Coal-based solid waste physicochemical property classification method and system based on regional ecological characteristics

Abstract

The invention relates to the technical field of data processing, and discloses a coal-based solid waste physicochemical property classification method and system based on regional ecological characteristics. The method comprises the following steps: collecting coal-based solid waste data, and establishing a resource and ecological database; analyzing association between the resource characteristics and the ecological elements, and obtaining association parameters and influence factors; performing classification modeling to form a classification model and characteristic prediction; evaluating a commercial value, and generating a value score and a market scheme; monitoring the whole life cycle, and obtaining circulation data and benefit evaluation; analyzing and deciding, and making a resource management scheme. According to the method, correlation analysis of coal-based solid waste resource characteristics and regional ecological elements is realized, a scientific classification model is constructed, and a full-life-cycle resource management scheme is provided, so that the utilization efficiency and the management level of coal-based solid waste resources are improved.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and particularly relates to a classification method and system for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics. Background Art

[0002] Coal-based solid waste is solid waste generated during coal mining, washing, and utilization, mainly including coal gangue, fly ash, coal slag, coal slime, etc. With the development of the coal industry, the accumulation of coal-based solid waste has been increasing year by year, occupying a large amount of land resources and having a serious impact on the ecological environment. In the prior art, the management and utilization of coal-based solid waste resources mainly rely on empirical classification methods, usually making simple classifications based on physical properties (such as particle size, density) and chemical compositions (such as calorific value, ash content). Although these traditional methods have promoted the resource utilization of coal-based solid waste to a certain extent, they lack systematicness and scientificity, fail to consider the influence of regional ecological characteristics on the physical and chemical properties of coal-based solid waste, and also fail to establish a correlation mechanism between the characteristics of coal-based solid waste and the ecological environment. In addition, the classification methods in the prior art are often static and single-dimensional, lacking dynamic monitoring and full-life cycle management, resulting in low resource utilization efficiency.

[0003] The deficiencies of the prior art are mainly manifested in the following aspects: First, it ignores the shaping effect of regional ecological factors on the physical and chemical properties of coal-based solid waste, resulting in the disconnection between the classification results and the actual application requirements; Second, there is a lack of correlation analysis between the resource characteristics of coal-based solid waste and regional ecological elements, and the internal connection between the two cannot be revealed; Third, most of the existing classification methods are experience-driven, lacking data support and scientific models, and the classification accuracy is not high; Fourth, the resource value evaluation system is imperfect, and it is difficult to achieve the optimal allocation and maximum value of resources; Fifth, there is a lack of full-life cycle monitoring and benefit evaluation mechanisms, and it is difficult to effectively manage and continuously optimize the resource utilization process. These deficiencies have severely restricted the efficient utilization of coal-based solid waste resources and the improvement of management levels. Summary of the Invention

[0004] The present application provides a classification method and system for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics, which is used to realize the correlation analysis between the resource characteristics of coal-based solid waste and regional ecological elements, construct a scientific classification model, and provide a full-life cycle resource management plan, so as to improve the utilization efficiency and management level of coal-based solid waste resources.

[0005] In a first aspect, the present application provides a classification method for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics. The classification method for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics includes: collecting and digitally processing coal-based solid waste resource data to obtain a coal-based solid waste resource database and a regional ecological characteristics database; based on the coal-based solid waste resource database and the regional ecological characteristics database, performing a correlation analysis on the characteristics of coal-based solid waste resources and regional ecological elements to obtain a set of characteristic correlation parameters and key influencing factors; according to the set of characteristic correlation parameters and the key influencing factors, performing multi-dimensional classification modeling on coal-based solid waste resources to obtain a coal-based solid waste resource classification model and a resource characteristic prediction result; based on the coal-based solid waste resource classification model and the resource characteristic prediction result, performing multi-dimensional evaluation on the commercial value of coal-based solid waste resources to obtain a resource value score and a market matching plan; monitoring the entire life cycle of the utilization process of coal-based solid waste resources in the market matching plan to obtain resource transfer data and a multi-dimensional benefit evaluation result; inputting the resource value score, the resource transfer data, and the multi-dimensional benefit evaluation result into a decision engine for comprehensive analysis to obtain a management decision plan for coal-based solid waste resources.

[0006] In a second aspect, the present application provides a classification system for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics. The classification system for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics includes: A processing module, configured to collect and digitally process coal-based solid waste resource data to obtain a coal-based solid waste resource database and a regional ecological characteristics database; An analysis module, configured to perform a correlation analysis on the characteristics of coal-based solid waste resources and regional ecological elements based on the coal-based solid waste resource database and the regional ecological characteristics database to obtain a set of characteristic correlation parameters and key influencing factors; A modeling module, configured to perform multi-dimensional classification modeling on coal-based solid waste resources according to the set of characteristic correlation parameters and the key influencing factors to obtain a coal-based solid waste resource classification model and a resource characteristic prediction result; An evaluation module, configured to perform multi-dimensional evaluation on the commercial value of coal-based solid waste resources based on the coal-based solid waste resource classification model and the resource characteristic prediction result to obtain a resource value score and a market matching plan; A monitoring module, configured to monitor the entire life cycle of the utilization process of coal-based solid waste resources in the market matching plan to obtain resource transfer data and a multi-dimensional benefit evaluation result; An input module, configured to input the resource value score, the resource transfer data, and the multi-dimensional benefit evaluation result into a decision engine for comprehensive analysis to obtain a management decision plan for coal-based solid waste resources.

[0007] In a third aspect, there is provided a classification device for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory to enable the classification device for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics to execute the above-mentioned classification method for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics.

[0008] In a fourth aspect, there is provided a computer-readable storage medium, in which instructions are stored, and when it runs on a computer, it enables the computer to execute the above-mentioned classification method for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics.

[0009] In the technical solution provided by this application, by collecting and digitally processing coal-based solid waste resource data, a coal-based solid waste resource database and a regional ecological characteristic database are constructed, providing high-quality basic data support for subsequent analysis and solving the problems of non-standard data collection and scattered storage in traditional methods; by conducting a correlation analysis between the characteristics of coal-based solid waste resources and regional ecological elements, a set of characteristic correlation parameters and key influencing factors are obtained, revealing the internal relationship between the physical and chemical properties of coal-based solid waste and the regional ecological environment. In particular, the application of artificial intelligence algorithms such as multivariate statistical analysis methods, principal component analysis, and canonical correlation analysis makes the correlation analysis results highly interpretable and scientific. The characteristics of these algorithms significantly improve the accuracy of correlation pattern recognition and the precision of ecological factor impact measurement; based on the set of characteristic correlation parameters and key influencing factors, multi-dimensional classification modeling is carried out, and intelligent algorithms such as hierarchical clustering, support vector machines, and random forests are used to construct a classification model. The characteristics of these algorithms enable the classification process to fully consider the non-linear relationship and high-dimensional characteristics of the data, greatly improving the accuracy and stability of coal-based solid waste resource classification; by conducting a multi-dimensional evaluation of the commercial value of coal-based solid waste resources, a resource value score and a market matching plan are obtained, building a bridge from resource classification to value realization. The characteristics of the multi-objective optimization algorithm applied effectively solve the multi-dimensional balance problem in resource value evaluation; the whole life cycle of the coal-based solid waste resource utilization process is monitored, and the Internet of Things technology and data mining algorithms are used to obtain resource transfer data and multi-dimensional benefit evaluation results. The characteristics of these algorithms perform well in processing time-series data and benefit evaluation, providing real-time feedback on the resource utilization effect; finally, the resource value score, resource transfer data, and multi-dimensional benefit evaluation results are input into the decision-making engine for comprehensive analysis to obtain a management decision-making plan for coal-based solid waste resources. The characteristics of algorithms such as Monte Carlo simulation and analytic hierarchy process enable the decision-making process to have the ability to handle uncertainty and multi-objective trade-offs, realizing the full-chain intelligent management from data to decision-making, and comprehensively improving the scientificity, accuracy, and efficiency of coal-based solid waste resource management. Description of the Drawings

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic diagram of an embodiment of the classification method for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics in the embodiments of the present application; Figure 2 It is a schematic diagram of an embodiment of the classification system for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics in the embodiments of the present application; Figure 3 It is a structural schematic block diagram of the classification equipment for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics in the embodiments of the present invention. Detailed implementation manners

[0012] The embodiments of the present application provide a classification method and system for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics. The terms first, second, third, fourth, etc. (if any) in the specification, claims, and above-mentioned accompanying drawings of the present application are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the term "including" or "having" and any deformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0013] For ease of understanding, the following describes the specific process of the embodiments of the present application. Please refer to Figure 1 , an embodiment of the classification method for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics in the embodiments of the present application includes: Step S101: Collect and digitally process the coal-based solid waste resource data to obtain a coal-based solid waste resource database and a regional ecological characteristic database; Step S102: Based on the coal-based solid waste resource database and the regional ecological characteristic database, conduct a correlation analysis on the coal-based solid waste resource characteristics and regional ecological elements to obtain a characteristic correlation parameter set and key influencing factors; Step S103: According to the characteristic correlation parameter set and the key influencing factors, conduct multi-dimensional classification modeling on the coal-based solid waste resources to obtain a coal-based solid waste resource classification model and a resource characteristic prediction result; Step S104: Based on the coal-based solid waste resource classification model and the resource characteristic prediction results, conduct a multi-dimensional assessment of the commercial value of coal-based solid waste resources to obtain a resource value score and a market matching plan; Step S105: Monitor the entire life cycle of the utilization process of coal-based solid waste resources in the market matching plan to obtain resource transfer data and multi-dimensional benefit assessment results; Step S106: Input the resource value score, resource transfer data, and multi-dimensional benefit assessment results into the decision-making engine for comprehensive analysis to obtain a management decision-making plan for coal-based solid waste resources.

