Dredging waste slag resource utilization potential assessment method based on layering characteristics

Abstract

The invention provides a dredging waste slag resource utilization potential assessment method based on layering characteristics, and relates to the technical field of dredging waste slag analysis. The method comprises the following steps: acquiring multiple groups of historical dredging analysis data about dredging waste slag, and constructing a sampling characteristic parameter matrix corresponding to each group of dredging waste slag sampling data; constructing a plurality of physicochemical characteristic parameter combinations and carrying out coupling effect analysis to construct a first coupling effect matrix corresponding to each group of dredging waste slag sampling data; performing feature sparsity analysis and feature stability analysis on each type of coupling feature parameters in the plurality of first coupling effect matrixes to generate a second coupling effect matrix corresponding to each first coupling effect matrix; and after a sample data set is constructed and a dredging waste slag resource evaluation model is obtained through training, dredging waste slag resource evaluation is carried out based on the dredging waste slag resource evaluation model. According to the method, the accuracy and comprehensiveness of dredging waste slag resource evaluation are improved.

Description

Technical Field

[0001] The invention relates to the technical field of dredging waste analysis, and in particular to a method for evaluating the resource utilization potential of dredging waste based on stratification characteristics. Background Art

[0002] With the continuous advancement of water conservancy projects, port construction, and waterway dredging, dredging operations have generated a large amount of dredging waste. Dredging waste is often regarded as waste when it is not treated. Random dumping may cause environmental pollution and affect the water ecosystem. However, dredging waste contains rich minerals, organic matter and other usable resources. Through scientific and reasonable evaluation and utilization, it can be converted into road foundation materials, building fillers, land conditioners, etc., thereby realizing resource utilization and promoting the recycling of dredging waste. How to scientifically and effectively evaluate the resource utilization potential of dredging waste is an important topic in current engineering practice and environmental protection fields.

[0003] Some existing technologies simply conduct resource assessment and analysis based on the physical and chemical properties of dredged waste, ignoring the coupling relationship between different properties. Since the composition of dredged waste has complex spatial distribution and stratification characteristics, and there are complex interactions between these characteristics, only surface analysis of characteristic parameters may lead to large deviations in the assessment results and fail to fully reveal the resource potential of dredged waste. Dredged waste has obvious stratification characteristics, and the physical and chemical properties at different depths vary greatly. It is difficult to reflect the distribution pattern of dredged waste in the vertical direction by simply using overall sampling or independent characteristic analysis. This makes it impossible to accurately assess the utilization value of dredged waste at different depths, thereby reducing the accuracy of the assessment. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a method for evaluating the resource utilization potential of dredged slag based on hierarchical characteristics. The method analyzes and mines the data in dredging operations through multi-dimensional feature coupling relationship analysis, feature selection and optimization strategies, thereby overcoming the problems of insufficient multi-dimensional feature analysis capabilities and low model prediction accuracy in the prior art, and achieving high-precision evaluation of the resource utilization potential of dredged slag.

[0005] The present invention provides a method for evaluating the resource utilization potential of dredged waste based on stratification characteristics, comprising: Acquire multiple groups of historical dredging analysis data on dredged waste, the historical dredging analysis data including dredged waste sampling data and dredging resource assessment data, the dredged waste sampling data including stratified characteristic sampling data of multiple sampling points; Extract multiple physical characteristic parameter vectors and chemical parameter characteristic vectors from each set of dredged slag sampling data, and construct a sampling characteristic parameter matrix corresponding to each set of dredged slag sampling data; Constructing multiple physical and chemical characteristic parameter combinations, performing coupling effect analysis on each physical and chemical characteristic parameter combination, and constructing a first coupling effect matrix corresponding to each group of dredged waste sampling data, including calculating characteristic coupling parameters and coupling effect factors of each physical and chemical characteristic parameter combination according to multiple parameter characteristic vectors, determining coupling characteristic parameters of each physical and chemical characteristic parameter combination according to the multiple characteristic coupling parameters and coupling effect factors, and constructing a first coupling effect matrix containing multiple coupling characteristic parameters; Performing feature sparsity analysis and feature stability analysis on each type of coupling feature parameters in a plurality of first coupling effect matrices, calculating feature sparsity parameters and feature stability parameters of each type of coupling feature parameters, performing feature screening on each first coupling effect matrix according to the feature sparsity parameters and feature stability parameters, and generating a second coupling effect matrix corresponding to each first coupling effect matrix; Based on the second coupling effect matrix, cluster analysis is performed on multiple groups of dredged waste sampling data to generate coupled classification labels for each group of dredged waste sampling data. Multiple coupled classification labels are added to multiple groups of historical dredging analysis data to construct a sample data set for training a dredged waste resource assessment model. After the dredged waste resource assessment model is obtained through training with the sample data set, dredged waste resource assessment is performed based on the dredged waste resource assessment model.

