were set up. The organic fertilizers of each ratio scheme were mixed with native microbial communities and applied to the test soil. After cultivation, the organic matter enhancement rate and microbial diversity enhancement rate of each test soil were measured.

[0164] The weights for organic matter enhancement rate and microbial diversity enhancement rate are determined based on organic matter content, and the sum of the weights for organic matter enhancement rate and microbial diversity enhancement rate is 1.

[0165] The organic matter improvement rate and the microbial diversity improvement rate were weighted and summed using the weights of organic matter improvement rate and microbial diversity improvement rate to obtain the comprehensive improvement effect index of each ratio scheme. The proportion of organic fertilizer corresponding to the maximum value of the comprehensive improvement effect index was selected as the basic ratio parameter.

[0166] Obtain the residual salt content after the salt removal process and set a standard salt content threshold.

[0167] Calculate the difference between the residual salt content and the standard salt content threshold, and determine the ratio correction coefficient based on the salt content difference;

[0168] Multiply the basic proportion parameters by the proportion correction factor to obtain the actual proportion parameters;

[0169] Organic fertilizer and native microorganisms are mixed according to the actual ratio parameters and then applied to the target saline-alkali soil to complete the soil microorganism transplantation. Salt-tolerant crops are then planted after the microorganism transplantation.

[0170] First, various organic fertilizer formulation schemes were established based on organic matter content. Each formulation was then mixed with native microbial communities and applied to the test soil. After cultivation, the organic matter enhancement rate and microbial diversity enhancement rate of each test soil were measured. Organic fertilizer refers to fertilizer made primarily from organic matter through fermentation and decomposition, used to replenish soil organic matter. Native microbial communities refer to a complex microbial community containing various beneficial functional bacteria such as Bacillus subtilis, Bacillus pumilus, and Bacillus amyloliquefaciens, isolated and screened from healthy soil or non-degraded soil in the target area, used to restore soil microbial diversity and ecological functions.

[0171] The organic fertilizer ratio refers to the mass proportion of organic fertilizer in the soil conditioner. The organic matter enhancement rate refers to the percentage increase in soil organic matter content after applying organic fertilizer and native microorganisms, relative to the state before application; it measures the effectiveness of the ratio in improving soil organic matter. The microbial diversity enhancement rate refers to the percentage increase in the microbial diversity index of the soil microbial community after applying organic fertilizer and native microorganisms, relative to the state before application; it measures the effectiveness of the ratio in restoring soil microbial diversity.

[0172] Specifically, when setting multiple organic fertilizer formulation schemes, different mass ratios of organic fertilizer were weighed and mixed with a fixed mass of native microbial communities to obtain mixtures for each formulation scheme. These mixtures were then applied to the soil of multiple experimental groups, while a control group without any amendments was also included. All experimental and control groups were incubated under the same conditions for a predetermined time. After incubation, the soil organic matter content and microbial diversity indices of each experimental and control group were measured, and the organic matter enhancement rate and microbial diversity enhancement rate of each experimental group were calculated. Organic matter enhancement rate = (Experimental group organic matter content - Control group organic matter content) / Control group organic matter content × 100%. Microbial diversity enhancement rate = (Experimental group microbial diversity index - Control group microbial diversity index) / Control group microbial diversity index × 100%.

[0173] For example, the soil baseline diagnostic module 11 measured an organic matter content of 2.96 g / kg. Well-rotted cow manure was used as the organic fertilizer, and the native microbial community was isolated and screened from the non-degraded meadow soil of the region. Four organic fertilizer ratios of 5%, 10%, 15%, and 20% were established. The organic fertilizer of each ratio was mixed with a fixed mass of native microbial community (50 kg / mu) and applied to the soil of the four experimental groups. A control group without any amendments was also set up. After 30 days of cultivation under the same conditions, the organic matter content and microbial diversity indices of each group were measured. The organic matter enhancement rate and microbial diversity enhancement rate of each ratio were calculated as follows: 5% organic fertilizer ratio corresponds to an organic matter enhancement rate of 8% and a microbial diversity enhancement rate of 45%; 10% organic fertilizer ratio corresponds to an organic matter enhancement rate of 18% and a microbial diversity enhancement rate of 52%; 15% organic fertilizer ratio corresponds to an organic matter enhancement rate of 32% and a microbial diversity enhancement rate of 48%; and 20% organic fertilizer ratio corresponds to an organic matter enhancement rate of 38% and a microbial diversity enhancement rate of 40%.

[0174] Secondly, the weights for organic matter enhancement rate and microbial diversity enhancement rate are determined based on organic matter content, with the sum of these two weights being 1. When soil organic matter content is low, it indicates severe soil infertility, and increasing organic matter content should be prioritized; therefore, the organic matter enhancement rate weight is [not specified]. It should be greater than the weight of the rate of increase in microbial diversity. When soil organic matter content is high, it indicates a good soil organic matter foundation, and the restoration of microbial diversity should be prioritized; therefore, the weight of the microbial diversity enhancement rate should be considered. It should be greater than the weight of the organic matter enhancement rate. .

[0175] In this embodiment, a first organic matter threshold of 6 g / kg and a second organic matter threshold of 15 g / kg are set. The first organic matter threshold of 6 g / kg is the critical value between extreme and general organic matter deficiency; soil below this value is considered unsuitable for agricultural cultivation. The second organic matter threshold of 15 g / kg is the inflection point for soil organic matter transitioning from low to medium-high fertility levels; exceeding this value, the marginal yield increase effect of increased organic matter on crops tends to balance out. When the organic matter content is less than 6 g / kg, , When the organic matter content is greater than or equal to 6 g / kg and less than or equal to 15 g / kg, , When the organic matter content is greater than 15g / kg, , For example, the organic matter content in this demonstration area was 2.96 g / kg, less than 6 g / kg, indicating a severe deficiency of organic matter. Therefore, a weight was set for the organic matter improvement rate. Weighting of microbial biodiversity improvement rate .