[0014] It can be understood that the execution subject of this application can be a coal-based solid waste physical and chemical property classification system based on regional ecological characteristics, or a terminal or a server. Specifically, it is not limited here. This application embodiment is described by taking the server as the execution subject as an example.

[0015] Specifically, collect and digitally process coal-based solid waste resource data to obtain a coal-based solid waste resource database and a regional ecological characteristic database. The data collection objects in this step mainly include two categories. One category is the coal-based solid waste resource ontology data, and the other category is the ecological environment parameter data of the sampling area. For the coal-based solid waste ontology data, the on-site collection end uses a mobile terminal device with GPS positioning, two-dimensional code encoding, and photographing functions to record the spatial position information and basic sampling characteristics of the samples, such as sampling depth, on-site humidity, etc. At the same time, a unique identification code is generated for each sample for subsequent association between fields in the data table. After the sample enters the laboratory, physical and chemical property tests are carried out through standard processes. The physical properties include bulk density, particle size composition, moisture content, etc., which are obtained through standard instruments and processes such as a particle size analyzer and the drying method, and are converted into structured data fields; chemical properties such as sulfur, iron, calcium, magnesium, heavy metal content, organic matter ratio, etc. are determined by X-ray fluorescence analysis (XRF) or inductively coupled plasma mass spectrometry (ICP-MS), and the obtained data are stored in the database in the form of elemental mass percentages. At the same time, the regional ecological characteristic data are obtained through multi-source data integration methods such as remote sensing images, geographic information system (GIS) platforms, and national ecological databases, including factors such as climate (annual average precipitation, sunshine duration, annual average temperature, etc.), soil (pH, organic matter content, texture category, etc.), hydrology (basin type, water body density, etc.), and vegetation (NDVI index, vegetation coverage rate, etc.). These regional data are corresponding to the sampling points in a raster or vector manner through spatial overlay technology, so as to establish a coal-based solid waste resource database and a regional ecological characteristic database with the unique identification code as the connection field.

[0016] Perform a correlation analysis on these two databases to extract the feature correlation parameter set and key influencing factors. In the data preprocessing stage, standardize the physical and chemical data and ecological data separately. Using the Min-Max normalization method, convert physical parameters with different units and dimensions (such as particle size range of 0.01 - 5 mm, moisture content of 0% - 100%) into comparable ratios between 0 and 1 in the same way for ecological factor parameters. Then construct a two-dimensional data matrix, where the rows are different coal-based solid waste samples and the columns are their standardized attribute parameters. Calculate the linear correlation degree between the physical and chemical properties of each coal-based solid waste and the ecological parameters of each region using the Pearson correlation coefficient, and set threshold conditions (|r| > 0.5, significance p < 0.05) to screen out significantly correlated variable pairs. Further apply principal component analysis (PCA) to the physical and chemical property data, and extract the principal component factors with a cumulative variance contribution rate reaching 85% according to the eigenvector matrix. The principal component factors are comprehensive indicators formed by the linear combination of several highly correlated original variables, used to reduce redundant features. Further introduce canonical correlation analysis (CCA) to perform canonical variable mapping between the principal component factors and the ecological feature set to find the maximum correlation structure between the two groups of variables. Finally, evaluate the feature importance by introducing the above-obtained significant parameter pairs and canonical variable pairs into the random forest model. The random forest consists of multiple decision trees, each tree is trained with different samples and feature subsets, and the average influence of features in the classification task is evaluated through information gain to obtain the final feature correlation parameter set and key influencing factors.

[0017] For multi-dimensional classification and modeling of coal-based solid waste resources, the above-mentioned feature set is used as the input variable. First, the original data set is divided into a training set and a test set at a ratio of 7:3. During the division process, it is necessary to maintain the balance of the distribution of different classification samples to prevent training bias. The hierarchical clustering algorithm is applied to the training set, and the silhouette coefficient and Davies-Bouldin index are calculated for different numbers of clusters (such as 2 to 10 classes). The closer the silhouette coefficient is to 1, the higher the clustering rationality, and the lower the Davies-Bouldin index, the better the separation between classes. The number of clusters is determined by analyzing the inflection points of these two indicators. After determining the number of classifications, three algorithms, K-means, support vector machine (SVM), and random forest, are introduced to establish classification models respectively. K-means assigns samples to the class closest to the centroid according to the least square error, SVM finds the hyperplane with the largest margin for boundary division, and random forest conducts classification prediction based on the decision tree ensemble method. Each model is trained and evaluated using 10-fold cross-validation, and the one with the highest average F1 score is selected as the final classifier. Finally, typical feature profiles are extracted based on the mean and standard deviation of the samples in each class, and a resource characteristic prediction model is constructed to predict the category and physical and chemical properties of unknown samples. The prediction results are evaluated for value and matched with the market. An indicator system for three dimensions, namely economy, environment, and society, is constructed. For the economic dimension, such as the cost of substitute materials and transportation costs; for the environmental dimension, such as the content of pollution factors and resource utilization efficiency; for the social dimension, such as the estimated number of job positions and regional resource dependence, etc. The predicted resource category and characteristic data are input into the value evaluation model, and the multi-dimensional scoring results are calculated by the linear weighting method. Then, application scenario analysis is carried out. For example, solid waste with low calorific value and high calcium content is suitable for building materials; those with high moisture content and low pollution factors are suitable for ecological landfill. According to the application characteristics of each type of resource, an application potential matrix is constructed, and matching calculations are performed in combination with regional user data (demand, resource quality requirements, transportation radius). Finally, the matching degree score of resources-users and priority supply suggestions are obtained.

[0018] By generating electronic identity cards for each resource and binding its unique identification code with the detection data. The Internet of Things devices are used to collect the status data of resources in the processes of generation, transportation, processing, and use in real time (such as GPS location information, weighing data, temperature and humidity, etc.), and combined with the environmental sensors (PM2.5, water quality pH, soil heavy metal content) deployed in the use area to collect environmental response data, so as to establish a complete resource transfer trajectory and impact database. Subsequently, a multi-dimensional benefit evaluation system is constructed, and benchmark expectation indicators are set, such as saving 10 yuan in cost per unit of resource and reducing pollutants by 20%. The difference in indicators obtained by comparing the actually collected data statistics is used to calculate the benefit achievement degree and deviation. Finally, by archiving and analyzing the historical data of the project, rules are extracted for establishing a knowledge base and a prediction mechanism. The above various scoring results (resource value scoring, transfer trajectory data, multi-dimensional benefit evaluation results) are input into the decision tree model to form a structured decision path. Classification branches under different conditions are constructed, and a possible path diagram is generated for each branch through simulation experiments, and a multi-objective optimization strategy is used to sort and evaluate the economic, environmental, and social values. Finally, the analytic hierarchy process (AHP) is introduced to assign different weight combinations for scoring, and the optimal classification and resource allocation path are output.

