A comprehensive analysis system and method for carbon emission of contaminated soil

Abstract

The application relates to the technical field of carbon emission analysis, and discloses a contaminated soil carbon emission comprehensive analysis system and method, which comprises the following steps: obtaining initial process data of a whole process of contaminated soil treatment of each space unit and performing pretreatment, determining target process data, screening the target process data based on an association rule, determining analysis indexes of three dimensions of soil characteristics, transportation parameters and cement kiln operation, performing dimension correlation analysis on the analysis indexes, determining in-site carbon emission accounting and out-site carbon emission accounting by adopting a difference method and a proportional distribution method based on an influence relationship and index combination, generating model carbon emission scores based on carbon emission spatial clustering rules and all carbon emission accounting based on a spatial autocorrelation analysis algorithm and in combination with carbon emission accounting, and determining a carbon emission risk grade of a target region based on the model carbon emission scores. The application ensures the reliability of comprehensive analysis of carbon emission.

Description

Technical Field

[0001] This invention relates to the field of carbon emission analysis technology, and more specifically, to a comprehensive carbon emission analysis system and method for contaminated soil. Background Technology

[0002] With the acceleration of global urbanization and industrial restructuring, soil pollution has become increasingly prominent. Cement kiln co-processing technology (CKCS), with its advantages of thoroughness, efficiency, and wide applicability, can effectively decompose pollutants in contaminated soil, thereby meeting the needs of land development. However, the implementation of cement kiln co-processing involves long-distance transportation of contaminated soil and high-carbon-emission cement clinker production, making carbon emissions a key focus for the sustainable remediation of contaminated soil. Existing carbon emission studies are mostly conducted from the perspective of cement production, failing to cover the entire process of excavation, transportation, and in-kiln treatment, and also failing to reflect the role of soil characteristics in carbon emissions. This neglects the influence of factors such as soil characteristics, transportation distance, and cement industry distribution, making it impossible to deeply analyze regional carbon emission characteristics and emission reduction potential. Furthermore, it fails to reveal the spatial heterogeneity of carbon emissions, resulting in a lack of targeted regional-scale assessments of emission reduction from cement kiln co-processing of contaminated soil.

[0003] Therefore, it is necessary to design a comprehensive analysis system and method for carbon emissions from contaminated soil to address the problems existing in current technologies. Summary of the Invention

[0004] In view of this, the present invention proposes a comprehensive carbon emission analysis system and method for contaminated soil, aiming to solve the above-mentioned problems.

[0005] In one aspect, the present invention proposes a comprehensive analysis method for carbon emissions from contaminated soil, comprising: The target area is divided into several spatial units according to a geographic grid. Initial process data of the entire process of contaminated soil treatment in each spatial unit is obtained and preprocessed to determine the target process data. The target process data is filtered based on association rules to determine the analysis indicators in three dimensions: soil characteristics, transportation parameters, and cement kiln operation. Dimensional association analysis is then performed on each analysis indicator to determine the impact relationship between each analysis indicator and carbon emissions, as well as the combination of indicators. Based on the aforementioned influencing relationships and index combinations, the difference method and proportional allocation method are used to determine the on-site carbon emission accounting and off-site carbon emission accounting. Based on the on-site carbon emission accounting and off-site carbon emission accounting, the carbon emission accounting for the entire process of contaminated soil disposal is determined. Based on the spatial autocorrelation analysis algorithm and combined with the carbon emission accounting, the spatial clustering pattern of carbon emissions in the target area is determined. Based on the carbon emission model, the spatial clustering pattern of carbon emissions and the calculation of all carbon emissions are used to generate a model carbon emission score. Based on the model carbon emission score, the carbon emission risk level of the target area is determined.

[0006] Furthermore, when acquiring and preprocessing the initial process data for the entire contaminated soil disposal process in each spatial unit to determine the target process data, the following steps are included: Soil composition data, transportation data, and disposal data of contaminated soil in each spatial unit are acquired. Before collecting the transportation data, the WGS-84 geographic coordinate system is uniformly used to preprocess the soil composition data and disposal data. The preprocessing includes data noise reduction and data standardization. The target process data is determined based on the transportation data and the preprocessed results. The soil composition data includes soil organic carbon content, soil chemical property parameters, and soil composition parameters; The transportation data includes transportation distance, load capacity, and transportation fuel consumption data; The processing data includes calcination temperature, residence time, raw material ratio, calcination fuel consumption, and electricity consumption data.

[0007] Furthermore, when filtering the target process data based on association rules to determine the analytical indicators for the three dimensions of soil characteristics, transportation parameters, and cement kiln operation, the following are included: The correlation degree between each candidate indicator in the target process data and carbon emissions is determined based on the association rule algorithm. The target process data is then reduced based on the correlation degree, and a three-level hierarchical indicator is constructed based on the reduction result. The first-level indicators of the three-level hierarchical indicator are soil characteristics, transportation parameters and cement kiln operation; the second-level indicators are soil composition, transportation efficiency and kiln operation status; and the third-level indicators are specific values.

[0008] Furthermore, when conducting dimensional correlation analysis on each analytical indicator to determine the impact relationship between each analytical indicator and carbon emissions, and the combination of indicators, this includes: Pearson correlation analysis and Spearman rank correlation analysis were used to analyze the soil properties and transport parameters to determine the correlation coefficients between the soil properties and transport parameters and carbon emissions. The absolute values ​​of the correlation coefficients were then sorted in descending order, and the influence relationship was determined based on the sorting results. A coupling coordination analysis was conducted on the cross-analysis indicators of cement kiln operation and soil characteristics to determine the coupling coordination degree between the two types of analysis indicators, and the indicator combination was determined based on the coupling coordination degree.

[0009] Furthermore, when determining the on-site carbon emission accounting and off-site carbon emission accounting based on the aforementioned influence relationships and indicator combinations using the difference method and proportional allocation method, and when determining the carbon emission accounting for the entire contaminated soil disposal process based on the on-site and off-site carbon emission accounting, the process includes: Based on the aforementioned influence relationships and indicator combinations, the difference method is used to compare the carbon emission data of cement kiln clinker production before and after the addition of the contaminated soil, determine the difference between them, and determine the changes in carbonate decomposition emissions caused by soil organic carbon decomposition and raw material ratio adjustment. Based on the aforementioned influence relationships and indicator combinations, the proportional allocation method is used to determine the allocation of indirect carbon emissions corresponding to fossil fuel combustion and electricity consumption according to the proportion of the contaminated soil in cement production raw materials. The carbon emission data, changes in carbonate decomposition emissions, and indirect carbon emissions are determined as on-site carbon emission accounting.