[0176] Next, the organic matter improvement rate and microbial diversity improvement rate are weighted and summed using the weights of organic matter improvement rate and microbial diversity improvement rate to obtain the comprehensive improvement effect index of each formulation scheme. The proportion of organic fertilizer corresponding to the maximum value of the comprehensive improvement effect index is selected as the basic formulation parameter. The comprehensive improvement effect index F is calculated according to the following formula: .in, To comprehensively improve the effect index, As the weight for the organic matter enhancement rate, To improve the organic matter enhancement rate, As a weight for the rate of increase in microbial diversity, To determine the rate of improvement in microbial diversity, the proportion of organic fertilizer corresponding to the maximum value of the comprehensive improvement effect index was selected as the basic ratio parameter.

[0177] For example, the demonstration area , The comprehensive improvement effect index of each formulation is calculated as follows: When the organic fertilizer ratio is 5%, F = 0.8 × 8% + 0.2 × 45% = 15.4%; when the organic fertilizer ratio is 10%, F = 0.8 × 18% + 0.2 × 52% = 24.8%; when the organic fertilizer ratio is 15%, F = 0.8 × 32% + 0.2 × 48% = 35.2%; when the organic fertilizer ratio is 20%, F = 0.8 × 38% + 0.2 × 40% = 38.4%. The maximum comprehensive improvement effect index of 38.4% corresponds to an organic fertilizer ratio of 20%. Therefore, the basic formulation parameter is 20%, meaning that organic fertilizer accounts for 20% of the total mass of the soil conditioner.

[0178] Then, the residual salt content after the salt removal process is obtained, and a standard salt content threshold is set; the difference between the residual salt content and the standard salt content threshold is calculated, and a ratio correction coefficient is determined based on the salt content difference. The standard salt content threshold refers to the critical value at which the salt content in the soil has a significant inhibitory effect on the decomposition of organic fertilizer and the colonization of microorganisms. In this embodiment, the standard salt content threshold is set to 0.20%.

[0179] Specifically, the difference in salinity is calculated using the following formula: .in, This represents the difference in salinity, expressed in percentage points. This refers to the residual salt content. The standard salinity threshold is used. The ratio correction factor η is determined based on the salinity difference: when... hour, No correction indicates that the residual salt content does not exceed the standard salt content threshold, and the decomposition of organic fertilizer and colonization of microorganisms are not significantly inhibited; when hour, The correction coefficient increases linearly with the difference in salinity. Coefficient 2 is a preset adjustment slope, meaning that for every 0.1 percentage point increase in residual salinity above the standard salinity threshold, the correction coefficient increases by 0.2. For example, the residual salinity after the salt removal process... It is 0.21%, the standard salinity threshold. The difference in salinity is 0.20%. Mixing ratio correction factor .

[0180] Then, multiply the basic ratio parameters by the ratio correction factor to obtain the actual ratio parameters. The actual ratio parameters refer to the mass percentage of organic fertilizer in the mixture of organic fertilizer and native microorganisms after the residual salt content has been corrected following salt removal. The actual ratio parameters are calculated using the following formula: .in, These are the actual mixing ratio parameters. Basic proportion parameters, This is a proportioning correction factor. For example, the basic proportioning parameter. The proportion is 20%, and the ratio correction factor is 20%. The actual proportion is 1.02. .

[0181] Finally, the organic fertilizer and native microbial community are mixed according to the actual ratio parameters and applied to the target saline-alkali soil to complete the soil microbial community transplantation. Salt-tolerant crops are then planted after the transplantation. Salt-tolerant crops are crop varieties that can grow normally and complete their life cycle under certain saline-alkali conditions, including any one or more of salt-tolerant rice, corn, and sunflower. Specifically, organic fertilizer is weighed according to the actual ratio parameters and thoroughly mixed with a fixed mass of native microbial community in a mixing container to ensure even distribution of the organic fertilizer and native microbial community. The mixture is then evenly spread on the soil surface and tilled to ensure thorough mixing with the topsoil. Before planting, salt-tolerant crop varieties are selected based on the target soil conditions after remediation. The salt tolerance threshold of the selected crop should not be lower than the aforementioned target range. Planting is carried out according to the standard planting density and cultivation management measures for that crop variety.

[0182] For example, the fixed application rate of the native microbial community is 50 kg / mu, and the actual ratio parameter is 20.4%, that is, organic fertilizer accounts for 20.4% of the total mass of the mixture (organic fertilizer + native microbial community). Let the organic fertilizer application rate be x kg / mu, then x / (x+50) = 20.4%, solving for x gives x ≈ 12.8 kg / mu. Approximately 12.8 kg of organic fertilizer per mu is mixed with 50 kg of native microbial community and applied to the soil, then tilled and mixed thoroughly to complete the soil microbial community transplantation. Three days after the microbial community transplantation, the salt-tolerant rice variety Yanfeng 47 is selected for planting. The tolerance threshold of Yanfeng 47 is pH ≤ 8.5 and salt content ≤ 0.3%, which matches the remediation target of pH 7.5~8.5 and salt content ≤ 0.3%. The planting density is 30cm × 15cm, with 3 to 4 seedlings per hole, and conventional water and fertilizer management measures are used for field management.

[0183] In this invention, a complete process from designing organic fertilizer formulation schemes to actual microbial transplantation was established, enabling the optimization target to adaptively adjust according to soil organic matter content. When organic matter content is low, the focus is on increasing organic matter; when organic matter content is high, the focus is on restoring microbial diversity. Quantification and scientific rigor were achieved through experimental measurements and weighted summation. Simultaneously, the residual salt content during the desalination stage was introduced to correct the basic formulation parameters, compensating for the inhibitory effect of residual salt on organic fertilizer decomposition and microbial colonization. After microbial transplantation, salt-tolerant crops were planted, establishing a stable symbiotic relationship between microorganisms, plants, and soil, realizing a complete closed loop from salinity regulation to soil fertility restoration and crop production.

