Microbial mineralization remediation method, system and application for coastal salinization disturbed soil

Abstract

The present application provides a microbial mineralization rehabilitation method and system for coastal salinization disturbed soil and application, relates to the soil improvement technical field, carries out regional disturbance comparative analysis to the coastal salinization region, obtains characteristic disturbance extraction data, carries out weight and influence analysis to the characteristic disturbance extraction data, obtains characteristic disturbance proportion analysis data, and then obtains disturbance collection analysis data;According to the structure improvement operation and the structure improvement of the characteristic disturbance proportion analysis data to the coastal salinization disturbed soil and the improvement comparative analysis of the improvement, improvement comparative analysis data are obtained, and the improvement factor adjustment data are obtained according to the improvement comparative analysis data, the disturbance improvement adjustment data are obtained according to the improvement factor adjustment, and the accuracy and intelligence of soil improvement are greatly enhanced through the present application.

Description

Technical Field

[0001] This invention proposes a method, system, and application for microbial mineralization restoration of coastal salinized and disturbed soils, relating to the field of disturbed soil technology, specifically to the field of microbial mineralization technology for coastal salinized and disturbed soils. Background Technology

[0002] Traditional techniques for analyzing disturbed soils in salinization and erosion typically involve comprehensive data analysis. This process is complex and inaccurate, and it is easy to misjudge the characteristics and types of disturbances, further exacerbating the analysis errors. Consequently, it is difficult to accurately determine and quantify disturbance information, and it is also difficult to identify the regulating factors for microbial mineralization, making further improvement and optimization difficult. Summary of the Invention

[0003] This invention provides a method, system, and application for microbial mineralization restoration of coastal salinized and disturbed soils to solve the above-mentioned problems: The present invention proposes a method, system, and application for microbial mineralization restoration of coastal salinized and disturbed soils. The method includes: S1. Conduct regional disturbance comparison analysis on the coastal salinization area to obtain characteristic disturbance extraction data. Perform weight and impact analysis on the characteristic disturbance extraction data to obtain characteristic disturbance proportion analysis data, and then obtain disturbance collection and analysis data. S2. Based on the characteristic disturbance ratio analysis data, structural improvement operations and comparative analysis of structural improvement are carried out on the coastal salinized disturbed soil to obtain improvement comparative analysis data. Based on the improvement comparative analysis data, improvement factor adjustment data are obtained, and adjustment is carried out according to the improvement factors to obtain disturbance improvement adjustment data.

[0004] Further, S1 includes: To obtain regional data, we collected data on the location of power transmission and transformation projects in the coastal salinized area and obtained regional disturbance data. Acquire initial regional data for the site of power transmission and transformation projects; The regional disturbance data is compared with the initial regional data to obtain regional disturbance comparison data. Based on the disturbance comparison data, disturbance damage data analysis is performed to obtain disturbance collection and analysis data.

[0005] Furthermore, the step of performing disturbance damage data analysis based on disturbance comparison data to obtain disturbance acquisition and analysis data includes: Establish a regional disturbance comparison table based on disturbance comparison data; Determine the regional disturbance comparison relationship and the regional non-disturbance comparison relationship based on the information in the regional disturbance comparison table; The aforementioned regional perturbation comparison relationships are combined to obtain regional perturbation comparison combinations; The non-disturbance comparison relationships of the regions are combined to obtain the non-disturbance comparison combination of regions; The distribution range and disturbance characteristics of the regional disturbance comparison combination are extracted to obtain the disturbance range and disturbance characteristic information of the regional disturbance comparison combination; Feature perturbation analysis is performed based on the perturbation feature extraction information to obtain feature perturbation analysis data.

[0006] Furthermore, the step of performing feature perturbation analysis based on the perturbation feature extraction information to obtain feature perturbation analysis data includes: Sort the perturbation feature type data by feature quantity to obtain the feature perturbation type sequence; Weights are generated for the feature perturbation types based on the feature perturbation type sequence to obtain perturbation feature type weight data; The feature perturbation ratio analysis was performed on the multiple perturbation feature extraction data of the perturbation feature type weight data to obtain the feature perturbation ratio analysis coefficient; The weights of the perturbation feature extraction data are generated by combining the perturbation feature type weight data with the feature perturbation proportion analysis coefficient, and feature perturbation analysis data is obtained.

[0007] Furthermore, the step of performing feature perturbation proportion analysis on multiple perturbation feature extraction data of perturbation feature type weight data to obtain feature perturbation proportion analysis coefficients includes: The influence coefficient of the disturbance feature is obtained by comparing the difference between the extracted data of each disturbance feature and the corresponding feature comparison data in the regional disturbance comparison combination. The ratio of the perturbation feature influence coefficient of each perturbation feature extraction data to the sum of the perturbation feature influence coefficients of all perturbation feature extraction data in the regional perturbation comparison combination is obtained to obtain the feature perturbation proportion analysis data.