[0010] Furthermore, when determining the on-site carbon emission accounting and off-site carbon emission accounting based on the aforementioned influence relationships and indicator combinations using the difference method and proportional allocation method, and when determining the carbon emission accounting for the entire contaminated soil disposal process based on the on-site and off-site carbon emission accounting, the method further includes: Based on the rated energy consumption and working time of the excavation equipment and combined with the carbon emission coefficient of fuel combustion, the carbon emissions of the excavation process are determined. Based on the transportation distance, fuel consumption per unit mileage of the transportation vehicle, and load capacity, the carbon emissions of the transportation process are determined according to the ton-kilometer energy consumption standard. The off-site carbon emission accounting is determined based on the carbon emissions of the excavation process and the carbon emissions of the transportation process. The on-site carbon emission accounting and off-site carbon emission accounting are summarized to determine the carbon emission accounting for the entire process of contaminated soil disposal.

[0011] Furthermore, when determining the spatial clustering pattern of carbon emissions in the target region based on the spatial autocorrelation analysis algorithm and in conjunction with the aforementioned carbon emission accounting, the process includes: The carbon emission accounting of the entire process of contaminated soil disposal in each spatial unit is linked with the geographic coordinates to construct a three-dimensional spatial dataset. Based on the spatial autocorrelation analysis algorithm and the three-dimensional spatial dataset, the local Moran index is determined. Based on the local Moran index, the LISA map is obtained and the LISA map is parsed. If the attribute value of a spatial unit is greater than the attribute value threshold, and the attribute value of adjacent spatial units is also greater than the attribute value threshold, then the spatial clustering pattern of carbon emissions is determined to be positive synergistic clustering. If the attribute value of a spatial unit is less than the attribute value threshold, and the attribute value of adjacent spatial units is also less than the attribute value threshold, then the spatial clustering pattern of carbon emissions is determined to be positively correlated. If the attribute value of a spatial unit is greater than the attribute value threshold, and the attribute value of adjacent spatial units is less than the attribute value threshold, then the spatial clustering pattern of carbon emissions is determined to be negatively correlated. If the attribute value of a spatial unit is less than the attribute value threshold, and the attribute value of an adjacent spatial unit is greater than the attribute value threshold, then the spatial clustering pattern of carbon emissions is determined to be a negative correlation.

[0012] Furthermore, when generating a carbon emission score based on the spatial clustering pattern of carbon emissions and the total carbon emission accounting based on the carbon emission model, the following is included: Obtain a carbon emission sample dataset, divide the carbon emission sample dataset into a training set and a test set, determine the model parameters based on grid search, and build a random forest model; The training set is used to train the random forest model. The test set is substituted into the trained random forest model and the accuracy is calculated. When the accuracy is greater than or equal to the accuracy threshold, the trained random forest model is determined as the carbon emission model. Otherwise, the number of decision trees in the trained random forest model is adjusted and training continues. The carbon emission spatial clustering pattern and the total carbon emission accounting are substituted into the carbon emission model to determine the carbon emission score of the model.

[0013] Furthermore, when determining the carbon emission risk level of the target area based on the model's carbon emission score, the process includes: Set a first model carbon emission score and a second model carbon emission score, with the first model carbon emission score being greater than the second model carbon emission score; When the carbon emission score of the model is greater than the carbon emission score of the first model, the carbon emission risk level of the target area is determined to be Level 1 risk. When the carbon emission score of the model is less than or equal to the carbon emission score of the first model and greater than or equal to the carbon emission score of the second model, the carbon emission risk level of the target area is determined to be Level II risk. When the carbon emission score of the model is less than the carbon emission score of the second model, the carbon emission risk level of the target area is determined to be Level III risk. The emission reduction priorities for Level 1, Level 2, and Level 3 risks decrease sequentially.

[0014] Compared with existing technologies, the advantages of this invention are as follows: By comprehensively covering the entire process of contaminated soil excavation, transportation, and in-kiln disposal, and simultaneously incorporating the mechanism by which soil characteristics affect carbon emissions, this invention effectively avoids the problem of neglecting factors such as soil characteristics, transportation distance, and cement industry distribution. It comprehensively captures the contribution of each stage to carbon emissions, providing complete data support for accurate calculation of carbon emissions throughout the entire process. Based on association rules, it selects analytical indicators from three dimensions: soil characteristics, transportation parameters, and cement kiln operation. Based on dimensional correlation analysis, it clarifies the influencing relationships and indicator combinations. Furthermore, it accurately separates on-site carbon emission accounting from off-site carbon emission accounting using the difference method and proportional allocation method, ensuring the comprehensiveness and accuracy of carbon emission accounting. This method ensures accuracy and avoids data bias caused by single-dimensional or one-sided calculations. By dividing the target area into spatial units of a geographic grid and combining it with spatial autocorrelation analysis algorithms, it accurately identifies the spatial clustering patterns of carbon emissions in the target area. This clearly presents the spatial distribution patterns of carbon emissions from contaminated soil, including clustering, randomness, and dispersion. It provides a spatial dimension basis for understanding the emission reduction potential of the target area. By generating a carbon emission score through a carbon emission model, it quantifies the carbon emission risk level and clarifies the emission reduction potential of different target areas. This provides a basis for formulating differentiated and precise emission reduction strategies, thereby comprehensively improving the reliability of carbon emission analysis for co-processing of contaminated soil in cement kilns (CKCS).

[0015] On the other hand, this application also provides a comprehensive carbon emission analysis system for contaminated soil, used to apply the above-mentioned comprehensive carbon emission analysis method for contaminated soil, including: The data acquisition unit is configured to divide the target area into several spatial units according to a geographic grid, acquire the initial process data of the entire process of contaminated soil treatment in each spatial unit and perform preprocessing to determine the target process data; The analysis unit is configured to filter the target process data based on association rules, determine the analysis indicators in three dimensions: soil characteristics, transportation parameters and cement kiln operation, and perform dimensional association analysis on each analysis indicator to determine the impact relationship between each analysis indicator and carbon emissions and the combination of indicators. The processing unit is configured to determine the on-site carbon emission accounting and off-site carbon emission accounting based on the influence relationship and index combination using the difference method and the proportional allocation method; determine the carbon emission accounting of the entire process of contaminated soil disposal based on the on-site carbon emission accounting and off-site carbon emission accounting; and determine the spatial clustering pattern of carbon emissions in the target area based on the spatial autocorrelation analysis algorithm and in combination with the carbon emission accounting. The emission unit is configured to generate a model carbon emission score based on the spatial clustering pattern of carbon emissions and the accounting of all carbon emissions using a carbon emission model, and to determine the carbon emission risk level of the target area based on the model carbon emission score.

[0016] It is understandable that the aforementioned comprehensive carbon emission analysis system and method for contaminated soil have the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a comprehensive carbon emission analysis method for contaminated soil provided in an embodiment of the present invention; Figure 2 This is a functional block diagram of a comprehensive carbon emission analysis system for contaminated soil provided in an embodiment of the present invention. Detailed Implementation

[0019] 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.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] See Figure 1 As shown in some embodiments of this application, a comprehensive analysis method for carbon emissions from contaminated soil includes: S100: Divide the target area into several spatial units according to the geographic grid, obtain the initial process data of the entire process of contaminated soil treatment in each spatial unit and perform preprocessing to determine the target process data.

