Decision-making method and system for resource utilization direction of multi-source coal-based solid waste

Abstract

The invention discloses a decision-making method and system for a multi-source coal-based solid waste resource utilization direction, and relates to the technical field of environmental engineering, and the method comprises the steps: constructing a coal-based solid waste cooperation characteristic model, constructing a solid waste characteristic spectrum through multi-dimensional physical and chemical characteristic detection, analyzing a key cooperation factor, and generating an interaction relation matrix; based on the interaction relation matrix, establishing a matching relation between the coal-based solid waste combination and a recycling technology chain, and outputting a technical scheme cluster; generating a scheme priority sequence through dynamic influence factor analysis and a policy response mechanism; generating a configuration strategy set based on the scheme priority sequence and the regional space-time constraint; and establishing a closed-loop feedback engine, and triggering dynamic updating of the configuration strategy set when external condition changes are monitored. According to the method, the complex synergistic effect among the solid waste components is accurately quantified, the problem that coal-based solid waste treatment lacks systematic evaluation and overall planning in a traditional method is solved, the resource conversion efficiency is improved, and economic benefits and environmental performance are optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental engineering, and particularly to a decision-making method and system for the resource utilization direction of multi-source coal-based solid wastes. Background Art

[0002] Coal-based solid wastes (such as coal gangue, fly ash, desulfurized gypsum, etc.) are a large number of solid wastes generated during coal mining, washing, and combustion. Their cumulative stacking not only occupies a large amount of land resources but may also cause environmental pollution problems. At present, the resource utilization of coal-based solid wastes mainly focuses on single fields such as building material production, road base course, and land reclamation, lacking a systematic comprehensive utilization decision-making method. Traditional solid waste treatment usually adopts a simple linear decision-making model, relying on empirical judgment or single-index evaluation, and it is difficult to cope with the complex component changes and diverse utilization requirements of multi-source coal-based solid wastes.

[0003] In recent years, with the tightening of environmental protection policies and the promotion of the concept of circular economy, the resource utilization of coal-based solid wastes has received extensive attention. However, there are still obvious deficiencies in the existing technology during the decision-making process of coal-based solid waste resource utilization: on the one hand, the understanding of the complex synergistic effects among solid waste components is limited, making it difficult to achieve efficient component matching; on the other hand, there is a lack of a dynamic response mechanism, making it difficult to adapt to external factors such as policy changes and market fluctuations. These problems lead to low resource conversion efficiency and poor economic benefits, restricting the depth and breadth of the resource utilization of coal-based solid wastes. Summary of the Invention

[0004] The present invention provides a decision-making method and system for the resource utilization direction of multi-source coal-based solid wastes, which are used to solve the technical problems that the decision-making of coal-based solid waste resource utilization in the existing technology lacks systematicness and is difficult to cope with complex component changes and dynamic adjustments of the external environment.

[0005] In view of this, the first aspect of the present invention provides a decision-making method for the resource utilization direction of multi-source coal-based solid wastes, including: Construct a coal-based solid waste collaborative characteristic model, construct a solid waste characteristic spectrum through multi-dimensional physical and chemical characteristic detection, analyze key collaborative factors, and generate an interaction relationship matrix; Based on the interaction relationship matrix, establish a matching relationship between the coal-based solid waste combination and the resource utilization technology chain through a multi-objective optimization algorithm, and output a cluster of technical solutions; Based on the cluster of technical solutions, generate a scheme priority sequence through dynamic influencing factor analysis and policy response mechanism; Based on the scheme priority sequence and regional space-time constraints, generate a configuration strategy set through a constraint optimization method; Establish a closed-loop feedback engine, and when external conditions change are monitored, trigger the dynamic update of the configuration strategy set.

[0006] Optionally, building a collaborative characteristic model for coal-based solid waste includes: Preprocess the original samples of multi-source coal-based solid waste to obtain a standardized sample set; Conduct multi-dimensional physical and chemical property detections on the standardized sample set to obtain physical and chemical property data; Build a feature space based on the physical and chemical property data and perform dimensionality reduction processing to generate a solid waste feature spectrum; Based on the physical and chemical property data, calculate and generate a set of key collaborative factors; Based on the set of key collaborative factors and the solid waste feature spectrum, build an interaction relationship matrix between the components of coal-based solid waste and generate a collaborative conversion characteristic data set.