[0008] Further, S2 includes: By analyzing the proportion of characteristic disturbances in microbial-induced calcium carbonate mineralization, structural improvement operations were carried out on coastal salinized disturbed soils to obtain regional disturbance data after structural improvement operations. The data collected on the disturbance in the region after the structural improvement operation is compared with the data collected on the initial region, so as to obtain the characteristic disturbance analysis data after the structural improvement operation. The characteristic perturbation analysis data before and after the structural improvement operation are compared to obtain the improvement comparison data. Perform improvement comparison analysis on the improvement comparison data to obtain improvement comparison analysis data; Based on the improved comparative analysis data, the improvement factor adjustment data is determined, and the adjustment is carried out according to the improvement factors to obtain the disturbance improvement adjustment data.

[0009] Furthermore, the step of performing improvement comparison analysis on the improvement comparison data to obtain improvement comparison analysis data includes: Salt content data were collected from disturbed coastal salinized soils. The soil salinity data is combined with a preset salinization grading standard to determine the degree of salinization of coastal salinized disturbed soils. By improving and comparing the data, the absolute value of the difference between the degree of salinization before and after the improvement is obtained, and the first improvement coefficient is obtained. Physical structure tests were conducted on the disturbed salinized soil in the coastal area to obtain physical structure test data; Based on the comprehensive correlation analysis data, the control parameters for microbial-induced calcium carbonate mineralization were determined, and mineralization control parameter information was obtained.

[0010] By improving and comparing the physical structure test data before and after the improvement, the absolute value of the physical structure difference is obtained to obtain the second improvement coefficient. The first improvement coefficient and the second improvement coefficient are the improvement comparison analysis data.

[0011] Further, based on the improved comparative analysis data, the adjustment data for improvement factors are determined, and adjustments are made according to the improvement factors to obtain disturbance improvement adjustment data, including: The first and second improvement coefficients of the improved comparative analysis data are normalized to obtain normalized data. The correlation coefficient was calculated using the Pearson correlation coefficient on the normalized data to obtain the improved correlation coefficient. The improved correlation coefficient is compared with the preset improved correlation threshold to obtain the improved correlation comparison result; Based on the improved correlation comparison results, determine the adjustment data for the improvement factors; Based on the aforementioned improvement factor adjustment data, the parameters of the preset improvement factors are adjusted to obtain disturbance improvement adjustment data.

[0012] Further, determining the adjustment data for improvement factors based on the improved correlation comparison results includes: When the improvement correlation coefficient is greater than the preset improvement correlation threshold, the ratio of the improvement correlation coefficient to the preset improvement correlation threshold is obtained to obtain the improvement level coefficient. Obtain the corresponding preset target improvement factor data based on the improvement level coefficient; Construct a database of relationships between improvement factors and their effects; Obtain the adjustment data of each factor from the correlation effect database of the improved factors, and obtain the disturbance improvement adjustment data.

[0013] Furthermore, the system includes: The disturbance analysis module is used to conduct regional disturbance comparative analysis of coastal salinization areas, obtain characteristic disturbance extraction data, perform weight and impact analysis on the characteristic disturbance extraction data, obtain characteristic disturbance proportion analysis data, and then obtain disturbance collection and analysis data. The improvement analysis module is used to perform structural improvement operations and comparative analysis of structural improvement on coastal salinized disturbed soils based on characteristic disturbance ratio analysis data, obtain improvement comparison analysis data, obtain improvement factor adjustment data based on improvement comparison analysis data, and adjust according to improvement factors to obtain disturbance improvement adjustment data.

[0014] The beneficial effects of this invention are as follows: By extracting data through feature perturbation, the data can be accurately summarized, reducing the amount of data to be analyzed. Focusing on core perturbation features significantly improves the efficiency of data analysis and processing. Weight analysis determines the attention given to different types of perturbation features, and impact analysis determines the degree of damage caused by these features to the soil. This greatly improves the flexibility and accuracy of perturbation feature analysis, enabling different emphases in the analysis of the impact of different perturbation features. The obtained perturbation data allows for the analysis of the degree of impact of perturbation features, improving the accuracy of the impact degree analysis. Structural improvement operations can enhance the quality of soil improvement, reduce soil salinization, and mitigate the soil impact of power transmission and transformation project sites. Comparative analysis of the improvements allows for the evaluation of the improvement effects, identifying which improvement factors are effective and which are ineffective. Adjustments based on the comparative analysis can optimize the improvement factors, avoiding blind improvements that may lead to unsatisfactory results and further improving the overall soil improvement effect. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of a microbial mineralization restoration method for coastal salinized and disturbed soils. Detailed Implementation

