A Data Analysis-Based Method and System for Post-Highway Engineering Ecological Restoration

Abstract

This invention proposes a data analysis-based method and system for post-construction ecological restoration of highway projects, belonging to the field of ecological engineering technology. The method involves acquiring soil samples before highway construction, analyzing these samples using XRF (Extremely Radio Frequency) to establish a mapping relationship between the soil samples and the concentrations of various nutrients in the soil, then sampling soil samples at the construction site after construction, and analyzing these samples using XRF to obtain a second XRF soil data sample. Finally, nutrient analysis is performed on the second XRF soil data sample based on the mapping relationship, thereby generating a method for post-construction ecological restoration of highway projects. This invention overcomes the deficiency in existing technologies regarding the lack of research on post-construction ecological restoration of highway projects; it simplifies the soil nutrient concentration analysis process by establishing a mapping relationship between soil samples and the concentrations of various nutrients in the soil; and it improves the accuracy of analysis by associating the principal component variables in the improved PLSR algorithm with the eigenvalues ​​of the correlation matrix of the soil samples at the construction site before highway construction.

Description

Technical Field

[0001] This invention belongs to the field of ecological engineering technology, and in particular relates to a method and system for ecological restoration after highway engineering based on data analysis. Background Technology

[0002] Highway construction inevitably damages the soil structure and vegetation around the project site, leading to adverse consequences such as soil erosion, ecological imbalance, and potential landslides. Post-construction ecological restoration, especially soil restoration, helps to restore the micro-ecological chain in the damaged area, improve soil stability and nutrients, and to some extent prevent geological disasters. The technology with patent number 2025102653439 collects soil condition data for various road sections along the route of highway construction. It then analyzes this data to obtain sampling frequency adjustment coefficients for each sampling point. Based on these coefficients, the preset sampling frequency for each sampling point is adjusted, and sampling and monitoring are performed at each point according to the adjusted frequency. The technology with patent number 2024113183184 first selects multiple healthy soil samples and calculates the average healthy nutrient data. Then, it selects multiple problematic soil samples and obtains nutrient data for each sample. Next, it calculates nutrient data for multiple extended samples and, based on a specific distance model, calculates the average problematic nutrient data for multiple problematic planting sub-regions. Finally, it obtains diagnostic comparison results and plans fertilization adjustments. Existing technologies, represented by these examples, lack direct analysis of post-highway engineering ecological restoration methods. Furthermore, they typically require custom models or parameters for soil nutrient analysis, making the process complex. Custom models or parameters are also prone to introducing empirical biases, affecting the analysis results. Summary of the Invention

[0003] To address the aforementioned problems in existing technologies, this invention proposes a method and system for post-construction ecological restoration of highway projects based on data analysis.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a data analysis-based method for post-construction ecological restoration of highway projects, comprising the following steps: Step S1: Obtaining soil samples from the site before highway construction and preprocessing them; Step S2: Analyzing the soil samples from the site before highway construction using XRF (X-ray Fluorescence) to obtain a first XRF soil data sample S1; Step S3: Establishing a mapping relationship between the first XRF soil data sample S1 and the concentration C of various nutrients in the soil; Step S4: Sampling soil samples from the site after highway construction, analyzing the soil samples from the site after highway construction using XRF to obtain a second XRF soil data sample S2, performing nutrient analysis on the second XRF soil data sample S2 based on the mapping relationship, and generating a post-construction ecological restoration method based on the nutrient analysis results.

[0005] Furthermore, the pretreatment in step S1 includes preparing soil samples from the site before highway construction into multiple sets of uniform powders or multiple tablets.

[0006] Furthermore, in step S2, the dimension of the first XRF soil data sample S1 is the number of soil samples to be analyzed multiplied by the number of XRF energy channels.

[0007] Further, in step S3, establishing the mapping relationship between the first XRF soil data sample S1 and the concentrations C of each nutrient in the soil includes: Step S31: defining the concentration of each nutrient in the soil as C in matrix form; Step S32: establishing the mapping relationship C=S1×A between the first XRF soil data sample S1 and the concentrations C of each nutrient in the soil; where A represents the mapping coefficient; Step S33: calculating the mean and standard deviation of S1 and C respectively, and standardizing S1 and C based on the mean and standard deviation to obtain S1' and C'; Step S34: calculating the mapping coefficient based on S1' and C', and using the mapping coefficient as the mapping relationship.