[0019] In the embodiments of the present application, by collecting and digitally processing the data of coal-based solid waste resources, a coal-based solid waste resource database and a regional ecological characteristic database are constructed, providing high-quality basic data support for subsequent analysis and solving the problems of irregular data collection and scattered storage in traditional methods; by conducting a correlation analysis on the characteristics of coal-based solid waste resources and regional ecological elements, a set of characteristic correlation parameters and key influencing factors are obtained, revealing the internal relationship between the physical and chemical properties of coal-based solid waste and the regional ecological environment. In particular, by applying artificial intelligence algorithms such as multivariate statistical analysis methods, principal component analysis, and canonical correlation analysis, the correlation analysis results are highly interpretable and scientific, and the characteristics of these algorithms significantly improve the accuracy of correlation pattern recognition and the precision of ecological factor impact measurement; based on the set of characteristic correlation parameters and key influencing factors, multi-dimensional classification modeling is carried out, and intelligent algorithms such as hierarchical clustering, support vector machines, and random forests are used to construct classification models. The characteristics of these algorithms enable the classification process to fully consider the non-linear relationship and high-dimensional characteristics of the data, greatly improving the accuracy and stability of coal-based solid waste resource classification; by conducting a multi-dimensional evaluation of the commercial value of coal-based solid waste resources, a resource value score and a market matching plan are obtained, building a bridge from resource classification to value realization. The characteristics of the multi-objective optimization algorithm applied effectively solve the multi-dimensional balance problem in resource value evaluation; the whole life cycle of the coal-based solid waste resource utilization process is monitored, and the Internet of Things technology and data mining algorithms are used to obtain resource transfer data and multi-dimensional benefit evaluation results. The characteristics of these algorithms perform well in processing time series data and benefit evaluation, providing real-time feedback on the resource utilization effect; finally, the resource value score, resource transfer data, and multi-dimensional benefit evaluation results are input into the decision-making engine for comprehensive analysis to obtain a management decision-making plan for coal-based solid waste resources. The characteristics of algorithms such as Monte Carlo simulation and analytic hierarchy process enable the decision-making process to have the ability to handle uncertainty and multi-objective trade-offs, realizing the full-chain intelligent management from data to decision-making and comprehensively improving the scientificity, precision, and efficiency of coal-based solid waste resource management.

[0020] In a specific embodiment, the process of executing step S101 may specifically include the following steps: Conduct on-site collection of coal-based solid waste samples to obtain original coal-based solid waste samples and sampling point location data; Encode and preprocess the original coal-based solid waste samples to obtain standardized coal-based solid waste samples and unique sample identification codes; Conduct physical property detection on the standardized coal-based solid waste samples to obtain coal-based solid waste physical property parameters including particle composition, bulk density, and porosity; Conduct chemical composition analysis on the standardized coal-based solid waste samples to obtain coal-based solid waste chemical property parameters including element composition, organic matter content, and heavy metal content; Ecological element data is collected based on the area corresponding to the sampling point location data to obtain the original regional ecological characteristic data including climate data, terrain data, and vegetation cover data; The sample unique identification code, physical property parameters of coal-based solid waste, chemical property parameters of coal-based solid waste, and the original regional ecological characteristic data are standardized, integrated, and stored to obtain a coal-based solid waste resource database and a regional ecological characteristic database.

[0021] Specifically, in the on-site collection of coal-based solid waste, systematic sampling is carried out at locations such as coal mine goafs, gangue dumps, or coal chemical tailings discharge areas using a handheld terminal device with positioning and sampling recording functions. In each sampling operation, the longitude and latitude information, altitude, on-site photos, sampling depth, and basic description information of the sampling point are recorded and bound to a collection record form. The sample numbering rule is area code + sampling batch + serial number to form a unique identification code. For example, the sample number is B0401-23-07, where B0401 is the area code, 23 is the sampling year, and 07 is the serial number of the day.

[0022] Subsequently, the collected original samples are pre-treated. This treatment process includes impurity removal, uniform mixing of samples, drying to constant weight (treated at 105°C for 24 hours), and particle size screening. After the pre-treatment is completed, the sample unique number is input through the sample registration system and the treatment status is set to standardized. This status means that the sample already has unified conditions to enter the analysis link, excluding detection deviations caused by moisture fluctuations and impurity interference.

[0023] The samples that have completed the pre-treatment are respectively sent to the physical property detection and chemical property analysis links. Physical property detection includes particle composition analysis, bulk density determination, and porosity calculation. Particle composition is completed using a laser particle size analyzer, and the output includes particle size percentile indicators such as D10, D50, and D90; the bulk density is converted by weighing after filling the sample into a standard test mold; the porosity is calculated by the mercury intrusion method to calculate the proportion of the total pore volume. The physical detection data is recorded through the Laboratory Information Management System (LIMS) and automatically bound to the sample number to generate a parameter data table. For example, D50 particle size = 0.35mm, bulk density = 1.47g / cm³, porosity = 28.5%.

[0024] Chemical composition analysis includes the determination of elemental composition, organic matter content, and heavy metal content. The elemental composition is detected by XRF (X-ray fluorescence spectroscopy) technology to detect major inorganic elements such as Si, Al, Fe, Ca, etc.; the organic matter content is measured by titration with potassium dichromate-sulfuric acid oxidation and expressed as a percentage of total organic carbon (TOC); the heavy metal content is analyzed by ICP-MS (inductively coupled plasma mass spectrometry), including elements such as Zn, Pb, Cr, Cd, etc., with the unit of mg / kg. The results of each parameter in this stage correspond to its detection method number, instrument number, and tester to ensure the traceability and structuring of data.

[0025] After obtaining the physical and chemical parameters of the samples, the associated collection of regional ecological characteristic data is further carried out based on the location coordinate data of the sample sampling points, mainly through the spatial information management platform and the remote sensing data interface to obtain ecological element information. Climate data includes annual average precipitation, annual average temperature, and relative humidity, which are imported from the meteorological satellite database and raster values are extracted according to the geographical coding of the sampling points; topographic data is used to extract slope, aspect, and altitude values from DEM (Digital Elevation Model); vegetation data is obtained from the remote sensing NDVI value, which is used as a quantitative index of vegetation coverage. All ecological factors are made to correspond to the sample coordinates through spatial interpolation or raster resampling. For example, the annual average rainfall in the area corresponding to a sampling point is 980 mm, the NDVI value is 0.65, and the slope is 12.7°. These data are uniformly encoded and integrated together with the sample number B0401-23-07.

[0026] The unique sample number, physical property parameter set, chemical property parameter set, and ecological characteristic parameter set are stored in the database in two main tables with a standard structure: the coal-based solid waste resource database and the regional ecological characteristic database. In the coal-based solid waste resource database, each row record corresponds to a sample number and contains attribute fields such as D10, D50, bulk density, porosity, SiO2%, Fe2O3%, organic matter%, Zn content, etc., with a total of no less than 30 fields; in the regional ecological characteristic database, with the sample number as the foreign key field, the record fields include ecological information fields such as annual average rainfall, NDVI, slope, soil pH, etc., with no less than 15 fields. The data tables are connected by the sample number as the connection key value to form a data system with a unified primary key structure. During the data import process, all fields are subject to type verification through the data standardization module (for example, physical parameters require a numerical floating-point format and unit consistency check). The missing value filling strategy adopts the mean imputation method, and outliers are screened through the box plot principle and marked in the outlier mark table for manual review.

[0027] For example, for a coal-based solid waste sample numbered C0311-24-09, its median particle size D50 is 0.42 mm, bulk density is 1.55 g / cm³, Zn content is 305 mg / kg, the altitude of the sampling point is 430 meters, the average annual temperature of the corresponding area is 11.2 °C, and the NDVI is 0.58. After obtaining the above data through a laser particle size analyzer, ICP-MS, and GIS spatial positioning system respectively, the data cleaning program screens out the outliers in the bulk density (setting marks outside the IQR range) and replaces them with the median of the bulk density of other samples in this area, which is 1.51 g / cm³. After final integration, the sample record is written into the database and associated with the regional ecological characteristic data, serving as standardized input data with complete physical and chemical and ecological attributes in subsequent analysis for the feature analysis and modeling classification tasks in the next stage. The above process reflects the whole process from on-site sampling to the establishment of a standard data structure, ensuring the consistency of physical and chemical properties and ecological elements in the logical structure and the operability in calculation and processing.

[0028] In a specific embodiment, the process of executing step S102 may specifically include the following steps: Standardize the physical and chemical property parameters of coal-based solid waste in the coal-based solid waste resource database to obtain a standardized coal-based solid waste characteristic data set; Standardize the ecological element parameters in the regional ecological characteristic database to obtain a standardized regional ecological characteristic data set; Based on the standardized coal-based solid waste characteristic data set and the standardized regional ecological characteristic data set, calculate the correlation coefficient matrix between the physical and chemical properties of coal-based solid waste and regional ecological elements to obtain significantly correlated parameter pairs; Apply the principal component analysis method to the standardized coal-based solid waste characteristic data set for dimensionality reduction processing to obtain the principal component factors of coal-based solid waste characteristics with a cumulative variance contribution rate of not less than 85%; Based on the principal component factors of coal-based solid waste characteristics and the standardized regional ecological characteristic data set, conduct canonical correlation analysis to obtain the canonical correlation pattern between the characteristics of coal-based solid waste and regional ecological elements; According to the canonical correlation pattern and significantly correlated parameter pairs, use the random forest algorithm to calculate the feature importance index to obtain a feature association parameter set and key influencing factors.

