A method for improving soil acidification

Abstract

This application discloses a method for improving soil acidification, relating to the field of soil improvement technology. This method employs multi-level diagnostics to calculate the soil acidification coefficient, physical barrier coefficient, and buffering capacity coefficient to assess soil properties, ensuring the comprehensiveness of soil acidification analysis and overcoming the limitations of current soil acidification analysis processes. Simultaneously, it uses threshold-driven decision control process branches to flexibly upgrade improvement schemes based on real-time monitoring data, enhancing the effectiveness of soil improvement. Closed-loop learning with dual feedback—S6 feeding back data to S1—enables system self-optimization, adapting to soil changes and providing timely warnings of abnormal states after improvement, ensuring soil safety. Applicable to fields such as land remediation, this method improves soil improvement efficiency and reduces resource waste through data-driven intelligent decision-making, upgrading soil acidification improvement and providing a technological engine for sustainable agricultural development and arable land protection.

Description

Technical Field

[0001] This application relates to the field of soil improvement technology, specifically to a method for improving soil acidification. Background Technology

[0002] Soil acidification poses a serious threat to agricultural development, especially in southern China where more than one-fifth of arable land is acidified. Traditional soil acidification remediation technologies still have many shortcomings, particularly the contradiction between the rapid effectiveness and limitations of lime remediation. Therefore, it is crucial to develop new soil remediation technologies.

[0003] Existing technology, such as the invention application patent with announcement number CN119742003A, discloses a method for predicting soil acidification sensitivity based on regional big data, including: acquiring the data to be tested and predicting using a soil acidification sensitivity prediction model; the training process of the soil acidification sensitivity prediction model includes: constructing a basic dataset, preprocessing the dataset, partitioning the dataset, training the model, and screening. This invention, by introducing a machine learning model, supplements the shortcomings of the traditional laboratory method of determining acidification sensitivity using continuous acid-base titration, and achieves rapid and accurate prediction of the acidification sensitivity of a batch of soil samples at a regional scale.

[0004] Regarding the above-mentioned solutions, the inventors of this application have found that the above-mentioned technologies have at least the following technical problems: 1. Currently, there is a lack of calculation of soil acidification coefficient, physical barrier coefficient and buffering capacity coefficient to complete the assessment of soil properties, which cannot ensure the comprehensiveness of soil acidification analysis, cannot solve the limitations of the current soil acidification analysis process, and at the same time, there is no use of threshold-driven decision control process branches, there is a lack of flexible upgrade and improvement schemes based on real-time monitoring data, which cannot improve the effect of soil improvement, there is a lack of closed-loop learning dual feedback, which cannot realize system self-optimization, adapt to soil changes, and cannot provide a technical engine for sustainable agricultural development and arable land protection.

[0005] 2. Currently, there is a lack of analysis on the first comprehensive improvement coefficient integrating topsoil and subsoil data, making it impossible to optimize resource allocation and avoid single-factor bias. There is also a lack of analysis on the second comprehensive improvement coefficient to address the negative buffer risk of the subsoil layer, thus failing to enhance the adaptability of the scheme. Furthermore, there is a lack of analysis on how electrochemical-assisted deep tillage can synergistically improve soil quality. Migration efficiency. Summary of the Invention

[0006] In view of the above-mentioned technical shortcomings, the purpose of this application is to provide a method for improving soil acidification.

[0007] To solve the above-mentioned technical problems, this application adopts the following technical solution: This application provides a method for improving soil acidification, which includes the following steps: S1, calculating the soil acidification coefficient based on pre-acquired topsoil data.

[0008] S2. Calculate the physical barrier coefficient of the soil based on the pre-acquired plow pan data, and decide whether to execute S3 or S5 based on the soil acidification coefficient threshold and the physical barrier coefficient threshold.

[0009] S3. Calculate the soil buffer capacity coefficient based on the pre-acquired subsoil data, and make a decision to execute S4 based on the buffer capacity coefficient threshold.