[0184] The collaborative feedback control module 15 is used to collect real-time soil monitoring parameters and crop growth status parameters, and adjust the control parameters for the release of alkaliphilic microorganisms based on the real-time soil monitoring parameters and crop growth status parameters.

[0185] In this embodiment of the invention, the remediation process of saline-alkali land is not linearly controllable. Changes in the external environment may cause the reduced salinity to rise again, and pH values ​​that have not been fully met in the previous stages may delay the effective progress of subsequent stages. Simultaneously, the actual growth status of salt-tolerant crops also needs to be included in the real-time assessment of the remediation effect. Therefore, a feedback control mechanism needs to be established outside the main remediation process. During the remediation process, real-time soil monitoring parameters and crop growth status parameters should be continuously collected. When abnormal soil conditions or abnormal crop growth are detected, corresponding supplementary remediation measures can be triggered, enabling the entire remediation system to have closed-loop regulation capabilities.

[0186] In one embodiment, the collaborative feedback control module 15 is further configured to:

[0187] During the remediation process, real-time soil monitoring parameters were continuously collected, including at least soil salinity and pH value.

[0188] Collect crop growth status parameters, which should include at least the normalized vegetation index.

[0189] The system determines whether to trigger a reverse callback based on the real-time monitoring value of soil salinity: when the soil salinity rises above the preset threshold, the amount of halophilic microorganisms to be added is calculated based on the difference in rise, and salt removal and replenishment are performed.

[0190] After the salt replenishment is completed, the soil pH value is re-acquired. If the pH value exceeds the preset target range, the amount and time interval of alkali-loving microorganism replenishment are re-determined based on the pH value after the salt replenishment, and alkali-adjusting replenishment is carried out.

[0191] The deviation between the normalized vegetation index and the preset normal value is calculated. If the deviation exceeds the preset threshold, the amount of carbon source replenishment is determined based on the deviation, and carbon source is replenished to the crop root zone.

[0192] First, during the remediation process, real-time soil monitoring parameters are continuously collected, including at least soil salinity and pH value. Soil salinity and pH value are acquired in real time through a sensor network deployed in the target saline-alkali land. The sensor network consists of multiple multi-parameter soil sensor nodes, each buried in the topsoil at different locations within the target saline-alkali land according to a pre-defined spatial distribution scheme. Each sensor node automatically collects soil salinity and pH value at preset time intervals. The collected data is uploaded to the central control system via wireless transmission, where it is stored and displayed in real time.

[0193] For example, the demonstration area deploys 12 sets of soil multi-parameter sensors in a 50m×50m grid, with each sensor buried in the topsoil. Each sensor collects soil salinity and pH values ​​every 4 hours, and the data is uploaded to the central control system via LoRa wireless transmission for real-time status monitoring and anomaly detection by the collaborative feedback control module 15.

[0194] Secondly, crop growth status parameters are collected, including at least the Normalized Difference Vegetation Index (NDVI). The NDVI is a dimensionless index reflecting crop growth and vegetation cover using remote sensing. Its value ranges from -1 to 1; a higher value indicates better crop growth and a larger leaf area index. The NDVI is acquired using a UAV equipped with a multispectral sensor. The UAV conducts aerial photography of the target saline-alkali land according to a preset flight path and time period. The multispectral sensor collects reflectance data of the crop canopy in the near-infrared and red light bands. The NDVI is calculated using the following formula: .in, This represents the reflectance value in the near-infrared band. This represents the reflectivity value in the red light band. The normalized vegetation index is used.

[0195] For example, on the 35th day after planting, when the rice in the demonstration area was in the tillering stage, a drone equipped with a multispectral sensor took aerial photos of the demonstration area along a preset route, collecting reflectance data of the crop canopy in the near-infrared and red light bands. The NDVI distribution map of the entire area was then calculated. The average NDVI of the entire area was 0.62, while the NDVI value of the northwest corner area was 0.48, significantly lower than the average of the entire area.

[0196] Next, the system determines whether to trigger a reverse callback based on real-time monitoring values ​​of soil salinity: when soil salinity rises above a preset threshold, the amount of halophilic microorganisms to be added is calculated based on the difference in salinity, and salt removal supplementation is performed. The preset threshold is the residual salinity at the end of the salt removal phase, set at the end of the salt removal process; in this embodiment, it is 0.31%. When the real-time monitoring value of soil salinity rises above this preset threshold, it indicates that changes in the external environment have carried deep-seated salts to the surface, requiring a restart of the salt removal process. The difference in salinity is equal to the real-time monitoring value of soil salinity minus the preset threshold; the amount of halophilic microorganisms to be added is calculated based on the difference in salinity, and salt removal supplementation is performed.

[0197] Specifically, the amount of halophilic microorganisms to be added is calculated according to the following formula: ,in, To replenish the amount of halophilic microorganisms, This refers to the application coefficient corresponding to the salinity level. This is a real-time monitoring value of soil salinity. The preset threshold is the residual salt content at the end of the salt removal process, and D is the soil bulk density correction coefficient. The values ​​of D are consistent with those in the salt-loving and salt-clearing control module 12.

[0198] For example, on the 45th day of restoration in the demonstration area, sensor network monitoring detected that the average salinity of the entire area had increased from 0.21% to 0.34%. (Preset threshold) The concentration is 0.31%, and the recovery difference is 0.34% - 0.31% = 0.03%. The dosage coefficient corresponding to severe salinity is... The soil bulk density correction factor D is 1.0, with a value of 1.5. The amount of halophilic microorganisms added is... Apply 0.045 L / acre of halophilic microbial agent as a supplementary application to remove salt deposits.