[0022] S200: Based on association rules, target process data is filtered to determine three dimensions of analysis indicators: soil characteristics, transportation parameters, and cement kiln operation. Dimensional association analysis is then performed on each analysis indicator to determine the impact relationship between each analysis indicator and carbon emissions, as well as the combination of indicators.

[0023] S300: Based on the influence relationship and the combination of indicators, the difference method and the proportional allocation method are used to determine the carbon emission accounting on site and off site. Based on the carbon emission accounting on site and off site, the carbon emission accounting of the whole process of contaminated soil disposal is determined. Based on the spatial autocorrelation analysis algorithm and combined with the carbon emission accounting, the spatial clustering pattern of carbon emissions in the target area is determined.

[0024] S400: Based on the carbon emission model, the spatial clustering pattern of carbon emissions and the accounting of all carbon emissions are used to generate a model carbon emission score, and the carbon emission risk level of the target area is determined based on the model carbon emission score.

[0025] Specifically, the target area can be an area defined by administrative jurisdiction (such as a province, city, county, district, etc.) or a specific area with clear functional attributes (such as an industrial park cluster, a contaminated site remediation cluster, a cement kiln co-processing facility radiation coverage area, etc.). The target area is divided into several spatial units according to a geographical grid. The number of units can be dynamically adjusted according to the size of the target area. Dividing the target area into several spatial units breaks the blindness of carbon emission assessment based on a single space, allowing each spatial unit to become an independent analysis entity, and enabling subsequent carbon emission analysis to focus on a more refined geographical range. Initial process data for the entire contaminated soil treatment process in each spatial unit was obtained. The treatment of contaminated soil involves three stages: soil composition, transportation, and disposal. The initial process data covers the complete treatment process of excavation, transportation, and in-kiln disposal, overcoming the fragmentation and incomplete coverage of existing data. This eliminates interference and standardizes data, ensuring accuracy and completeness and preventing distortion of subsequent analysis results due to data quality issues. Analysis indicators were selected based on association rules and divided into three dimensions. Data mining was used to extract indicators strongly correlated with carbon emissions from the complex target process data to reduce redundancy in the analysis. The soil characteristics dimension covers attributes such as the texture and degree of contamination of the contaminated soil; the transportation parameters dimension includes information such as transportation distance and mode of transportation; and the cement kiln operation dimension involves data such as kiln energy consumption and operating conditions. The three-dimensional indicator system comprehensively covers the entire chain process of soil-transportation-disposal. Dimensional correlation analysis delves into the intrinsic relationships between different indicators, such as the correlation between soil moisture content and cement kiln energy consumption, and the correlation between transportation distance and transportation carbon emissions. This avoids the limitations of single-dimensional analysis, reveals the synergistic effect of multiple factors on carbon emissions, and determines the influence relationship and combination of indicators on carbon emissions. The difference method and proportional allocation method are used to determine on-site and off-site carbon emission accounting. Appropriate accounting methods are selected based on the characteristics of different carbon emission stages. On-site carbon emission accounting mainly corresponds to the increased carbon emissions compared to conventional cement production during the co-processing of contaminated soil in cement kilns, while off-site carbon emission accounting covers stages such as contaminated soil excavation, loading, and long-distance transportation. The carbon emission sources in these stages are relatively dispersed, and the responsible parties are diversified. The full-process carbon emission accounting integrates on-site and off-site carbon emission accounting, achieving comprehensive and accurate quantification of carbon emissions.

[0026] Understandably, spatial clustering patterns are determined by combining spatial autocorrelation analysis algorithms with carbon emission accounting results. This integrates carbon emission accounting results with spatial location, analyzes the correlation between carbon emission levels in different spatial units and adjacent spatial units, identifies the spatial heterogeneity of carbon emissions in the target area, clarifies the distribution characteristics and aggregation patterns of regional carbon emissions, and provides spatial basis for precise emission reduction. Based on carbon emission models, model carbon emission scores are generated and risk levels are determined. The entire process of carbon emission accounting and spatial clustering information for all spatial units is integrated. The model quantifies the model carbon emission score of the target area, and the carbon emission risk level of the target area is determined based on the model carbon emission score. This transforms abstract carbon emission data into concrete risk assessment results, clearly presenting the comprehensive carbon emission risk level of spatial units. This allows the target area to implement differentiated emission reduction measures based on the carbon emission risk level. Higher-risk areas require enhanced emission reduction, while lower-risk areas require optimized processes to consolidate effectiveness. This improves the targeting and reliability of emission reduction and promotes the sustainable development of cement kiln co-processing of contaminated soil.

[0027] In some embodiments of this application, when acquiring and preprocessing the initial process data of the entire contaminated soil disposal process for each spatial unit to determine the target process data, the process includes: acquiring soil composition data, transportation data, and disposal data of the contaminated soil for each spatial unit; before collecting the transportation data, uniformly using the WGS-84 geographic coordinate system to preprocess the soil composition data and disposal data, including data noise reduction and data standardization; and determining the target process data based on the transportation data and the preprocessed results. The soil composition data includes soil organic carbon content, soil chemical characteristic parameters, and soil composition parameters; the transportation data includes transportation distance, loading capacity, and transportation fuel consumption data; and the disposal data includes calcination temperature, residence time, raw material ratio, calcination fuel consumption, and power consumption data.

[0028] Specifically, the target area is divided into several spatial units according to a geographic grid. This small-scale gridding breaks the limitations of holistic regional analysis, enabling precise anchoring of carbon emission analysis and laying a spatial foundation for revealing the spatial heterogeneity of carbon emissions. Initial process data for the entire contaminated soil treatment process in each spatial unit is obtained, comprehensively covering three dimensions: soil composition data, transportation data, and treatment data. Soil organic carbon content represents the total amount of carbon in various organic substances in contaminated soil, directly affecting carbon emissions during cement kiln calcination. Soil chemical characteristic parameters represent various indicators related to the chemical properties of contaminated soil, including pollutant type (such as heavy metals and organic matter), pollutant concentration, pH value, and cation exchange capacity. These parameters affect the calcination conditions during cement kiln treatment, thus indirectly affecting energy consumption and carbon emissions. Soil composition parameters represent the basic material composition indicators of contaminated soil, including soil texture (sand, clay, loam, etc.), particle size distribution, moisture content, and bulk density. These parameters determine the efficiency of transporting and treating contaminated soil in the kiln (e.g., texture affects calcination uniformity). The transport distance represents the actual mileage of the contaminated soil from the excavation site in the spatial unit to the cement kiln plant that undertakes the treatment (it is uniformly calibrated using the WGS-84 geographic coordinate system to ensure the accuracy of distance calculation). The longer the transport distance, the more fuel is consumed and the higher the carbon emissions. Loading capacity refers to the weight of contaminated soil that a single transport vehicle (such as a truck or specialized transport equipment) can carry. Transport fuel consumption data refers to the total amount of fuel, electricity, and other fuels consumed by the transport vehicle during the transport of contaminated soil. This is the direct basis for carbon emission accounting in the transportation process. Calcination temperature indicates the temperature level inside the cement kiln when treating contaminated soil. The temperature directly affects the kiln's fuel and electricity consumption, and thus the carbon emission level of the treatment process. Residence time is the length of time the contaminated soil remains in the cement kiln during calcination. Raw material ratio is the mixing ratio of contaminated soil to cement production raw materials (such as limestone, clay, and iron powder). It takes into account both the treatment effect of contaminated soil and the quality of cement clinker. Different ratios will lead to differences in reaction efficiency inside the kiln, thus affecting fuel consumption and ultimately carbon emissions. Calcination fuel consumption refers to the total amount of fuel (such as coal, natural gas, and biomass fuel) consumed by the cement kiln to maintain the calcination temperature and treat contaminated soil. Electricity consumption data represents the total amount of electricity consumed during the operation of the cement kiln (such as kiln drive, ventilation, and material conveying). Electricity consumption indirectly translates into carbon emissions (related to carbon emissions from the power generation process). Preprocessing the soil composition and treatment data involved data denoising to eliminate interference and abnormal data generated during data acquisition. Data standardization unified the units and scales of data from different sources and formats, eliminating dimensional differences and preventing distortion of subsequent analysis results due to inconsistent data definitions. Based on the calibrated transportation data and the preprocessed soil composition and treatment data, the target process data was comprehensively determined, providing data support for subsequent analysis.