[0010] S4. Calculate the first comprehensive improvement coefficient or the second comprehensive improvement coefficient based on the soil acidification coefficient, physical barrier coefficient and buffering capacity coefficient to trigger S5.

[0011] S5. Implement the primary soil improvement program based on the soil acidification coefficient, the first comprehensive improvement coefficient, or the second comprehensive improvement coefficient, and monitor the soil over the first time series. The value is then used to decide whether to implement the secondary improvement plan or execute S6, thereby obtaining the second timing sequence. value.

[0012] S6, based on the second time sequence The threshold value triggers an alert and update mechanism, and sends the updated data back to S1.

[0013] Preferably, the calculation of the soil acidification coefficient based on the pre-acquired topsoil data is carried out as follows: the topsoil data includes the topsoil layer... Value, exchange acidity Content and content;

[0014] According to the calculation formula The soil acidification coefficient was calculated. ,in Represented as the pre-defined soil exchangeability Maximum content Represented as preset soil Maximum content , and These are respectively represented as the topsoil layer. The corresponding weighting factor and exchange acidity of the value Weighting factors corresponding to content and Weighting factors corresponding to content.

[0015] Preferably, the calculation of the soil physical barrier coefficient based on pre-acquired plow pan data is performed as follows: the plow pan data includes the compaction of the soil plow pan and... Content, substitute into the calculation formula The physical barrier coefficient of the soil was calculated. ,in This refers to the compaction of the soil subsoil. This represents the preset maximum soil compaction value. and These represent the weighting factors corresponding to the compaction of the soil subsurface and, respectively. Weighting factors corresponding to content.

[0016] Preferably, the decision-making process based on the soil acidification coefficient threshold and physical barrier coefficient threshold, performed in step S3 or step S5, includes: according to the decision formula. Output a decision result of 1 or 0. If the decision result is 1, execute S3; if the decision result is 0, execute operation B1 in S5. This is represented as the preset soil acidification coefficient threshold. This is represented as the preset threshold for the physical barrier coefficient of the soil.

[0017] Preferably, the calculation of the soil buffering capacity coefficient based on pre-acquired subsoil data is performed as follows: the subsoil data includes the clay content of the soil subsoil and Value, substitute into the calculation formula The buffering capacity coefficient of the soil was obtained. ,in Represented as a natural constant term, This refers to the clay content of the soil subsoil. and These represent the weighting factors corresponding to the clay content of the soil subsoil and, respectively. The weighting factor corresponding to the value.

[0018] Preferably, the decision-making process based on the buffer capacity coefficient threshold S4 includes: according to the decision formula. Output the decision result as 1 or 0. If the decision result is 0, execute A1 in S4; if the decision result is 1, execute A2 in S4. This represents the preset buffer capacity threshold.

[0019] Preferably, the calculation of the first comprehensive improvement coefficient or the second comprehensive improvement coefficient based on the soil acidification coefficient, physical barrier coefficient, and buffering capacity coefficient is carried out as follows: A1. Substitute the soil acidification coefficient and physical barrier coefficient into the calculation formula. The first comprehensive improvement coefficient was obtained. ,in and These represent the weighting factors corresponding to the soil acidification coefficient and the physical barrier coefficient, respectively.

[0020] A2. Substitute the soil acidification coefficient, physical barrier coefficient, and buffering capacity coefficient into the calculation formula. The second comprehensive improvement coefficient was obtained. ,in , and These are respectively represented as the weighting factors corresponding to the soil acidification coefficient, the physical barrier coefficient, and the buffering capacity coefficient.

[0021] Preferably, the first-level improvement scheme is implemented based on the soil acidification coefficient, the first comprehensive improvement coefficient, or the second comprehensive improvement coefficient, and the first time series of soil monitoring is performed. The values ​​include: B1, obtaining the amount of lime based on the soil type and acidification coefficient, mixing the soil and lime, turning the soil to the target depth, and turning the soil again in the cross direction of the turning to complete the soil mixing, and then watering the soil based on the soil dryness, and monitoring the first time series value of the soil.

