Polluted site soil production function reconstruction feasibility analysis and evaluation method, device, equipment and medium

Abstract

The invention discloses a contaminated site soil production function reconstruction feasibility analysis and evaluation method, device and equipment and a medium, and relates to the field of soil remediation and utilization, and the method comprises the steps: obtaining an index data matrix of a to-be-analyzed site; the indexes comprise a soil quality index, a remediation potential index, a function utilization potential index and a carbon sequestration and emission reduction potential index; according to the index data matrix, based on the game theory, adopting multiple objective weighting methods to determine the comprehensive weight of each index; determining the assigned score of each index of each point location according to the index data matrix and a predetermined score assignment table; and determining a comprehensive score of the to-be-analyzed site according to the score of each index of each point location and the comprehensive weight of each index so as to determine the soil production function reconstruction feasibility of the to-be-analyzed site. According to the method, the analysis precision of farmland production function feasibility achieved through contaminated site soil reconstruction and reutilization is improved, and the actual potential of site soil production function reconstruction is visually reflected.

Description

Technical Field

[0001] This application relates to the field of soil remediation and utilization, and in particular to a method, apparatus, equipment and medium for feasibility analysis and evaluation of reconstructing the production function of soil in contaminated sites. Background Technology

[0002] With the acceleration of industrialization, soil pollution has become increasingly prominent, posing a serious threat to the ecological environment and human health. At the same time, arable land resources face a "three-fold shortage" dilemma: low per capita arable land area, limited high-yield arable land, and scarce reserve arable land, with high costs and difficulties in developing and utilizing reserve arable land. Against this backdrop, the effective remediation of contaminated sites and the reconstruction of arable land's productive functions have become crucial pathways to ensuring food security and ecological security.

[0003] Currently, a differentiated risk management system and a relatively mature standard framework have been established for the environmental management of soil in construction land. However, assessment methods for determining whether remediated contaminated sites can be converted into arable land remain lacking. Compared to construction land, which focuses solely on human health risks, arable land soil environmental quality is more concerned with the impact of pollutants on agricultural products. Whether remediated soil in contaminated sites can meet the actual use needs of arable land depends not only on whether the pollutant content in the soil meets relevant standards, but also on a systematic assessment of the multi-dimensional key conditions necessary for the realization of arable land production functions, such as soil fertility, topographic features, and water availability. Although some studies have attempted to assess the suitability of remediated contaminated soil for arable land development, most studies focus on the single objective of pollution risk management and are limited to issues such as technical feasibility or a single aspect of environmental risk. This "fragmented assessment" often leads to the contradiction of "remediation meeting standards but being unsuitable for cultivation" in practice, hindering the standardized advancement of the arable land function reconstruction of contaminated site soil.

[0004] Against this backdrop, the establishment of a systematic feasibility analysis and evaluation method for the reconstruction of farmland functions in contaminated sites has become an urgent need. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus, equipment and medium for feasibility analysis and evaluation of soil production function reconstruction in contaminated sites, which can improve the analysis accuracy of the feasibility of soil production function reconstruction in contaminated sites and intuitively reflect the actual potential of soil production function reconstruction in sites.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for feasibility analysis and evaluation of soil production function reconfiguration at contaminated sites, including: Obtain the indicator data matrix of the site to be analyzed; the indicator data matrix includes multiple points and the values ​​of multiple indicators at each point; the indicators include soil quality indicators, remediation potential indicators, functional utilization potential indicators and carbon sequestration and emission reduction potential indicators; Based on the aforementioned indicator data matrix, and using game theory, a variety of objective weighting methods are employed to determine the comprehensive weight of each indicator. Based on the indicator data matrix and the pre-determined grading and assignment table, the score for each indicator at each point is determined. Based on the scores assigned to each indicator at each location and the comprehensive weight of each indicator, the comprehensive score of the site to be analyzed is determined in order to determine the feasibility of reconstructing the soil production function of the site to be analyzed.