[0029] In some embodiments of this application, when filtering target process data based on association rules to determine the three dimensions of analysis indicators—soil characteristics, transportation parameters, and cement kiln operation—the process includes: determining the degree of correlation between each candidate indicator in the target process data and carbon emissions based on the association rule algorithm; reducing the target process data based on the degree of correlation; and constructing a three-level hierarchical indicator based on the reduction result. The first-level indicators of the three-level hierarchical indicator are soil characteristics, transportation parameters, and cement kiln operation; the second-level indicators are soil composition, transportation efficiency, and kiln operation status; and the third-level indicators are specific numerical values.

[0030] Specifically, after acquiring the target process data, an association rule algorithm is used to conduct in-depth analysis of various candidate indicators in the target process data. Candidate indicators are all potential indicators in the target process data that may be related to carbon emissions. They cover various quantitative parameters related to the characteristics of contaminated soil itself, the transportation process, and the in-kiln treatment stages. The association rule algorithm is used to uncover the inherent correlation logic between candidate indicators and carbon emissions, ensuring that the selected candidate indicators can truly have a substantial impact on carbon emissions and avoiding irrelevant or weakly correlated indicators from being included in subsequent analyses. Based on the identified correlation degree, the target process data is pruned, retaining candidate indicators that have a substantial impact on carbon emissions, reducing the interference of invalid information on subsequent analyses. A three-level hierarchical indicator system is constructed based on the pruned candidate indicators. The first-level indicators define three dimensions: soil characteristics, transportation parameters, and cement kiln operation, covering the key links affecting carbon emissions in the contaminated soil treatment process. The second-level indicators are correspondingly broken down into soil composition, transportation efficiency, and kiln operating status, serving as a basis for further analysis. The connection between primary indicators and specific quantitative indicators is crucial. Soil composition, transportation efficiency, and kiln operation status are secondary indicators in the three-level hierarchical indicator system. They serve as connecting dimensions between the primary indicators of soil characteristics, transportation parameters, and cement kiln operation, respectively. Soil composition focuses on the material composition attributes of contaminated soil itself and is a specific decomposition of the primary indicator of soil characteristics. It covers data such as soil organic carbon content, soil chemical characteristic parameters, and soil composition parameters, directly reflecting the intrinsic characteristics of contaminated soil. Transportation efficiency revolves around the efficiency and related attributes of the contaminated soil transfer process. Its corresponding primary indicator of transportation parameters covers transportation distance, loading capacity, and transportation fuel consumption data, reflecting the transfer efficiency of contaminated soil to the cement kiln and its energy consumption characteristics. Kiln operation status addresses the operational attributes of the cement kiln when treating contaminated soil, covering data such as calcination temperature, calcination fuel consumption, and electricity consumption, reflecting the operating conditions of the cement kiln in the process of co-processing contaminated soil. The tertiary indicators are then implemented as specific numerical values, ensuring the reliability of carbon emission quantification for each dimension. Association rule algorithms ensure a strong correlation between candidate indicators and carbon emissions, avoiding subjectivity and blindness in indicator selection. The construction of three-level hierarchical indicators makes the indicator system structured and organized, clarifying the attribution and hierarchical relationship, defining the impact relationship between each dimension of analysis indicators and carbon emissions, and avoiding the risk of chaotic research indicators.

[0031] In some embodiments of this application, when performing dimensional correlation analysis on various analytical indicators to determine the influence relationship between each analytical indicator and carbon emissions and the combination of indicators, the following steps are taken: Pearson correlation analysis and Spearman rank correlation analysis are used for the analytical indicators of soil properties and transport parameters to determine the correlation coefficients between the analytical indicators of soil properties and transport parameters and carbon emissions, and the absolute values ​​of the correlation coefficients are sorted in descending order. The influence relationship is determined based on the sorting results. Coupled coordination analysis is used for the cross-analysis indicators of cement kiln operation and soil properties to determine the coupling coordination degree between the two types of analytical indicators, and the combination of indicators is determined based on the coupling coordination degree.

[0032] Specifically, the analytical indicators refer to the specific quantitative indicators that are filtered by association rules and categorized into three dimensions: soil characteristics, transportation parameters, and cement kiln operation, and that are related to carbon emissions. Specifically, they correspond to various specific values ​​in the three-level hierarchical indicators, such as soil composition, transportation efficiency, and kiln operation status. For the analytical indicators under soil properties and transport parameters, a combination of Pearson correlation analysis and Spearman rank correlation analysis was used to comprehensively capture the linear and nonlinear relationships between the analytical indicators and carbon emissions. By obtaining the correlation coefficients between each analytical indicator and carbon emissions, the absolute values ​​of the correlation coefficients were sorted in descending order. Based on the sorting results, the degree of correlation between different analytical indicators and carbon emissions was clarified, and the influence relationship between each analytical indicator and carbon emissions was determined. The larger the absolute value of the correlation coefficient, the closer the correlation between the analytical indicator and carbon emissions. For the cross-analysis indicators of cement kiln operation and soil properties, these cross-analysis indicators are those that simultaneously relate to both cement kiln operation and soil properties, reflecting the interaction between the two. They are not new indicators independent of the analytical indicators. For example, soil organic carbon content and calcination temperature. Soil organic carbon content in soil properties directly affects the adjustment of calcination temperature in cement kiln operation. When the soil organic carbon content of contaminated soil is high, the calcination temperature needs to be increased accordingly to ensure the complete decomposition of organic pollutants. Coupled coordination analysis was employed to explore the interaction and mutual influence between two types of analytical indicators from different dimensions. The degree of coupling coordination between the two types of indicators was calculated, and based on this degree, cross-analysis indicators with significant synergistic effects were screened. This process then determined the indicator combination, which, after multiple rounds of analysis from three dimensions—soil properties, transportation parameters, and cement kiln operation—resulted in a set of indicators strongly correlated with carbon emissions and capable of synergistically characterizing the carbon emission impact of contaminated soil disposal. Therefore, coupled coordination analysis overcomes the limitations of single-dimensional analysis, uncovering the synergistic effects of cross-dimensional analytical indicators. The determined influence relationships and indicator combinations more closely align with the actual carbon emission formation mechanism of contaminated soil disposal. The core of the association rule algorithm is used for preliminary screening. It quickly identifies candidate indicators with potential correlation to carbon emissions from the target process data, mitigating the risks of too many or too varied candidate indicators, and interference from irrelevant indicators. While it determines whether candidate indicators are related to carbon emissions, it cannot determine the strength of the correlation or whether there is a synergistic effect among the candidate indicators.Pearson correlation analysis, Spearman rank correlation analysis, and coupling coordination analysis are used to more accurately define the relationships between effective analytical indicators (i.e., analytical indicators) selected by association rules. Pearson correlation analysis is good at capturing linear associations, while Spearman rank correlation analysis is good at capturing nonlinear associations. The combination of the two can comprehensively cover the association types between soil properties, transport parameters, and carbon emissions. It can also clarify the strength of the impact of different analytical indicators on carbon emissions by ranking the correlation coefficients. Coupling coordination analysis is specifically for cross-analysis indicators of cement kiln operation and soil properties, and explores the synergistic effects between indicators of different dimensions (such as how changes in soil properties affect the role of cement kiln operation indicators in carbon emissions). This provides data support for subsequent accurate carbon emission accounting and the formulation of targeted emission reduction measures.