[0022] B2. Based on the soil type and the first comprehensive improvement coefficient, the amount of dolomite powder used is obtained, and then the electrochemical-assisted deep loosening of the soil is completed, and the first time series value of the soil is monitored.

[0023] B3. The amount of amendment applied is obtained based on the soil type and the second comprehensive amendment coefficient. The amendment is a paste made of lime, phosphogypsum and biochar. The amendment is then injected into the subsoil layer, and the first time series value of the soil is monitored.

[0024] Preferably, the decision on whether to implement the secondary improvement plan or to implement S6 includes: when the soil's first time series When the value shows a downward trend or fluctuates, a second-level remediation plan is implemented; simultaneously, the soil is monitored again to obtain a second time series data. Value, when the soil's first time series When the value shows a downward trend or fluctuates up and down, the decision is executed in step S6.

[0025] Preferably, the secondary improvement scheme includes: C1, applying organic fertilizer.

[0026] C2. The amount of amendment used is obtained based on the first comprehensive improvement coefficient. The amendment is phosphogypsum dolomite powder microcapsules, which are then injected into the plow pan of the soil.

[0027] C3. Implement agronomic management of the soil.

[0028] The beneficial effects of this application are as follows: 1. The soil acidification improvement method provided in this application completes the assessment of soil properties by calculating the soil acidification coefficient, physical barrier coefficient, and buffering capacity coefficient through multi-level diagnosis, ensuring the comprehensiveness of soil acidification analysis and solving the limitations of current soil acidification analysis processes. At the same time, it uses threshold-driven decision control process branches to flexibly upgrade the improvement scheme based on real-time monitoring data, thereby improving the effect of soil improvement. Closed-loop learning with dual feedback, with S6 feeding back data to S1, enables the system to self-optimize, adapt to soil changes, and promptly warn of abnormal states after improvement, ensuring soil safety. It is applicable to fields such as land remediation, improving soil improvement efficiency and reducing resource waste through data-driven intelligent decision-making, upgrading soil acidification improvement, and providing a technological engine for sustainable agricultural development and arable land protection.

[0029] 2. This application quantifies the acidification degree of the topsoil layer through the acidification coefficient, ensures efficient resource allocation and avoids overtreatment through threshold decision-making, assesses the mechanical obstacles of the plow layer through the physical obstacle coefficient, optimizes the diagnostic process through threshold decision-making and reduces redundant operations, quantifies the acid neutralization capacity of the subsoil layer through the buffer capacity coefficient and improves diagnostic accuracy, integrates the topsoil layer and plow layer data through the first comprehensive improvement coefficient to optimize resource allocation and avoid single-factor bias, and addresses the negative buffer risk of the subsoil layer through the second comprehensive improvement coefficient to enhance the adaptability of the scheme.

[0030] 3. This application improves the synergistic effect of electrochemical-assisted deep tillage. Migration efficiency; two-layer timing Threshold-based decision-making ensures precise control and reduces energy consumption. Data-driven closed-loop systems improve diagnostic accuracy, avoid ineffective operations, and establish early warning and feedback mechanisms, while optimizing them for soil-specific dynamic circulation. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating the implementation steps of the method described in this application.

[0033] Figure 2 This is a schematic diagram of the decision-making process for this application. Detailed Implementation

[0034] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0035] Please see Figure 1 As shown, this application provides a method for improving soil acidification, including: S1, calculating the soil acidification coefficient based on pre-acquired topsoil data.

[0036] In a specific example, the calculation of the soil acidification coefficient based on pre-acquired topsoil data is carried out as follows: the topsoil data includes the topsoil layer... Value, exchange acidity Content and content.