[0006] Preferably, a coupling effect analysis is performed on each combination of physical and chemical characteristic parameters to construct a first coupling effect matrix corresponding to each set of dredged waste sampling data, including: The physicochemical characteristic covariance is calculated based on the physical characteristic parameter vector and the chemical parameter characteristic vector corresponding to the physicochemical characteristic parameter combination, the standard deviation of the physical characteristic parameter vector and the chemical parameter characteristic vector are calculated respectively, and the characteristic coupling parameter of the physicochemical characteristic parameter combination is calculated based on the physicochemical characteristic covariance, the standard deviation of the physical characteristic parameter vector and the chemical parameter characteristic vector; Calculate the mutual information between the physical characteristic parameter vector and the chemical parameter characteristic vector corresponding to the physical and chemical characteristic parameter combination, calculate the information entropy of the physical characteristic parameter vector and the chemical parameter characteristic vector respectively, and calculate the coupling effect factor of the physical and chemical characteristic parameter combination according to the mutual information, the information entropy of the physical characteristic parameter vector and the chemical parameter characteristic vector; The coupling characteristic parameters of the physical and chemical characteristic parameter combination are calculated using the following formula:

[0007] In the formula, For the The physical characteristic parameters and The coupled characteristic parameters of the physical and chemical characteristic parameter combination composed of chemical characteristic parameters, is the coupling effect factor of the physical and chemical characteristic parameter combination, is the physicochemical characteristic covariance of the physicochemical characteristic parameter combination, , are the standard deviations of the physical characteristic parameter vector and chemical parameter characteristic vector corresponding to the physical and chemical characteristic parameter combination, is the adjustment coefficient; The first coupling effect matrix of each group of dredged slag sampling data is constructed according to the coupling characteristic parameters corresponding to the respective combinations of multiple physical and chemical characteristic parameters.

[0008] Preferably, the calculation of the characteristic sparse parameters and characteristic stable parameters of the coupling characteristic parameters includes: The characteristic sparse parameters of each type of coupling characteristic parameters are calculated according to the multiple first coupling effect matrices, wherein for any type of coupling characteristic parameters, the characteristic sparse parameters are calculated as follows:

[0009] In the formula, is the characteristic sparse parameter of this type of coupling characteristic parameter, is the indicator function, , The characteristic parameters of this type of coupling are The value of the first coupling effect matrix, is the number of the first coupling effect matrix; The coupling characteristic vectors of each type of coupling characteristic parameters are extracted according to multiple first coupling effect matrices, the Kendall correlation coefficients between any two types of coupling characteristic parameters are calculated according to the multiple coupling characteristic vectors, and the average value of the Kendall correlation coefficients between each type of coupling characteristic parameters and the remaining multiple types of coupling characteristic parameters is calculated and recorded as the characteristic stability parameter of each type of coupling characteristic parameter.

[0010] Preferably, feature screening is performed on each first coupling effect matrix according to the feature sparsity parameter and the feature stability parameter to generate a second coupling effect matrix corresponding to each first coupling effect matrix, including: The characteristic screening parameters of each type of coupling characteristic parameters are calculated based on the characteristic sparsity parameters and the characteristic stability parameters, where:

[0011] In the formula, is the feature screening parameter for the coupled feature parameters, is the characteristic stability parameter of the coupled characteristic parameter, , are the weight parameters of feature sparsity parameter and feature stability parameter respectively; Based on the feature screening threshold, multiple coupling feature parameters whose feature screening parameters are smaller than the feature screening threshold in each first coupling effect matrix are removed to generate a second coupling effect matrix corresponding to each first coupling effect matrix.

[0012] Preferably, cluster analysis is performed on multiple groups of dredged slag sampling data based on the second coupling effect matrix to generate coupling classification labels for each group of dredged slag sampling data, including: Determine frequency scores corresponding to multiple types of coupling feature parameters in multiple second coupling effect matrices, determine multiple target coupling feature parameters in the multiple second coupling effect matrices based on the frequency scores and feature screening parameters, and construct a feature reference template based on the multiple target coupling feature parameters; Performing feature reconstruction processing on a plurality of second coupling effect matrices based on a feature reference template to generate a third coupling effect matrix corresponding to each second coupling effect matrix; Based on multiple third coupling effect matrices, cluster analysis is performed on multiple groups of dredged slag sampling data to generate coupling classification labels for each group of dredged slag sampling data.

[0013] Preferably, it is characterized in that, based on the characteristic reference template, a characteristic reconstruction process is performed on a plurality of second coupling effect matrices, comprising: Matching the coupling characteristic parameters existing in each second coupling effect matrix with the target coupling characteristics in the characteristic parameter template, retaining the coupling characteristic parameters that successfully match the target coupling characteristics in the characteristic parameter template, and removing the coupling characteristic parameters that fail to match the target coupling characteristics in the characteristic parameter template; The coupling feature parameters that exist in the feature reference template but are missing in the second coupling effect matrix are marked as missing, and the coupling feature parameters with missing marks in each second coupling effect matrix are completed based on the mean filling method to complete the feature reconstruction of each second coupling effect matrix and generate a third coupling effect matrix corresponding to each second coupling effect matrix.

[0014] Preferably, constructing a sample data set for training a dredging waste resource assessment model also includes: Based on the dredging resource assessment data in the historical dredging analysis data, multiple resource assessment labels are determined, and the coupling classification labels of each group of historical dredging analysis data are added to the corresponding dredging spoil sampling data, and each group of dredging spoil sampling data is associated with the corresponding resource assessment label and the coupling classification label to construct a sample data set; Taking multiple groups of dredged slag sampling data containing coupled classification labels in the sample data set as the input of the model, and taking multiple resource evaluation labels in the sample data set as the training target of the model, a dredged slag resource evaluation model is trained, wherein the dredged slag resource evaluation model is an LSTM model.

[0015] Preferably, a DBSCAN clustering algorithm is used to perform cluster analysis on multiple groups of dredging waste sampling data based on multiple third coupling effect matrices.