[0029] Specifically, the sample data in the coal-based solid waste resource database is grouped for the training and validation of supervised and unsupervised learning. Randomly dividing the sample dataset into a training dataset and a test dataset at a ratio of 7:3 is to ensure sufficient data and generalization ability during the model construction phase. When dividing, it is necessary to maintain the representativeness of the samples in terms of spatial location and type distribution. The division operation adopts a stratified sampling strategy. After stratifying according to regional ecological characteristics or preliminary classification labels, random division is carried out to prevent a certain type of sample from having too high a proportion in the training or test set, resulting in model bias. After obtaining the training dataset, an hierarchical clustering algorithm is used to perform exploratory grouping analysis on the samples. Hierarchical clustering is a bottom-up clustering method based on distance measurement between samples. By calculating the Euclidean distance or Mahalanobis distance between samples, a distance matrix is formed, and then the sample groups with the closest distance are continuously merged until a tree-like clustering structure is finally formed. The clustering structure is visualized by drawing a dendrogram, and the silhouette coefficient and Davies-Bouldin index are calculated for evaluation at each clustering number. The silhouette coefficient represents the difference between the tightness of the sample to the center of the same class and the separation from the center of different classes, with a value range of [-1,1]. The closer it is to 1, the more reasonable the clustering is; the Davies-Bouldin index reflects the ratio of the distance between classes to the tightness within classes, and the smaller the value, the better the clustering effect. The optimal number of clusters is determined by analyzing the extreme value inflection points of the two indicators. For example, when the number of clusters is 4, the silhouette coefficient is maximized at 0.71 and the Davies-Bouldin index is minimized at 0.37, so 4 classes are selected as the final classification number. Using the feature correlation parameter set and key influencing factors as model input variables, a K-means clustering model, a support vector machine (SVM) model, and a random forest classification model are constructed respectively. The K-means model sets the class center points and iteratively adjusts the sample attribution to minimize the squared distance from the sample to the class center; the SVM model constructs an optimal hyperplane to divide the boundaries of each class in the high-dimensional space, maximizing the interval between different class samples; the random forest model consists of several decision trees, each tree is constructed by a random subset of the training set, and the sample class is determined under the majority voting mechanism. Each model is trained on the training set and the model performance is evaluated on the test set through a 10-fold cross-validation method. The evaluation indicators include accuracy, recall, precision, and F1-score. The F1-score, as a balanced indicator considering both accuracy and recall, is often used for imbalanced datasets. By comparing the average F1 scores of each model, the one with the highest score is selected as the final classification model. For example, in this instance, the average F1 value of the random forest model in the 10-fold cross-validation is 0.91, which is significantly higher than 0.83 of K-means and 0.87 of SVM, so the random forest is selected as the optimal classification model.

[0030] After classification, statistical analysis is performed on the characteristic parameters of each category of samples to extract their characteristic description files. Statistical indicators include the mean, median, standard deviation, and interquartile range of each parameter in each category. For example, the average Zn content of a certain type of resource is 280 mg / kg, the median particle size D50 is 0.38 mm, the bulk density is 1.52 g / cm³, the average NDVI is 0.61, and the average slope is 12.1°. Through these statistical characteristics, the typical physical, chemical, and ecological characteristic profiles of each category can be constructed, providing a reference basis for the classification of subsequent new samples. The category characteristic files are saved in a structured form, and each category independently records its key parameter intervals. Based on the above category characteristic files and the previously identified key influencing factors, a resource characteristic prediction model is established. This model takes the ecological parameters and some physical and chemical parameters of the samples as inputs to predict some physical and chemical properties of the undetected samples (such as missing heavy metal content or porosity). For example, in a certain category, the Zn content is negatively correlated with NDVI. For every 0.1 unit decrease in NDVI, the Zn content increases by an average of 27 mg / kg. A multiple regression or random forest-based regression model can be constructed for property prediction. The property prediction model is used to complete the physical and chemical parameters in the stage of incomplete sample detection or rapid estimation. Its output results complement the output of the classification model, providing a complete description of the sample attributes.

[0031] In a specific embodiment, the process of executing step S103 may specifically include the following steps: Randomly divide the sample data in the coal-based solid waste resource database into a training data set and a test data set according to a ratio of 7:3 to obtain a coal-based solid waste resource training set and a test set; Apply the hierarchical clustering algorithm to the coal-based solid waste resource training set, and determine the optimal clustering value by calculating the silhouette coefficient and the Davies-Bouldin index to obtain the classification quantity of the coal-based solid waste resources; Based on the feature correlation parameter set and the key influencing factors, construct a classification model including K-means clustering, support vector machines, and random forests, and select the model with the best performance through cross-validation to obtain the optimal classification model of coal-based solid waste resources; Perform statistical analysis on the characteristic parameters of each category of resources, calculate the characteristic distribution characteristics of each category, and obtain the characteristic description files of each category of resources; Based on the characteristic description files and the key influencing factors, construct a resource characteristic prediction model, perform characteristic prediction on the coal-based solid waste samples, and obtain the resource characteristic prediction results.

[0032] Specifically, the sample data of coal-based solid waste resources is divided to provide a dataset for model training and evaluation. In this process, the sample dataset is randomly divided into a training dataset and a test dataset at a ratio of 7:3. When dividing the data, in order to ensure the consistency of the class distribution between the training set and the test set, the method of stratified sampling is usually adopted. That is, to ensure that the proportion of coal-based solid waste samples of each category is the same in the training set and the test set, thus avoiding model bias caused by sample imbalance. For example, if a certain type of coal-based solid waste resource accounts for 40% in the total data, then the samples of this category in the training set and the test set should also maintain the same proportion. In this way, the representativeness of the training set is ensured, and the data imbalance problem that may be brought about by random division is reduced. After obtaining the training dataset, the next step is to apply the hierarchical clustering algorithm to the training set and determine the optimal number of clusters by calculating the evaluation index of the clustering effect. Hierarchical clustering is an algorithm that gradually groups based on the similarity or distance between samples, and finally forms a hierarchical tree structure (clustering tree). The key to this process is to calculate the distance metric between samples (usually using the Euclidean distance), and then merge samples or subsets according to the proximity of the distances. By constructing the clustering tree, we can evaluate different numbers of clusters and select the best number of clusters through the silhouette coefficient and the Davies-Bouldin index.

[0033] The silhouette coefficient measures the compactness of each sample with other samples in the same class and its separation from samples in other classes. The value of this coefficient ranges from -1 to 1, and the closer the value is to 1, the better the classification effect of the sample. When calculating the silhouette coefficient, for each sample, first calculate the average distance between it and other samples in the same class, and then calculate the average distance between it and the samples in the nearest class. The silhouette coefficient is the ratio of the difference between the two to the maximum value. The Davies-Bouldin index measures the compactness of samples within a class and the separation of samples between classes, and the smaller the value, the better the clustering effect.

[0034] By calculating the silhouette coefficient and Davies-Bouldin index under different numbers of clusters, the optimal number of clusters can be determined. For example, in a certain experiment, the silhouette coefficients obtained by the hierarchical clustering algorithm with 3, 4, and 5 clusters are 0.67, 0.75, and 0.70 respectively, while the Davies-Bouldin indices are 0.83, 0.72, and 0.78 respectively. At this time, when the number of clusters is 4, the silhouette coefficient is the largest and the Davies-Bouldin index is the smallest, so 4 is selected as the final number of clusters. After determining the number of clusters, a classification model including machine learning algorithms such as K-means clustering, support vector machine (SVM), and random forest is constructed based on the feature correlation parameter set and key influencing factors of the training set. The K-means clustering algorithm is a method of clustering based on the distance between samples and the center points of categories. In each iteration, the samples are divided into corresponding categories according to the center point of the category with the smallest distance, and the center point of each category is recalculated. The support vector machine (SVM) is a binary classification model that classifies by constructing a hyperplane that can maximize the interval between categories. In multi-class problems, SVM usually adopts the one-versus-one or one-versus-all strategy for extension. Random forest is an ensemble learning algorithm based on decision trees. By generating multiple decision trees, the category prediction result is finally obtained through the majority voting mechanism. The construction of each decision tree is based on the random sampling of data and the random selection of features, thereby increasing the robustness of the model.