[0037] According to the calculation formula The soil acidification coefficient was calculated. ,in Represented as the pre-defined soil exchangeability Maximum content Represented as preset soil Maximum content , and These are respectively represented as the topsoil layer. The corresponding weighting factor and exchange acidity of the value Weighting factors corresponding to content and Weighting factors corresponding to content.

[0038] It should be noted that the soil was divided into several zones, and pH data were collected from the topsoil layer of each zone using a pH sensor. Value and exchange acidity The content of KCl was collected in the topsoil of each region by extraction and spectral analysis. Content, and thus the topsoil layer of each region Value, exchange acidity Content and The average content was calculated to obtain the topsoil data.

[0039] It should be noted that, .

[0040] It should be noted that the default setting... , preset .

[0041] It should be noted that, , , , ,like , and .

[0042] It should be noted that the topsoil layer was obtained using factor analysis. The corresponding weighting factor and exchange acidity of the value Weighting factors corresponding to content and The weighting factors corresponding to the content are first determined by the topsoil layer. Value, exchange acidity Content and The information on content is condensed, and then the variance explained after rotation is obtained. The weights are obtained by dividing the cumulative variance explained.

[0043] It should be noted that factor analysis is a well-known technique. It is a multivariate statistical analysis method that starts by studying the internal dependencies of variables and reduces some variables with complex relationships to a few comprehensive factors. Information condensation is expressed as the calculation of the median. The variance explained rate is the amount of information extracted by the factors. Variance explained rate = eigenvalues ​​ / total number of analysis terms. The rotated variance explained rate is expressed as the variance explained by the factors after maximum variance rotation.

[0044] S2. Calculate the physical barrier coefficient of the soil based on the pre-acquired plow pan data, and decide whether to execute S3 or S5 based on the soil acidification coefficient threshold and the physical barrier coefficient threshold.

[0045] In a specific example, the calculation of the soil physical barrier coefficient based on pre-acquired plow pan data is performed as follows: the plow pan data includes the soil compaction and... Content, substitute into the calculation formula The physical barrier coefficient of the soil was calculated. ,in This refers to the compaction of the soil subsoil. This represents the preset maximum soil compaction value. and These represent the weighting factors corresponding to the compaction of the soil subsurface and, respectively. Weighting factors corresponding to content.

[0046] It should be noted that the compaction of the plow pan in each area was collected using a soil compaction meter, and the compaction of the plow pan in each area was collected by combining KCl extraction with spectral analysis. Content, which in turn affects the compaction of the plow pan in each region and The content was averaged to obtain soil plow pan data.

[0047] It should be noted that, , , .

[0048] It should be noted that the weighting factors corresponding to the compaction of the plow pan were obtained through factor analysis. The weighting factors corresponding to the content are first determined by the compaction of the soil subsoil and... The information on content is condensed, and then the variance explained after rotation is obtained. The weights are obtained by dividing the cumulative variance explained.

[0049] In a specific instance, the decision-making process based on the soil acidification coefficient threshold and physical barrier coefficient threshold, either S3 or S5, includes: [the following steps are taken based on the decision formula]. Output a decision result of 1 or 0. If the decision result is 1, execute S3; if the decision result is 0, execute operation B1 in S5. This is represented as the preset soil acidification coefficient threshold. This is represented as the preset threshold for the physical barrier coefficient of the soil.

[0050] It should be noted that the amount of lime used is determined based on the soil acidification coefficient, and the specific calculation formula is as follows: ,in The amount of lime applied corresponds to the soil type. For example, the amount of lime applied to clay soil is greater than that applied to sandy soil. The unit of lime application is tons per acre.

[0051] S3. Calculate the soil buffer capacity coefficient based on the pre-acquired subsoil data, and make a decision to execute S4 based on the buffer capacity coefficient threshold.