[0016] The present invention has the following beneficial effects: The present invention introduces the coupling effect analysis between physical properties and chemical properties, constructs a coupling feature matrix, reveals the complex interactive relationship between different combinations of physical properties and chemical properties, and screens out stable and representative features in each sample through the sparsity and stability analysis of the coupling features. Cluster analysis is performed in combination with the coupling features in multiple groups of historical data, and coupling classification labels are generated and merged with the original features to construct a sample data set containing coupling feature labels. The sample data set is used to train a dredged slag resource assessment model, so that the model can learn the coupling relationship between physical properties and chemical properties and its impact on resource utilization potential, thereby improving the model's ability to understand different dredged slag samples and enhancing the ability to capture complex feature relationships, achieving high-precision prediction of the resource utilization potential of dredged slag, and effectively improving the accuracy and comprehensiveness of dredged slag resource assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention discloses a flow chart of a method for evaluating the potential for resource utilization of dredged waste based on stratification characteristics disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make those skilled in the art better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] like Figure 1 As shown, a method for evaluating the resource utilization potential of dredged waste based on stratification characteristics disclosed in an embodiment of the present invention comprises the following steps: Step S10, obtaining multiple groups of historical dredging analysis data on dredged waste, the historical dredging analysis data including dredged waste sampling data and dredging resource assessment data, extracting multiple physical characteristic parameter vectors and chemical parameter characteristic vectors from each group of dredged waste sampling data, and constructing a sampling characteristic parameter matrix corresponding to each group of dredged waste sampling data.

[0020] In this embodiment, the historical dredging analysis data may be data related to dredged waste collected during dredging operations, including sampling data of dredged waste and resource assessment data of dredged waste. In order to evaluate the resource utilization potential of dredged waste, corresponding stratified characteristic sampling data are often collected from multiple sampling points based on stratified sampling technology, which includes physical and chemical property data of dredged waste at different sampling depths, that is, for example, physical properties include particle size distribution, water content, density, etc., and chemical properties include organic matter content, heavy metal content, pH value, etc. The resource utilization potential of dredged waste can be evaluated through dredged waste sampling data, and the dredging resource assessment data can be the performance of each group of dredged waste in actual utilization, such as strength, permeability, etc., or the resource utilization potential obtained by evaluating experimental results, such as applicability as a building material, the effectiveness of a soil conditioner, etc. The historical dredging analysis data contains rich characteristic information, which provides a good data basis for the subsequent evaluation of the resource utilization potential of dredged waste.

[0021] After obtaining multiple sets of historical dredging analysis data on dredged spoil, the physical characteristic parameter vectors and chemical characteristic parameter vectors of each sampling point at different sampling depths are extracted from each set of dredged spoil sampling data. Each parameter vector represents the value of a characteristic such as density, water content, heavy metal content, etc. at different sampling points and depths. These physical and chemical characteristic parameter vectors are arranged according to the distribution characteristics of each sampling point and depth, and the sampling characteristic parameter matrix corresponding to each set of dredged spoil sampling data is constructed.

[0022] Step S20: construct multiple physical and chemical characteristic parameter combinations, perform coupling effect analysis on each physical and chemical characteristic parameter combination, and construct a first coupling effect matrix corresponding to each group of dredged waste sampling data.

[0023] In this embodiment, the combination of physical and chemical characteristic parameters refers to the combination of different physical characteristics and chemical characteristics in pairs to form several characteristic pairs. For example, the combination can be "particle size distribution-organic matter content", "density-heavy metal content", etc. The construction of the combination of physical and chemical characteristic parameters helps to reveal the mutual influence between each characteristic pair, thereby providing a basis for the subsequent coupling effect analysis. After determining multiple physical and chemical characteristic parameter combinations, a coupling effect analysis is performed on each physical and chemical characteristic parameter combination, and the degree of mutual influence between each physical and chemical characteristic parameter combination is quantified by data analysis techniques such as correlation analysis and interaction effect analysis. Specifically, the characteristic coupling parameters and coupling effect factors of each physical and chemical characteristic parameter combination are calculated according to multiple parameter characteristic vectors, and the coupling characteristic parameters of each physical and chemical characteristic parameter combination are determined according to multiple characteristic coupling parameters and coupling effect factors. Then, the first coupling effect matrix corresponding to each group of dredged slag sampling data is constructed by multiple coupling characteristic parameters representing the coupling relationship between different characteristic pairs. The matrix quantitatively describes the characteristic coupling effect of dredged slag, expresses the coupling relationship of each physical and chemical characteristic combination in each group of dredged slag sampling data, and provides a data basis for subsequent characteristic analysis and model training.

[0024] Step S30, performing feature sparsity analysis and feature stability analysis on each type of coupling feature parameters in the plurality of first coupling effect matrices, and calculating feature sparsity parameters and feature stability parameters of each type of coupling feature parameters.

[0025] In this embodiment, it is considered that some coupling features may not show significant coupling relationships in some samples, and the coupling feature relationship between different samples may be unstable, for example, strong coupling relationships are shown in some samples, while weak coupling relationships are shown in other samples. In order to avoid these factors from having a greater impact on the generalization ability and prediction effect of the model, the first coupling effect matrix is ​​further analyzed, and the coupling feature parameters in multiple first coupling effect matrices are specifically subjected to feature sparsity analysis and feature stability analysis. Feature sparsity and feature stability are important indicators for measuring feature parameters, which are used to evaluate the distribution and performance consistency of features in samples, wherein feature sparsity is used to measure whether a certain coupling feature is widely present in all samples, and feature stability is used to evaluate the performance consistency of the same coupling feature in different samples. By performing feature sparsity analysis and feature stability analysis on each type of coupling feature parameters in multiple first coupling effect matrices, the feature sparsity parameters and feature stability parameters of each type of coupling feature parameters are calculated, which are important indicators for measuring the performance of each coupling feature in the sample, and provide a quantitative basis for subsequent feature screening.