[0199] Furthermore, after the salt replenishment is completed, the soil pH value is re-acquired. If the pH value exceeds the preset target range, the amount and time interval for replenishing alkaliphilic microorganisms are re-determined based on the pH value after the salt replenishment, and alkali-adjusting replenishment is performed. The amount and time interval for replenishing alkaliphilic microorganisms are determined as follows: using the soil pH value re-acquired after the salt replenishment as the current pH value input, the corresponding fuzzy rules are re-matched from the corrected fuzzy rule library according to the method in the alkali-adjusting module 13 to calculate the amount and time interval for replenishing alkaliphilic microorganisms.

[0200] For example, 10 days after the salt replenishment was completed, the sensor network retested and found that the salinity had dropped to 0.20%, and the soil pH was 8.1, which was still within the preset target range of 7.5 to 8.5. Therefore, there was no need to perform any more salt replenishment, and the system returned to normal monitoring status during the replenishment phase.

[0201] Finally, the deviation between the normalized vegetation index (NVI) and the preset normal value is calculated. If the deviation exceeds a preset threshold, the carbon source replenishment amount is determined based on the deviation, and carbon source is replenished to the crop root zone. The preset normal value is the average NVI value of the area with normal crop growth in the target saline-alkali land. The deviation is equal to the preset normal value minus the real-time monitoring value of the NVI. The carbon source replenishment amount is calculated according to the following formula: .in, To replenish the carbon source, The normalized vegetation index is the preset normal value. This represents the real-time monitoring value of the Normalized Difference Vegetation Index. This is the carbon source replenishment coefficient.

[0202] Carbon sources refer to organic carbon sources that can be rapidly utilized by soil microorganisms, such as molasses solution, to stimulate microbial activity and promote the maintenance and function of the microbial community when crop root exudates are insufficient. Carbon source replenishment coefficient. The preset values ​​are pre-calibrated based on the type of carbon source and soil conditions.

[0203] For example, during the rice tillering stage in this demonstration area, UAV multispectral imagery showed an average NDVI of 0.62 for the entire area, with an NDVI of 0.48 in the northwest corner. (Preset normal value) The value is 0.62, and the deviation is 0.62 - 0.48 = 0.14. The preset deviation threshold is 0.12; 0.14 > 0.12, thus meeting the trigger condition. Carbon source replenishment coefficient. The carbon source replenishment amount is 10 L / mu, meaning 1 L / mu is added for every 0.1 NDVI unit deviation, converted to a coefficient of 10. For the northwest corner area, molasses solution was applied at a rate of 1.4 L / acre. Ten days after application, the NDVI value in this area was retested and rose to 0.58, returning to normal levels.

[0204] In this embodiment of the invention, real-time monitoring parameters such as soil salinity, pH value, and normalized vegetation index (NDI) are continuously collected during the main remediation process, enabling dynamic perception and closed-loop regulation of soil and crop growth status during the remediation process. When soil salinity rises above a preset threshold, a reverse callback mechanism triggers salt replenishment, performing replenishment treatment using the same quantitative calculation method as the salt replenishment stage, and reassessing the alkali adjustment requirement based on the pH value after salt replenishment. When the NDI is lower than a preset normal value, the amount of carbon source replenishment is quantitatively calculated based on the degree of deviation, and carbon source is replenished to the crop root zone to stimulate microbial activity. The above feedback control mechanism enables the remediation system to automatically trigger corresponding replenishment measures when external environmental changes or abnormal situations occur, ensuring the long-term stability and sustainability of the remediation effect.

[0205] Through the specific implementation methods described above, the embodiments of the present invention achieve the following technical effects:

[0206] This invention provides a system and method for the synergistic remediation of saline-alkali soil using salt-tolerant crops and microorganisms. It obtains initial soil physicochemical parameters through soil baseline diagnosis as a data foundation, performs salt removal treatment and extracts salt removal state parameters, and uses these parameters to drive adaptive corrections of fuzzy rule weights, application rate levels, activation thresholds, activity compensation coefficients, and intervention time windows. This allows the application strategy during the alkali adjustment stage to be dynamically adjusted based on the actual salt removal effect. The microbial fertilizer ratio is optimized based on soil organic matter and nutrient content and corrected by residual salt content. Real-time monitoring triggers reverse callbacks when salt content rises and quantitative carbon source supplementation when crop growth is abnormal. Through the transmission and feedback correction of state parameters, a composite control system combining serial progressive and reverse regulation based on real-time soil state is formed, achieving precise, adaptive, and closed-loop control throughout the entire process of saline-alkali land remediation.

[0207] Example 2, as Figure 2 , Figure 3 As shown, this invention provides a method for the synergistic remediation of saline-alkali soil using salt-tolerant crops and microorganisms, the method comprising:

[0208] S100: Collect soil samples from the target saline-alkali land, determine the soil physicochemical properties, and obtain soil physicochemical parameters. The soil physicochemical parameters include at least the initial pH value, salinity, organic matter content, soil bulk density, and nutrient content.

[0209] Step S100 in the method provided in this embodiment of the invention includes:

[0210] The target saline-alkali land was divided into multiple grid units. Sampling points were set in each grid unit to collect surface soil samples. The surface soil samples were mixed evenly to obtain representative soil samples.

[0211] Take a representative soil sample and mix it with distilled water in a certain proportion. After stirring, let it stand and insert a pH electrode into the supernatant to measure the initial pH value.

[0212] Take a representative soil sample and mix it with distilled water. After extraction by shaking, filter the sample, evaporate the filtrate to dryness and dry it to constant weight. Weigh the residue and calculate the salt content.

[0213] A representative soil sample was added to potassium dichromate solution and sulfuric acid, heated to react, and then titrated with ferrous sulfate standard solution. The organic matter content was calculated based on the amount consumed.

[0214] Press the ring cutter into the soil and remove it. After drying, weigh the dry soil and calculate the mass of dry soil per unit volume to obtain the soil bulk density.