[0007] Secondly, this application provides a device for feasibility analysis and evaluation of soil production function reconfiguration at contaminated sites, comprising: The data acquisition module is used to acquire the indicator data matrix of the site to be analyzed; the indicator data matrix includes multiple points and the values ​​of multiple indicators at each point; the indicators include soil quality indicators, remediation potential indicators, functional utilization potential indicators and carbon sequestration and emission reduction potential indicators. The weight determination module is used to determine the comprehensive weight of each indicator based on the indicator data matrix and using multiple objective weighting methods based on game theory. The scoring module is used to determine the score of each indicator at each point based on the indicator data matrix and the pre-determined grading and scoring table. The feasibility determination module is used to determine the comprehensive score of the site to be analyzed based on the scores of each indicator at each location and the comprehensive weight of each indicator, so as to determine the feasibility of reconstructing the soil production function of the site to be analyzed.

[0008] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned feasibility analysis and evaluation method for reconstructing the production function of contaminated site soil.

[0009] Fourthly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-mentioned feasibility analysis and evaluation method for reconstructing the production function of contaminated site soil.

[0010] According to the specific embodiments provided in this application, this application has the following technical effects: The indicator system covers four core indicators: soil quality, remediation potential, functional utilization potential, and carbon sequestration and emission reduction potential. It comprehensively covers the key influencing factors of soil production function reconstruction, avoiding the one-sidedness of single-dimensional assessment. Furthermore, it employs game theory combined with multiple objective weighting methods to determine the comprehensive weight, abandoning the arbitrariness of subjective weighting and fully integrating the advantages of different objective weighting methods, making the weight allocation more scientific and reasonable, and improving the credibility of the assessment results. By combining indicator scoring with comprehensive weights to calculate the comprehensive score, the feasibility assessment is quantified and presented, intuitively reflecting the actual potential of site soil production function reconstruction. This provides accurate and implementable reference for subsequent decision-making, improves the pertinence and efficiency of scheme formulation, and reduces the risk of reconstruction implementation. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments 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.

[0012] Figure 1 This is an application environment diagram of a feasibility analysis and evaluation method for reconstructing the production function of soil in a contaminated site, as described in one embodiment of this application.

[0013] Figure 2 This is a flowchart illustrating a feasibility analysis and evaluation method for reconstructing the production function of soil in a contaminated site, provided as an embodiment of this application.

[0014] Figure 3 This is a frequency analysis chart of literature collection indicators in one embodiment of this application.

[0015] Figure 4 This is a schematic diagram of the functional modules of a feasibility analysis and evaluation device for reconstructing the production function of soil in a contaminated site, provided as an embodiment of this application.

[0016] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0017] 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.

[0018] This application fully considers evaluation factors such as soil environmental quality, soil remediation potential, potential for production functions, and potential for carbon sequestration and emission reduction. It establishes a feasibility analysis and evaluation method for soil function reconstruction of contaminated sites from the aspects of necessity, technical feasibility, benefit feasibility, and risk. This method can provide a scientific basis for decision-making on the reclamation of contaminated sites into arable land and help achieve the goals of arable land resource protection and soil pollution prevention and control in a coordinated manner.

[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] The feasibility analysis and evaluation method for reconstructing the productive function of contaminated site soil provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be set up independently, integrated into server 102, or placed in the cloud or on another server. Terminal 101 can send the indicator data matrix of the site to be analyzed to server 102. After receiving the indicator data matrix, server 102 determines the comprehensive score of the site. Server 102 can then feed back the comprehensive score of the site to terminal 101. Furthermore, in some embodiments, the feasibility analysis and evaluation method for reconstructing the production function of contaminated site soil can also be implemented independently by server 102 or terminal 101.

[0021] The terminal 101 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 102 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0022] In one exemplary embodiment, such as Figure 2 As shown, a feasibility analysis and evaluation method for reconstructing the productive functions of soil in contaminated sites is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 102 as an example, the explanation includes the following steps 201 to 204.