[0052] In a specific example, the calculation of the soil's buffering capacity coefficient based on pre-acquired subsoil data is performed as follows: the subsoil data includes the clay content of the soil subsoil and... Value, substitute into the calculation formula The buffering capacity coefficient of the soil was obtained. ,in Represented as a natural constant term, This refers to the clay content of the soil subsoil. and These represent the weighting factors corresponding to the clay content of the soil subsoil and, respectively. The weighting factor corresponding to the value.

[0053] It should be noted that the soil was divided into several zones, and pH data were collected from the topsoil layer of each zone using a pH sensor. Value and exchange acidity The content of KCl was collected in the topsoil of each region by extraction and spectral analysis. Content, and thus the topsoil layer of each region Value, exchange acidity Content and The average content was calculated to obtain the topsoil data.

[0054] It should be noted that pH sensors were used to collect data from the subsoil layers in each area. The value was determined, and the clay content of the subsoil in each region was collected using a laser particle size analyzer, thereby classifying the subsoil content of each region. The mean values ​​of soil core content and clay content are calculated separately to obtain the subsoil data.

[0055] It should be noted that, .

[0056] It should be noted that, , , .

[0057] It should be noted that the weighting factors corresponding to the compaction of the soil subsurface were obtained through factor analysis. The weighting factors corresponding to the content are first determined by the compaction of the soil subsoil and... The information on content is condensed, and then the variance explained after rotation is obtained. The weights are obtained by dividing the cumulative variance explained.

[0058] In a specific example, the decision-making process based on the buffer capacity coefficient threshold, S4, includes: according to the decision formula... Output the decision result as 1 or 0. If the decision result is 0, execute A1 in S4; if the decision result is 1, execute A2 in S4. This represents the preset buffer capacity threshold.

[0059] S4. Calculate the first comprehensive improvement coefficient or the second comprehensive improvement coefficient based on the soil acidification coefficient, physical barrier coefficient and buffering capacity coefficient to trigger S5.

[0060] In a specific example, the calculation of the first comprehensive improvement coefficient or the second comprehensive improvement coefficient based on the soil acidification coefficient, physical barrier coefficient, and buffering capacity coefficient is carried out as follows:

[0061] A1. Substitute the soil acidification coefficient and physical barrier coefficient into the calculation formula. The first comprehensive improvement coefficient was obtained. ,in and These represent the weighting factors corresponding to the soil acidification coefficient and the physical barrier coefficient, respectively.

[0062] A2. Substitute the soil acidification coefficient, physical barrier coefficient, and buffering capacity coefficient into the calculation formula. The second comprehensive improvement coefficient was obtained. ,in , and These are respectively represented as the weighting factors corresponding to the soil acidification coefficient, the physical barrier coefficient, and the buffering capacity coefficient.

[0063] This application quantifies the acidification degree of the topsoil layer using an acidification coefficient, employs threshold decision-making to ensure efficient resource allocation and avoid overtreatment, assesses mechanical obstacles in the plow layer using a physical obstacle coefficient, optimizes the diagnostic process using threshold decision-making to reduce redundant operations, quantifies the acid neutralization capacity of the subsoil layer using a buffer capacity coefficient to improve diagnostic accuracy, integrates topsoil and plow layer data using a first comprehensive improvement coefficient to optimize resource allocation and avoid single-factor bias, and addresses the negative buffer risk of the subsoil layer using a second comprehensive improvement coefficient to enhance the adaptability of the solution.

[0064] S5. Implement the primary soil improvement program based on the soil acidification coefficient, the first comprehensive improvement coefficient, or the second comprehensive improvement coefficient, and monitor the soil over the first time series. The value is then used to decide whether to implement the secondary improvement plan or execute S6, thereby obtaining the second timing sequence. value.

[0065] It should be noted that the first time series of soil The values ​​represent the soil after the first-stage soil improvement program was completed, based on the data collected at each time point. value.