[0215] Nutrient content includes total nitrogen content, available phosphorus content, and available potassium content. Total nitrogen content is determined by titration after digestion and distillation of a representative soil sample. Available phosphorus content is determined by colorimetric analysis after extraction. Available potassium content is determined by flame photometry after extraction.

[0216] S200: Based on the initial pH value and salinity, the degree of salinization is classified to obtain the salinity level. Based on the salinity level and salinity, the amount of halophilic microorganisms to be added is calculated, the salinization treatment is performed, and the salinization status parameters are obtained.

[0217] Step S200 in the method provided in this embodiment of the invention includes:

[0218] The degree of salinization is classified according to the initial pH value and salinity to obtain the salinity level, which includes mild salinity, moderate salinity and severe salinity. Different dosage coefficients are set according to the salinity level.

[0219] Obtain the standard soil bulk density, and determine the soil bulk density correction factor based on the ratio of the soil bulk density to the standard soil bulk density;

[0220] Based on the application coefficient, salt content and soil bulk density correction coefficient corresponding to the salinity level, calculate the application amount of halophilic microorganisms and apply the halophilic microorganism agent into the soil to carry out salt removal treatment.

[0221] During the desalination process, soil salinity, pH value and halophilic microbial activity indicators were measured multiple times at preset time intervals to obtain the salinity decrease curve, pH change curve and microbial activity change curve.

[0222] The slope of salt decrease was extracted from the salt content decrease curve; the cumulative pH change at the end of salt removal was extracted from the pH change curve; and the peak activity time was extracted from the bacterial activity change curve.

[0223] To obtain the residual salt content after the salt removal process;

[0224] The slope of salt concentration decrease, cumulative pH change, peak activity time, and residual salt content were used as parameters for the salt removal state.

[0225] S300: Determine the control parameters for the addition of alkaliphilic microorganisms based on the salinity parameters. During the alkali adjustment process, obtain the pH value multiple times and update the control parameters for the addition of alkaliphilic microorganisms until the pH value reaches the preset target range. The control parameters for the addition of alkaliphilic microorganisms include the amount of alkaliphilic microorganisms added and the time interval between additions.

[0226] Step S300 in the method provided in this embodiment of the invention includes:

[0227] The activation threshold of the preset target range is adjusted based on the cumulative change in pH.

[0228] The activity compensation coefficient is determined based on the residual salt content;

[0229] The intervention time window for the alkali adjustment stage is determined based on the peak activity time.

[0230] Within the intervention time window, the current pH value and pH change rate are obtained. Based on the current pH value and pH change rate, the corresponding fuzzy rules are matched from the pre-constructed modified fuzzy rule library, and the first alkaliphilic microorganism dosage and the first dosage time interval are output.

[0231] Repeatedly obtain the current pH value and pH change rate, match the corresponding fuzzy rules from the modified fuzzy rule library, and output the amount of alkaliphilic microorganisms to be added and the time interval between additions, until the current pH value reaches the activation threshold;

[0232] The construction of a revised fuzzy rule base includes:

[0233] An initial fuzzy rule base is constructed, which contains multiple fuzzy rules. The fuzzy rules include the correspondence between pH value range, pH change rate range and alkaliphilic microorganism dosage level and dosage time interval level. Among them, the pH value range includes high value range, medium value range and low value range, and the pH change rate range includes positive value range, zero value range and negative value range.

[0234] When the slope of the salt decrease is greater than the first threshold, the weight of the fuzzy rules in the initial fuzzy rule base that have a high pH value range and a positive pH change rate range is reduced by the first correction amount.

[0235] When the slope of the salt decrease is less than the second threshold, the weight of the fuzzy rules in the initial fuzzy rule base that have a pH value range of median value and a pH change rate range of positive value is increased by the second correction amount.

[0236] When the slope of the salt concentration decrease is between the second threshold and the first threshold, the weights of the initial fuzzy rule base remain unchanged.

[0237] The modified fuzzy rule base is obtained by adjusting the fuzzy logic output of the alkaliphilic microorganism dosage level based on the slope of the salinity decrease.

[0238] Within the intervention time window, the current pH value and pH change rate are obtained. Based on the current pH value and pH change rate, corresponding fuzzy rules are matched from the modified fuzzy rule base to output the first alkaliphilic microorganism dosage and the first dosage time interval, including:

[0239] Obtain the current pH value and the current pH change rate. Determine the bias weight of each fuzzy rule matching the current pH value based on the position of the current pH value in each pH value interval. Determine the bias weight of each fuzzy rule matching the current pH change rate based on the position of the current pH change rate in each change rate interval.

[0240] The bias weights of the fuzzy rules matching the current pH value and the fuzzy rules matching the current pH change rate are multiplied pairwise to obtain the combined bias weights of each rule combination.

[0241] Based on the comprehensive bias weight, the reference values ​​of the delivery volume in each combination rule are weighted and averaged to obtain the initial value of the first delivery volume. The reference values ​​of the delivery time interval in each combination rule are weighted and averaged to obtain the initial value of the first delivery time interval.

[0242] The initial value of the first dosage is corrected according to the activity compensation coefficient to obtain the first dosage of alkalophilic microorganisms, and the initial value of the first dosage time interval is taken as the first dosage time interval.

[0243] Specifically, the bias weights of each fuzzy rule matching the current pH value are determined based on the position of the current pH value within each pH value interval, including:

[0244] Determine the current pH value range into which the current pH value falls, and determine the first common boundary value that the current pH value is closest to within the current pH value range. The first common boundary value is the boundary value corresponding to the smaller distance between the current pH value and each common boundary value of the current pH value range. Calculate the absolute value of the difference between the current pH value and the first common boundary value as the first deviation.

[0245] Calculate the ratio of the first deviation to the width of the current pH range, and use it as the first position ratio;

[0246] The bias weight of the fuzzy rule corresponding to the current pH value range is the first bias weight, which is equal to 1 minus the first position ratio;

[0247] The bias weight of the fuzzy rule corresponding to the adjacent pH value intervals corresponding to the first shared boundary value is the second bias weight, which is equal to the first position ratio.