[0033] In some embodiments of this application, when determining on-site carbon emission accounting and off-site carbon emission accounting using the difference method and proportional allocation method based on the influence relationship and index combination, and determining the carbon emission accounting of the entire process of contaminated soil disposal based on the on-site carbon emission accounting and off-site carbon emission accounting, the process includes: using the difference method based on the influence relationship and index combination to compare the carbon emission data of cement kiln clinker production before and after the addition of contaminated soil, determining the difference between them and determining the change in carbonate decomposition emissions caused by soil organic carbon decomposition and raw material ratio adjustment; and using the proportional allocation method based on the influence relationship and index combination to determine the allocation of indirect carbon emissions corresponding to fossil fuel combustion and electricity consumption according to the proportion of contaminated soil in cement production raw materials, and determining the carbon emission data, the change in carbonate decomposition emissions, and the indirect carbon emissions as on-site carbon emission accounting.

[0034] Specifically, based on soil characteristics, the relationship between cement kiln operation-related indicators and carbon emissions, and the selected indicator combination, the difference method is used for calculation. Taking cement kiln clinker production as a benchmark, the carbon emission data before and after the addition of contaminated soil are compared. Through the difference between the two, specific emissions caused by the introduction of contaminated soil are accurately identified, namely, carbon emissions from the decomposition of soil organic carbon, and the changes in carbonate decomposition emissions caused by the adjustment of raw material ratio due to differences in soil composition. This process makes full use of the direct influence relationship between soil characteristic-related indicators and carbon emissions, ensuring that the contribution of contaminated soil itself to on-site carbon emissions is accurately identified. Similarly, based on the calcination fuel consumption and electricity consumption data related to cement kiln operation in the indicator combination, as well as the correlation logic between contaminated soil and carbon emissions, the proportional allocation method is used to calculate common emission sources. Since the indirect carbon emissions corresponding to fossil fuel combustion and electricity consumption are jointly generated by cement production and the co-treatment of contaminated soil, they cannot be directly separated. Therefore, they are allocated according to the actual proportion of contaminated soil in cement production raw materials, ensuring that this part of the carbon emissions is included in the on-site emissions corresponding to contaminated soil treatment. The changes in soil organic carbon decomposition carbon emissions and carbonate decomposition emissions obtained by the difference method are integrated with the indirect carbon emissions from fossil fuel combustion and electricity consumption obtained by the proportional allocation method to jointly determine the on-site carbon emission accounting results. The difference method specifically captures the carbon emissions brought about by the introduction of contaminated soil, while the proportional allocation method solves the problem of apportionment of common emission sources and avoids the distortion of accounting results. The combination of the two fully reflects the comprehensive impact of soil characteristics on carbon emissions, thereby ensuring the reliability of the carbon emission analysis of contaminated soil.

[0035] In some embodiments of this application, when determining on-site carbon emission accounting and off-site carbon emission accounting based on the difference method and proportional allocation method using the influence relationship and index combination, and determining the carbon emission accounting of the entire process of contaminated soil disposal based on the on-site carbon emission accounting and off-site carbon emission accounting, the method further includes: determining the carbon emission of the excavation process based on the rated energy consumption and working time of the excavation equipment and combined with the carbon emission coefficient of fuel combustion; determining the carbon emission of the transportation process according to the ton-kilometer energy consumption standard based on the transportation distance, fuel consumption per unit mileage of the transportation vehicle, and load capacity; determining the off-site carbon emission accounting based on the carbon emission of the excavation process and the carbon emission of the transportation process; and summarizing the on-site carbon emission accounting and the off-site carbon emission accounting to determine the carbon emission accounting of the entire process of contaminated soil disposal.

[0036] Specifically, the dredging equipment is an engineering machinery and equipment specifically used to excavate, strip, and collect contaminated soil at the contaminated soil disposal site. It is the starting equipment in the off-site disposal process of contaminated soil. Its function is to dig out the contaminated soil from the original site and pile it up, preparing for subsequent closed transportation to the cement kiln co-disposal. It corresponds to the carbon emission source in the dredging process of off-site carbon emission accounting. For the off-site dredging of contaminated soil, taking the parameters related to dredging in the indicator combination as the core, combined with the correlation logic between equipment energy consumption and carbon emission in the impact relationship, the energy consumption benchmark of the equipment itself is determined through the rated energy consumption of the dredging equipment, and the total energy consumption input is determined by matching the actual working hours. Then, combined with the carbon emission coefficient of fuel combustion, the energy consumption data is converted into the corresponding carbon emission in the dredging process. For the off-site transportation link, relying on the transportation parameters (transportation distance, fuel consumption per unit mileage of the transportation vehicle, loading capacity) in the indicator combination, combined with the direct impact relationship between these parameters and transportation carbon emission, the transportation-related parameters are converted into quantified carbon emission in the transportation process according to the energy consumption standard per ton-kilometer, which not only conforms to the energy consumption law of the actual transportation scenario but also ensures the accuracy of the accounting. The carbon emission in the dredging process and the carbon emission in the transportation process are integrated to determine the result of off-site carbon emission accounting, which completely covers the carbon emission links outside the contaminated soil disposal site. The results of in-site carbon emission accounting determined by the difference method and the proportional distribution method are summarized with the results of off-site carbon emission accounting, and possible cross-repetition items are excluded. Finally, the carbon emission accounting results of the entire process of contaminated soil disposal are formed. The overall carbon emission accounting covers the complete process of contaminated soil from dredging, transportation to in-kiln disposal,弥补了现有聚焦单一环节且核算范围不全面的缺陷,同时,为后续空间自相关分析、风险等级评估提供了完整且可靠的基础数据,保障了分析结果的准确性和可靠性。