[0007] Optionally, establishing a matching relationship between the coal-based solid waste combination and the resource utilization technology chain through a multi-objective optimization algorithm includes: Build a knowledge base for the resource utilization technology chain, where the knowledge base for the resource utilization technology chain contains a set of resource utilization technology nodes and technology chain connection rules; Based on the interaction relationship matrix and the collaborative conversion characteristic data set, determine the coal-based solid waste combination, calculate the collaborative matching degree of different coal-based solid waste combinations, and generate a set of solid waste combination schemes through an incremental clustering algorithm; Using the set of key collaborative factors, establish an adaptability evaluation model between the coal-based solid waste combination and the resource utilization technology nodes; Based on the output results of the adaptability evaluation model, build a bipartite graph model between the set of solid waste combination schemes and the set of resource utilization technology nodes; Apply a multi-objective evolutionary algorithm on the bipartite graph model for path optimization to generate a Pareto optimal solution set; Conduct hierarchical clustering analysis on the Pareto optimal solution set to obtain a family of technical solutions, optimize and configure the process parameters of the family of technical solutions in combination with the collaborative conversion characteristic data set, and integrate to form a cluster of technical solutions.

[0008] Optionally, generating a scheme priority sequence through dynamic influencing factor analysis and policy response mechanism includes: Build a dynamic influencing factor evaluation system to conduct a temporal evaluation on each scheme in the cluster of technical solutions; Establish a policy impact matrix, quantify the impact coefficients of policy factors on each evaluation index, and calculate the policy sensitivity; Analyze the collaborative relationship between the schemes within the cluster of technical solutions, build a collaborative effect evaluation model, and generate a collaborative effect score; Design a dynamic coupling evaluation function to fuse the dynamic score, policy sensitivity, and collaborative effect score to generate a comprehensive evaluation value for the scheme; Based on the comprehensive evaluation value of the scheme, apply the temporal TOPSIS method to generate a scheme priority sequence.

[0009] Optionally, generating a configuration strategy set through a constraint optimization method includes: Construct a dynamic spatio-temporal constraint system, and quantify geographical distribution, resource flow, seasonal fluctuations, and infrastructure conditions into a set of constraint parameters; Based on the sequence of solution priorities, establish a timing diagram for the deployment of technical solutions, and set the implementation cycle and dependency relationships for each solution; Design a multi-stage constraint optimization model, with the comprehensive regional resource benefit as the objective function and the set of constraint parameters as the boundary conditions; Apply the piecewise linear programming method to solve the multi-stage constraint optimization model and generate a configuration plan; Perform a robustness analysis on the configuration plan, simulate the scenario of constraint condition fluctuations, and generate a set of configuration strategies.

[0010] Optionally, triggering the dynamic update of the set of configuration strategies includes: Real-time monitor the content of key components of solid waste, environmental protection policy information, and regional carbon emission data; Calculate the fluctuation value of the content of key components of solid waste, the update flag of the environmental protection policy standard version, and the deviation value of the regional carbon emission quota; Establish an event handling mechanism to monitor external conditions and determine whether the trigger criteria are met; Based on the bipartite graph model reconstruction algorithm, locate the affected resource utilization technology chain nodes and the solid waste combination relationship according to the trigger event type, and perform local reconstruction and parameter re-optimization on the matching relationship.

[0011] Optionally, the physical and chemical property data includes elemental composition data, specific surface area data, pore structure parameters, and pyrolysis characteristic parameters; The set of key synergy factors includes material conversion synergy factors, element migration synergy factors, and reaction kinetics synergy factors.

[0012] The second aspect of the present invention provides a decision-making system for the resource utilization direction of multi-source coal-based solid waste, including: A characteristic analysis module for constructing a coal-based solid waste synergy characteristic model, constructing a solid waste characteristic spectrum through multi-dimensional physical and chemical property detection, analyzing key synergy factors, and generating an interaction relationship matrix; A solution generation module for establishing a matching relationship between coal-based solid waste combinations and resource utilization technology chains through a multi-objective optimization algorithm based on the interaction relationship matrix and the synergy conversion characteristic data set, and outputting a cluster of technical solutions; A priority evaluation module for generating a sequence of solution priorities through dynamic influencing factor analysis and policy response mechanism based on the cluster of technical solutions; A strategy optimization module for generating a set of configuration strategies through a constraint optimization method based on the sequence of solution priorities and regional spatio-temporal constraints; A dynamic adjustment module for establishing a closed-loop feedback engine to trigger the dynamic update of the set of configuration strategies when external condition changes are detected.

[0013] The beneficial effects of the present invention are as follows: By accurately identifying the synergistic effects among the components of coal-based solid waste, the present invention improves the conversion efficiency of traditional single-component treatment, optimizes the material conversion process, and enhances the level of comprehensive resource utilization; The combination of a precisely matched technical path and a multi-objective optimization algorithm reduces ineffective conversion and resource waste, improves the input-output ratio, and enhances the economic efficiency of solid waste treatment; Through the optimization of process parameters and the collaborative configuration of the technical chain, harmful substance emissions and energy consumption are reduced, and the environmental friendliness of the solid waste treatment process is improved; The application of multi-dimensional physical and chemical property analysis and a collaborative model enables the system to handle more complex and diverse combinations of coal-based solid waste, overcoming the limitations of traditional methods in dealing with complex components. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a flowchart of a decision-making method for the resource utilization direction of multi-source coal-based solid waste.