[0248] Specifically, the bias weights of each fuzzy rule matching the current pH change rate are determined based on the position of the current pH change rate within each change rate interval, including:

[0249] Determine the current pH change rate within the current change rate interval, identify the second common boundary value that the current pH change rate is closest to within the current change rate interval, the second common boundary value is the boundary value corresponding to the smaller distance between the current pH change rate and each common boundary value of the current change rate interval, and calculate the absolute value of the difference between the current pH change rate and the second common boundary value as the second deviation.

[0250] Calculate the ratio of the second deviation to the width of the current rate of change interval, and use it as the second position ratio;

[0251] The bias weight of the fuzzy rule corresponding to the current rate of change interval is the third bias weight, which is equal to 1 minus the proportion of the second position.

[0252] The bias weight of the fuzzy rule corresponding to the adjacent rate of change intervals corresponding to the second shared boundary value is the fourth bias weight, which is equal to the second position ratio.

[0253] S400: Outputs the ratio parameters of organic fertilizer and native microbial community based on organic matter content and nutrient content, performs soil microbial community transplantation, and plants salt-tolerant crops.

[0254] Step S400 in the method provided in this embodiment of the invention includes:

[0255] Based on the organic matter content, various organic fertilizer ratio schemes were set up. The organic fertilizers of each ratio scheme were mixed with native microbial communities and applied to the test soil. After cultivation, the organic matter enhancement rate and microbial diversity enhancement rate of each test soil were measured.

[0256] The weights for organic matter enhancement rate and microbial diversity enhancement rate are determined based on organic matter content, and the sum of the weights for organic matter enhancement rate and microbial diversity enhancement rate is 1.

[0257] The organic matter improvement rate and the microbial diversity improvement rate were weighted and summed using the weights of organic matter improvement rate and microbial diversity improvement rate to obtain the comprehensive improvement effect index of each ratio scheme. The proportion of organic fertilizer corresponding to the maximum value of the comprehensive improvement effect index was selected as the basic ratio parameter.

[0258] Obtain the residual salt content after the salt removal process and set a standard salt content threshold.

[0259] Calculate the difference between the residual salt content and the standard salt content threshold, and determine the ratio correction coefficient based on the salt content difference;

[0260] Multiply the basic proportion parameters by the proportion correction factor to obtain the actual proportion parameters;

[0261] Organic fertilizer and native microorganisms are mixed according to the actual ratio parameters and then applied to the target saline-alkali soil to complete the soil microorganism transplantation. Salt-tolerant crops are then planted after the microorganism transplantation.

[0262] S500: Collects real-time soil monitoring parameters and crop growth status parameters, and adjusts the control parameters for the release of alkaliphilic microorganisms based on these parameters.

[0263] Step S500 in the method provided in this embodiment of the invention includes:

[0264] During the remediation process, real-time soil monitoring parameters were continuously collected, including at least soil salinity and pH value.

[0265] Collect crop growth status parameters, which should include at least the normalized vegetation index.

[0266] The system determines whether to trigger a reverse callback based on the real-time monitoring value of soil salinity: when the soil salinity rises above the preset threshold, the amount of halophilic microorganisms to be added is calculated based on the difference in rise, and salt removal and replenishment are performed.

[0267] After the salt replenishment is completed, the soil pH value is re-acquired. If the pH value exceeds the preset target range, the amount and time interval of alkali-loving microorganism replenishment are re-determined based on the pH value after the salt replenishment, and alkali-adjusting replenishment is carried out.

[0268] The deviation between the normalized vegetation index and the preset normal value is calculated. If the deviation exceeds the preset threshold, the amount of carbon source replenishment is determined based on the deviation, and carbon source is replenished to the crop root zone.

[0269] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A saline-alkali soil cleanup and remediation system based on the synergy of salt-tolerant crops and microorganisms, characterized in that, The system includes: The soil baseline diagnostic module is used to collect soil samples from the target saline-alkali land, measure the soil physicochemical properties, and obtain soil physicochemical parameters. The soil physicochemical parameters include at least the initial pH value, salinity, organic matter content, soil bulk density, and nutrient content. The halophilic cleansing control module is used to classify the degree of salinization based on the initial pH value and the salt content, obtain the salinity level, calculate the amount of halophilic microorganisms to be added based on the salinity level and the salt content, perform cleansing treatment, and obtain cleansing status parameters. The alkali-loving alkali-adjusting control module is used to determine the alkali-loving microbial release control parameters based on the salinity status parameters. During the alkali adjustment process, the pH value is acquired multiple times and the alkali-loving microbial release control parameters are updated until the pH value reaches a preset target range. The alkali-loving microbial release control parameters include the amount of alkali-loving microorganisms released and the release time interval. The microbial community transplantation and cultivation module is used to output the ratio parameters of organic fertilizer and native microbial community based on the organic matter content and the nutrient content, perform soil microbial community transplantation, and plant salt-tolerant crops. The collaborative feedback control module is used to collect real-time soil monitoring parameters and crop growth status parameters, and adjust the alkaliphilic microorganism release control parameters according to the real-time soil monitoring parameters and crop growth status parameters.