[0016] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0017] In one embodiment of the present invention, the present invention proposes a method, system, and application for microbial mineralization restoration of coastal salinized and disturbed soils, the method comprising: S1. Conduct regional disturbance comparison analysis on the coastal salinization area to obtain characteristic disturbance extraction data. Perform weight and impact analysis on the characteristic disturbance extraction data to obtain characteristic disturbance proportion analysis data, and then obtain disturbance collection and analysis data. S2. Based on the characteristic disturbance proportion analysis data, structural improvement operations and comparative analysis of structural improvement were conducted on the coastal salinized disturbed soil. Comparative analysis data of the improvement were obtained, and adjustment data of the improvement factors were acquired based on this data. Adjustments were then made according to the improvement factors to obtain disturbance improvement adjustment data, such as… Figure 1 As shown.

[0018] The working principle and technical effects of the above technical solution are as follows: Regional disturbance comparative analysis is conducted on coastal salinization areas to obtain characteristic disturbance extraction data. This data is then accurately summarized, reducing the amount of data to be analyzed. Focus is placed on core disturbance characteristics, significantly improving data analysis and processing efficiency. Weight and impact analysis is performed on the characteristic disturbance extraction data to obtain characteristic disturbance proportion analysis data. Weight analysis determines the attention level of different types of disturbance characteristics, and impact analysis determines the degree of damage to the soil caused by disturbance characteristics. This greatly improves the flexibility and accuracy of disturbance characteristic analysis, enabling different impact-focused analysis of different disturbance characteristics, thereby obtaining disturbance collection and analysis data. The obtained disturbance collection and analysis data is then used to analyze the degree of influence of disturbance characteristics, improving the accuracy of the influence degree analysis.

[0019] Based on the characteristic disturbance proportion analysis data, structural improvement operations and comparative analyses of structural improvement were conducted on coastal salinized disturbed soils. Comparative analysis data was obtained, showing that structural improvement operations can enhance soil improvement quality, reduce soil salinization, and mitigate the soil impact of power transmission and transformation project sites. The comparative analysis also allowed for the evaluation of the improvement effects, identifying which improvement factors were effective and which had low effectiveness. Adjustment data for these improvement factors was obtained from the comparative analysis data, and adjustments were made accordingly to obtain disturbance improvement adjustment data. Adjustments based on the comparative analysis enable the regulation and optimization of improvement factors, avoiding blind improvements that may lead to suboptimal results and further improving soil improvement outcomes.

[0020] In one embodiment of the present invention, S1 includes: To obtain regional data, we collected data on the location of power transmission and transformation projects in the coastal salinized area and obtained regional disturbance data. Acquire initial regional data for the site of power transmission and transformation projects; The regional disturbance data is compared with the initial regional data to obtain regional disturbance comparison data. The regional disturbance comparison data includes environmental comparison data, vegetation comparison data, and soil comparison data; Based on the disturbance comparison data, disturbance damage data analysis is performed to obtain disturbance collection and analysis data.

[0021] The working principle and technical effects of the above technical solution are as follows: By collecting regional data on the sites of power transmission and transformation projects in coastal salinization areas, abnormal soil disturbance analysis is achieved. Initial healthy soil data before the project's impact is retrieved by obtaining initial regional data. By comparing the initial regional data with regional disturbance data, disturbance comparison of multiple types of data before and after disturbance is achieved. Based on the disturbance comparison data of multiple types, disturbance quantification analysis of the characteristics of each type is performed, greatly enhancing the accuracy and comprehensiveness of disturbance data acquisition and analysis, enabling individual comparison of disturbance data of each type, and thus enabling individual disturbance comparison analysis of each type.

[0022] By directly comparing multiple types of data before and after disturbance, the accuracy of disturbance analysis of coastal salinized soil disturbance characteristics is greatly improved, and the complexity of traditional comprehensive disturbance comparison analysis is reduced. Decomposing the comparison data into environment, vegetation, and soil makes the data source and impact analysis have clear boundaries, enhancing the standardization of data analysis. Through quantitative analysis and comparison of disturbance characteristics, the distinction between engineering disturbance and primary salinization problems is enhanced, greatly improving the accuracy of data quantification.

[0023] In one embodiment of the present invention, the step of performing disturbance destruction data analysis based on disturbance comparison data to obtain disturbance acquisition and analysis data includes: Establish a regional disturbance comparison table based on disturbance comparison data; The regional disturbance comparison relationship and the regional non-disturbance comparison relationship are determined based on the information in the regional disturbance comparison table; the disturbance comparison relationship is determined by judging whether the disturbance comparison difference is greater than the preset disturbance comparison threshold. The aforementioned regional perturbation comparison relationships are combined to obtain regional perturbation comparison combinations; The non-disturbance comparison relationships of the regions are combined to obtain the non-disturbance comparison combination of regions; The distribution range and disturbance characteristics of the regional disturbance comparison combination are extracted to obtain the disturbance range and disturbance characteristic information of the regional disturbance comparison combination; Feature perturbation analysis is performed based on the perturbation feature extraction information to obtain feature perturbation analysis data.