[0037] 需要注意的是,原文中最后一句“弥补了现有聚焦单一环节且核算范围不全面的缺陷,同时,为后续空间自相关分析、风险等级评估提供了完整且可靠的基础数据,保障了分析结果的准确性和可靠性。”这部分内容在英文翻译中重复出现了,我按照原文进行了翻译,但可能存在表述不太准确的地方,你可以根据实际情况进行调整。In some embodiments of this application, when determining the spatial clustering pattern of carbon emissions in a target area based on spatial autocorrelation analysis algorithm and combined with carbon emission accounting, the process includes: associating the carbon emission accounting of the entire process of contaminated soil disposal in each spatial unit with geographic coordinates to construct a three-dimensional spatial dataset; determining the local Moran index based on the spatial autocorrelation analysis algorithm and the three-dimensional spatial dataset; obtaining the LISA map based on the local Moran index; and parsing the LISA map. If the attribute value of a spatial unit is greater than the attribute value threshold, and the attribute value of adjacent spatial units is greater than the attribute value threshold, then the spatial clustering pattern of carbon emissions is determined to be positive co-clustering; if the attribute value of a spatial unit is less than the attribute value threshold, and the attribute value of adjacent spatial units is less than the attribute value threshold, then the spatial clustering pattern of carbon emissions is determined to be positive correlation; if the attribute value of a spatial unit is greater than the attribute value threshold, and the attribute value of adjacent spatial units is less than the attribute value threshold, then the spatial clustering pattern of carbon emissions is determined to be negative correlation; and if the attribute value of a spatial unit is less than the attribute value threshold, and the attribute value of adjacent spatial units is greater than the attribute value threshold, then the spatial clustering pattern of carbon emissions is determined to be negative correlation.

[0038] Specifically, the carbon emission accounting results of the entire contaminated soil treatment process for each spatial unit are correlated one-to-one with the geographical coordinates of the corresponding spatial unit. This ensures that each carbon emission accounting result has a clear spatial location attribute, avoiding the risk of isolated analysis detached from actual geographical location. This constructs a three-dimensional spatial dataset containing spatial units, carbon emission accounting values, and geographical coordinate information. The three-dimensional spatial dataset retains the quantitative characteristics of carbon emissions while fully carrying spatial location information. Based on spatial autocorrelation analysis algorithms, the three-dimensional spatial dataset is processed to identify the spatial patterns of carbon emission data in each spatial unit and the carbon emission data of surrounding spatial units. Using GeoDa software, a LISA map is generated using spatial autocorrelation analysis algorithms. Spatial autocorrelation algorithms can explore the spatial distribution characteristics of a certain element and the degree of correlation between two elements, thereby determining the local Moran index. The local Moran index is a quantitative indicator measuring the degree of spatial correlation of carbon emissions within the target area, reflecting the spatial clustering pattern of high or low attribute values. The attribute value is the soil organic carbon content of each spatial unit. Spatial clustering patterns include four types: high-high, where the attribute value of the spatial unit is greater than the attribute value threshold, and the attribute values ​​of adjacent spatial units are greater than the attribute value threshold. The thresholds for carbon emission spatial clustering determine the following: Positive co-clustering (low-low): The attribute value of a spatial unit is less than the threshold, and the attribute values ​​of adjacent spatial units are also less than the threshold. Positive correlation (high-low): The attribute value of a spatial unit is greater than the threshold, and the attribute values ​​of adjacent spatial units are less than the threshold. Negative correlation (low-high): The attribute value of a spatial unit is less than the threshold, and the attribute values ​​of adjacent spatial units are greater than the threshold. While both high-low and low-high are negative correlations, the entities (spatial units) representing the high and low values ​​are completely different, resulting in two distinct spatial distribution patterns (isolated high values ​​or isolated low values). Spatial autocorrelation analysis algorithms and local Moran's indices define the spatial clustering patterns of carbon emissions, clearly revealing the spatial heterogeneity of carbon emissions in the target area, thereby improving the accuracy of emission reduction.

[0039] In some embodiments of this application, when generating a model carbon emission score based on the spatial clustering pattern of carbon emissions and the total carbon emission accounting based on the carbon emission model, the process includes: obtaining a carbon emission sample dataset and dividing the carbon emission sample dataset into a training set and a test set; determining model parameters based on grid search and establishing a random forest model; training the random forest model using the training set; substituting the test set into the trained random forest model and calculating the accuracy; when the accuracy is greater than or equal to the accuracy threshold, the trained random forest model is determined as the carbon emission model; otherwise, adjusting the number of decision trees in the trained random forest model and continuing training; substituting the spatial clustering pattern of carbon emissions and the total carbon emission accounting into the carbon emission model to determine the model carbon emission score.

[0040] In some embodiments of this application, when determining the carbon emission risk level of a target area based on the model carbon emission score, the method includes: setting a first model carbon emission score and a second model carbon emission score, wherein the first model carbon emission score is greater than the second model carbon emission score; when the model carbon emission score is greater than the first model carbon emission score, the carbon emission risk level of the target area is determined to be Level 1 risk; when the model carbon emission score is less than or equal to the first model carbon emission score and greater than or equal to the second model carbon emission score, the carbon emission risk level of the target area is determined to be Level 2 risk; and when the model carbon emission score is less than the second model carbon emission score, the carbon emission risk level of the target area is determined to be Level 3 risk. The emission reduction priority of Level 1 risk, Level 2 risk, and Level 3 risk decreases sequentially.