[0016] Figure 2 It is a flowchart for constructing a collaborative characteristic model of coal-based solid waste in a decision-making method for the resource utilization direction of multi-source coal-based solid waste.

[0017] Figure 3 It is a flowchart for generating a sequence of scheme priorities in a decision-making method for the resource utilization direction of multi-source coal-based solid waste. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to make the objectives, features, and advantages of the present invention more obvious and understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0019] Example 1, referring to Figures 1 to 3 , which is the first embodiment of the present invention. This embodiment provides a decision-making method for the resource utilization direction of multi-source coal-based solid waste. The flowchart of this method is as Figure 1 shown, and this method includes: S1: Construct a collaborative characteristic model for coal-based solid waste. Build a characteristic spectrum of solid waste through multi-dimensional physical and chemical characteristic detection, analyze key collaborative factors, and generate an interaction relationship matrix.

[0020] In the specific implementation manner of the present invention, the flow chart for constructing the collaborative characteristic model of coal-based solid waste is as Figure 2 shown, and specifically includes the following steps: S1.1: Uniquely identify and preprocess the original samples of multi-source coal-based solid waste to obtain a standardized sample set.

[0021] Specifically, the identification uses a five-tuple code, including a geographical region code, a source power plant code, a solid waste type classification code, a production date (in the format of YYYYMMDD), and a batch number. The preprocessing includes crushing, drying, screening, and homogenization.

[0022] S1.2: Perform multi-dimensional physical and chemical characteristic detection on the standardized sample set to obtain physical and chemical characteristic data, and record process environment parameters at the same time.

[0023] Among them, the physical and chemical characteristic data includes elemental composition data (major elements, trace elements), specific surface area data, pore structure parameters, and pyrolysis characteristic parameters. Specifically, the content data of major elements, including Al, Si, Ca, Fe, S, etc., is determined by double verification using X-ray fluorescence spectrometry (XRF) and inductively coupled plasma optical emission spectrometry (ICP-OES); the content data of trace elements, including Na, K, Mg, etc., is determined by inductively coupled plasma mass spectrometry (ICP-MS); the specific surface area data is obtained by the BET nitrogen adsorption method; the pore structure parameters are determined by the nitrogen adsorption-desorption method; under a nitrogen atmosphere (100 mL / min), starting from room temperature, the temperature is increased to 900 °C at three heating rates of 5 °C / min, 10 °C / min, and 15 °C / min respectively, and the pyrolysis characteristic parameters, including the maximum weight loss rate, the maximum weight loss temperature, and the residual mass percentage, are obtained through a synchronous thermal analyzer.

[0024] Furthermore, the process environment parameters include process operation parameters (temperature, pressure, gas flow rate, residence time) and external constraint parameters (environmental protection standard limits, resource utilization rate requirements). The process operation parameters are collected through the DCS system, and the external constraint parameters are obtained through the enterprise environmental protection management system. The system sets a data validity verification mechanism: when the detected data deviates from the mean range, a data review process is triggered; if the review confirms that the data is abnormal, re-sampling and detection are performed.

[0025] S1.3: Construct a feature space based on the physical and chemical characteristic data and perform dimensionality reduction processing to generate a solid waste characteristic spectrum.

[0026] Specifically, organize the physicochemical property data into a feature matrix; after performing Z-score standardization on the feature matrix, use the principal component analysis (PCA) method for dimensionality reduction: First, calculate the covariance matrix, solve for the eigenvalues and eigenvectors, arrange the eigenvalues in descending order, select the top k principal components whose cumulative contribution rate reaches the preset contribution rate threshold to generate the reduced-dimensional solid waste characteristic spectrum, and evaluate the dimensionality reduction effect by calculating the reconstruction error, with the error threshold set to 0.15. Preferably, the preset contribution rate threshold is 85%.

[0027] S1.4: Calculate and generate a key co-factor set based on the physicochemical property data.