[0023] Step 201: Obtain the indicator data matrix of the site to be analyzed. The indicator data matrix includes multiple data points and the values ​​of multiple indicators for each data point. The indicators include soil quality indicators, remediation potential indicators, functional utilization potential indicators, and carbon sequestration and emission reduction potential indicators.

[0024] This application focuses on the productive function of arable land, using relevant industry standards as the primary basis and supplemented by literature research to collect relevant indicators. Specifically, by reviewing relevant standards and technical specifications concerning soil environmental quality and arable land quality, relevant indicators for the feasibility evaluation of soil productive function reconstruction and reuse at contaminated sites were collected, as shown in Table 1. To ensure the comprehensiveness of the collected indicators, further supplementary indicators were obtained through literature review. Using keywords such as "soil reconstruction," "soil remediation," and "land reclamation," over 60 articles related to relevant indicators were screened from Chinese literature databases such as CNKI, VIP, and Wanfang, and English literature databases such as Web of Science and Science Direct, resulting in a total of 135 indicators. Based on the frequency analysis results, specific indicators appearing 3 times or less in the collected literature were removed. Through further merging and sorting, 22 indicators were obtained, including soil texture, pollutant concentration, pH value, and topographic slope. Figure 3 The frequency of the indicators shown determines soil texture, pollutant concentration, pH value, topographic slope, available (phosphorus), soil organic matter, (effective) soil layer thickness, (available) potassium, and (nitrate) nitrogen as core supplementary indicators.

[0025] To enhance the accessibility of indicator data and improve the operability of the weighting method, indicators with high relevance were selectively retained, while those that did not conform to soil reconstruction characteristics or regional features were removed. Considering the overall relevance, representativeness, and operability of the indicator system, some indicators were also removed. To clarify the soil functional characteristics represented by each indicator, the indicators were categorized into four aspects: soil quality, remediation potential, functional utilization potential, and carbon sequestration and emission reduction potential. This resulted in the final set of feasibility evaluation indicators for the reconstruction and reuse of contaminated site soil production functions, as shown in Table 2.

[0026] Table 1 Collection of evaluation indicators for farmland productivity

[0027] Table 2. Feasibility Evaluation Index Set for Reconstruction of Soil Production Functions at Contaminated Sites

[0028] In a specific application example, the values ​​of each indicator in the indicator data matrix are determined based on the actual collected data from each location, using the single-factor index method, the grade assignment method, and the Nemerow comprehensive pollution index method.

[0029] Specifically, to obtain the correct weights for each indicator, considering the inconsistencies in the initial data units and the existence of omissions, it is necessary to process the original indicator values ​​to obtain indicator data from which weights can be derived. For indicators with clearly defined standard values ​​(limits), a single-factor index method is used; for indicators without standard values ​​(limits), a graded assignment method is used; and for indicators with missing detection indicators, a comprehensive index method is used. Among these, the comprehensive index for remediation potential indicators... The modified Nemerow Integrated Pollution Index method was used for calculation: in, To repair the average of the highest-scoring indicators among all indicators in the potential category; This is the score of the indicator with the highest weight in the potential improvement category.

[0030] Step 202: Based on the indicator data matrix, and using game theory, determine the comprehensive weight of each indicator using multiple objective weighting methods.

[0031] Objective weighting methods, based on mathematical principles, avoid subjective interference and can solve the problem of human bias caused by subjective weighting. Entropy weighting methods, based on information theory principles, analyze the dispersion of data among various indicators; the smaller the information entropy, the lower the uncertainty, and the greater the weight assigned to the indicator. The Critic method determines weights through two dimensions: the information content of the indicator and the conflict between indicators. Information content is measured by standard deviation (the larger the standard deviation, the more significant the difference in indicator values, and the more information provided); conflict is measured by the negative correlation coefficient between indicators (the stronger the negative correlation, the less redundant information between indicators, and the higher the conflict). The final weight is the normalized result of "information content × conflict," meaning indicators with high information content and high conflict have greater weights. Principal component analysis extracts key information for weighting through dimensionality reduction: first, multiple related indicators are transformed into a few unrelated principal components; then, the weight is determined based on the variance contribution rate of each principal component (the larger the variance, the more original indicator information the principal component contains). This application selects a combined weighting method of "entropy weighting method + Critic method + principal component analysis method". In order to avoid the amplification of the shortcomings of the three objective weighting methods and the resulting deviation in the final weight value, a combination coefficient is assigned to the three methods in the comprehensive weight based on game theory.