[0026] Step S40: Perform feature screening on each first coupling effect matrix according to the feature sparsity parameter and the feature stability parameter to generate a second coupling effect matrix corresponding to each first coupling effect matrix.

[0027] In this embodiment, the purpose of feature screening for each first coupling effect matrix is ​​to remove features with high sparsity, that is, features that are missing or invalid in most samples, and features with poor stability, that is, features that are inconsistent in different samples, so as to retain a more representative and stable feature set. After feature screening, each first coupling effect matrix will only retain the screened features to generate the corresponding second coupling effect matrix. The second coupling effect matrix is ​​a feature matrix that has been screened and optimized, and can better reflect the key features of the potential for resource utilization of dredged slag.

[0028] Step S50: performing cluster analysis on multiple groups of dredged waste sampling data based on the second coupling effect matrix, generating coupling classification labels for each group of dredged waste sampling data, adding multiple coupling classification labels to multiple groups of historical dredging analysis data, and constructing a sample data set for training a dredged waste resource assessment model.

[0029] In this embodiment, cluster analysis is performed on all dredged waste sampling data based on the second coupling effect matrix generated previously. The purpose of cluster analysis is to group dredged waste samples with similar coupling characteristic parameters, thereby revealing the differences in resource utilization potential of different types of dredged waste. According to the clustering results, the coupling classification label of each dredged waste sample can be determined, such as "high resource utilization potential", "medium resource utilization potential", "low resource utilization potential", etc., or more detailed labels, such as more specific resource utilization potential levels of dredged waste in different application scenarios. These coupling classification labels are added to multiple groups of historical dredging analysis data to form a complete sample data set containing coupling characteristic parameters and coupling classification labels.

[0030] Step S60: After obtaining a dredging waste resource assessment model through training of the sample data set, a dredging waste resource assessment is performed based on the dredging waste resource assessment model.

[0031] In this embodiment, the sample data set containing historical dredging analysis data and coupled classification labels generated above is used to train the dredging waste resource assessment model. The trained dredging waste resource assessment model can predict the resource utilization potential of dredging waste based on the dredging waste sampling data that needs to be input and analyzed, and realize the automatic assessment of the resource utilization value of dredging waste. In this way, the accuracy of the resource utilization assessment of dredging waste can be improved.

[0032] It is worth noting that some existing technologies extract the relationship between physical and chemical property data and its actual utilization effect through multiple actual application records in historical data, and construct preliminary resource utilization value criteria. For example, given the sampling and testing data of a pile of dredged waste and its resource utilization results in actual scenarios, a similar rule is constructed: "When the particle size is greater than a certain parameter and the heavy metal content is less than a certain parameter, the sample's utilization value is at a certain level."

[0033] However, this process fails to consider the complex interactions between characteristics. This single or few characteristic analysis method makes it difficult to reveal the coupling effects between different characteristics, resulting in the inability of the trained model for resource assessment to effectively capture the complex relationships between characteristics. The interaction between physical and chemical properties plays an important role in the resource utilization potential of dredged waste. The analysis and modeling of simple and primitive physical or chemical properties ignores the complex coupling relationships that may exist between characteristics. This makes it difficult for the model to explore the deep laws in the data during the learning process, resulting in poor model prediction results.

[0034] The embodiment of the present invention provides a method for evaluating the resource utilization potential of dredged waste based on layered characteristics, which introduces coupling characteristics, that is, constructs a coupling feature matrix based on the coupling effect between physical characteristics and chemical characteristics. These coupling feature matrices can make the complex relationship between characteristics explicit, and reveal the mutual influence between different combinations of physical and chemical characteristics through coupling effect analysis. The model can capture the nonlinear relationship between characteristics and learn the influence of these feature combinations on the resource utilization potential, thereby improving the learning ability of the model.

[0035] For example, by introducing the coupling features of particle size distribution and organic matter content, heavy metal content and pH value, the model can understand the changing trends of these combined characteristics at different depths and sampling points, and establish a stronger association with the actual resource utilization value label. The introduction of this coupling feature enables the model to learn complex relationships in the data at a higher dimension, thereby improving the performance of the model. By introducing feature coupling effects and feature selection strategies, the model can effectively capture the feature differences of dredged waste in different engineering scenarios and different application environments, improving the applicability and versatility of the model to multi-scenario and multi-type dredged waste samples, and can output higher-precision resource assessment results in more application scenarios.

[0036] In step S20, a coupling effect analysis is performed on each combination of physical and chemical characteristic parameters to construct a first coupling effect matrix corresponding to each set of dredged waste sampling data, specifically including: The physical characteristic parameter vector and the chemical parameter characteristic vector corresponding to the physical and chemical characteristic parameter combination are analyzed, and the characteristic coupling parameters of the physical and chemical characteristic parameter combination are calculated.