[0028] Among them, the key co-factor set includes a material transformation co-factor, an element migration co-factor, and a reaction kinetics co-factor. Specifically, based on the major element content data, calculate the oxide molar ratios such as the aluminum-silicon ratio (Al 2 O 3 / SiO 2 ), the calcium-sulfur ratio (CaO / SO 3 ), the iron-calcium ratio (Fe 2 O 3 / CaO) as the material transformation co-factor; based on the trace element content data, calculate the standardized molar ratios of characteristic element pairs such as Na / K, Mg / Ca as the element migration co-factor; use the weighted geometric mean method to integrate the specific surface area data, pore structure parameters, and pyrolysis characteristic parameters to construct the reaction kinetics co-factor, and the weight coefficients are determined by analysis of variance.

[0029] S1.5: Construct an interaction relationship matrix among the components of coal-based solid waste based on the key co-factor set and the solid waste characteristic spectrum.

[0030] Specifically, associate the key co-factor set with the reduced-dimensional solid waste characteristic spectrum to construct an enhanced feature matrix. This matrix constructs the interaction relationship through the following steps: First, use partial correlation analysis to quantify the correlation strength among the material transformation co-factor, the element migration co-factor, and the reaction kinetics co-factor; then, construct a symmetric interaction relationship matrix, and the matrix element values are represented by standardized correlation coefficients, with the value range [-1, 1], where a positive value indicates a synergistic promotion effect, a negative value indicates an inhibitory competition effect, and a correlation coefficient absolute value greater than 0.5 indicates a significant correlation; finally, perform Z-score standardization on the interaction relationship matrix and conduct a significance test through Bootstrap resampling (the number of resampling times is 500 times, and the confidence level is 95%).

[0031] Preferably, the method for constructing the interaction relationship matrix innovatively integrates three types of synergistic factors, namely material transformation, element migration, and reaction kinetics, breaking through the limitations of traditional solid waste treatment that only rely on the characteristics of single components or simple linear superposition. By establishing an enhanced feature matrix and a standardized correlation coefficient system, the precise quantification and characterization of the complex synergistic effects among the components of coal-based solid waste are achieved. In particular, the method combining covariance analysis and partial least squares regression effectively reveals the synergistic promotion and inhibitory competition mechanisms among the components. At the same time, the significance test based on Bootstrap resampling ensures the statistical reliability of the interaction relationship matrix, providing reliable data support for the subsequent orthogonal test scheme design and technical path optimization.

[0032] S1.6: Establish a coal-based solid waste co-transformation experimental platform, conduct gradient combination experiments, and generate a co-transformation characteristic data set.

[0033] Specifically, select pairs of coal-based solid waste components that are significantly correlated in the interaction relationship matrix (through Bootstrap significance test and the absolute value of the correlation coefficient is greater than 0.5), design an orthogonal test scheme, take temperature, gas flow rate, residence time, and mixing ratio as the investigation factors, conduct co-transformation experiments within the range of externally constrained parameters, and obtain a co-transformation characteristic data set of each component under different process conditions.

[0034] S2: Based on the interaction relationship matrix and the co-transformation characteristic data set, establish the matching relationship between the coal-based solid waste combination and the resource utilization technology chain through a multi-objective optimization algorithm, and output a cluster of configurable technical solutions.

[0035] In the specific implementation manner of the present invention, S2 specifically includes the following steps: S2.1: Construct a knowledge base of the resource utilization technology chain, and the knowledge base of the resource utilization technology chain includes a set of resource utilization technology nodes and technology chain connection rules.

[0036] Specifically, the set of resource utilization technology nodes includes thermochemical conversion nodes (pyrolysis, gasification, combustion), physicochemical conversion nodes (materialization, extraction), and environmental protection treatment nodes (flue gas purification, wastewater treatment). Each technology node includes raw material adaptability parameters (substance composition range, particle size requirements), process operation parameters (temperature, pressure, gas flow rate, residence time), product characteristic parameters (yield, quality index), and technical and economic parameters (investment cost, operation cost). The technology chain connection rules define the pre-order dependence relationship, material flow direction constraints, and energy balance requirements among the technology nodes, are represented by a directed graph structure, and mark the compatibility conditions of each connection.

[0037] S2.2: Determine the coal-based solid waste combination based on the interaction relationship matrix and the co-transformation characteristic data set, calculate the co-matching degree of different coal-based solid waste combinations, and generate a set of solid waste combination schemes through an incremental clustering algorithm.

[0038] Specifically, extract significantly correlated (the absolute value of the correlation coefficient is greater than 0.5 and passes the Bootstrap resampling significance test) coal-based solid waste component pairs from the interaction relationship matrix to construct a candidate pool of coal-based solid waste combinations; combine the co-conversion characteristic data set to construct an evaluation index system (including conversion efficiency, co-effect intensity, and economic feasibility), and establish a fuzzy evaluation matrix; calculate the co-matching degree for each coal-based solid waste combination, where the co-matching degree is defined as the fuzzy comprehensive evaluation result; apply the incremental clustering algorithm (the initial clustering center is the coal-based solid waste combination with the highest co-matching degree, the clustering radius is 0.3, and the incremental step size is 0.05) to classify the coal-based solid waste combinations to form a set of solid waste combination schemes.