[0032] In a specific application example, step 202 includes steps 21 to 23.

[0033] Step 21: Based on the index data matrix, determine the weight of each index using multiple objective weighting methods to obtain the weight vector set corresponding to each objective weighting method.

[0034] Indicator Data Matrix X for: ;in, For the first i The first point j The values ​​of each indicator, m For the number of points, n This refers to the number of indicators.

[0035] The process of determining the weight of each indicator using the entropy weight method includes the following steps (11) to (15).

[0036] (11) Perform positive transformation on the indicator data matrix.

[0037] For extremely large (benefit-oriented) indicators, the value remains unchanged. For extremely small (cost-oriented) indicators, the formula is used. Determine its corresponding positive value. For intermediate indicators, use the formula... and Determine its corresponding positive value. For interval indicators, if the first... j The optimal range for each indicator is Then the forwarding formula is: .like ,but ;like If it is within the optimal range, then ;like ,but .in, The maximum value of the indicator. x For the value of the indicator, For the positive values ​​of the indicator, M The median value. To be the optimal value, For the first j The values ​​of each indicator.

[0038] Specifically, pH value and soil bulk density are range-type indicators. Effective soil layer thickness, total nitrogen, available phosphorus, available potassium, cation exchange capacity, biodiversity, photothermal productivity, climatic productivity, irrigation and drainage capacity, soil organic carbon content, topsoil texture, profile configuration, and carbon pool variation factors are very large indicators. Salinization degree, cadmium, mercury, arsenic, lead, chromium, copper, nickel, zinc, hexachlorocyclohexane (HCH), DDT, benzo[α]pyrene, topographic slope, and groundwater depth are very small indicators.

[0039] (12) Standardize the index data matrix after positive transformation to form a standardized matrix. R : ;in, For the first i The first venue jThe standard value of each indicator .

[0040] (13) Calculate the prior probability of each index at each point to obtain the probability matrix. P : ;in, For the first i The first venue j The prior probability of each indicator .

[0041] (14) Calculate the information entropy of each index based on the probability matrix: .in, For the first j Information entropy of each indicator k For constants: Further calculation of the first... j Information utility value of each indicator : The greater the information utility value, the greater its importance to the evaluation, and the greater its weight.

[0042] (15) Calculate the weight of each indicator based on its information entropy: .in, For the first j The weight of each indicator is determined by the entropy method. Essentially, the weight of each indicator is calculated using the value coefficient of that indicator's information; the higher the value coefficient, the greater its importance to the evaluation.

[0043] The process of determining the weight of each indicator using the Critic method includes the following steps (21) to (25).

[0044] (21) Perform dimensionless processing on the indicator data matrix: ;in, The index value is a dimensionless value.

[0045] (22) Calculate the variability of the indicators. Specifically, this is expressed in the form of standard deviation: ; ;in, For the first j The standard deviation of each indicator For the first j The mean of each indicator. In the Critic method, the standard deviation is used to represent the variation and fluctuation of the values ​​of each indicator. The larger the standard deviation, the greater the numerical variation of the indicator, the more information it can reflect, and the stronger the evaluation strength of the indicator itself. Therefore, more weight should be assigned to the indicator.

[0046] (23) Calculate the conflict of indicators. Specifically, this is expressed by the correlation coefficient: ;in, For the first f The first indicator and the first j The correlation coefficient between the indicators For the first j The conflict between indicators. The correlation coefficient is used to represent the correlation between indicators. The stronger the correlation between an indicator and other indicators, the less conflict there is. It reflects more of the same information, and the more repetitive the evaluation content it can reflect. To some extent, this also weakens the evaluation strength of the indicator, and the weight assigned to the indicator should be reduced.