[0024] The working principle and technical effect of the above technical solution are as follows: a structured regional disturbance comparison table is constructed through disturbance comparison data, and the degree of difference characteristics of the characteristic disturbance analysis data is analyzed; by using a preset disturbance comparison threshold set according to experience, it is determined whether the difference between multiple disturbance comparisons exceeds the threshold, and the regional disturbance comparison relationship and the regional non-disturbance comparison relationship are divided according to the judgment result; the regional disturbance comparison relationship and the regional non-disturbance comparison relationship are combined and classified to obtain disturbance comparison combination and non-disturbance comparison combination; based on the disturbance comparison combination, the distribution range analysis and disturbance feature extraction analysis are performed to obtain the spatial distribution boundary and core disturbance feature information, and then the disturbance range and feature information of the regional disturbance comparison combination are obtained.

[0025] This method achieves precise labeling of perturbation relationships through a regional perturbation comparison table, greatly enhancing the specificity of perturbation relationships and making perturbation judgment more systematic and clear. By setting a perturbation comparison threshold, it achieves precise quantification of perturbation data, enhancing the standardization and objectivity of perturbation judgment and reducing the error rate caused by subjective perturbation relationship classification. By extracting the range and features of perturbation combinations separately, it achieves precise determination of the perturbation range and features, further enhancing the accuracy of perturbation analysis. Classifying and combining perturbation and non-perturbation relationships greatly reduces interference from irrelevant data and improves the efficiency and accuracy of feature perturbation analysis.

[0026] In one embodiment of the present invention, the step of performing feature perturbation analysis based on perturbation feature extraction information to obtain feature perturbation analysis data includes: Sort the perturbation feature type data by feature quantity to obtain the feature perturbation type sequence; Weights are generated for the feature perturbation types based on the feature perturbation type sequence to obtain perturbation feature type weight data; The feature perturbation ratio analysis was performed on the multiple perturbation feature extraction data of the perturbation feature type weight data to obtain the feature perturbation ratio analysis coefficient; The weights of the perturbation feature extraction data are generated by combining the perturbation feature type weight data with the feature perturbation proportion analysis coefficient, and feature perturbation analysis data is obtained.

[0027] The working principle and technical effects of the above technical solution are as follows: Sorting the perturbation feature type data by feature quantity clarifies the difference in the number of perturbation features for each type. The perturbation feature type sequence determines the difference in the number of perturbation features for each type compared to other types, thus reflecting the degree of influence of the perturbation feature of one type compared to other types. Based on the sequence sorting information, weight values ​​are set for each perturbation feature type, generating perturbation feature type weight data. This weight data allows for the emphasization of each perturbation feature type, enhancing the accuracy of comprehensive perturbation analysis and avoiding the problem of a lack of focus and reduced accuracy caused by comprehensive perturbation analysis. Analyzing the perturbation proportion based on the extracted perturbation feature data enables the analysis of the perturbation focus for each feature, enhancing the accuracy of feature perturbation analysis and further improving the accuracy of comprehensive perturbation analysis.

[0028] This method significantly enhances the accuracy of weight quantification analysis of perturbation feature types by ranking the number of perturbation features. Weighting is set according to the ranking, highlighting the degree of differentiation of different types of perturbations and clarifying the dominance of key perturbation factors. Through weight analysis and perturbation feature proportion analysis, precise emphasis is achieved on types and features, greatly enhancing the accuracy and comprehensiveness of feature perturbation analysis.

[0029] In one embodiment of the present invention, the step of performing feature perturbation proportion analysis on multiple perturbation feature extraction data of perturbation feature type weight data to obtain feature perturbation proportion analysis coefficients includes: The influence coefficient of the disturbance feature is obtained by comparing the difference between the extracted data of each disturbance feature and the corresponding feature comparison data in the regional disturbance comparison combination. The ratio of the perturbation feature influence coefficient of each perturbation feature extraction data to the sum of the perturbation feature influence coefficients of all perturbation feature extraction data in the regional perturbation comparison combination is obtained to obtain the feature perturbation proportion analysis data.

[0030] The working principle and technical effect of the above technical solution are as follows: by calculating the difference ratio between the extracted data of each disturbance feature and the corresponding feature comparison data in the regional disturbance comparison combination, the influence degree of a single disturbance feature on the soil is quantitatively obtained, which improves the probability and accuracy of obtaining disturbance factors and obtains the disturbance feature influence coefficient; by obtaining the ratio of the sum of the disturbance feature influence coefficients of all disturbance feature extracted data in the regional disturbance comparison combination to the influence coefficient of each disturbance feature to the total sum, the disturbance degree of a single disturbance feature in the overall disturbance is quantitatively obtained, which enhances the quantification of the dominant influence of disturbance features and obtains feature disturbance proportion analysis data.