2. The saline-alkali soil cleanup and remediation system based on the synergistic effect of salt-tolerant crops and microorganisms as described in claim 1, characterized in that, Soil samples were collected from the target saline-alkali land, and their physicochemical properties were measured to obtain soil physicochemical parameters, including: The target saline-alkali land was divided into multiple grid units. Sampling points were set in each grid unit to collect surface soil samples. The surface soil samples were mixed evenly to obtain representative soil samples. Take the representative soil sample and mix it with distilled water in a certain proportion. After stirring, let it stand and insert a pH electrode into the upper clear liquid to measure the initial pH value. Take the representative soil sample and mix it with distilled water. After extraction by shaking, filter the sample, evaporate the filtrate to dryness and dry it to constant weight. Weigh the mass of the residue and calculate the salt content. Take the representative soil sample, add potassium dichromate solution and sulfuric acid, heat to react, and then titrate with ferrous sulfate standard solution. Calculate the organic matter content based on the amount consumed. The ring cutter is pressed into the soil and removed. After drying, the dry soil mass is weighed, and the dry soil mass per unit volume is calculated to obtain the soil bulk density. The nutrient content includes total nitrogen content, available phosphorus content, and available potassium content. The total nitrogen content is determined by titration after digestion and distillation of the representative soil sample. The available phosphorus content is determined by colorimetric analysis after extraction. The available potassium content is determined by flame photometry after extraction.

3. The saline-alkali soil cleanup and remediation system based on the synergistic effect of salt-tolerant crops and microorganisms as described in claim 1, characterized in that, The degree of salinization is graded based on the initial pH value and the salinity, and a salinity level is obtained. The amount of halophilic microorganisms to be added is calculated based on the salinity level and the salinity. A salinization treatment is then performed, and salinization status parameters are obtained, including: The degree of salinization is classified according to the initial pH value and the salt content to obtain a salinity level, which includes mild salinity, moderate salinity and severe salinity, and different dosage coefficients are set according to the salinity level. Obtain the standard soil bulk density, and determine the soil bulk density correction factor based on the ratio of the soil bulk density to the standard soil bulk density; Based on the application coefficient corresponding to the salinity level, the salt content, and the soil bulk density correction coefficient, the application amount of halophilic microorganisms is calculated, and the halophilic microorganism agent is applied to the soil to perform desalination treatment. During the desalination process, soil salinity, pH value and halophilic microbial activity indicators were measured multiple times at preset time intervals to obtain the salinity decrease curve, pH change curve and microbial activity change curve. The slope of salt decrease is extracted based on the salt content decrease curve, the cumulative pH change at the end of salt removal is extracted based on the pH change curve, and the peak activity time is extracted based on the bacterial community activity change curve. To obtain the residual salt content after the salt removal process is completed; The salt concentration decrease slope, the cumulative pH change, the peak activity time, and the residual salt content are used as the parameters of the salt removal state.

4. The saline-alkali soil cleanup and remediation system based on the synergistic effect of salt-tolerant crops and microorganisms as described in claim 3, characterized in that, Based on the salinity parameters, control parameters for the addition of alkaliphilic microorganisms are determined. During the alkali adjustment process, pH values ​​are repeatedly acquired and the control parameters for the addition of alkaliphilic microorganisms are updated until the pH value reaches a preset target range, including: The activation threshold of the preset target range is adjusted based on the cumulative pH change. The activity compensation coefficient is determined based on the residual salt content. The intervention time window for the alkali adjustment stage is determined based on the peak activity time. Within the intervention time window, the current pH value and pH change rate are obtained. Based on the current pH value and the current pH change rate, the corresponding fuzzy rules are matched from the pre-constructed modified fuzzy rule library, and the first alkaliphilic microorganism dosage and the first dosage time interval are output. Repeatedly acquire the current pH value and pH change rate, match the corresponding fuzzy rules from the modified fuzzy rule library, and output the amount of alkaliphilic microorganisms to be added and the time interval between additions, until the current pH value reaches the activation threshold; The construction of a revised fuzzy rule base includes: An initial fuzzy rule base is constructed, which contains multiple fuzzy rules. The fuzzy rules include the correspondence between pH value range, pH change rate range and alkaliphilic microorganism dosage level and dosage time interval level. The pH value range includes high value range, medium value range and low value range, and the pH change rate range includes positive value range, zero value range and negative value range. When the slope of the salt decrease is greater than the first threshold, the weight of the fuzzy rules in the initial fuzzy rule base that have a high pH value range and a positive pH change rate range is reduced by the first correction amount. When the slope of the salt decrease is less than the second threshold, the weight of the fuzzy rules in the initial fuzzy rule base that have a pH value range of median value and a pH change rate range of positive value is increased by the second correction amount. When the slope of the salt content decrease is between the second threshold and the first threshold, the weights of the initial fuzzy rule base remain unchanged; The weights in the initial fuzzy rule base and the alkaliphilic microorganism release levels output by the fuzzy rules are adjusted according to the salinity decrease slope to obtain the corrected fuzzy rule base.

5. The saline-alkali soil cleanup and remediation system based on the synergistic effect of salt-tolerant crops and microorganisms as described in claim 4, characterized in that, Within the intervention time window, the current pH value and pH change rate are obtained. Based on the current pH value and the current pH change rate, corresponding fuzzy rules are matched from the modified fuzzy rule base, and the first alkaliphilic microorganism dosage and the first dosage time interval are output, including: Obtain the current pH value and the current pH change rate; determine the bias weight of each fuzzy rule matching the current pH value based on the position of the current pH value in each pH value interval; determine the bias weight of each fuzzy rule matching the current pH change rate based on the position of the current pH change rate in each change rate interval. The bias weights of the fuzzy rules matching the current pH value and the fuzzy rules matching the current pH change rate are multiplied pairwise to obtain the comprehensive bias weights of each combination rule. Based on the comprehensive bias weight, the reference values ​​of the delivery volume in each combination rule are weighted and averaged to obtain the initial value of the first delivery volume. The reference values ​​of the delivery time interval in each combination rule are weighted and averaged to obtain the initial value of the first delivery time interval. The initial value of the first dosage is corrected according to the activity compensation coefficient to obtain the dosage of the first alkalophilic microorganism, and the initial value of the first dosage time interval is taken as the first dosage time interval.