[0037] In this embodiment, the Pearson correlation coefficient between the physical property parameter vector and the chemical parameter characteristic vector is used as the characteristic coupling parameter of the physicochemical property parameter combination. Specifically, the physicochemical characteristic covariance of the physicochemical characteristic parameter combination, that is, the covariance between the physical property parameter vector and the chemical parameter characteristic vector, is calculated based on the physical property parameter vector and the chemical parameter characteristic vector, and the standard deviations of the physical property parameter vector and the chemical parameter characteristic vector are calculated respectively. Then, the Pearson correlation coefficient between the physical property parameter vector and the chemical parameter characteristic vector is calculated based on the physicochemical characteristic covariance and the standard deviation of the physical property parameter vector and the chemical parameter characteristic vector to obtain the characteristic coupling parameter of the physicochemical property parameter combination.

[0038] The physical characteristic parameter vector and chemical parameter characteristic vector corresponding to the physical and chemical characteristic parameter combination are analyzed, and the coupling effect factor of the physical and chemical characteristic parameter combination is calculated.

[0039] In this embodiment, the coupling effect factor of the physicochemical characteristic parameter combination is determined based on the mutual information between the physical characteristic parameter vector and the chemical parameter characteristic vector. Specifically, the mutual information between the physical characteristic parameter vector and the chemical parameter characteristic vector corresponding to the physicochemical characteristic parameter combination is calculated, and the information entropy of the physical characteristic parameter vector and the chemical parameter characteristic vector are calculated respectively. Then, the coupling effect factor of the physicochemical characteristic parameter combination is calculated according to the mutual information, the information entropy of the physical characteristic parameter vector and the chemical parameter characteristic vector. Among them, the coupling effect factor is the ratio of the mutual information to the sum of the information entropy of the physical characteristic parameter vector and the chemical parameter characteristic vector. The coupling effect factor can measure the intensity and complexity of the interaction between physical properties and chemical properties.

[0040] Finally, the coupling characteristic parameters of the combination of physical and chemical characteristic parameters are calculated by characteristic coupling parameters and coupling effect factors. The coupling characteristic parameters can comprehensively quantify the complex interaction between physical and chemical properties. Through the coupling characteristic parameters, the distribution law of the characteristic combination at different sampling points and depths can be better described, revealing the nonlinear relationship between different characteristic pairs and their impact on resource utilization potential.

[0041] In this embodiment, the coupling characteristic parameters of the physical and chemical characteristic parameter combination are calculated using the following formula:

[0042] In the formula, For the The physical characteristic parameters and The coupled characteristic parameters of the physical and chemical characteristic parameter combination composed of chemical characteristic parameters, is the coupling effect factor of the physical and chemical characteristic parameter combination, is the physicochemical characteristic covariance of the physicochemical characteristic parameter combination, , are the standard deviations of the physical characteristic parameter vector and chemical parameter characteristic vector corresponding to the physical and chemical characteristic parameter combination, is the adjustment coefficient.

[0043] After obtaining the coupling characteristic parameters of each combination of physical and chemical characteristic parameters through the above calculation, the first coupling effect matrix corresponding to each set of dredged spoil sampling data is constructed based on multiple coupling characteristic parameters. Each element of the first coupling effect matrix represents the coupling effect intensity of one of the physical and chemical characteristic combinations on the dredged spoil sampling data, thereby comprehensively describing the interaction between the physical and chemical properties of each set of dredged spoil sampling data as a whole, revealing the complex relationship between the physical and chemical properties in dredged spoil and its impact on the potential for resource utilization.

[0044] In step 30, the calculation of the characteristic sparse parameters and characteristic stable parameters of the coupling characteristic parameters specifically includes: The feature sparsity parameter of each type of coupling feature parameter is calculated based on multiple first coupling effect matrices, which is used to measure the widespread existence of a certain type of coupling feature in all samples. The feature sparsity parameter can quantify whether the coupling feature exists in most samples. If a coupling feature is zero or missing in most samples, the feature may not be representative or valuable.

[0045] In this embodiment, for any type of coupling characteristic parameters, the characteristic sparse parameters are calculated as follows:

[0046] In the formula, is the characteristic sparse parameter of this type of coupling characteristic parameter, is the indicator function, , The characteristic parameters of this type of coupling are The value of the first coupling effect matrix, is the number of the first coupling effect matrix.

[0047] The feature sparsity parameter ranges from 0 to 1. The closer the value is to 1, the more likely the feature is to be present in most samples, i.e., the sparsity is low and it is a representative feature. The closer the value is to 0, the less likely the feature is to be present in most samples, i.e., the sparsity is high and it may be an invalid or unstable feature. By calculating the feature sparsity parameter, we can effectively filter out features that are missing or invalid in most samples and reduce the interference of invalid features on the model.

[0048] The consistency of each type of coupling characteristic parameter in different samples, that is, different first coupling effect matrices, is further analyzed, and the performance of a certain type of coupling characteristic in all samples is measured by calculating the characteristic stability parameter. Specifically, the coupling characteristic vector of each type of coupling characteristic parameter is extracted according to multiple first coupling effect matrices, the Kendall correlation coefficient between any two types of coupling characteristic parameters is calculated according to multiple coupling characteristic vectors, and the average value of the Kendall correlation coefficient between each type of coupling characteristic parameter and the remaining multiple types of coupling characteristic parameters is calculated and recorded as the characteristic stability parameter of each type of coupling characteristic parameter.