[0031] In one embodiment of the present invention, S2 includes: By analyzing the proportion of characteristic disturbances in microbial-induced calcium carbonate mineralization, structural improvement operations were carried out on coastal salinized disturbed soils to obtain regional disturbance data after structural improvement operations. The data collected on the disturbance in the region after the structural improvement operation is compared with the data collected on the initial region, so as to obtain the characteristic disturbance analysis data after the structural improvement operation. The characteristic perturbation analysis data before and after the structural improvement operation are compared to obtain the improvement comparison data. Perform improvement comparison analysis on the improvement comparison data to obtain improvement comparison analysis data; Based on the improved comparative analysis data, the improvement factor adjustment data is determined, and the adjustment is carried out according to the improvement factors to obtain the disturbance improvement adjustment data.

[0032] The working principle and technical effect of the above technical solution are as follows: by inducing calcium carbonate mineralization through microorganisms, the structure of coastal salinized and disturbed soil is improved based on the characteristic disturbance ratio analysis data. The structure improvement operation enhances soil quality, alleviates soil pollution, achieves targeted regulation of soil improvement, greatly enhances soil improvement efficiency, and realizes the quantification of improved data through regional disturbance data collection after the structure improvement operation, clearly demonstrating the improvement effect and role. The data collected on disturbances in the region after structural improvement operations are compared with the data collected on the initial region. This comparison allows for a benchmarking and analysis of the situation before and after improvement, making the effects of the improvement clearer. By obtaining the characteristic disturbance analysis data after structural improvement operations, the disturbance characteristic improvement data is quantified, reducing the complexity and computational load of the improvement analysis.

[0033] The characteristic perturbation analysis data before and after the structural improvement operation are compared to obtain improvement comparison data; a quantitative scheme for improvement comparison is provided to enhance the accuracy and processing efficiency of improvement comparison. Improvement comparison analysis was performed on the improved comparison data to obtain improved comparison analysis data; through improvement comparison analysis, feature extraction of the improvement effect data was realized, which enhanced the accuracy of effect evaluation.

[0034] Based on the comparative analysis data of the improved soil, adjustment data for improvement factors were determined. Adjustments were then made according to these factors to obtain disturbance improvement adjustment data. By adjusting the improvement factors, the types and quantities of soil amendments could be clearly identified, thus optimizing the improvement operation and enhancing the accuracy and efficiency of soil improvement.

[0035] In one embodiment of the present invention, the step of performing improvement comparison analysis on the improvement comparison data to obtain improvement comparison analysis data includes: Salt content data were collected from disturbed coastal salinized soils. The soil salinity data is combined with a preset salinization grading standard to determine the degree of salinization of coastal salinized disturbed soils; the degree of salinization includes at least mild salinization, moderate salinization, severe salinization and extremely severe salinization. By improving and comparing the data, the absolute value of the difference between the degree of salinization before and after the improvement is obtained, and the first improvement coefficient is obtained. Physical structure tests were conducted on disturbed coastal salinized soils to obtain physical structure test data. The soil physical structure test included porosity testing. The porosity test used the ring sampler method to determine the proportion of soil pores to the total soil volume. Based on the comprehensive correlation analysis data, the control parameters for microbial-induced calcium carbonate mineralization were determined, and mineralization control parameter information was obtained.

[0036] By improving and comparing the physical structure test data before and after the improvement, the absolute value of the physical structure difference is obtained to obtain the second improvement coefficient. The first improvement coefficient and the second improvement coefficient are the improvement comparison analysis data.

[0037] The working principle and technical effect of the above technical solution are as follows: the salt content of the disturbed coastal salinized soil is collected to obtain soil salt content data; the salt content collection can quantify the degree of soil salinization, and can further analyze the changes in salt content of soil improvement to obtain the effect of salinization improvement.

[0038] By combining the soil salinity data with a preset salinization grading standard, the degree of salinization of coastal salinized disturbed soils is determined. The degree of salinization includes at least mild salinization, moderate salinization, severe salinization, and extremely severe salinization. The degree of salinization is quantified by determining the degree of salinization, thereby enabling visualization of salinization improvement.