[0066] In a specific example, the first-level improvement scheme is implemented based on the soil acidification coefficient, the first comprehensive improvement coefficient, or the second comprehensive improvement coefficient, and the first time series of soil monitoring is conducted. The values ​​include: B1, obtaining the amount of lime based on the soil type and acidification coefficient, mixing the soil and lime, turning the soil to the target depth, and turning the soil again in the cross direction of the turning to complete the soil mixing, and then watering the soil based on the soil dryness, and monitoring the first time series value of the soil.

[0067] It should be noted that the amount of lime used is obtained based on the soil type and acidification coefficient, and the database stores a table of lime usage corresponding to the soil type and acidification coefficient.

[0068] It should be noted that a lime spreader is used to spread lime on the topsoil layer, and then a rotary tiller is used to mix the soil and lime; the target depth is 15 to 20 centimeters; based on the soil dryness, soil watering is carried out, and the specific process is as follows: a humidity sensor is used to obtain the soil dryness, and the corresponding watering amount is retrieved from the database based on the soil dryness and soil type, thereby completing the soil watering.

[0069] B2. Based on the soil type and the first comprehensive improvement coefficient, the amount of dolomite powder used is obtained, and then the electrochemical-assisted deep loosening of the soil is completed, and the first time series value of the soil is monitored.

[0070] It should be noted that the amount of dolomite powder used is obtained based on the soil type and the first comprehensive improvement coefficient, and the database stores a table of dolomite powder usage corresponding to the soil type and the first comprehensive improvement coefficient.

[0071] It should be noted that electrochemical-assisted deep tillage of soil involves attaching electrodes to the deep tillage plow blades to generate a low-voltage electric field, such as 5V / cm, during the loosening process, which drives... Migrating towards the cathode, while the anode releases dolomite powder... This achieves neutralization of the acidity gradient in the plow layer.

[0072] B3. The amount of amendment applied is obtained based on the soil type and the second comprehensive amendment coefficient. The amendment is a paste made of lime, phosphogypsum and biochar. The amendment is then injected into the subsoil layer, and the first time series value of the soil is monitored.

[0073] It should be noted that the amount of soil amendment used is obtained based on the soil type and the second comprehensive improvement coefficient. The database stores a table of soil amendment usage corresponding to the soil type and the second comprehensive improvement coefficient.

[0074] It should be noted that the moisture content of the amendment is less than 30%, and it is injected into the deep soil through boreholes using a high-pressure pump.

[0075] In a specific instance, the decision of whether to implement the secondary remediation plan or to implement S6 includes: when the soil's first time series When the value shows a downward trend or fluctuates, a second-level remediation plan is implemented; simultaneously, the soil is monitored again to obtain a second time series data. Value, when the soil's first time series When the value shows a downward trend or fluctuates up and down, the decision is executed in step S6.

[0076] In a specific example, the secondary improvement scheme includes: C1, applying organic fertilizer.

[0077] C2. The amount of amendment used is obtained based on the first comprehensive improvement coefficient. The amendment is phosphogypsum dolomite powder microcapsules, which are then injected into the plow pan of the soil.

[0078] C3. Implement agronomic management of the soil.

[0079] It should be noted that the secondary improvement schemes include C1, C2, and C3, where C1, C2, and C3 correspond to B1, B2, and B3, respectively. For example, when implementing B1, the first time series of soil monitoring is performed. When the value shows a downward trend or fluctuates up and down, the decision is to implement the secondary improvement plan C1.

[0080] It should be noted that the amount of soil amendment used is obtained based on the soil type and the second comprehensive improvement coefficient. The database stores a table of soil amendment usage corresponding to the soil type and the second comprehensive improvement coefficient.

[0081] It should be noted that the phosphogypsum-dolomite powder gel microcapsules are a composite of phosphogypsum and dolomite powder, wherein the phosphogypsum contains It is formulated as microcapsules. When the pH of deep soil is <5.0, the capsule shell dissolves, releasing calcium and magnesium ions, which replace aluminum ions and neutralize acidity. Theoretical basis: The calcium in phosphogypsum can fix active aluminum. Dolomite provides sustained alkalinity .