[0047] (24) Calculate the information content of the indicators: .in, For the first j The amount of information in each indicator The larger, the more j The greater the role of an indicator in the overall evaluation indicator system, the more weight it should be assigned.

[0048] (25) Calculate the weight of the indicators based on the amount of information: .

[0049] The process of determining the weights of each indicator using principal component analysis includes the following steps (31) to (38).

[0050] (31) Standardize the indicator data matrix to obtain the standard indicator data matrix. : .

[0051] Calculate the mean by column. = and standard deviation Calculate standardized data: .in, For the standardized first i The first point j The values ​​of each indicator.

[0052] (32) Calculate the covariance matrix L of the standard index data matrix: ;in, .

[0053] in, For the first f The first indicator and the first j Covariance between the indicators.

[0054] (33) Calculate L The eigenvalues ​​λ1≥λ2≥…≥λ n ≥0 and eigenvectors , , ..., .

[0055] (34) Calculate the principal component contribution rate ( j =1, 2, ..., n ) and cumulative contribution rate ( j =1, 2, ..., n ).

[0056] (35) Determine the principal components: Generally, the first, second, ..., third principal components are selected from the eigenvalues ​​whose cumulative contribution rate exceeds 80%. s ( s ≤ n ) principal components. j Principal components: F j = a 1j X1+ a 2j X2+…+ a nj X n ( j =1, 2, ..., s ).

[0057] (36) Determine the coefficient matrix of the linear combination: .

[0058] (37) Calculate the overall score coefficient: A1= A2= A s = A j This represents the overall score of the j-th indicator.

[0059] (38) Calculate the weights: .

[0060] Step 22: Perform linear combination of each weight vector set, and optimize the linear combination coefficients with the goal of minimizing the deviation, to obtain the linear combination coefficients corresponding to each objective weighting method.

[0061] To avoid amplifying the shortcomings of the three objective weighting methods and causing deviations in the final weight values, a combination coefficient is assigned to the three methods in the comprehensive weighting based on game theory. Let { 1, 2, 3} represents the linear combination coefficients, then the linear combination of the three weight vectors is: Where W1 is the weight vector set of the entropy method; W2 is the weight vector set of the Critic method; and W3 is the weight vector set of the principal component analysis method. , , These are the linear combination coefficients for the entropy weight method, the Critic method, and the principal component analysis method, respectively.

[0062] Based on the idea of ​​game theory aggregation model, the linear combination coefficients are optimized with the goal of minimizing deviation to obtain the most satisfactory weights. The objective function is established as follows: ;in, U The number of objective empowerment methods, For the first u Linear combination coefficients of objective weighting method, W u For the first u The weight vector set of an objective weighting method The vector set representing the summation weights. T This is for the transpose operation.

[0063] Based on the properties of matrix differentiation, the above equation can be equivalently transformed into a system of linear equations with conditions for the first derivative of the optimization: ; Solving for the optimal combination coefficients , , And perform normalization: ; ; ;in, These are the linear combination coefficients corresponding to the entropy weight method. These are the linear combination coefficients corresponding to the Critic method. These are the linear combination coefficients corresponding to principal component analysis.

[0064] Step 23: Determine the comprehensive weight of each indicator based on the linear combination coefficient and weight vector set corresponding to each objective weight.

[0065] Step 203: Based on the indicator data matrix and the pre-determined grading and assignment table, determine the score for each indicator at each location. The grading and assignment table is pre-determined according to the standards for each indicator. The grading and assignment table includes the scores corresponding to different levels for each indicator.

[0066] The feasibility evaluation index system includes qualitative and quantitative indicators. This application adopts an improved fuzzy comprehensive evaluation method for the comprehensive feasibility evaluation of the reconstruction of the production function of contaminated site soil. The feasibility evaluation indicators are classified and graded according to various industry standards, and values ​​are assigned to each grade. The index classification and values ​​are shown in Table 3.