[0039] By improving and comparing the data, the absolute value of the difference between the salinization level determination information before and after the improvement was obtained, and the first improvement coefficient was obtained. By obtaining the salinization level difference, the degree of change in the level after the improvement was obtained, and the impact of the improvement operation on the change in salinization level was clarified. Physical structure tests were conducted on disturbed coastal salinized soils to obtain physical structure test data. The soil physical structure test included porosity testing. The porosity test used the ring sampler method to determine the proportion of soil pores to the total soil volume. Through physical structure testing, the quality analysis of soil physical structure can be realized, and the physical structure information can be quantified. Based on the comprehensive correlation analysis data, the control parameters for microbial-induced calcium carbonate mineralization were determined, and mineralization control parameter information was obtained.

[0040] By improving and comparing the physical structure test data before and after the improvement, the absolute value of the difference in physical structure is obtained to obtain the second improvement coefficient. By obtaining the difference in physical structure, the degree of change in the physical structure after the improvement is obtained, and the impact of the improvement operation on the degree of change in the logistics structure is clarified. The first improvement coefficient and the second improvement coefficient are the improvement comparison analysis data.

[0041] In one embodiment of the present invention, determining improvement factor adjustment data based on the improved comparative analysis data, and adjusting according to the improvement factors to obtain disturbance improvement adjustment data includes: The first and second improvement coefficients of the improved comparative analysis data are normalized to obtain normalized data. The correlation coefficient was calculated using the Pearson correlation coefficient on the normalized data to obtain the improved correlation coefficient. The improved correlation coefficient is compared with the preset improved correlation threshold to obtain the improved correlation comparison result; Based on the improved correlation comparison results, determine the adjustment data for the improvement factors; Based on the aforementioned improvement factor adjustment data, the parameters of the preset improvement factors are adjusted to obtain disturbance improvement adjustment data.

[0042] The working principle and technical effect of the above technical solution are as follows: The first and second improvement coefficients in the comparative analysis data are normalized to eliminate analytical errors caused by the different dimensions of the two types of coefficients, thus obtaining normalized data; the normalized data are calculated using the Pearson correlation coefficient method to obtain the degree of correlation between salinization improvement and soil structure improvement, generating an improvement correlation coefficient; the improvement correlation coefficient is compared with a preset improvement correlation threshold, and the adjustment direction and magnitude of the improvement factors are determined based on the comparison results, generating improvement factor adjustment data; based on the improvement factor adjustment data, the preset improvement factors are optimized to obtain disturbance improvement adjustment data that clearly defines the improvement operation.

[0043] By normalizing the multi-dimensional improvement coefficients, the objectivity and accuracy of the results are greatly improved. Furthermore, the Pearson correlation coefficient analysis quantifies the correlation between the improvement effects, thus revealing the mutual influence mechanism between salinization improvement and soil structure improvement. At the same time, the direction of regulation can be determined based on the comparison between the correlation coefficient and the threshold, enabling precise control of improvement factors and greatly avoiding the waste of resources caused by blind regulation.

[0044] In one embodiment of the present invention, determining the adjustment data of the improvement factors based on the improved correlation comparison results includes: When the improvement correlation coefficient is greater than the preset improvement correlation threshold, the ratio of the improvement correlation coefficient to the preset improvement correlation threshold is obtained to obtain the improvement level coefficient. Obtain the corresponding preset target improvement factor data based on the improvement level coefficient; Construct a database of relationships between improvement factors and their effects; Obtain the adjustment data of each factor from the correlation effect database of the improved factors, and obtain the disturbance improvement adjustment data.

[0045] The working principle and technical effect of the above technical solution are as follows: When the improvement correlation coefficient is greater than the preset improvement correlation threshold, the ratio of the improvement correlation coefficient to the preset improvement correlation threshold is calculated. The degree of achievement of the improvement effect is quantified by the ratio to obtain the improvement level coefficient. Based on the improvement level coefficient, the preset target improvement factor data is retrieved to clarify the adjustment direction of the factor that matches the current improvement level. An improvement factor correlation effect relationship database is constructed, which contains the correspondence between different improvement factor parameters and improvement effects. Factor adjustment data that matches the target improvement factor data is retrieved in the relationship database to generate disturbance improvement adjustment data that can directly guide the improvement operation.

[0046] By relying on the precise quantitative expression of improvement (i.e., by calculating the corresponding improvement level coefficient), the degree to which the improvement effect meets the target can be accurately quantified. At the same time, a database of the relationships between the effects of various improvement factors is constructed, thereby realizing a systematic correspondence between improvement parameters and improvement effects, which greatly avoids the empirical and randomness in the adjustment process. Furthermore, based on the matching retrieval of the level coefficient and the database, the optimal adjustment scheme for the improvement factors can be quickly located, thereby greatly improving the efficiency and accuracy of the optimization of improvement parameters.