[0035] During the model training process, the cross-validation method is used to evaluate the model. Cross-validation divides the dataset into multiple subsets. By taking turns using each subset as the test set and the remaining subsets as the training set, the performance metrics of the model are calculated, and the stability and accuracy of the model are evaluated by taking the average value. The evaluation metrics include accuracy, precision, recall, and F1 score. By performing cross-validation on the K-means, SVM, and random forest models on the training set and comparing their performances, the model with the best performance is finally selected as the final classifier.

[0036] After training the finally selected optimal classification model, the next task is to perform statistical analysis on the characteristics of various types of resources, calculate the characteristic distribution characteristics of each category, and obtain the characteristic description file. The characteristic description file contains the statistics of each characteristic parameter in each category, such as mean, standard deviation, maximum value, minimum value, etc. Through these statistics, we can understand the characteristic distribution of different types of coal-based solid wastes and provide a reference for the prediction of new samples. For example, the Zn content of a certain type of coal-based solid waste may be concentrated between 250-300 mg / kg, the bulk density is 1.55 g / cm³, the NDVI is 0.65, and the porosity is 30%. These statistical data provide an important benchmark for the classification and characteristic prediction of new samples.

[0037] Based on the feature description file and key influencing factors, a resource characteristic prediction model is constructed. This model is mainly used to predict the characteristics of untested samples, such as predicting unmeasured parameters such as the heavy metal content and particle size distribution of coal-based solid waste. By inputting the standardized features into the prediction model, the model can output the characteristic prediction values of coal-based solid waste samples. This process usually uses a regression model (such as regression based on random forest) for prediction. For example, when the NDVI of a new sample is 0.62, the particle size D50 is 0.35 mm, and the bulk density is 1.50 g / cm³, after inputting into the prediction model, the model may predict that the Zn content of this sample is 290 mg / kg, with an error range of ±5 mg / kg. The prediction results can provide guidance for resource management and utilization.

[0038] In a specific embodiment, the process of executing step S104 may specifically include the following steps: Construct a multi-dimensional value evaluation framework for coal-based solid waste resources including the economic value dimension, environmental value dimension, and social value dimension, conduct hierarchical quantification on the resource characteristic prediction results, and obtain the coal-based solid waste resource value evaluation index system; Based on the coal-based solid waste resource classification model, conduct application scenario analysis on various types of coal-based solid waste resources to obtain the coal-based solid waste resource application potential matrix; Calculate the economic benefits of each application scenario in the coal-based solid waste resource application potential matrix to obtain the return on investment and investment payback period data under different utilization methods; Based on the return on investment and investment payback period data, conduct economic feasibility ranking on each application scenario, select the top three application scenarios, and obtain the resource utilization path for priority development; Construct a regional market demand database, match and analyze the demand parameters of potential users in the region with the resource utilization path for priority development, and obtain the resource-user matching degree score; Generate a comprehensive resource value score and the optimal market matching plan according to the resource-user matching degree score and the coal-based solid waste resource value evaluation index system.

[0039] Specifically, a multi-dimensional value evaluation framework for coal-based solid waste resources is constructed, which includes economic value dimension, environmental value dimension, and social value dimension. In the economic dimension, factors such as the direct utilization value, processing value-added potential, and substitution cost of coal-based solid waste are mainly considered; in the environmental dimension, attention is paid to the environmental risks, ecological friendliness, and carbon emission reduction potential of coal-based solid waste; the social value dimension focuses on the social benefits such as employment contribution and regional economic development potential of coal-based solid waste. According to the evaluation indicators of different dimensions, a multi-level weight system is adopted to comprehensively evaluate each dimension. For example, the evaluation of economic value can include the application value of coal-based solid waste in fields such as building materials, soil remediation, and energy recovery, while the evaluation of environmental value involves the positive impacts of resource recovery and utilization on the environment, such as reducing pollution, energy conservation, and emission reduction.

[0040] Based on the prediction results of the characteristics of coal-based solid waste, a hierarchical quantification method is used to quantify the characteristics of each coal-based solid waste sample, and an evaluation index system for coal-based solid waste resources is constructed. For example, the physical and chemical characteristics of a coal-based solid waste sample, such as heavy metal content, particle size, moisture content, and its impact on the ecological environment, can be converted into specific values through a scoring system. The economic dimension may calculate its potential market value based on the comprehensive characteristics of the sample, the environmental dimension calculates its potential for reducing environmental pollution or carbon emissions, and the social value dimension scores based on the social benefits of the resource in actual applications.

[0041] Based on the classification model of coal-based solid waste resources, an application scenario analysis is carried out for various types of coal-based solid waste resources, so as to obtain an application potential matrix of coal-based solid waste resources. Application scenario analysis refers to the feasibility analysis of different application methods of resources according to the physical and chemical characteristics, ecological environment requirements, and market demands of coal-based solid waste. For example, for a certain type of coal-based solid waste, if it contains rich organic matter and low heavy metal content, it may be suitable for soil improvement and organic fertilizer production; if its particle size is small and it has a high calorific value, it may be suitable for energy recovery or building materials production. According to the characteristics of each application scenario, an application potential matrix is constructed, and different types of coal-based solid waste are classified according to their applicability in each application scenario, and their potential market demands are evaluated. Based on the application potential matrix of coal-based solid waste resources, economic benefits are calculated for each application scenario to obtain data on the internal rate of return (IRR) and payback period under different utilization methods. For example, if a certain type of coal-based solid waste is used to produce building materials, its initial investment includes equipment procurement, transportation, and processing costs, and the subsequent returns are the revenues obtained from selling building materials. By calculating these economic parameters, the financial feasibility of this application scenario can be predicted and support can be provided for decision-making.

[0042] After obtaining the economic benefit calculation data for different application scenarios, based on the return on investment and the payback period, the economic feasibility of each application scenario is ranked, and the top three application scenarios are selected as the resource utilization paths for priority development. The goal of this stage is to identify the application scenarios with the highest economic return and the shortest payback period, so as to provide a direction for the efficient utilization of coal-based solid waste resources. For example, after comparing several application scenarios, assume that the return on investment of a certain coal-based solid waste in the energy recovery field is 15% and the payback period is 4 years, while the return on investment in the soil remediation field is 10% and the payback period is 6 years. Obviously, the application scenario in the energy recovery field will be given priority for development.

[0043] Next, by constructing a regional market demand database, the demand parameters of potential users within the region are matched and analyzed with the resource utilization paths for priority development to obtain a resource-user matching degree score. The regional market demand database includes parameters such as the demand volume of users in different regions, resource quality requirements, and purchasing capabilities. For example, building materials enterprises within a certain region may have higher requirements for the particle size and moisture content of coal-based solid waste, while agricultural companies may be more concerned about its organic matter content. On this basis, through a matching algorithm, the priority development paths are docked with the market demand to obtain the matching degree score of each resource application scenario and the market demand.

[0044] Combining the resource-user matching degree score and the previously constructed coal-based solid waste resource value evaluation index system, a comprehensive resource value score and an optimal market matching plan are generated. In this process, the comprehensive score will comprehensively consider the economic, environmental, and social values of coal-based solid waste resources, as well as the adaptability and potential of this resource in the market. Through this multi-dimensional comprehensive evaluation, a coal-based solid waste resource utilization plan that best meets the regional market demand and has high economic and social values can be obtained, further promoting the efficient utilization and sustainable development of coal-based solid waste.

[0045] For example, for a certain coal-based solid waste sample, assume that its application potential in the building materials production field is 0.85 (according to the application potential matrix), its economic benefit calculation shows that the return on investment is 16% and the payback period is 3.5 years, and this application scenario is highly matched with the market demand. Finally, the comprehensive resource value score is obtained as 90, and the resource-user matching degree score is 85, indicating that it has high commercial value and market prospects in this field, so it can be preferentially selected as the main application scenario for this region.

[0046] In a specific embodiment, the process of executing step S105 may specifically include the following steps: The coal-based solid waste resources in the market matching plan are identified and managed using electronic tag technology, recording the unique identification code and basic characteristic parameters of the resources to obtain the electronic identity card of the coal-based solid waste resources; Based on the electronic ID cards of coal-based solid waste resources and Internet of Things sensing devices, data is collected throughout the whole process of resource generation, collection, transportation, treatment to final application, and the transfer trajectory data of coal-based solid waste resources is obtained; An environmental monitoring sensor network is deployed in the application area of coal-based solid waste resources to monitor air quality, water quality and soil conditions in real time, and dynamic environmental impact data is obtained; Based on the transfer trajectory data of coal-based solid waste resources and the dynamic environmental impact data, a multi-dimensional benefit evaluation system of economic benefit indicators, environmental benefit indicators and social benefit indicators is constructed, and the quantitative standard of the utilization benefit of coal-based solid waste resources is obtained; The quantitative standard of the utilization benefit of coal-based solid waste resources is compared and analyzed with the preset target, the achievement rate and deviation value of each benefit indicator are calculated, and the benefit realization degree score and gap analysis result are obtained; Based on the benefit realization degree score and gap analysis result, machine learning algorithms are applied to mine patterns and summarize rules from historical project data, and transfer data and multi-dimensional benefit evaluation results are obtained.