[0082] It should be noted that soil agronomic management includes water optimization, planting structure adjustment, and organic carbon addition.

[0083] S6, based on the second time sequence The threshold value triggers an alert and update mechanism, and sends the updated data back to S1.

[0084] It should be noted that, according to the second timing sequence in S5 Value, data update initiated based on warning type, second time series A level 1 warning is issued when the value is greater than 5.5, followed by a second time series warning. A level-two early warning will be issued when the value is greater than 6.5, in the second time series. When the value is greater than 7, a level 3 warning will be issued, and the updated data will be fed back to S1 to re-execute each step.

[0085] This application utilizes electrochemical-assisted deep tillage synergistic improvement Migration efficiency; two-layer timing Threshold-based decision-making ensures precise control and reduces energy consumption. Data-driven closed-loop systems improve diagnostic accuracy, avoid ineffective operations, and establish early warning and feedback mechanisms optimized for soil-specific dynamic circulation.

[0086] This application provides a soil acidification remediation method that, through multi-level diagnosis, calculates the soil acidification coefficient, physical barrier coefficient, and buffering capacity coefficient to complete the assessment of soil properties, ensuring the comprehensiveness of soil acidification analysis and solving the limitations of current soil acidification analysis processes. Simultaneously, it uses threshold-driven decision control process branches to flexibly upgrade the remediation scheme based on real-time monitoring data, improving the effectiveness of soil remediation. Closed-loop learning with dual feedback—S6 feeding back data to S1—enables the system to self-optimize, adapt to soil changes, and promptly alert to abnormal states after remediation, ensuring soil safety. Applicable to fields such as land remediation, this method improves soil remediation efficiency and reduces resource waste through data-driven intelligent decision-making, upgrading soil acidification remediation and providing a technological engine for sustainable agricultural development and arable land protection.

[0087] In the several embodiments provided by this invention, it should be understood that the disclosed methods can be implemented in other ways.

[0088] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0089] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, and technology that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for improving soil acidification, characterized in that, include: S1. Calculate the soil acidification coefficient based on the pre-acquired topsoil data; S2. Calculate the physical barrier coefficient of the soil based on the pre-acquired plow pan data, and decide whether to execute S3 or B1 operation in S5 based on the soil acidification coefficient threshold and the physical barrier coefficient threshold. The decision based on the soil acidification coefficient threshold and physical barrier coefficient threshold is executed in S3 or in operation B1 of S5, including: According to the decision formula Output a decision result of 1 or 0. If the decision result is 1, execute S3; if the decision result is 0, execute operation B1 in S5. This is represented as the preset soil acidification coefficient threshold. This is represented as a preset threshold for the physical barrier coefficient of the soil. S3. Calculate the soil buffer capacity coefficient based on the pre-acquired subsoil data, and make a decision to execute S4 based on the buffer capacity coefficient threshold. S4. Calculate the first comprehensive improvement coefficient or the second comprehensive improvement coefficient based on the soil acidification coefficient, physical barrier coefficient and buffering capacity coefficient to trigger S5; S5. Implement the primary soil improvement program based on the soil acidification coefficient, the first comprehensive improvement coefficient, or the second comprehensive improvement coefficient, and monitor the soil over the first time series. The value is then used to decide whether to implement the secondary improvement plan or execute S6, thereby obtaining the second timing sequence. value; The first-level improvement scheme is implemented based on the soil acidification coefficient, the first comprehensive improvement coefficient, or the second comprehensive improvement coefficient, and the first time series of soil monitoring is conducted. Values, including: B1. Based on the soil type and acidification coefficient, obtain the amount of lime to be used, mix the soil and lime, turn the soil over to the target depth, and turn the soil over again in the cross direction of the turning to complete the soil mixing. Based on the soil dryness, replenish the soil water and monitor the first time series value of the soil. B2. Based on the soil type and the first comprehensive improvement coefficient, the amount of dolomite powder used is obtained, and then the electrochemical-assisted deep loosening of the soil is completed, and the first time series value of the soil is monitored. B3. Based on the soil type and the second comprehensive improvement coefficient, the amount of amendment is obtained. The amendment is a paste made of lime, phosphogypsum and biochar. The amendment is then injected into the subsoil layer and the first time series value of the soil is monitored. S6, based on the second time sequence The threshold value triggers an alert and update mechanism, and sends the updated data back to S1.