[0047] According to one embodiment of the present invention, the system includes: The disturbance analysis module is used to conduct regional disturbance comparative analysis of coastal salinization areas, obtain characteristic disturbance extraction data, perform weight and impact analysis on the characteristic disturbance extraction data, obtain characteristic disturbance proportion analysis data, and then obtain disturbance collection and analysis data. The improvement analysis module is used to perform structural improvement operations and comparative analysis of structural improvement on coastal salinized disturbed soils based on characteristic disturbance ratio analysis data, obtain improvement comparison analysis data, obtain improvement factor adjustment data based on improvement comparison analysis data, and adjust according to improvement factors to obtain disturbance improvement adjustment data.

[0048] The working principle and technical effects of the above technical solution are as follows: Regional disturbance comparative analysis is conducted on coastal salinization areas to obtain characteristic disturbance extraction data. This data is then accurately summarized, reducing the amount of data to be analyzed. Focus is placed on core disturbance characteristics, significantly improving data analysis and processing efficiency. Weight and impact analysis is performed on the characteristic disturbance extraction data to obtain characteristic disturbance proportion analysis data. Weight analysis determines the attention level of different types of disturbance characteristics, and impact analysis determines the degree of damage to the soil caused by disturbance characteristics. This greatly improves the flexibility and accuracy of disturbance characteristic analysis, enabling different impact-focused analysis of different disturbance characteristics, thereby obtaining disturbance collection and analysis data. The obtained disturbance collection and analysis data is then used to analyze the degree of influence of disturbance characteristics, improving the accuracy of the influence degree analysis.

[0049] Based on the characteristic disturbance proportion analysis data, structural improvement operations and comparative analyses of structural improvement were conducted on coastal salinized disturbed soils. Comparative analysis data was obtained, showing that structural improvement operations can enhance soil improvement quality, reduce soil salinization, and mitigate the soil impact of power transmission and transformation project sites. The comparative analysis also allowed for the evaluation of the improvement effects, identifying which improvement factors were effective and which had low effectiveness. Adjustment data for these improvement factors was obtained from the comparative analysis data, and adjustments were made accordingly to obtain disturbance improvement adjustment data. Adjustments based on the comparative analysis enable the regulation and optimization of improvement factors, avoiding blind improvements that may lead to suboptimal results and further improving soil improvement outcomes.

[0050] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for microbial mineralization restoration of coastal salinized and disturbed soils, characterized in that, The method includes: S1. Conduct regional disturbance comparison analysis on the coastal salinization area to obtain characteristic disturbance extraction data. Perform weight and impact analysis on the characteristic disturbance extraction data to obtain characteristic disturbance proportion analysis data, and then obtain disturbance collection and analysis data. S2. Based on the characteristic disturbance ratio analysis data, structural improvement operations and comparative analysis of structural improvement are carried out on the coastal salinized disturbed soil to obtain improvement comparative analysis data. Based on the improvement comparative analysis data, improvement factor adjustment data are obtained, and adjustment is carried out according to the improvement factors to obtain disturbance improvement adjustment data.

2. The method for microbial mineralization restoration of coastal salinized and disturbed soils according to claim 1, characterized in that, S1 includes: To obtain regional data, we collected data on the location of power transmission and transformation projects in the coastal salinized area and obtained regional disturbance data. Acquire initial regional data for the site of power transmission and transformation projects; The regional disturbance data is compared with the initial regional data to obtain regional disturbance comparison data. Based on the disturbance comparison data, disturbance damage data analysis is performed to obtain disturbance collection and analysis data.

3. The method for microbial mineralization restoration of coastal salinized and disturbed soils according to claim 2, characterized in that, The step of analyzing disturbance damage data based on disturbance comparison data to obtain disturbance acquisition and analysis data includes: Establish a regional disturbance comparison table based on disturbance comparison data; Determine the regional disturbance comparison relationship and the regional non-disturbance comparison relationship based on the information in the regional disturbance comparison table; The aforementioned regional perturbation comparison relationships are combined to obtain regional perturbation comparison combinations; The non-disturbance comparison relationships of the regions are combined to obtain the non-disturbance comparison combination of regions; The distribution range and disturbance characteristics of the regional disturbance comparison combination are extracted to obtain the disturbance range and disturbance characteristic information of the regional disturbance comparison combination; Feature perturbation analysis is performed based on the perturbation feature extraction information to obtain feature perturbation analysis data.

4. The method for microbial mineralization restoration of coastal salinized and disturbed soils according to claim 3, characterized in that, The step of performing feature perturbation analysis based on perturbation feature extraction information to obtain feature perturbation analysis data includes: Sort the perturbation feature type data by feature quantity to obtain the feature perturbation type sequence; Weights are generated for the feature perturbation types based on the feature perturbation type sequence to obtain perturbation feature type weight data; The feature perturbation ratio analysis was performed on the multiple perturbation feature extraction data of the perturbation feature type weight data to obtain the feature perturbation ratio analysis coefficient; The weights of the perturbation feature extraction data are generated by combining the perturbation feature type weight data with the feature perturbation proportion analysis coefficient, and feature perturbation analysis data is obtained.