[0047] Specifically, the electronic tag technology is applied to the identification management of coal-based solid waste resources. Each batch of coal-based solid waste resources is assigned a unique identification code, which runs through the data collection, processing and storage processes to ensure the data traceability and independence of each sample. Through electronic tags (such as RFID tags), the basic characteristic parameters of each coal-based solid waste sample, such as sampling location, sampling time, physical and chemical properties (such as particle composition, heavy metal content, bulk density, etc.) will be recorded to generate an "electronic ID card for coal-based solid waste resources". This process provides data support for subsequent transfer tracking, monitoring and management.

[0048] Based on electronic ID cards and IoT sensing devices for coal-based solid waste resources, data is collected throughout the entire process of coal-based solid waste resources from generation, collection, transportation, treatment to final application, generating the transfer trajectory data of coal-based solid waste resources. For example, in the collection link of coal-based solid waste, sensors with GPS positioning functions are used to record the geographical location of the resources; during transportation, information such as the temperature, humidity, and loading capacity of the transportation vehicles is collected in real time through IoT technology to ensure real-time monitoring of the quality and quantity of coal-based solid waste resources during transportation; in the treatment link, sensors record data such as the temperature and treatment time of coal-based solid waste. These information are ultimately uploaded to the central database to form a digital transfer record of the resources, ensuring data transparency and traceability at each link. In the application area of coal-based solid waste resources, an environmental monitoring sensor network is deployed to monitor the impact of the utilization of coal-based solid waste resources on the surrounding environment in real time. The monitoring network includes air quality sensors, water quality sensors, and soil condition sensors. The air quality sensors detect the concentration of pollutants such as PM2.5 and CO2 in real time; the water quality sensors monitor parameters such as heavy metal ions, dissolved oxygen, and pH value in the water body; the soil sensors measure indicators such as heavy metal content, pH value, and soil humidity in the soil. These environmental monitoring data provide real-time data support for the ecological impact assessment of coal-based solid waste resources and lay the foundation for subsequent environmental risk early warning.

[0049] After collecting the transfer trajectory data of coal-based solid waste resources and the dynamic data of environmental impacts, a multi-dimensional benefit evaluation system is constructed, which includes economic benefit indicators, environmental benefit indicators, and social benefit indicators. The economic benefit indicators mainly include cost savings of coal-based solid waste resources, return on investment, improvement of production efficiency, etc.; the environmental benefit indicators cover the degree of environmental improvement after resource recovery, such as the reduction of pollutants and the reduction of carbon emissions; the social benefit indicators include employment creation, local economic development, health impacts, etc. By quantifying these indicators, a benefit evaluation standard for the utilization of coal-based solid waste resources is constructed, providing a scientific basis for resource management decisions. A comparative analysis is carried out between the benefit quantification standard of coal-based solid waste resource utilization and the preset goals. The preset goals can be environmental protection standards, economic return rates, social benefit goals, etc. for the utilization of coal-based solid waste resources. For each type of coal-based solid waste resource, the system calculates the achievement rate and deviation value of each benefit indicator through benefit achievement degree scoring and gap analysis. For example, if the expected carbon emission reduction target for a certain type of coal-based solid waste is 50 tons, but the actual reduction is 48 tons, with a gap of -2 tons, the benefit achievement degree score will be affected. This analysis result helps managers understand the benefit deviations existing in the resource utilization process and adjust the resource management strategy in a timely manner.

[0050] Based on the benefit realization degree scoring and gap analysis results, machine learning algorithms are applied to mine patterns and summarize rules from historical project data. Through the training and analysis of a large amount of historical project data, machine learning algorithms can identify potential rules in aspects such as resource flow, environmental monitoring, and economic benefits, providing guidance for future management of coal-based solid waste resources. For example, the algorithm can discover that in a certain area during the recycling process of coal-based solid waste resources, the recycling efficiency of a certain type of resource is relatively high and the environmental benefits are significant. Based on these rules, the algorithm will give optimization suggestions for future projects to ensure a more efficient and environmentally friendly resource utilization path.

[0051] In a specific embodiment, the process of executing step S106 may specifically include the following steps: Perform data standardization processing on the resource value score, resource flow data, and multi-dimensional benefit evaluation results, convert them into decision input parameters with a unified dimension, and obtain a comprehensive decision-making dataset for coal-based solid waste resources; Construct a decision tree model based on the comprehensive decision-making dataset for coal-based solid waste resources, conduct hierarchical analysis on resource classification, utilization methods, and value realization paths, and obtain a decision tree for coal-based solid waste resource management; Apply the Monte Carlo simulation method to the decision tree for coal-based solid waste resource management, generate multi-round scenario simulation data under different decision paths, and obtain a decision risk probability distribution map; Based on the decision risk probability distribution map and multi-objective optimization algorithms, conduct trade-off calculations for the goals of maximizing economic benefits, minimizing environmental risks, and optimizing social values, and obtain a set of Pareto optimal decision-making schemes; Apply the analytic hierarchy process to the set of Pareto optimal decision-making schemes, and conduct comprehensive evaluation in combination with regional development needs and policy guidance factors to obtain the optimal decision-making scheme for coal-based solid waste resource management; Match the optimal decision-making scheme for coal-based solid waste resource management with historical successful cases, extract key success factors and implementation paths, and obtain a decision-making scheme for coal-based solid waste resource management.

[0052] Specifically, data standardization processing is performed on the resource value scores, resource transfer data, and multi-dimensional benefit evaluation results. The purpose of this process is to eliminate the difficulty in comparison caused by different measurement units between different indicators and ensure that all decision-making input parameters are processed under a unified measurement unit. Common standardization methods include Min-Max normalization or Z-score normalization. For example, the range of resource value scores may be from 0 to 100, the quantity range of transfer data is relatively large, and the results of benefit evaluation may vary greatly between different units. By uniformly converting these data into standardized values between 0 and 1, the fairness and accuracy of subsequent analysis can be ensured. After standardization processing, a comprehensive decision-making dataset of coal-based solid waste resources with a unified measurement unit is obtained. This dataset contains the value of various resources, transfer data, and benefit evaluation information, providing basic data for subsequent decision support.

[0053] Based on this comprehensive decision-making dataset, a decision tree model is then constructed to conduct hierarchical analysis on resource classification, utilization methods, and value realization paths. The decision tree model is a common classification and regression analysis method. Through the division and hierarchical analysis of different resource utilization paths, it can clearly show the resource selection and value realization methods under different decision paths. For example, in the decision tree of a certain type of coal-based solid waste, it may conduct step-by-step screening based on the physical and chemical properties of the resources (such as particle size, moisture content, etc.) and the economic benefits and environmental impacts of the application scenarios, and finally determine the most suitable resource utilization path. At each layer of the decision tree, there will be a corresponding decision node, and the nodes are connected through judgment conditions to finally form a complete decision path tree. After the decision tree is established, the Monte Carlo simulation method is used to conduct multiple rounds of scenario simulations. This method randomly generates different input parameters (such as economic benefits, environmental impacts, etc.) to simulate the possible results under different decision paths and generates multiple rounds of scenario simulation data. For example, during the simulation process, factors such as the market demand for resources, resource price fluctuations, and policy environment changes may be changed to obtain the resource utilization results under multiple simulation scenarios. The Monte Carlo simulation assigns probability distributions to each decision path through multiple random samplings and finally generates a decision risk probability distribution graph. This graph presents the probability distribution of indicators such as economic benefits and environmental impacts under different decision paths, helping decision-makers understand the risks and benefits of different paths.

[0054] Based on the Monte Carlo simulation results, a multi-objective optimization algorithm is used to calculate the trade-off among the goals of maximizing economic benefits, minimizing environmental risks, and optimizing social values. These goals are usually conflicting. For example, in some cases, to increase economic benefits, a part of environmental protection or social benefits may need to be sacrificed. Through the multi-objective optimization algorithm, these conflicting goals can be comprehensively considered to find the best balance point among the goals. Using the concept of Pareto optimal solutions, the optimization algorithm will generate a set of Pareto optimal decision-making solution sets, which achieve the optimal state in the balance of economic benefits, environmental risks, and social values, that is, maximize other goals without sacrificing one goal.