[0008] Furthermore, step S34 specifically includes the following sub-steps: Step S341: Construct the correlation matrix M of S1' and C', where M = [S1' C'] T ×[S1' C']; Step S342: Calculate the eigenvalues ​​of the correlation matrix M using the eig function; Step S343: Calculate the principal component number n based on the eigenvalues ​​of the correlation matrix M; Step S344: Use S1', C', and n as input variables of the plsr (partial least-square regression) function, calculate the mapping coefficient A, and then use the mapping coefficient A as the mapping relationship.

[0009] Further, step S343 specifically includes the following sub-steps: Step S343-1: Find the eigenvalues ​​greater than 1 in the eigenvalues ​​of the correlation matrix M, and sum the eigenvalues ​​greater than 1 to obtain the first principal component number n1; Step S343-2: Construct the eigenvalue matrix DIAG of the correlation matrix M, calculate the first-order difference DIFF matrix of the DIAG matrix, and calculate the second principal component number n2 based on the first-order difference DIFF matrix: n2=k×(max[D1. / D2]+1), where D1 is the matrix constructed after selecting the first element to the second-to-last element in the first-order difference DIFF matrix, D2 is the matrix constructed after selecting the second element to the last element in the first-order difference DIFF matrix, . / represents element-wise division, and k represents the empirical coefficient; Step S343-3: Take the maximum value of the first principal component number n1 and the second principal component number n2 as the principal component number n.

[0010] Further, step S344 specifically involves first inputting variables S1', C', and n into the plsr function to obtain the return value BETA, and then using BETA as the mapping coefficient A.

[0011] Further, in step S4, nutrient analysis is performed on the second XRF soil data sample S2 based on the mapping relationship, and an ecological restoration method for highway engineering is generated based on the nutrient analysis results. Specifically, this includes: constructing a second XRF soil data sample S2 in matrix form, containing XRF energy channels and the number of soil samples to be analyzed; multiplying S2 by the mapping coefficient A; calculating the concentration of each nutrient in the second XRF soil data sample S2 based on the multiplication result; and determining whether soil conditioner needs to be added based on the relationship between each nutrient concentration and the corresponding concentration threshold.

[0012] This invention also proposes a data analysis-based post-construction ecological restoration system for highway engineering, used to execute the aforementioned ecological restoration method. The system includes a data acquisition unit, a data analysis unit, and a restoration method generation unit. The data acquisition unit is connected to the data analysis unit, and the data analysis unit is connected to the restoration method generation unit. The data acquisition unit is used to acquire soil samples, the data analysis unit is used to perform nutrient analysis on soil samples from the construction site after highway engineering, and the restoration method generation unit is used to generate a soil remediation method based on the results of the data analysis unit.

[0013] The beneficial technical effects of this invention compared with the prior art are as follows:

[0014] (1) A method for ecological restoration after highway engineering is proposed, which takes soil nutrients after highway engineering as the analysis object, making up for the lack of research on ecological restoration after highway engineering in the existing technology; and the present invention can use portable XRF to acquire soil data samples, thereby implementing the method for ecological restoration after highway engineering, and can realize on-site detection of soil nutrients.

[0015] (2) By establishing the mapping relationship between XRF soil data samples and the concentration of each nutrient in the soil, the concentration of each nutrient in the soil can be intuitively associated with the soil sample data, reducing the construction, setting and use of intermediate variables or intermediate algorithm models in the soil nutrient concentration analysis process, and simplifying the soil nutrient concentration analysis process.

[0016] (3) Using the improved PLSR algorithm, the principal component number variable in the improved PLSR algorithm is associated with the correlation matrix eigenvalue of the soil sample at the site before the highway construction, so as to ensure the balance between the retention of soil information at the site before the highway construction and the introduction of empirical bias. This allows for a more accurate analysis of the soil conditions after the highway construction based on the soil characteristics such as structure, pH, and water content of the soil sample at the site before the highway construction, thereby improving the accuracy of the analysis. Attached Figure Description

[0017] To more clearly illustrate the embodiments of the present invention or the technical solutions in 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 merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0018] Figure 1 This is a flowchart of a data analysis-based method for post-construction ecological restoration of highway projects according to the present invention.