[0049] In this embodiment, the coupling feature vector of each type of coupling feature extracted from multiple first coupling effect matrices contains the value of the feature in different samples, and then the correlation between any two types of coupling feature parameters is calculated. In this implementation, the correlation between any two types of coupling feature parameters in different samples is measured based on the Kendall correlation coefficient, and then the Kendall correlation coefficient between each type of coupling feature parameter and the other types of coupling feature parameters is averaged to obtain the feature stability parameter of each type of coupling feature parameter. The feature stability parameter can effectively evaluate the consistency of a coupling feature in multiple samples and screen out stable and reliable features in different dredged waste samples. In this way, features that fluctuate greatly and are unstable in different samples can be filtered out.

[0050] In step S40, feature screening is performed on each first coupling effect matrix according to the feature sparsity parameter and the feature stability parameter to generate a second coupling effect matrix corresponding to each first coupling effect matrix, specifically including: The feature screening parameters of each type of coupling feature parameters are calculated based on the feature sparsity parameters and feature stability parameters, which are used to measure the effectiveness and performance stability of the feature in all samples.

[0051] Specific:

[0052] In the formula, is the feature screening parameter for the coupled feature parameters, is the characteristic stability parameter of the coupling characteristic parameter, indicating the consistency of a coupling characteristic in different samples. , They are the weight parameters of the feature sparsity parameter and the feature stability parameter, respectively, which are used to control the influence weight of the two on the feature screening parameter.

[0053] The feature screening parameter combines the information of the feature sparsity parameter and the feature stability parameter, and is a comprehensive indicator to measure whether the coupling feature should be retained. The higher the value, the lower the sparsity of the feature (existing in most samples) and the better the stability (consistent performance in different samples), which is a high-quality coupling feature. By setting the threshold of the feature screening parameter, features with too high sparsity or poor stability can be effectively filtered out, and only representative and stable coupling features are retained. Based on the feature screening threshold, multiple coupling feature parameters whose feature screening parameters are less than the feature screening threshold in each first coupling effect matrix are removed to generate the second coupling effect matrix corresponding to each first coupling effect matrix. Compared with the first coupling effect matrix, the second coupling effect matrix is ​​more refined, and only contains the key coupling features that have been screened and optimized, which reduces the noise interference caused by feature redundancy and can be better used for model training.

[0054] In step S50, cluster analysis is performed on multiple groups of dredged slag sampling data based on the second coupling effect matrix to generate coupling classification labels for each group of dredged slag sampling data, including: Determine the frequency scores corresponding to the multiple types of coupling feature parameters in the multiple second coupling effect matrices, determine the multiple target coupling feature parameters in the multiple second coupling effect matrices based on the frequency scores and the feature screening parameters, and construct a feature reference template based on the multiple target coupling feature parameters.

[0055] In this embodiment, considering that in the first coupling effect matrix corresponding to different dredging waste sampling data, the coupling relationship strength between different characteristic parameters is inconsistent, the retained characteristic pairs are inconsistent in the multiple second coupling effect matrices generated after feature screening. For example, the same coupling characteristic parameter may be retained in this matrix but missing in another matrix, resulting in feature misalignment, which affects the subsequent data clustering.

[0056] In this case, a feature reconstruction strategy based on a feature reference template is designed. Specifically, the frequency of occurrence of each type of coupling feature parameter in all samples is counted to determine the corresponding frequency score, which is used to measure the degree to which a coupling feature is widely present in all samples, and the frequency score and feature screening parameters are combined to screen out multiple representative target coupling feature parameters. For example, features with high frequency scores and feature screening parameters greater than a certain threshold are recorded as target coupling feature parameters. By reasonably setting the corresponding thresholds of the two, the selected target coupling feature parameters can be stably present in most samples and show good sparsity and stability. Then, a feature parameter template is constructed based on the selected target coupling feature parameters. The feature parameter template represents the set of high-quality coupling feature parameters in all second coupling effect matrices.

[0057] After feature reconstruction of multiple second coupling effect matrices based on the feature reference template, a third coupling effect matrix corresponding to each second coupling effect matrix is ​​generated. Subsequently, cluster analysis of multiple groups of dredged spoil sampling data is performed based on multiple third coupling effect matrices to generate coupling classification labels for each group of dredged spoil sampling data. The purpose of feature reconstruction is to feature align multiple second coupling effect matrices so that the data dimensions of all samples are consistent with the feature template, ensuring the consistency and integrity of the feature data, and facilitating subsequent cluster analysis.

[0058] In this embodiment, the feature reconstruction process of the plurality of second coupling effect matrices specifically includes: The coupling characteristic parameters existing in each second coupling effect matrix are matched with the target coupling characteristics in the characteristic parameter template, the coupling characteristic parameters that successfully match the target coupling characteristics in the characteristic parameter template are retained, and the coupling characteristic parameters that fail to match the target coupling characteristics in the characteristic parameter template are removed, thereby completing the preliminary feature screening step.

[0059] Then, the coupling feature parameters that exist in the feature reference template and are missing in the second coupling effect matrix are marked as missing, and the coupling feature parameters with missing marks in each second coupling effect matrix are completed based on the mean filling method. Specifically, for the coupling feature parameters with missing marks, the mean of the coupling feature parameters in multiple second coupling effect matrices is calculated to complete the coupling feature parameters with missing marks, and the feature reconstruction of each second coupling effect matrix is ​​completed through feature screening and feature completion, and finally a third coupling effect matrix corresponding to each second coupling effect matrix is ​​generated.