[0055] The Analytic Hierarchy Process (AHP) is applied to further evaluate the Pareto optimal decision-making solution set. The AHP is a decision analysis method that decomposes complex decision-making problems into multiple levels and conducts weighted scoring on different decision-making solutions by constructing a judgment matrix. According to the regional development needs and policy orientation factors, combined with expert scoring and weight analysis, each solution can be scored to obtain a comprehensive evaluation value. The AHP helps to comprehensively evaluate the feasibility of each solution from different dimensions. For example, if a certain solution has an advantage in economic benefits but has a low score in environmental risk control, the weights of each goal can be appropriately adjusted according to the policy orientation of this region to obtain the most suitable decision-making solution.

[0056] The optimal decision-making solution is pattern-matched with historical successful cases, and key success factors and implementation paths are extracted from historical data. Through the analysis of past successful cases, the system can identify which strategies, technologies, and management measures have been successful under similar conditions and apply these factors to the current decision-making. For example, in the process of coal-based solid waste resource recovery in some regions, by optimizing the transportation route, improving the treatment efficiency, strengthening environmental monitoring, etc., the balance of high economic benefits and low environmental risks has been successfully achieved. Summarizing and integrating these experiences into the current decision-making can effectively improve the implementation effect of the solution.

[0057] The above describes the classification method of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics in the embodiments of the present application. Next, the classification system of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics in the embodiments of the present application will be described. Please refer to Figure 2 One embodiment of the classification system of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics in the embodiments of the present application includes: A processing module 201, configured to collect and digitally process coal-based solid waste resource data to obtain a coal-based solid waste resource database and a regional ecological characteristic database; An analysis module 202, configured to perform a correlation analysis on the characteristics of coal-based solid waste resources and regional ecological elements based on the coal-based solid waste resource database and the regional ecological feature database, so as to obtain a set of characteristic correlation parameters and key influencing factors; A modeling module 203, configured to perform multi-dimensional classification modeling on the coal-based solid waste resources according to the set of characteristic correlation parameters and the key influencing factors, so as to obtain a coal-based solid waste resource classification model and a resource characteristic prediction result; An evaluation module 204, configured to perform multi-dimensional evaluation on the commercial value of the coal-based solid waste resources based on the coal-based solid waste resource classification model and the resource characteristic prediction result, so as to obtain a resource value score and a market matching plan; A monitoring module 205, configured to perform full-life cycle monitoring on the utilization process of the coal-based solid waste resources in the market matching plan, so as to obtain resource transfer data and a multi-dimensional benefit evaluation result; An input module 206, configured to input the resource value score, the resource transfer data, and the multi-dimensional benefit evaluation result into a decision-making engine for comprehensive analysis, so as to obtain a management decision-making plan for the coal-based solid waste resources.

[0058] Through the collaborative cooperation of the above-mentioned various components, by collecting and digitally processing the data of coal-based solid waste resources, a coal-based solid waste resource database and a regional ecological characteristic database are constructed, providing high-quality basic data support for subsequent analysis and solving the problems of non-standard data collection and scattered storage in traditional methods; through the correlation analysis of the characteristics of coal-based solid waste resources and regional ecological elements, a set of characteristic correlation parameters and key influencing factors are obtained, revealing the internal connection between the physical and chemical properties of coal-based solid waste and the regional ecological environment. In particular, the application of artificial intelligence algorithms such as multivariate statistical analysis methods, principal component analysis, and canonical correlation analysis makes the correlation analysis results highly interpretable and scientific. The characteristics of these algorithms significantly improve the accuracy of correlation pattern recognition and the precision of ecological factor impact measurement; based on the set of characteristic correlation parameters and key influencing factors, multi-dimensional classification modeling is carried out, and intelligent algorithms such as hierarchical clustering, support vector machines, and random forests are used to construct classification models. The characteristics of these algorithms enable the classification process to fully consider the non-linear relationship and high-dimensional characteristics of the data, greatly improving the accuracy and stability of coal-based solid waste resource classification; through the multi-dimensional evaluation of the commercial value of coal-based solid waste resources, a resource value score and a market matching plan are obtained, building a bridge from resource classification to value realization. The characteristics of the multi-objective optimization algorithm applied effectively solve the multi-dimensional balance problem in resource value evaluation; the whole life cycle of the coal-based solid waste resource utilization process is monitored, and Internet of Things technology and data mining algorithms are used to obtain resource transfer data and multi-dimensional benefit evaluation results. The characteristics of these algorithms perform well in processing time-series data and benefit evaluation, providing real-time feedback on the resource utilization effect; finally, the resource value score, resource transfer data, and multi-dimensional benefit evaluation results are input into the decision-making engine for comprehensive analysis to obtain a management decision-making plan for coal-based solid waste resources. The characteristics of algorithms such as Monte Carlo simulation and analytic hierarchy process enable the decision-making process to have the ability to handle uncertainty and multi-objective trade-offs, realizing the full-chain intelligent management from data to decision-making, and comprehensively improving the scientificity, accuracy, and efficiency of coal-based solid waste resource management.

[0059] Above Figure 2 The classification system of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics in the embodiments of the present invention is described in detail from the perspective of modular functional entities. Next, the classification device of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics in the embodiments of the present invention is described in detail from the perspective of hardware processing.

[0060] Figure 3FIG. 0 is a schematic structural diagram of a coal-based solid waste physical and chemical property classification device based on regional ecological characteristics provided by an embodiment of the present invention. The coal-based solid waste physical and chemical property classification device 300 based on regional ecological characteristics may vary greatly due to configuration or performance, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 for storing application programs 333 or data 332 (for example, one or more mass storage device terminals). Among them, the memory 320 and the storage media 330 may be transient storage or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the coal-based solid waste physical and chemical property classification device 300 based on regional ecological characteristics. Further, the processor 310 may be configured to communicate with the storage media 330 and execute a series of instruction operations in the storage media 330 on the coal-based solid waste physical and chemical property classification device 300 to implement the steps of the above-mentioned coal-based solid waste physical and chemical property classification method based on regional ecological characteristics.

[0061] The coal-based solid waste physical and chemical property classification device 300 based on regional ecological characteristics may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or, one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that Figure 3 The shown structure of the coal-based solid waste physical and chemical property classification device based on regional ecological characteristics does not limit the coal-based solid waste physical and chemical property classification device provided by the present invention, and may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0062] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the coal-based solid waste physical and chemical property classification method based on regional ecological characteristics.

[0063] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units may refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0064] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to enable a coal-based solid waste physical and chemical property classification device based on regional ecological characteristics (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0065] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A classification method for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics, characterized in that, The method includes: Collecting and digitally processing coal-based solid waste resource data to obtain a coal-based solid waste resource database and a regional ecological characteristic database; Based on the coal-based solid waste resource database and the regional ecological characteristic database, conducting a correlation analysis on the characteristics of coal-based solid waste resources and regional ecological elements to obtain a set of characteristic correlation parameters and key influencing factors; According to the set of characteristic correlation parameters and the key influencing factors, performing multi-dimensional classification modeling on coal-based solid waste resources to obtain a coal-based solid waste resource classification model and resource characteristic prediction results; Based on the coal-based solid waste resource classification model and the resource characteristic prediction results, conducting multi-dimensional evaluation of the commercial value of coal-based solid waste resources to obtain a resource value score and a market matching plan; Monitoring the entire life cycle of the utilization process of coal-based solid waste resources in the market matching plan to obtain resource transfer data and multi-dimensional benefit evaluation results; Inputting the resource value score, the resource transfer data, and the multi-dimensional benefit evaluation results into a decision-making engine for comprehensive analysis to obtain a management decision-making plan for coal-based solid waste resources.

2. The classification method of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics according to claim 1, wherein The collecting and digitally processing coal-based solid waste resource data to obtain a coal-based solid waste resource database and a regional ecological characteristic database includes: Conducting on-site collection of coal-based solid waste samples to obtain original coal-based solid waste samples and sampling point location data; Encoding and preprocessing the original coal-based solid waste samples to obtain standardized coal-based solid waste samples and unique sample identification codes; Detecting the physical properties of the standardized coal-based solid waste samples to obtain coal-based solid waste physical property parameters including particle composition, bulk density, and porosity; Analyzing the chemical composition of the standardized coal-based solid waste samples to obtain coal-based solid waste chemical property parameters including elemental composition, organic matter content, and heavy metal content; Collecting ecological element data for the area corresponding to the sampling point location data to obtain original regional ecological characteristic data including climate data, terrain data, and vegetation cover data; Standardizing, integrating, and storing the unique sample identification code, the coal-based solid waste physical property parameters, the coal-based solid waste chemical property parameters, and the original regional ecological characteristic data to obtain a coal-based solid waste resource database and a regional ecological characteristic database.