[0019] Figure 2 This is a flowchart illustrating the mapping relationship between the first XRF soil data sample and the concentrations of various nutrients in the soil in this invention.

[0020] Figure 3 This is a flowchart of the method for calculating the mapping relationship in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The concepts involved in this application will first be described with reference to the accompanying drawings. It should be noted that the following descriptions of various concepts are only for the purpose of making the content of this application easier to understand and do not constitute a limitation on the scope of protection of this application; furthermore, the embodiments and features in the embodiments of this application can be combined with each other unless otherwise specified. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] Refer to the instruction manual. Figure 1 This invention proposes a data analysis-based method for post-construction ecological restoration of highway projects, comprising the following steps: Step S1: Obtain soil samples from the site before highway construction and perform pretreatment; The sources of soil samples from the site before highway construction include soil from the temporary land occupation site and slope site before highway construction. The pretreatment includes preparing the soil samples from the site before highway construction into multiple sets of uniform powder or multiple tablets through steps such as coarse grinding, fine grinding, and flat spreading.

[0024] Step S2: Analyze soil samples from the site before highway construction using XRF to obtain the first XRF soil data sample S1; the dimension of the first XRF soil data sample S1 is the number of soil samples to be analyzed multiplied by the number of XRF energy channels, and the data of the first XRF soil data sample S1 is the relative intensity of the XRF energy spectrum.

[0025] Step S3: Establish a mapping relationship between the first XRF soil data sample S1 and the concentrations C of various nutrients in the soil; Step S4: Sample soil samples from the site after highway construction, analyze the soil samples from the site after highway construction using XRF to obtain the second XRF soil data sample S2, perform nutrient analysis on the second XRF soil data sample S2 based on the mapping relationship, and generate an ecological restoration method for highway construction based on the nutrient analysis results.

[0026] Refer to the instruction manual. Figure 2Step S3 establishes a mapping relationship between the first XRF soil data sample S1 and the concentrations C of various nutrients in the soil, including: Step S31: Define the concentration of each nutrient in the soil as C in matrix form. The dimension of C can be the number of soil samples to be analyzed multiplied by the number of each nutrient in the sample to be analyzed (for example, when iron and zinc need to be analyzed, the number of nutrients is 2). The data of C is the concentration of each nutrient that has been measured in advance; Step S32: Establish a mapping relationship between the first XRF soil data sample S1 and the concentrations C of various nutrients in the soil, C=S1×A; where A represents the mapping coefficient, and the above mapping coefficient A is a mapping coefficient matrix; Step S33: Calculate the mean and standard deviation of S1 and C respectively, and standardize S1 and C based on the mean and standard deviation to obtain S1' and C'; where S1' can be equal to each element in S1 minus the mean of S1 divided by the standard deviation, and C' can be equal to each element in C minus the mean of C divided by the standard deviation. Step S34: Calculate the mapping coefficients based on S1' and C', and use the mapping coefficients as the mapping relationship.

[0027] Refer to the instruction manual. Figure 3 Step S34 specifically includes the following sub-steps: Step S341: Construct the correlation matrix M of S1' and C', where M = [S1' C'] T ×[S1' C'], the above-mentioned correlation matrix M is used to describe the cross covariance of the characteristic matrices constructed by S1' and C', which can reflect the linear correlation between S1' and C' and provide a decomposition basis for the plsr function; Step S342: Calculate the eigenvalues ​​of the correlation matrix M using the eig function; it can be understood that the above-mentioned eig function is a function for calculating the eigenvalues ​​and eigenvectors of a matrix, and T represents the transpose of the matrix.

[0028] Step S343: Calculate the principal component number n based on the eigenvalues ​​of the correlation matrix M; Step S344: Use S1', C' and n as input variables of the plsr function to calculate the mapping coefficient A, and then use the mapping coefficient A as the mapping relationship.