[0039] S2.3: Use the key co-factor set to establish an adaptability evaluation model between the coal-based solid waste combination and the resource utilization technology node by using the BP neural network.

[0040] Specifically, construct a three-layer neural network model. The input layer includes the material conversion co-factor, element migration co-factor, and reaction kinetics co-factor of the solid waste combination. The hidden layer uses the ReLU activation function, and the output layer corresponds to the adaptability score of the resource utilization technology node; use the co-conversion characteristic data set as the training set and adopt five-fold cross-validation to optimize the model parameters; for each coal-based solid waste combination, predict its adaptability score with each resource utilization technology node and calculate the 95% confidence interval; when the adaptability score is higher than the preset adaptability threshold (0.7), establish a preliminary matching relationship between the coal-based solid waste combination and the corresponding resource utilization technology node.

[0041] S2.4: Based on the output results of the adaptability evaluation model, construct a bipartite graph model between the set of solid waste combination schemes and the set of resource utilization technology nodes.

[0042] Specifically, the left nodes of the bipartite graph model represent each coal-based solid waste combination in the set of solid waste combination schemes, and the right nodes represent each technology node in the set of resource utilization technology nodes; the weight of the edge is determined by the adaptability score and is given three attributes: resource conversion attribute (based on the co-conversion efficiency data in S1.6), economic attribute (based on the cost-benefit data of the technology node), and environmental impact attribute (based on the emission data of the technology node); when the adaptability score is lower than the critical value (0.4), no connection edge is established.

[0043] S2.5: Apply the improved NSGA-Ⅲ algorithm to the bipartite graph model, and perform path optimization with the goals of maximizing resource conversion efficiency, optimizing economic benefits, and minimizing environmental impact to generate a Pareto optimal solution set.

[0044] Specifically, set the population size to 100 individuals, the number of evolutionary generations to 500, the crossover probability to 0.8, and the mutation probability to 0.1. Adopt an adaptive reference point generation strategy to enhance the search space coverage, and introduce crowding distance to maintain the diversity of the solution set. For each resource-based technology chain path, comprehensively evaluate its performance in three objective dimensions, and apply dominance sorting to determine the non-dominated solution set. Introduce the technology chain connection rule as a constraint condition to eliminate the paths that do not meet the process logic, and finally form the Pareto optimal solution set.

[0045] S2.6: Conduct hierarchical clustering analysis on the Pareto optimal solution set to obtain the technology solution families, optimize and configure the process parameters for the technology solution families in combination with the co-transformation characteristic dataset, and integrate them to form configurable technology solution clusters.

[0046] Specifically, construct an affinity matrix based on the similarity of the technology chain paths, determine the optimal number of clusters K using the Ward minimum variance method, and generate the technology solution families using the hierarchical clustering algorithm. For each technology solution family, in combination with the co-transformation characteristic dataset in S1.6, optimize the process parameter configuration (temperature, gas flow rate, residence time, mixing ratio) using the response surface method, select the Box-Behnken design to construct the experimental plan, and establish a second-order polynomial regression equation. Use analysis of variance to evaluate the significance of the model, and adopt cross-validation to prevent overfitting. Calculate the standardized sensitivity coefficients of each parameter and their interaction effects to determine the parameter regulation priority. Mark the applicable coal-based solid waste types, operating parameter ranges, and environmental constraint conditions for each technology solution, and finally integrate all technology solution families and their optimized parameter configurations to form a complete configurable technology solution cluster.

[0047] Preferably, in this step, a resource-based technology chain knowledge base and a multi-objective optimization framework are established to achieve a systematic mapping from solid waste characteristics to technology solutions. In particular, a method combining fuzzy comprehensive evaluation and BP neural network is used to establish an accurate matching mechanism between solid waste combinations and technology nodes, and the multi-objective optimization of the technology chain is realized through an improved NSGA-Ⅲ algorithm. The finally generated configurable technology solution cluster not only ensures the resource conversion efficiency but also takes into account economic benefits and environmental impacts, reflecting the systematicness and scientificity of the solution selection.

[0048] S3: Based on the configurable technology solution cluster, generate a sequence of solution priorities through dynamic influencing factor analysis and policy response mechanism.

[0049] In the specific implementation manner of the present invention, the flowchart for generating the sequence of solution priorities is as Figure 3 shown, specifically including the following steps: S3.1: Construct a dynamic influencing factor evaluation system to conduct a temporal evaluation on each solution in the configurable technology solution cluster.