[0067] Table 3. Indicator Classification and Value Assignment Table

[0068] Step 204: Based on the assigned scores for each indicator at each location and the overall weight of each indicator, determine the overall score of the site to be analyzed, in order to determine the feasibility of reconstructing the soil production function of the site to be analyzed. If the overall score is >50 points, it is feasible; if the overall score is ≤50 points, it is not feasible.

[0069] This application addresses the lack of effective assessment methods for determining whether soil remediation at contaminated sites can achieve arable land functionality. It proposes a feasibility analysis and evaluation method for reconstructing the productive functions of contaminated site soils based on soil environmental quality, remediation potential, potential for productive use, and carbon sequestration and emission reduction potential. This method enables predictive analysis of the reconstructed and reused productive functions of contaminated site soils. Breaking through the limitations of traditional single-faceted pollution risk assessment, this method achieves a closed-loop demonstration from "remediation meeting standards" to "full verification of arable land function," fundamentally solving the pain point in practice where "remediated soil meets standards but cannot be cultivated due to functional defects." It provides scientific and feasible decision-making support for the arable land utilization of contaminated sites. This method not only scientifically explains the specific key factors that can affect the reconstructed productive functions of soil in actual engineering projects but also provides targeted suggestions for possible anomalies in specific engineering practices, thereby reducing resource waste and improving the effectiveness of ecological governance and restoration.

[0070] Based on the same inventive concept, this application also provides a feasibility analysis and evaluation device for reconstructing the productive function of soil in contaminated sites, used to implement the above-mentioned method for feasibility analysis and evaluation of soil productive function reconstruction in contaminated sites. The solution provided by this device is similar to the solution described in the above-described method. Therefore, the specific limitations of one or more embodiments of the feasibility analysis and evaluation device for reconstructing the productive function of soil in contaminated sites provided below can be found in the limitations of the feasibility analysis and evaluation method for reconstructing the productive function of soil in contaminated sites described above, and will not be repeated here.

[0071] In one exemplary embodiment, such as Figure 4 As shown, a feasibility analysis and evaluation device for reconstructing the production function of soil in a contaminated site is provided, comprising: a data acquisition module 401, a weight determination module 402, a scoring module 403, and a feasibility determination module 404.

[0072] The data acquisition module 401 is used to acquire the indicator data matrix of the site to be analyzed. The indicator data matrix includes multiple points and the values ​​of multiple indicators at each point: the indicators include soil quality indicators, remediation potential indicators, functional utilization potential indicators, and carbon sequestration and emission reduction potential indicators.

[0073] The weight determination module 402 is used to determine the comprehensive weight of each indicator based on the indicator data matrix and using multiple objective weighting methods based on game theory.

[0074] The scoring module 403 is used to determine the score of each indicator at each point according to the indicator data matrix and the pre-determined grading and scoring table.

[0075] The feasibility determination module 404 is used to determine the comprehensive score of the site to be analyzed based on the scores of each indicator at each point and the comprehensive weight of each indicator, so as to determine the feasibility of reconstructing the soil production function of the site to be analyzed.

[0076] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores a matrix of indicator data for the site to be analyzed. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a feasibility analysis and evaluation method for the reconfiguration of soil production functions in a contaminated site.

[0077] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0078] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0079] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0080] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0081] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0082] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0083] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0084] 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. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for feasibility analysis and evaluation of soil production function reconfiguration in contaminated sites, characterized in that, The method includes: Obtain the indicator data matrix of the site to be analyzed; the indicator data matrix includes multiple points and the values ​​of multiple indicators at each point; the indicators include soil quality indicators, remediation potential indicators, functional utilization potential indicators and carbon sequestration and emission reduction potential indicators; Based on the aforementioned indicator data matrix, and using game theory, a variety of objective weighting methods are employed to determine the comprehensive weight of each indicator. Based on the indicator data matrix and the pre-determined grading and assignment table, the score for each indicator at each point is determined. Based on the scores assigned to each indicator at each location and the comprehensive weight of each indicator, the comprehensive score of the site to be analyzed is determined in order to determine the feasibility of reconstructing the soil production function of the site to be analyzed.