[0060] The process of clustering analysis of multiple groups of dredged slag sampling data based on multiple third coupling effect matrices, in this embodiment, a density-based clustering algorithm is used to cluster analysis of multiple groups of dredged slag sampling data based on multiple third coupling effect matrices. Exemplarily, after flattening multiple third coupling effect matrices to obtain corresponding one-dimensional vectors, multiple groups of dredged slag sampling data are clustered based on the DBSCAN clustering algorithm. The DBSCAN clustering algorithm is suitable for situations where there may be density differences, outliers or abnormal points in the samples, and can automatically identify clustering areas with higher density, and treat sample points with lower density as noise points. This method can better deal with the complex distribution characteristics and abnormal samples that may exist in dredged slag sampling data. Finally, the clustering results generated by the DBSCAN clustering algorithm can obtain the coupling classification labels about the coupling characteristics corresponding to each group of dredged waste sampling data, which includes the coupling correlation between the physical and chemical property parameters. The sample data set constructed by the combined coupling classification labels reveals the potential characteristic relationship in the dredged waste, and fully explores the complex internal characteristics of the generated dredged waste samples, which can enable the dredged waste resource assessment model to better understand and analyze the comprehensive characteristics of the dredged waste, thereby improving the accuracy and comprehensiveness of the resource assessment.

[0061] In step S50, for the construction of a sample data set for training the dredged waste resource assessment model, a conventional data set may be first constructed based on multiple groups of historical dredging analysis data, for example, multiple resource assessment labels may be determined based on the dredged resource assessment data, for example, taking the building material application scenario as an example, labels related to strength assessment, durability assessment, density and volume assessment, etc. may be obtained, which may be multiple assessment levels or specific values ​​set based on empirical knowledge, etc. A conventional data set is first constructed with the dredged waste sampling data and multiple resource assessment labels in multiple groups of historical dredging analysis data, and on this basis, corresponding coupling classification labels are added to the dredged waste sampling data in each group of historical dredging analysis data, the coupling classification labels including coupling features between physical and chemical properties, and after associating each group of dredged waste sampling data with the corresponding resource assessment label and coupling classification label, a sample data set containing rich feature information is constructed.

[0062] In the process of training the dredged waste resource assessment model through sample data sets, multiple groups of dredged waste sampling data containing coupled classification labels are used as the input of the model, and multiple resource assessment labels are used as the training targets of the model. The dredged waste resource assessment model that can be used to evaluate the potential of dredged waste resource utilization is trained, thereby realizing an accurate assessment of the potential of dredged waste resource utilization.

[0063] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A method for evaluating the resource utilization potential of dredged waste based on stratification characteristics, characterized in that: include: Acquire multiple groups of historical dredging analysis data on dredged waste, the historical dredging analysis data including dredged waste sampling data and dredging resource assessment data, the dredged waste sampling data including stratified characteristic sampling data of multiple sampling points; Extract multiple physical characteristic parameter vectors and chemical parameter characteristic vectors from each set of dredged slag sampling data, and construct a sampling characteristic parameter matrix corresponding to each set of dredged slag sampling data; Constructing multiple physical and chemical characteristic parameter combinations, performing coupling effect analysis on each physical and chemical characteristic parameter combination, and constructing a first coupling effect matrix corresponding to each group of dredged waste sampling data, including calculating characteristic coupling parameters and coupling effect factors of each physical and chemical characteristic parameter combination according to multiple parameter characteristic vectors, determining coupling characteristic parameters of each physical and chemical characteristic parameter combination according to the multiple characteristic coupling parameters and coupling effect factors, and constructing a first coupling effect matrix containing multiple coupling characteristic parameters; Performing feature sparsity analysis and feature stability analysis on each type of coupling feature parameters in a plurality of first coupling effect matrices, calculating feature sparsity parameters and feature stability parameters of each type of coupling feature parameters, performing feature screening on each first coupling effect matrix according to the feature sparsity parameters and feature stability parameters, and generating a second coupling effect matrix corresponding to each first coupling effect matrix; Based on the second coupling effect matrix, cluster analysis is performed on multiple groups of dredged waste sampling data to generate coupled classification labels for each group of dredged waste sampling data. Multiple coupled classification labels are added to multiple groups of historical dredging analysis data to construct a sample data set for training a dredged waste resource assessment model. After the dredged waste resource assessment model is obtained through training with the sample data set, dredged waste resource assessment is performed based on the dredged waste resource assessment model.

2. The method for evaluating the resource utilization potential of dredged waste based on stratification characteristics according to claim 1 is characterized in that: The coupling effect analysis is performed on each combination of physical and chemical characteristic parameters to construct the first coupling effect matrix corresponding to each set of dredged waste sampling data, including: The physicochemical characteristic covariance is calculated based on the physical characteristic parameter vector and the chemical parameter characteristic vector corresponding to the physicochemical characteristic parameter combination, the standard deviation of the physical characteristic parameter vector and the chemical parameter characteristic vector are calculated respectively, and the characteristic coupling parameter of the physicochemical characteristic parameter combination is calculated based on the physicochemical characteristic covariance, the standard deviation of the physical characteristic parameter vector and the chemical parameter characteristic vector; Calculate the mutual information between the physical characteristic parameter vector and the chemical parameter characteristic vector corresponding to the physical and chemical characteristic parameter combination, calculate the information entropy of the physical characteristic parameter vector and the chemical parameter characteristic vector respectively, and calculate the coupling effect factor of the physical and chemical characteristic parameter combination according to the mutual information, the information entropy of the physical characteristic parameter vector and the chemical parameter characteristic vector; The coupling characteristic parameters of the physical and chemical characteristic parameter combination are calculated using the following formula: In the formula, For the The physical characteristic parameters and The coupled characteristic parameters of the physical and chemical characteristic parameter combination composed of chemical characteristic parameters, is the coupling effect factor of the physical and chemical characteristic parameter combination, is the physicochemical characteristic covariance of the physicochemical characteristic parameter combination, , are the standard deviations of the physical characteristic parameter vector and chemical parameter characteristic vector corresponding to the physical and chemical characteristic parameter combination, is the adjustment coefficient; The first coupling effect matrix of each group of dredged slag sampling data is constructed according to the coupling characteristic parameters corresponding to the respective combinations of multiple physical and chemical characteristic parameters.