3. The classification method of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics according to claim 1, wherein The conducting a correlation analysis on the characteristics of coal-based solid waste resources and regional ecological elements based on the coal-based solid waste resource database and the regional ecological characteristic database to obtain a set of characteristic correlation parameters and key influencing factors includes: Standardizing the physical and chemical property parameters of coal-based solid waste in the coal-based solid waste resource database to obtain a standardized coal-based solid waste characteristic data set; Standardizing the ecological element parameters in the regional ecological characteristic database to obtain a standardized regional ecological characteristic data set; Based on the standardized coal-based solid waste characteristic data set and the standardized regional ecological characteristic data set, calculating the correlation coefficient matrix between the physical and chemical properties of coal-based solid waste and regional ecological elements to obtain significantly correlated parameter pairs; Applying the principal component analysis method to the standardized coal-based solid waste characteristic data set for dimensionality reduction processing to obtain coal-based solid waste characteristic principal component factors with a cumulative variance contribution rate of not less than 85%; Based on the principal component factors of the coal-based solid waste characteristics and the standardized regional ecological characteristic dataset, perform canonical correlation analysis to obtain the canonical correlation pattern between the coal-based solid waste characteristics and regional ecological elements; According to the canonical correlation pattern and the significant correlation parameter pair, use the random forest algorithm to calculate the feature importance index, and obtain the feature association parameter set and the key influencing factors.

4. The classification method of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics according to claim 1, wherein According to the feature association parameter set and the key influencing factors, perform multi-dimensional classification modeling on the coal-based solid waste resources to obtain the coal-based solid waste resource classification model and the resource characteristic prediction results, including: Randomly divide the sample data in the coal-based solid waste resource database into a training dataset and a test dataset according to a ratio of 7:3 to obtain the coal-based solid waste resource training set and test set; Apply the hierarchical clustering algorithm to the coal-based solid waste resource training set, determine the optimal clustering value by calculating the silhouette coefficient and the Davies-Bouldin index, and obtain the classification number of the coal-based solid waste resources; Based on the feature association parameter set and the key influencing factors, construct a classification model including K-means clustering, support vector machine, and random forest, select the model with the optimal performance through cross-validation, and obtain the optimal classification model of the coal-based solid waste resources; Perform statistical analysis on the characteristic parameters of various types of resources, calculate the characteristic distribution characteristics of each type, and obtain the characteristic description files of various types of resources; Based on the characteristic description files and the key influencing factors, construct a resource characteristic prediction model, predict the characteristics of the coal-based solid waste samples, and obtain the resource characteristic prediction results.

5. The classification method of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics according to claim 1, wherein Based on the coal-based solid waste resource classification model and the resource characteristic prediction results, perform multi-dimensional evaluation on the commercial value of the coal-based solid waste resources to obtain the resource value score and the market matching plan, including: Construct a multi-dimensional value evaluation framework for coal-based solid waste resources including economic value dimension, environmental value dimension, and social value dimension, perform hierarchical quantification on the resource characteristic prediction results, and obtain the coal-based solid waste resource value evaluation index system; Based on the coal-based solid waste resource classification model, perform application scenario analysis on various coal-based solid waste resources to obtain the coal-based solid waste resource application potential matrix; Calculate the economic benefits of each application scenario in the coal-based solid waste resource application potential matrix to obtain the return on investment and investment payback period data under different utilization methods; Based on the return on investment and investment payback period data, rank the economic feasibility of each application scenario, select the top three application scenarios, and obtain the resource utilization path for priority development; Construct a regional market demand database, match and analyze the demand parameters of potential users in the region with the resource utilization path for priority development, and obtain the resource-user matching degree score; According to the resource-user matching degree score and the coal-based solid waste resource value evaluation index system, generate the comprehensive resource value score and the optimal market matching plan.

6. The classification method of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics according to claim 1, wherein, Monitor the entire life cycle of the utilization process of the coal-based solid waste resources in the market matching plan to obtain the resource transfer data and the multi-dimensional benefit evaluation results, including: Apply electronic tag technology to the coal-based solid waste resources in the market matching scheme for identification management, record the unique identification code and basic characteristic parameters of the resources, and obtain the electronic identity card of coal-based solid waste resources; Based on the electronic identity card of coal-based solid waste resources and Internet of Things sensing devices, collect data on the whole process of resources from generation, collection, transportation, treatment to final application, and obtain the transfer trajectory data of coal-based solid waste resources; Deploy an environmental monitoring sensor network in the application area of coal-based solid waste resources to monitor air quality, water quality and soil conditions in real time, and obtain dynamic environmental impact data; Based on the transfer trajectory data of coal-based solid waste resources and the dynamic environmental impact data, construct a multi-dimensional benefit evaluation system of economic benefit indicators, environmental benefit indicators and social benefit indicators, and obtain the quantitative standard of the utilization benefit of coal-based solid waste resources; Compare and analyze the quantitative standard of the utilization benefit of coal-based solid waste resources with the preset target, calculate the achievement rate and deviation value of each benefit indicator, and obtain the benefit realization degree score and gap analysis result; Based on the benefit realization degree score and the gap analysis result, apply machine learning algorithms to mine patterns and summarize rules from historical project data, and obtain resource transfer data and multi-dimensional benefit evaluation results.

7. The classification method of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics according to claim 1, wherein Input the resource value score, the resource transfer data and the multi-dimensional benefit evaluation results into the decision-making engine for comprehensive analysis to obtain the management decision-making scheme for coal-based solid waste resources, including: Perform data standardization processing on the resource value score, the resource transfer data and the multi-dimensional benefit evaluation results, and convert them into decision-making input parameters with a unified dimension to obtain the comprehensive decision-making data set for coal-based solid waste resources; Based on the comprehensive decision-making data set of coal-based solid waste resources, construct a decision tree model, and perform hierarchical analysis on resource classification, utilization methods and value realization paths to obtain the management decision tree for coal-based solid waste resources; Apply the Monte Carlo simulation method to the management decision tree of coal-based solid waste resources to generate multi-round scenario simulation data under different decision-making paths, and obtain the decision-making risk probability distribution map; Based on the decision-making risk probability distribution map and multi-objective optimization algorithms, perform trade-off calculations on the goals of maximizing economic benefits, minimizing environmental risks and optimizing social values to obtain the Pareto optimal decision-making scheme set; Apply the analytic hierarchy process to the Pareto optimal decision-making scheme set, and conduct comprehensive evaluation in combination with regional development needs and policy orientation factors to obtain the optimal management decision-making scheme for coal-based solid waste resources; Match the optimal management decision-making scheme for coal-based solid waste resources with historical successful cases, extract key success factors and implementation paths, and obtain the management decision-making scheme for coal-based solid waste resources.

8. A classification system for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics, characterized in that, Used to implement the classification method of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics as described in any one of claims 1-7, and the classification system of the physical and chemical properties of coal-based solid waste based on regional ecological characteristics includes: A processing module for collecting and digitally processing coal-based solid waste resource data to obtain a coal-based solid waste resource database and a regional ecological characteristic database; An analysis module, configured to perform a correlation analysis on the characteristics of coal-based solid waste resources and regional ecological elements based on the coal-based solid waste resource database and the regional ecological characteristic database, so as to obtain a characteristic correlation parameter set and key influencing factors; A modeling module, configured to perform multi-dimensional classification modeling on coal-based solid waste resources according to the characteristic correlation parameter set and the key influencing factors, so as to obtain a coal-based solid waste resource classification model and a resource characteristic prediction result; An evaluation module, configured to perform multi-dimensional evaluation on the commercial value of coal-based solid waste resources based on the coal-based solid waste resource classification model and the resource characteristic prediction result, so as to obtain a resource value score and a market matching plan; A monitoring module, configured to perform full-life cycle monitoring on the utilization process of coal-based solid waste resources in the market matching plan, so as to obtain resource transfer data and a multi-dimensional benefit evaluation result; An input module, configured to input the resource value score, the resource transfer data, and the multi-dimensional benefit evaluation result into a decision engine for comprehensive analysis, so as to obtain a management decision plan for coal-based solid waste resources.

9. A classification device for the physical and chemical properties of coal-based solid waste based on regional ecological characteristics, characterized in that, It includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the method for classifying the physical and chemical properties of coal-based solid waste based on regional ecological characteristics according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, the processor is caused to execute the method for classifying the physical and chemical properties of coal-based solid waste based on regional ecological characteristics according to any one of claims 1 to 7.

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