2. The method for improving soil acidification according to claim 1, characterized in that, The soil acidification coefficient is calculated based on pre-acquired topsoil data. The specific calculation process is as follows: Topsoil data includes the topsoil layer of the soil. Value, exchange acidity Content and content; According to the calculation formula The soil acidification coefficient was calculated. ,in Represented as the pre-defined soil exchangeability Maximum content Represented as preset soil Maximum content , and These are respectively represented as the topsoil layer. The corresponding weighting factor and exchange acidity of the value Weighting factors corresponding to content and Weighting factors corresponding to content.

3. The method for improving soil acidification according to claim 2, characterized in that, The physical barrier coefficient of the soil is calculated based on the pre-acquired plow pan data. The specific calculation process is as follows: Plow pan data includes soil compaction and Content, substitute into the calculation formula The physical barrier coefficient of the soil was calculated. ,in This refers to the compaction of the soil subsoil. This represents the preset maximum soil compaction value. and These represent the weighting factors corresponding to the compaction of the soil subsurface and, respectively. Weighting factors corresponding to content.

4. The method for improving soil acidification according to claim 3, characterized in that, The soil buffering capacity coefficient is calculated based on the pre-acquired subsoil data. The specific calculation process is as follows: Subsoil data includes the clay content of the soil subsoil and Value, substitute into the calculation formula The buffering capacity coefficient of the soil was obtained. ,in Represented as a natural constant term, This refers to the clay content of the soil subsoil. and These represent the weighting factors corresponding to the clay content of the soil subsoil and, respectively. The weighting factor corresponding to the value.

5. A method for improving soil acidification according to claim 4, characterized in that, The decision-making process based on the buffer capacity coefficient threshold, S4, includes: According to the decision formula Output a decision result of 1 or 0. If the decision result is 0, execute A1 in S4; if the decision result is 1, execute A2 in S4. This represents the preset buffer capacity threshold.

6. The method for improving soil acidification according to claim 5, characterized in that, The calculation of the first or second comprehensive improvement coefficient based on the soil acidification coefficient, physical barrier coefficient, and buffering capacity coefficient is as follows: A1. Substitute the soil acidification coefficient and physical barrier coefficient into the calculation formula. The first comprehensive improvement coefficient was obtained. ,in and These are respectively represented as the weighting factors corresponding to the soil acidification coefficient and the weighting factors corresponding to the physical barrier coefficient; A2. Substitute the soil acidification coefficient, physical barrier coefficient, and buffering capacity coefficient into the calculation formula. The second comprehensive improvement coefficient was obtained. ,in , and These are respectively represented as the weighting factors corresponding to the soil acidification coefficient, the physical barrier coefficient, and the buffering capacity coefficient.

7. A method for improving soil acidification according to claim 6, characterized in that, The decision on whether to implement the secondary improvement plan or to implement S6 includes: When the first time sequence of soil When the value shows a downward trend or fluctuates, a second-level remediation plan is implemented; simultaneously, the soil is monitored again to obtain a second time series data. Value, when the soil's first time series When the value shows a downward trend or fluctuates up and down, the decision is executed in step S6.

8. A method for improving soil acidification according to claim 7, characterized in that, The secondary improvement scheme includes: C1. Apply organic fertilizer; C2. The amount of amendment is obtained based on the first comprehensive improvement coefficient. The amendment is phosphogypsum dolomite powder microcapsules, which are then injected into the plow pan of the soil. C3. Implement agronomic management of the soil.

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