2. The feasibility analysis and evaluation method for reconstructing the productive function of contaminated site soil according to claim 1, characterized in that, The soil quality indicators include: pH value, salinization degree, effective soil layer thickness, total nitrogen, available phosphorus, available potassium, cation exchange capacity, soil bulk density, and biodiversity; The remediation potential indicators include: cadmium, mercury, arsenic, lead, chromium, copper, nickel, zinc, hexachlorocyclohexane (HCH), DDT, and benzo[α]pyrene; The functional utilization potential indicators include: light and temperature / climate production potential, terrain slope, irrigation and drainage capacity, and groundwater depth; The indicators for carbon sequestration and emission reduction potential include: soil organic carbon content, topsoil texture, profile configuration, and carbon pool variation factors.

3. The feasibility analysis and evaluation method for reconstructing the productive function of contaminated site soil according to claim 1, characterized in that, The values ​​of each indicator in the indicator data matrix are determined based on the actual collected data from each location, using the single-factor index method, the grade assignment method, and the Nemerow comprehensive pollution index method.

4. The feasibility analysis and evaluation method for reconstructing the productive function of contaminated site soil according to claim 1, characterized in that, Based on the aforementioned indicator data matrix, and using game theory, various objective weighting methods are employed to determine the comprehensive weight of each indicator, including: Based on the index data matrix, the weights of each index are determined using multiple objective weighting methods, resulting in a weight vector set corresponding to each objective weighting method. Linear combination of each weight vector set is performed, and the linear combination coefficients are optimized with the goal of minimizing the deviation, to obtain the linear combination coefficients corresponding to each objective weighting method; The comprehensive weight of each indicator is determined based on the linear combination coefficients and weight vector set corresponding to each objective weight.

5. The feasibility analysis and evaluation method for reconstructing the productive function of contaminated site soil according to claim 1, characterized in that, The various objective weighting methods are entropy weighting, Critic method, and principal component analysis.

6. The feasibility analysis and evaluation method for reconstructing the productive function of soil in contaminated sites according to claim 4, characterized in that, The objective function for optimizing the linear combination coefficients is: ;in, U The number of objective empowerment methods, For the first u Linear combination coefficients of objective weighting method, W u For the first u The weight vector set of an objective weighting method T This is for the transpose operation.

7. The feasibility analysis and evaluation method for reconstructing the productive function of contaminated site soil according to claim 1, characterized in that, The grading and assignment table is determined in advance according to the standards of each indicator; the grading and assignment table includes the score corresponding to different levels of each indicator.

8. A device for feasibility analysis and evaluation of soil production function reconstruction in contaminated sites, characterized in that, The apparatus performs the feasibility analysis and evaluation method for reconstructing the productive function of soil in contaminated sites as described in any one of claims 1-7, and the apparatus comprises: The data acquisition module is used to acquire the indicator data matrix of the site to be analyzed; the indicator data matrix includes multiple points and the values ​​of multiple indicators at each point; the indicators include soil quality indicators, remediation potential indicators, functional utilization potential indicators and carbon sequestration and emission reduction potential indicators. The weight determination module is used to determine the comprehensive weight of each indicator based on the indicator data matrix and using multiple objective weighting methods based on game theory. The scoring module is used to determine the score of each indicator at each point based on the indicator data matrix and the pre-determined grading and scoring table. The feasibility determination module is used to determine the comprehensive score of the site to be analyzed based on the scores of each indicator at each location and the comprehensive weight of each indicator, so as to determine the feasibility of reconstructing the soil production function of the site to be analyzed.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the feasibility analysis and evaluation method for reconfiguration of soil production functions at a contaminated site as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the feasibility analysis and evaluation method for reconstructing the production function of soil at a contaminated site as described in any one of claims 1-7.

Images