3. The method for evaluating the resource utilization potential of dredged waste based on stratification characteristics according to claim 2 is characterized in that: The calculation of characteristic sparse parameters and characteristic stable parameters of coupled characteristic parameters includes: The characteristic sparse parameters of each type of coupling characteristic parameters are calculated according to the multiple first coupling effect matrices, wherein for any type of coupling characteristic parameters, the characteristic sparse parameters are calculated as follows: In the formula, is the characteristic sparse parameter of this type of coupling characteristic parameter, is the indicator function, , The characteristic parameters of this type of coupling are The value of the first coupling effect matrix, is the number of the first coupling effect matrix; The coupling characteristic vectors of each type of coupling characteristic parameters are extracted according to multiple first coupling effect matrices, the Kendall correlation coefficients between any two types of coupling characteristic parameters are calculated according to the multiple coupling characteristic vectors, and the average value of the Kendall correlation coefficients between each type of coupling characteristic parameters and the remaining multiple types of coupling characteristic parameters is calculated and recorded as the characteristic stability parameter of each type of coupling characteristic parameter.

4. The method for evaluating the resource utilization potential of dredged waste based on stratification characteristics according to claim 3 is characterized in that: Performing feature screening on each first coupling effect matrix according to the feature sparse parameter and the feature stable parameter, generating a second coupling effect matrix corresponding to each first coupling effect matrix, including: The characteristic screening parameters of each type of coupling characteristic parameters are calculated based on the characteristic sparsity parameters and the characteristic stability parameters, where: In the formula, is the feature screening parameter for the coupled feature parameters, is the characteristic stability parameter of the coupled characteristic parameter, , are the weight parameters of feature sparsity parameter and feature stability parameter respectively; Based on the feature screening threshold, multiple coupling feature parameters whose feature screening parameters are smaller than the feature screening threshold in each first coupling effect matrix are removed to generate a second coupling effect matrix corresponding to each first coupling effect matrix.

5. The method for evaluating the resource utilization potential of dredged waste based on stratification characteristics according to claim 4 is characterized in that: Based on the second coupling effect matrix, cluster analysis is performed on multiple groups of dredged slag sampling data to generate coupling classification labels for each group of dredged slag sampling data, including: Determine frequency scores corresponding to multiple types of coupling feature parameters in multiple second coupling effect matrices, determine multiple target coupling feature parameters in the multiple second coupling effect matrices based on the frequency scores and feature screening parameters, and construct a feature reference template based on the multiple target coupling feature parameters; Performing feature reconstruction processing on a plurality of second coupling effect matrices based on a feature reference template to generate a third coupling effect matrix corresponding to each second coupling effect matrix; Based on multiple third coupling effect matrices, cluster analysis is performed on multiple groups of dredged waste sampling data to generate coupling classification labels for each group of dredged waste sampling data.

6. The method for evaluating the resource utilization potential of dredged waste based on stratification characteristics according to claim 5 is characterized in that: Performing feature reconstruction processing on multiple second coupling effect matrices based on the feature reference template includes: Matching the coupling characteristic parameters existing in each second coupling effect matrix with the target coupling characteristics in the characteristic parameter template, retaining the coupling characteristic parameters that successfully match the target coupling characteristics in the characteristic parameter template, and removing the coupling characteristic parameters that fail to match the target coupling characteristics in the characteristic parameter template; The coupling feature parameters that exist in the feature reference template but are missing in the second coupling effect matrix are marked as missing, and the coupling feature parameters with missing marks in each second coupling effect matrix are completed based on the mean filling method to complete the feature reconstruction of each second coupling effect matrix and generate a third coupling effect matrix corresponding to each second coupling effect matrix.

7. The method for evaluating the resource utilization potential of dredged waste based on stratification characteristics according to claim 6 is characterized in that: The sample data set constructed for training the dredging spoil resource assessment model also includes: Based on the dredging resource assessment data in the historical dredging analysis data, multiple resource assessment labels are determined, and the coupling classification labels of each group of historical dredging analysis data are added to the corresponding dredging spoil sampling data, and each group of dredging spoil sampling data is associated with the corresponding resource assessment label and the coupling classification label to construct a sample data set; Taking multiple groups of dredged slag sampling data containing coupled classification labels in the sample data set as the input of the model, and taking multiple resource evaluation labels in the sample data set as the training target of the model, a dredged slag resource evaluation model is trained, wherein the dredged slag resource evaluation model is an LSTM model.

8. The method for evaluating the resource utilization potential of dredged waste based on stratification characteristics according to claim 7 is characterized in that: The DBSCAN clustering algorithm was used to perform cluster analysis on multiple groups of dredging waste sampling data based on multiple third coupling effect matrices.

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