[0050] Specifically, the dynamic influencing factor evaluation system includes a market volatility indicator group (raw material supply stability, product demand changes, price elasticity), a regional development indicator group (industrial planning fit, infrastructure completeness, technical support ability), and a social effect indicator group (employment driving ability, industrial chain contribution degree, public acceptance degree); the time series analysis method is used to dynamically track each indicator, a quarterly rolling evaluation mechanism is established, the tracking period is set to one year, historical database and market research data are utilized, combined with Monte Carlo simulation, to calculate the dynamic scores of each plan and their 95% confidence intervals, forming a dynamic scoring matrix of technical solutions.

[0051] S3.2: Establish a policy impact matrix, quantify the impact coefficients of policy factors on each evaluation indicator, and calculate the policy sensitivity of each technical solution.

[0052] Specifically, first identify key policy factors, including environmental protection emission standards, comprehensive utilization policies of resources, energy structure adjustment policies, and regional industrial planning, and mark the trend of policy intensity; secondly, construct an impact matrix of policy factors on evaluation indicators, and use the expert scoring method to determine the impact coefficients (range [-1, 1]), positive values indicate a promoting effect, negative values indicate an inhibitory effect, 0 indicates no impact, and a dynamic time coefficient is set for trend policies; finally, based on the implementation progress and impact degree of each policy, calculate the comprehensive impact score as the policy sensitivity of the plan.

[0053] S3.3: Analyze the synergistic relationship among the solutions within the configurable technical solution cluster, construct a synergistic effect evaluation model, and generate a synergistic effect score.

[0054] Specifically, based on the classification results of the technical solution families in S2.6, analyze the possible synergistic types among the solutions, mainly including material synergy type (closed-loop material utilization), energy synergy type (cascaded utilization of waste heat), and process synergy type (sharing of equipment and facilities); construct a benefit ratio function to quantify the synergistic effect, and measure the synergistic intensity by calculating the ratio of the comprehensive benefit of the solution combination to the sum of the benefits of individual solutions; calculate the marginal contribution rate of each solution in different combinations; construct a comprehensive synergistic effect evaluation model, use the benefit ratio and marginal contribution rate as core indicators, and use the entropy weight method to determine the weight coefficients of each indicator to obtain the synergistic effect score.

[0055] S3.4: Design a dynamic coupling evaluation function, integrate the dynamic score, policy sensitivity, and synergistic effect score, and generate a comprehensive evaluation value of the plan.

[0056] Specifically, construct a time-series weighted coupling evaluation function, and the specific formula is as follows:

[0057] Among them, E t is the comprehensive evaluation value at time t, Dt is the dynamic score at time t, is the risk adjustment term of the dynamic score, S is the policy sensitivity, C is the synergy effect score, is the time-varying weight coefficient at time t, is the risk aversion coefficient, is the policy sensitivity weight index, is the synergy effect weight index. The time-varying weight coefficient is optimized by the dynamic programming algorithm, using historical case data as the training set to minimize the prediction error; when the policy sensitivity is higher than the sensitivity threshold (0.7) or the synergy effect score is lower than the synergy threshold (0.3), the evaluation value is penalized and adjusted by adjusting the risk aversion coefficient.

[0058] S3.5: Based on the comprehensive evaluation value of the solution, apply the time series TOPSIS method to generate the solution priority sequence.

[0059] Specifically, after standardizing the time series comprehensive evaluation value, construct a time-varying weighted standardized decision matrix; calculate the positive and negative ideal solutions considering the time lag effect, and solve the time series distance from each solution to the positive and negative ideal solutions; sort the solutions based on the time integral value of the relative closeness to generate the solution priority sequence.

[0060] Preferably, this step breaks through the limitations of traditional static evaluation, establishes a time series evaluation system for technical solutions by introducing dynamic influencing factors such as market fluctuations, regional development, and social effects. Especially through time-varying policy sensitivity analysis and the time series TOPSIS method, a dynamic response mechanism for solution evaluation is realized, improving the forward-looking and adaptability of decision-making. This evaluation system complements the technical and economic environment evaluation in S2, jointly constructing a comprehensive solution optimization framework.

[0061] S4: Based on the solution priority sequence and regional spatio-temporal constraints, generate a configuration strategy set through a constraint optimization method.

[0062] In the specific implementation manner of the present invention, S4 specifically includes the following steps: S4.1: Construct a dynamic spatio-temporal constraint system, and quantify geographical distribution, resource flow, seasonal fluctuations, and infrastructure conditions into a constraint parameter set.