[0029] Step S343 specifically includes the following sub-steps: Step S343-1: Find the eigenvalues ​​greater than 1 in the eigenvalues ​​of the correlation matrix M, and sum the eigenvalues ​​greater than 1 to obtain the first principal component number n1; Step S343-2: Construct the eigenvalue matrix DIAG of the correlation matrix M, calculate the first-order difference DIFF matrix of the DIAG matrix, and calculate the second principal component number n2 based on the first-order difference DIFF matrix: n2=k×(max[D1. / D2]+1), where D1 is the selected first-order difference DIFF matrix. D2 is a matrix constructed from the first element to the second-to-last element in the first-order difference DIFF matrix. ". / " indicates element-wise division, and k represents the empirical coefficient. Step S343-3: Take the maximum value of the first principal component number n1 and the second principal component number n2 as the principal component number n. Step S344 specifically involves first inputting variables S1', C', and n into the plsr function to obtain the return value BETA, and then using BETA as the mapping coefficient A. The first and second principal component numbers represent different degrees of variance deviation. By comparing the first and second principal component numbers and taking the larger value, the balance between the retention of soil information at the site before highway construction and the introduction of empirical bias can be maximized, thereby improving the accuracy of soil nutrient analysis.

[0030] In step S4, nutrient analysis is performed on the second XRF soil data sample S2 based on the mapping relationship, and an ecological restoration method for highway engineering is generated based on the nutrient analysis results. Specifically, this includes: constructing a second XRF soil data sample S2 in matrix form, which contains XRF energy channels and the number of soil samples to be analyzed; then multiplying S2 with the mapping coefficient A; and calculating the concentration of each nutrient in the second XRF soil data sample S2 based on the multiplication result.

[0031] The above-mentioned method of determining whether soil conditioner needs to be added based on the relationship between the concentration of each nutrient and its corresponding concentration threshold can be as follows: when all nutrient concentrations are less than their corresponding concentration thresholds, a first quantity of soil conditioner is added to the soil after the highway project; when one or more nutrient concentrations are less than their corresponding concentration thresholds, a second quantity of soil conditioner is added to the soil after the highway project; and when all nutrient concentrations are greater than their corresponding concentration thresholds, no soil conditioner is added to the soil after the highway project.

[0032] This invention also proposes a data analysis-based post-construction ecological restoration system for highway engineering, used to execute the aforementioned ecological restoration method. The system includes a data acquisition unit, a data analysis unit, and a restoration method generation unit. The data acquisition unit is connected to the data analysis unit, and the data analysis unit is connected to the restoration method generation unit. The data acquisition unit is used to acquire soil samples, the data analysis unit is used to perform nutrient analysis on soil samples from the construction site after highway engineering, and the restoration method generation unit is used to generate a soil remediation method based on the results of the data analysis unit.

[0033] The embodiments and / or implementation methods described above are merely preferred embodiments and / or implementation methods for implementing the technology of the present invention, and are not intended to limit the implementation methods of the technology of the present invention in any way. Any person skilled in the art can make some modifications or alterations to other equivalent embodiments without departing from the scope of the technical means disclosed in the content of the present invention, but they should still be regarded as the technology or embodiments that are substantially the same as the present invention.

[0034] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. The above descriptions are only preferred embodiments of this application. It should be noted that due to the limitations of written expression, while there are objectively infinite specific structures, those skilled in the art can make several improvements, modifications, or changes without departing from the principles of this application, and can also combine the above technical features in an appropriate manner. These improvements, modifications, changes, or combinations, or the direct application of the inventive concept and technical solution to other situations without modification, should all be considered within the scope of protection of this application.