5. The method for microbial mineralization restoration of coastal salinized and disturbed soils according to claim 4, characterized in that, The step of performing feature perturbation proportion analysis on multiple perturbation feature extraction data of perturbation feature type weight data to obtain feature perturbation proportion analysis coefficients includes: The influence coefficient of the disturbance feature is obtained by comparing the difference between the extracted data of each disturbance feature and the corresponding feature comparison data in the regional disturbance comparison combination. The ratio of the perturbation feature influence coefficient of each perturbation feature extraction data to the sum of the perturbation feature influence coefficients of all perturbation feature extraction data in the regional perturbation comparison combination is obtained to obtain the feature perturbation proportion analysis data.

6. The method for microbial mineralization restoration of coastal salinized and disturbed soils according to claim 1, characterized in that, S2 includes: By analyzing the proportion of characteristic disturbances in microbial-induced calcium carbonate mineralization, structural improvement operations were carried out on coastal salinized disturbed soils to obtain regional disturbance data after structural improvement operations. The data collected on the disturbance in the region after the structural improvement operation is compared with the data collected on the initial region, so as to obtain the characteristic disturbance analysis data after the structural improvement operation. The characteristic perturbation analysis data before and after the structural improvement operation are compared to obtain the improvement comparison data. Perform improvement comparison analysis on the improvement comparison data to obtain improvement comparison analysis data; Based on the improved comparative analysis data, the improvement factor adjustment data is determined, and the adjustment is carried out according to the improvement factors to obtain the disturbance improvement adjustment data.

7. The method for microbial mineralization restoration of coastal salinized and disturbed soils according to claim 6, characterized in that, The improvement comparison analysis of the improved comparison data, to obtain improved comparison analysis data, includes: Salt content data were collected from disturbed coastal salinized soils. The soil salinity data is combined with a preset salinization grading standard to determine the degree of salinization of coastal salinized disturbed soils. By improving and comparing the data, the absolute value of the difference between the degree of salinization before and after the improvement is obtained, and the first improvement coefficient is obtained. Physical structure tests were conducted on the disturbed salinized soil in the coastal area to obtain physical structure test data; Based on the comprehensive correlation analysis data, the control parameters for microbial-induced calcium carbonate mineralization were determined, and mineralization control parameter information was obtained. By improving and comparing the physical structure test data before and after the improvement, the absolute value of the physical structure difference is obtained to obtain the second improvement coefficient. The first improvement coefficient and the second improvement coefficient are the improvement comparison analysis data.

8. The method for microbial mineralization restoration of coastal salinized and disturbed soils according to claim 6, characterized in that, Based on the improved comparative analysis data, the adjustment data for improvement factors are determined, and adjustments are made according to the improvement factors to obtain the disturbance improvement adjustment data, including: The first and second improvement coefficients of the improved comparative analysis data are normalized to obtain normalized data. The correlation coefficient was calculated using the Pearson correlation coefficient on the normalized data to obtain the improved correlation coefficient. The improved correlation coefficient is compared with the preset improved correlation threshold to obtain the improved correlation comparison result; Based on the improved correlation comparison results, determine the adjustment data for the improvement factors; Based on the aforementioned improvement factor adjustment data, the parameters of the preset improvement factors are adjusted to obtain disturbance improvement adjustment data.

9. The method for microbial mineralization restoration of coastal salinized and disturbed soils according to claim 8, characterized in that, The step of determining the adjustment data for improvement factors based on the improved correlation comparison results includes: When the improvement correlation coefficient is greater than the preset improvement correlation threshold, the ratio of the improvement correlation coefficient to the preset improvement correlation threshold is obtained to obtain the improvement level coefficient. Obtain the corresponding preset target improvement factor data based on the improvement level coefficient; Construct a database of relationships between improvement factors and their effects; Obtain the adjustment data of each factor from the correlation effect database of the improved factors, and obtain the disturbance improvement adjustment data.

10. A microbial mineralization restoration system for coastal salinized and disturbed soils, characterized in that, The system includes: The disturbance analysis module is used to conduct regional disturbance comparative analysis of coastal salinization areas, obtain characteristic disturbance extraction data, perform weight and impact analysis on the characteristic disturbance extraction data, obtain characteristic disturbance proportion analysis data, and then obtain disturbance collection and analysis data. The improvement analysis module is used to perform structural improvement operations and comparative analysis of structural improvement on coastal salinized disturbed soils based on characteristic disturbance ratio analysis data, obtain improvement comparison analysis data, obtain improvement factor adjustment data based on improvement comparison analysis data, and adjust according to improvement factors to obtain disturbance improvement adjustment data.

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