[0041] Specifically, the carbon emission sample dataset includes the full-process carbon emission accounting results for each spatial unit corresponding to historically contaminated soil, the geographic coordinate data of the corresponding spatial unit, the carbon emission spatial clustering characteristics of the spatial unit, the labeled carbon emission risk-related reference data (boundary definitions under internationally accepted carbon accounting standards (such as GHG Protocol, ISO 14064) and regional adaptation standards (such as the EU PEF method, domestic industry carbon emission accounting guidelines), emission factor data (such as IEA power upstream life cycle emission factors, industry emission factors from databases such as Ecoinvent), social carbon cost (SCC) data), etc. The carbon emission sample dataset provides comprehensive input features and validation basis for model training. The carbon emission sample dataset is divided into a training set and a test set in a 3:2 ratio. The training set is used to train the model, and the test set is used to verify the model's performance. A grid search method is used to traverse the parameter range, select model parameters, and build a random forest model. This avoids the subjectivity of manually setting model parameters. The random forest model is trained using the training set, allowing the model to learn the inherent logic of data such as the spatial clustering rules of carbon emissions and the full-process carbon emission accounting. After training, the test set is substituted into the trained model to determine the accuracy. The accuracy threshold is preferably 0.8. If the accuracy reaches or exceeds the accuracy threshold, it indicates that the model has reliable prediction ability, and it is determined to be a carbon emission model. If the accuracy threshold is not reached, the model structure is optimized by adjusting the number of decision trees, and the model is retrained until the accuracy threshold is reached or exceeded. Using the spatial clustering patterns of carbon emissions in the target area and the carbon emission accounting results of the entire process of contaminated soil treatment in all spatial units as inputs, and substituting them into the established carbon emission model, the model will output a carbon emission score that comprehensively reflects the carbon emission level and spatial distribution characteristics based on the logic learned during training. The model carbon emission score reflects the cumulative emission level of contaminated soil in the target area from excavation, transportation to in-kiln treatment. It includes emissions from soil organic carbon decomposition, carbonate decomposition, and fuel / electricity consumption within the site, as well as emissions from energy consumption of excavation equipment and transportation fuel consumption outside the site. Furthermore, if the carbon emissions in the target area are clustered, the corresponding model carbon emission score will also increase accordingly. The level of the model carbon emission score directly corresponds to the urgency and difficulty of emission reduction. The higher the model carbon emission score, the higher the absolute level of carbon emissions in the entire process or the emission intensity per unit treatment volume, and the greater the regional emission reduction pressure brought about by the clustering of high emission areas. Therefore, the degree of emission reduction required is also higher, thus providing data support for the development of targeted emission reduction strategies for the target area.

[0042] In summary, the beneficial effects of this invention are as follows: By comprehensively covering the entire process of contaminated soil excavation, transportation, and in-kiln disposal, and simultaneously incorporating the mechanism by which soil characteristics affect carbon emissions, it effectively avoids the problem of neglecting factors such as soil characteristics, transportation distance, and cement industry distribution. It comprehensively captures the contribution of each stage to carbon emissions, providing complete data support for accurate calculation of carbon emissions throughout the entire process. Based on association rules, it selects analytical indicators from three dimensions: soil characteristics, transportation parameters, and cement kiln operation. Based on dimensional correlation analysis, it clarifies the influencing relationships and indicator combinations. Furthermore, it accurately separates on-site carbon emission accounting from off-site carbon emission accounting using the difference method and proportional allocation method, ensuring the comprehensiveness and accuracy of carbon emission accounting. This approach avoids data bias caused by single-dimensional or one-sided calculations. By dividing the target area into spatial units of a geographic grid and combining it with spatial autocorrelation analysis algorithms, it accurately identifies the spatial clustering patterns of carbon emissions in the target area. This clearly presents the spatial distribution patterns of carbon emissions from contaminated soil, including clustering, randomness, and dispersion. It provides a spatial dimension basis for understanding the emission reduction potential of the target area. By generating a carbon emission score through a carbon emission model, it quantifies the carbon emission risk level and clarifies the emission reduction potential of different target areas. This provides a basis for formulating differentiated and precise emission reduction strategies, thereby comprehensively improving the reliability of carbon emission analysis for co-processing of contaminated soil in cement kilns (CKCS).

[0043] In another preferred embodiment based on the above embodiments, see [reference] Figure 2 As shown, this embodiment provides a comprehensive carbon emission analysis system for contaminated soil, used to apply a comprehensive carbon emission analysis method for contaminated soil, including: The data acquisition unit is configured to divide the target area into several spatial units according to a geographic grid, acquire the initial process data of the entire process of contaminated soil treatment in each spatial unit, perform preprocessing, and determine the target process data.

[0044] The analysis unit is configured to filter target process data based on association rules, determine analysis indicators in three dimensions: soil characteristics, transportation parameters, and cement kiln operation, and perform dimensional association analysis on each analysis indicator to determine the impact relationship and combination of each analysis indicator with carbon emissions.

[0045] The processing unit is configured to determine on-site and off-site carbon emission accounting based on the difference method and proportional allocation method using the influence relationship and index combination. Based on the on-site and off-site carbon emission accounting, it determines the carbon emission accounting of the entire process of contaminated soil disposal. Based on the spatial autocorrelation analysis algorithm and combined with the carbon emission accounting, it determines the spatial clustering pattern of carbon emissions in the target area.

[0046] The emission unit is configured to generate a model carbon emission score based on the spatial clustering pattern of carbon emissions and the accounting of all carbon emissions using a carbon emission model, and to determine the carbon emission risk level of the target area based on the model carbon emission score.

[0047] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A comprehensive analysis method for carbon emissions from contaminated soil, characterized in that, include: The target area is divided into several spatial units according to a geographic grid. Initial process data of the entire process of contaminated soil treatment in each spatial unit is obtained and preprocessed to determine the target process data. The target process data is filtered based on association rules to determine the analysis indicators in three dimensions: soil characteristics, transportation parameters, and cement kiln operation. Dimensional association analysis is then performed on each analysis indicator to determine the impact relationship between each analysis indicator and carbon emissions, as well as the combination of indicators. Based on the aforementioned influencing relationships and index combinations, the difference method and proportional allocation method are used to determine the on-site carbon emission accounting and off-site carbon emission accounting. Based on the on-site carbon emission accounting and off-site carbon emission accounting, the carbon emission accounting for the entire process of contaminated soil disposal is determined. Based on the spatial autocorrelation analysis algorithm and combined with the carbon emission accounting, the spatial clustering pattern of carbon emissions in the target area is determined. Based on the carbon emission model, the spatial clustering pattern of carbon emissions and the calculation of all carbon emissions are used to generate a model carbon emission score. Based on the model carbon emission score, the carbon emission risk level of the target area is determined.

2. The comprehensive analysis method for carbon emissions from contaminated soil according to claim 1, characterized in that, When acquiring and preprocessing the initial process data for the entire contaminated soil disposal process in each spatial unit to determine the target process data, the following steps are included: Soil composition data, transportation data, and disposal data of contaminated soil in each spatial unit are acquired. Before collecting the transportation data, the WGS-84 geographic coordinate system is uniformly used to preprocess the soil composition data and disposal data. The preprocessing includes data noise reduction and data standardization. The target process data is determined based on the transportation data and the preprocessed results. The soil composition data includes soil organic carbon content, soil chemical property parameters, and soil composition parameters; The transportation data includes transportation distance, load capacity, and transportation fuel consumption data; The processing data includes calcination temperature, residence time, raw material ratio, calcination fuel consumption, and electricity consumption data.

3. The comprehensive analysis method for carbon emissions from contaminated soil according to claim 2, characterized in that, When filtering the target process data based on association rules to determine the analytical indicators for three dimensions—soil characteristics, transportation parameters, and cement kiln operation—the following are included: The correlation degree between each candidate indicator in the target process data and carbon emissions is determined based on the association rule algorithm. The target process data is then reduced based on the correlation degree, and a three-level hierarchical indicator is constructed based on the reduction result. The first-level indicators of the three-level hierarchical indicator are soil characteristics, transportation parameters and cement kiln operation; the second-level indicators are soil composition, transportation efficiency and kiln operation status; and the third-level indicators are specific values.