Claims

1. A data-driven method for post-construction ecological restoration of highways, characterized in that, Includes the following steps: Step S1: Obtain soil samples from the construction site before highway construction and perform pretreatment; Step S2: Use XRF to analyze soil samples from the site before highway construction to obtain the first XRF soil data sample S1; Step S3: Establish the mapping relationship between the first XRF soil data sample S1 and the concentrations C of various nutrients in the soil; Step S4: Sample soil samples from the construction site of the highway project, analyze the soil samples using XRF to obtain a second XRF soil data sample S2, perform nutrient analysis on the second XRF soil data sample S2 based on the mapping relationship, and generate an ecological restoration method for the highway project based on the nutrient analysis results. In step S2, the dimension of the first XRF soil data sample S1 is the number of soil samples to be analyzed multiplied by the number of XRF energy channels. Step S3, which establishes the mapping relationship between the first XRF soil data sample S1 and the concentrations C of various nutrients in the soil, includes: Step S31: Define the concentration of each nutrient in the soil as C in matrix form; Step S32: Establish the mapping relationship between the first XRF soil data sample S1 and the concentrations of various nutrients C in the soil: C = S1 × A; where A represents the mapping coefficient. Step S33: Calculate the mean and standard deviation of S1 and C respectively, and standardize S1 and C based on the mean and standard deviation to obtain S1' and C'; Step S34: Calculate the mapping coefficients based on S1' and C', and use the mapping coefficients as the mapping relationship.

2. The method for post-construction ecological restoration of highways based on data analysis according to claim 1, characterized in that, The pretreatment in step S1 includes preparing soil samples from the site before highway construction into multiple sets of uniform powders or multiple tablets.

3. The method for post-construction ecological restoration of highways based on data analysis according to claim 1, characterized in that, Step S34 specifically includes the following sub-steps: Step S341: Construct the incidence matrix M of S1' and C', where M = [S1' C'] T ×[S1' C']; Step S342: Calculate the eigenvalues ​​of the correlation matrix M using the eig function; Step S343: Calculate the number of principal components n based on the eigenvalues ​​of the correlation matrix M; Step S344: Using S1', C', and n as input variables of the plsr function, calculate the mapping coefficient A, and then use the mapping coefficient A as the mapping relationship.

4. The method for post-construction ecological restoration of highways based on data analysis according to claim 3, characterized in that, Step S343 specifically includes the following sub-steps: Step S343-1: Find the eigenvalues ​​greater than 1 in the eigenvalues ​​of the correlation matrix M, and sum the eigenvalues ​​greater than 1 to obtain the number of the first principal components n1; Step S343-2: Construct the eigenvalue matrix DIAG of the correlation matrix M, find the first-order difference DIFF matrix of the DIAG matrix, and calculate the second principal component n2 based on the first-order difference DIFF matrix: n2=k×(max[D1. / D2]+1), where D1 is the matrix constructed after selecting the first element to the second-to-last element in the first-order difference DIFF matrix, D2 is the matrix constructed after selecting the second element to the last element in the first-order difference DIFF matrix, . / means element-wise division, and k represents the empirical coefficient; Step S343-3: Take the maximum value of the first principal component number n1 and the second principal component number n2 as the principal component number n.

5. The method for post-construction ecological restoration of highways based on data analysis according to claim 3, characterized in that, Specifically, step S344 involves first inputting variables S1', C', and n into the plsr function to obtain the return value BETA, and then using BETA as the mapping coefficient A.

6. The method for post-construction ecological restoration of highways based on data analysis according to claim 3, characterized in that, In step S4, nutrient analysis is performed on the second XRF soil data sample S2 based on the mapping relationship, and an ecological restoration method for highway engineering is generated based on the nutrient analysis results. Specifically, this includes: constructing a second XRF soil data sample S2 in matrix form, containing XRF energy channels and the number of soil samples to be analyzed; multiplying S2 by the mapping coefficient A; calculating the concentration of each nutrient in the second XRF soil data sample S2 based on the multiplication result; and determining whether soil conditioner needs to be added based on the relationship between each nutrient concentration and the corresponding concentration threshold.

7. A data-based ecological restoration system for highway engineering projects, used to execute the ecological restoration method as described in any one of claims 1-6, comprising a data acquisition unit, a data analysis unit, and a restoration method generation unit, wherein the data acquisition unit is connected to the data analysis unit, and the data analysis unit is connected to the restoration method generation unit, characterized in that, The data acquisition unit is used to acquire soil samples, the data analysis unit is used to perform nutrient analysis on soil samples at the site after highway construction, and the remediation method generation unit is used to generate a soil remediation method based on the results of the data analysis unit.

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