6. The saline-alkali soil cleanup and remediation system based on the synergistic effect of salt-tolerant crops and microorganisms as described in claim 5, characterized in that, The bias weights of each fuzzy rule matching the current pH value are determined based on the position of the current pH value within each pH range, including: Determine the current pH value range into which the current pH value falls, determine the first common boundary value that the current pH value is closest to within the current pH value range, the first common boundary value is the boundary value corresponding to the smaller distance between the current pH value and each common boundary value of the current pH value range, and calculate the absolute value of the difference between the current pH value and the first common boundary value as the first deviation. Calculate the ratio of the first deviation to the width of the current pH value range, and use it as the first position ratio; The bias weight of the fuzzy rule corresponding to the current pH value range is the first bias weight, which is equal to 1 minus the first position ratio. The bias weight of the fuzzy rule corresponding to the adjacent pH value intervals corresponding to the first shared boundary value is the second bias weight, and the second bias weight is equal to the first position ratio.

7. The saline-alkali soil cleanup and remediation system based on the synergistic effect of salt-tolerant crops and microorganisms as described in claim 5, characterized in that, The bias weights of each fuzzy rule matching the current pH change rate are determined based on the position of the current pH change rate within each change rate interval, including: Determine the current pH change rate within the current change rate interval, identify the second common boundary value that the current pH change rate is closest to within the current change rate interval, the second common boundary value is the boundary value corresponding to the smaller distance between the current pH change rate and each common boundary value of the current change rate interval, and calculate the absolute value of the difference between the current pH change rate and the second common boundary value as the second deviation. Calculate the ratio of the second deviation to the width of the current rate of change interval, and use it as the second position ratio; The bias weight of the fuzzy rule corresponding to the current rate of change interval is the third bias weight, which is equal to 1 minus the second position ratio. The bias weight of the fuzzy rule corresponding to the adjacent rate of change intervals corresponding to the second shared boundary value is the fourth bias weight, which is equal to the second position ratio.

8. The saline-alkali soil cleanup and remediation system based on the synergistic effect of salt-tolerant crops and microorganisms as described in claim 1, characterized in that, Based on the organic matter content and nutrient content, output the ratio parameters of organic fertilizer and native microbial community, perform soil microbial community transplantation, and plant salt-tolerant crops, including: Based on the organic matter content, various organic fertilizer ratio schemes were set up. The organic fertilizers of each ratio scheme were mixed with native microbial communities and applied to the test soil. After cultivation, the organic matter enhancement rate and microbial diversity enhancement rate of each test soil were measured. The weights for organic matter enhancement rate and microbial diversity enhancement rate are determined based on the organic matter content, and the sum of the organic matter enhancement rate weights and the microbial diversity enhancement rate weights is 1. The organic matter improvement rate and the microbial diversity improvement rate are weighted and summed using the weight of the organic matter improvement rate and the weight of the microbial diversity improvement rate to obtain the comprehensive improvement effect index of each ratio scheme. The proportion of organic fertilizer corresponding to the maximum value of the comprehensive improvement effect index is selected as the basic ratio parameter. Obtain the residual salt content after the salt removal process and set a standard salt content threshold. Calculate the difference between the residual salt content and the standard salt content threshold, and determine the ratio correction coefficient based on the salt content difference; Multiply the basic proportion parameters by the proportion correction coefficient to obtain the actual proportion parameters; After mixing the organic fertilizer with the native microbial community according to the actual ratio parameters, the mixture is applied to the target saline-alkali soil to complete the soil microbial community transplantation. Salt-tolerant crops are then planted after the microbial community transplantation.

9. The saline-alkali soil cleanup and remediation system based on the synergistic effect of salt-tolerant crops and microorganisms as described in claim 1, characterized in that, Collect real-time soil monitoring parameters and crop growth status parameters, and adjust the alkaliphilic microorganism release control parameters based on the real-time soil monitoring parameters and crop growth status parameters, including: During the remediation process, real-time soil monitoring parameters are continuously collected, including at least soil salinity and pH value. Collect crop growth status parameters, which include at least the normalized vegetation index; Determine whether to trigger a reverse callback based on the real-time monitoring value of the soil salinity: when the soil salinity rises above a preset threshold, calculate the amount of halophilic microorganisms to be added based on the difference in rise, and perform salt removal and replenishment. After the salt replenishment is completed, the soil pH value is re-acquired. If the pH value exceeds the preset target range, the amount and time interval of alkali-loving microorganism replenishment are re-determined based on the pH value after the salt replenishment, and alkali-adjusting replenishment is performed. The deviation between the normalized vegetation index and the preset normal value is calculated. If the deviation exceeds the preset threshold, the amount of carbon source replenishment is determined based on the deviation, and carbon source is replenished to the crop root area.

10. A method for the synergistic remediation of saline-alkali soil by salt-tolerant crops and microorganisms, characterized in that, The method applied to the saline-alkali soil cleanup and remediation system of salt-tolerant crops and microorganisms as described in any one of claims 1 to 9 includes: Soil samples were collected from the target saline-alkali land, and the soil physicochemical properties were measured to obtain soil physicochemical parameters. The soil physicochemical parameters include at least the initial pH value, salinity, organic matter content, soil bulk density, and nutrient content. The degree of salinization is classified according to the initial pH value and the salt content to obtain the salinity level. The amount of halophilic microorganisms to be added is calculated based on the salinity level and the salt content. Salt removal treatment is performed, and salt removal status parameters are obtained. The control parameters for the addition of alkaliphilic microorganisms are determined based on the salinity parameters. During the alkali adjustment process, the pH value is acquired multiple times and the control parameters for the addition of alkaliphilic microorganisms are updated until the pH value reaches the preset target range. The control parameters for the addition of alkaliphilic microorganisms include the amount of alkaliphilic microorganisms added and the time interval between additions. Based on the organic matter content and nutrient content, output the ratio parameters of organic fertilizer and native microbial community, perform soil microbial community transplantation, and plant salt-tolerant crops; Collect real-time soil monitoring parameters and crop growth status parameters, and adjust the control parameters for the release of alkaliphilic microorganisms based on the real-time soil monitoring parameters and crop growth status parameters.

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