4. The comprehensive analysis method for carbon emissions from contaminated soil according to claim 3, characterized in that, When conducting dimensional correlation analysis on various analytical indicators to determine the impact relationship and combination of indicators with carbon emissions, the following should be included: Pearson correlation analysis and Spearman rank correlation analysis were used to analyze the soil properties and transport parameters to determine the correlation coefficients between the soil properties and transport parameters and carbon emissions. The absolute values ​​of the correlation coefficients were then sorted in descending order, and the influence relationship was determined based on the sorting results. A coupling coordination analysis was conducted on the cross-analysis indicators of cement kiln operation and soil characteristics to determine the coupling coordination degree between the two types of analysis indicators, and the indicator combination was determined based on the coupling coordination degree.

5. The comprehensive analysis method for carbon emissions from contaminated soil according to claim 4, characterized in that, When determining on-site and off-site carbon emission accounting based on the aforementioned influencing relationships and indicator combinations using the difference method and proportional allocation method, and when determining the carbon emission accounting for the entire contaminated soil disposal process based on the on-site and off-site carbon emission accounting, the following are included: Based on the aforementioned influence relationships and indicator combinations, the difference method is used to compare the carbon emission data of cement kiln clinker production before and after the addition of the contaminated soil, determine the difference between them, and determine the changes in carbonate decomposition emissions caused by soil organic carbon decomposition and raw material ratio adjustment. Based on the aforementioned influence relationships and indicator combinations, the proportional allocation method is used to determine the allocation of indirect carbon emissions corresponding to fossil fuel combustion and electricity consumption according to the proportion of the contaminated soil in cement production raw materials. The carbon emission data, changes in carbonate decomposition emissions, and indirect carbon emissions are determined as on-site carbon emission accounting.

6. The comprehensive analysis method for carbon emissions from contaminated soil according to claim 5, characterized in that, When determining on-site and off-site carbon emission accounting using the difference method and proportional allocation method based on the aforementioned influence relationships and indicator combinations, and when determining the carbon emission accounting for the entire contaminated soil disposal process based on the on-site and off-site carbon emission accounting, the method further includes: Based on the rated energy consumption and working time of the excavation equipment and combined with the carbon emission coefficient of fuel combustion, the carbon emissions of the excavation process are determined. Based on the transportation distance, fuel consumption per unit mileage of the transportation vehicle, and load capacity, the carbon emissions of the transportation process are determined according to the ton-kilometer energy consumption standard. The off-site carbon emission accounting is determined based on the carbon emissions of the excavation process and the carbon emissions of the transportation process. The on-site carbon emission accounting and off-site carbon emission accounting are summarized to determine the carbon emission accounting for the entire process of contaminated soil disposal.

7. The comprehensive analysis method for carbon emissions from contaminated soil according to claim 6, characterized in that, When determining the spatial clustering pattern of carbon emissions in the target region based on the spatial autocorrelation analysis algorithm and in conjunction with the aforementioned carbon emission accounting, the following steps are included: The carbon emission accounting of the entire process of contaminated soil disposal in each spatial unit is linked with the geographic coordinates to construct a three-dimensional spatial dataset. Based on the spatial autocorrelation analysis algorithm and the three-dimensional spatial dataset, the local Moran index is determined. Based on the local Moran index, the LISA map is obtained and the LISA map is parsed. If the attribute value of a spatial unit is greater than the attribute value threshold, and the attribute value of adjacent spatial units is also greater than the attribute value threshold, then the spatial clustering pattern of carbon emissions is determined to be positive synergistic clustering. If the attribute value of a spatial unit is less than the attribute value threshold, and the attribute value of adjacent spatial units is also less than the attribute value threshold, then the spatial clustering pattern of carbon emissions is determined to be positively correlated. If the attribute value of a spatial unit is greater than the attribute value threshold, and the attribute value of adjacent spatial units is less than the attribute value threshold, then the spatial clustering pattern of carbon emissions is determined to be negatively correlated. If the attribute value of a spatial unit is less than the attribute value threshold, and the attribute value of an adjacent spatial unit is greater than the attribute value threshold, then the spatial clustering pattern of carbon emissions is determined to be a negative correlation.

8. The comprehensive analysis method for carbon emissions from contaminated soil according to claim 7, characterized in that, When generating a carbon emission score based on the spatial clustering pattern of carbon emissions and the total carbon emission accounting based on the carbon emission model, the following are included: Obtain a carbon emission sample dataset, divide the carbon emission sample dataset into a training set and a test set, determine the model parameters based on grid search, and build a random forest model; The training set is used to train the random forest model. The test set is substituted into the trained random forest model and the accuracy is calculated. When the accuracy is greater than or equal to the accuracy threshold, the trained random forest model is determined as the carbon emission model. Otherwise, the number of decision trees in the trained random forest model is adjusted and training continues. The carbon emission spatial clustering pattern and the total carbon emission accounting are substituted into the carbon emission model to determine the carbon emission score of the model.

9. The comprehensive analysis method for carbon emissions from contaminated soil according to claim 8, characterized in that, When determining the carbon emission risk level of the target area based on the carbon emission score of the model, the following are included: Set a first model carbon emission score and a second model carbon emission score, with the first model carbon emission score being greater than the second model carbon emission score; When the carbon emission score of the model is greater than the carbon emission score of the first model, the carbon emission risk level of the target area is determined to be Level 1 risk. When the carbon emission score of the model is less than or equal to the carbon emission score of the first model and greater than or equal to the carbon emission score of the second model, the carbon emission risk level of the target area is determined to be Level II risk. When the carbon emission score of the model is less than the carbon emission score of the second model, the carbon emission risk level of the target area is determined to be Level III risk. The emission reduction priorities for Level 1, Level 2, and Level 3 risks decrease sequentially.

10. A comprehensive carbon emission analysis system for contaminated soil, used to apply the comprehensive carbon emission analysis method for contaminated soil as described in any one of claims 1-9, characterized in that, include: The data acquisition unit is configured to divide the target area into several spatial units according to a geographic grid, acquire the initial process data of the entire process of contaminated soil treatment in each spatial unit and perform preprocessing to determine the target process data; The analysis unit is configured to filter the target process data based on association rules, determine the analysis indicators in three dimensions: soil characteristics, transportation parameters and cement kiln operation, and perform dimensional association analysis on each analysis indicator to determine the impact relationship between each analysis indicator and carbon emissions and the combination of indicators. The processing unit is configured to determine the on-site carbon emission accounting and off-site carbon emission accounting based on the influence relationship and index combination using the difference method and the proportional allocation method; determine the carbon emission accounting of the entire process of contaminated soil disposal based on the on-site carbon emission accounting and off-site carbon emission accounting; and determine the spatial clustering pattern of carbon emissions in the target area based on the spatial autocorrelation analysis algorithm and in combination with the carbon emission accounting. The emission unit is configured to generate a model carbon emission score based on the spatial clustering pattern of carbon emissions and the accounting of all carbon emissions using a carbon emission model, and to determine the carbon emission risk level of the target area based on the model carbon emission score.

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