[0063] Specifically, use the geographic information system to construct a digital map of the regional coal-based solid waste distribution, mark the solid waste generation points, potential treatment points, and transportation networks; construct a resource flow topology map, mark the upper limit of the processing capacity and the minimum economic scale of each node; analyze the seasonal fluctuation pattern based on the recorded process environment parameters and historical data of the past 5 years, and apply the Fourier transform method to predict seasonal supply and demand changes; quantify the infrastructure conditions through infrastructure evaluation indicators (including access level, stability coefficient, expansion potential value); all parameters are standardized to form a constraint parameter set.

[0064] S4.2: Based on the solution priority sequence, establish a timing diagram for the deployment of technical solutions, and set the implementation cycle and dependency relationships for each solution.

[0065] Specifically, take the solution priority sequence as the input, assign a three-stage time frame (preparation period, construction period, operation period) to each technical solution; analyze the dependency chain among the technical solution families to determine the necessary precedence relationships; use the critical path method to calculate the shortest implementation cycle, and optimize the resource allocation timing through resource load balancing technology; integrate the implementation cycle and dependency relationships of each solution to form a complete timing diagram for the deployment of technical solutions.

[0066] S4.3: Design a multi-stage constraint optimization model, with the comprehensive regional resource benefit as the objective function and the constraint parameter set as the boundary conditions.

[0067] Specifically, construct a multi-stage objective function, with the objective of each stage set as the sum of the present values of the comprehensive resource benefits of the stage; use the analytic hierarchy process to determine the weight coefficients of economic net income, environmental benefits, and social value for the quantitative evaluation of the comprehensive resource benefits; transform the constraint parameter set into linear constraints and identify non-linear constraints; design connection constraints between stages to ensure the continuity of resource flow and the smooth transition of facility utilization rates.

[0068] S4.4: Apply the piecewise linear programming method to solve the multi-stage constraint optimization model and generate a configuration plan.

[0069] Specifically, transform the non-linear constraints into linear forms through piecewise linear approximation, and select the break points based on the results of sensitivity analysis; use the rolling horizon method to decompose the multi-stage problem into a series of short-term optimization problems, set the rolling window width to 3 years and the step size to 1 year; use the Lagrangian relaxation method to handle the constraints that are difficult to linearize directly, and iteratively update the Lagrange multipliers through the subgradient method; use a commercial solver to solve the multi-stage constraint optimization model, set the relative gap tolerance to 0.5%, and obtain the optimal configuration plan under all constraint conditions.

[0070] S4.5: Perform a robustness analysis on the configuration plan, simulate the scenarios of constraint condition fluctuations, and generate a set of configuration strategies.

[0071] Specifically, use an improved scenario tree construction method to analyze the uncertainty of constraint conditions and design typical fluctuation scenarios; conduct a simulation evaluation of the configuration plan under each scenario and calculate the fluctuation range of key performance indicators; determine the robustness index of the plan based on reliability theory and screen out the configuration plans with good adaptability under different scenarios; formulate emergency adjustment strategies for high-frequency fluctuation factors, and finally form a set of configuration strategies including multiple alternative plans.

[0072] Preferably, in this step, by constructing a dynamic spatio-temporal constraint system and a multi-stage optimization model, the transformation from the priority sequence to the specific configuration strategy is realized. In particular, the piecewise linear programming and scenario analysis methods are adopted, which not only ensure the feasibility of the scheme but also improve the robustness of the configuration strategy. This method fully considers the regional characteristics and resource endowments, providing a systematic implementation path for the resource utilization of coal-based solid waste.

[0073] S5: Establish a closed-loop feedback engine to trigger the dynamic update of the configuration strategy set when external condition changes are detected.

[0074] In the specific implementation manner of the present invention, S5 specifically includes the following steps: S5.1: Construct a multi-source data acquisition network to monitor the content of key components of solid waste, environmental protection policy information, and regional carbon emission data in real time.

[0075] Specifically, deploy on-line monitoring equipment to collect the content of key components in the physicochemical property data, and the sampling frequency is adaptively adjusted according to the process fluctuation characteristics, and the sampling interval ranges from 1 to 24 hours; establish an environmental protection policy information scraping system to cover the policy release channels at the central, provincial, and local levels, and set keyword triggers and semantic analysis modules; access the data interface of the regional carbon emission trading platform to update the carbon emission quota and price information daily; all data are stored in a distributed time series database after preprocessing (denoising, outlier detection, missing value filling), and the data retention period is 3 years.

[0076] S5.2: Calculate the fluctuation value of the content of key components of solid waste, the update identifier of the environmental protection policy standard version, and the deviation value of the regional carbon emission quota.

[0077] Specifically, calculate the fluctuation value of the content of key components of solid waste, which is defined as the relative deviation percentage of the current value from the moving average of the reference period (the previous 30 days); through the policy text similarity comparison and key parameter extraction algorithm, generate the update identifier of the environmental protection policy standard version, and the identifier value of 0 indicates no update, and the non-zero value coding indicates the impact degree and type of the update; the regional carbon emission quota deviation value is calculated as the percentage of the difference between the actual cumulative emissions and the quota in the quota.

[0078] S5.3: Establish an event processing mechanism to monitor external conditions and judge whether the trigger standard is reached.

[0079] Among them, the trigger standard includes that the fluctuation of the content of key components of solid waste exceeds the threshold, the update of the environmental protection policy standard version, or the breakthrough of the warning value of the regional carbon emission quota.

[0080] Furthermore, set the hierarchical trigger criteria: the first-level threshold for the fluctuation of the key component content of solid waste is ±10%, the second-level threshold is ±20%, and the third-level threshold is ±30%; the update identification of the environmental protection policy standard version triggers as long as it is non-zero, and it is divided into three levels: low, medium, and high according to the degree of influence; the warning value of the regional carbon emission quota deviation is set to 80%, the early warning value is 90%, and the critical value is 95%; establish a priority evaluation mechanism for trigger events, and allocate processing resources according to the scope of influence and urgency; implement the event correlation analysis function to identify the cross-influence of multiple triggers.

[0081] S5.4: Based on the bipartite graph model reconstruction algorithm, locate the relationship between the affected resource-based technology chain nodes and solid waste combinations according to the type of trigger event, and perform local reconstruction and parameter re-optimization on the matching relationship.

[0082] Furthermore, for trigger events, map and identify the affected nodes and edges of the bipartite graph model; for the trigger caused by the fluctuation of the key component content of solid waste, recalculate the cooperative matching degree of the coal-based solid waste combination and update the connection weight with the resource-based technology node; for the trigger caused by the update of the environmental protection policy standard version, adjust the relevant coefficients of the policy impact matrix and re-evaluate the compliance of the plan; for the trigger of the regional carbon emission quota, preferentially adjust the configuration ratio of the high-carbon emission technology chain; adopt a gradient-based incremental optimization method for local search, only reconstruct the affected area, keep other parts stable, and achieve high computational efficiency.

[0083] S5.5: Execute the policy verification process, conduct consistency checks and feasibility evaluations on the reconstructed policy, and generate an updated configuration policy set.

[0084] Furthermore, conduct consistency checks on the reconstructed plan, verify the resource balance constraint, process compatibility constraint, and timing logic constraint; evaluate the short-term response performance and long-term stability of the reconstructed plan through rapid simulation; calculate the ratio of the plan adjustment cost to the expected benefit, and set a minimum benefit threshold (configurable parameter, default is 1.5 times the adjustment cost); sort the plans that pass the verification by priority to form a hierarchical response strategy; generate a policy implementation roadmap, including decision nodes, implementation steps, and effect evaluation indicators; finally, update the configuration policy set and feedback the update result to the system knowledge base to complete the closed-loop optimization.

[0085] S5.6: Establish a continuous learning and optimization module, and feedback the execution results to the relevant models.

[0086] Furthermore, the newly collected data is used to update the solid waste characteristic spectrum and the set of key collaborative factors; the prediction accuracy of the adaptability evaluation model is evaluated according to the actual operation effect, and the model parameters are updated using an online learning algorithm; the parameters of the dynamic coupling evaluation function are optimized using the newly added time-series samples; the solution efficiency and solution quality of the multi-stage constraint optimization model are regularly evaluated, and the model performance is improved by adjusting the adaptive algorithm parameters; a quarterly report mechanism is established to summarize the system response effect, identify the optimization direction, and automatically generate the monitoring focus for the next cycle.

[0087] Preferably, the present invention breaks through the limitations of the static configuration of traditional solid waste treatment technologies, innovatively constructs a multi-source data acquisition network and a multi-level trigger mechanism, can monitor the fluctuations of solid waste components, changes in the policy environment, and carbon emission constraints in real time, and accurately judge the timing of system adjustment. In particular, the local optimization strategy implemented through the bipartite graph model reconstruction algorithm greatly improves the system's response efficiency and computing performance to external changes, and achieves precise adjustment of key nodes while maintaining overall stability. At the same time, based on the closed-loop feedback mechanism of continuous learning and optimization, the entire resource utilization system has the ability of self-iteration and evolution, can continuously accumulate experience and knowledge, optimize model parameters and decision-making logic, and form an increasingly accurate strategy configuration ability.

[0088] Further, this embodiment also provides a decision-making system for the utilization direction of multi-source coal-based solid waste, including: a characteristic analysis module, used to construct a collaborative characteristic model of coal-based solid waste, construct a solid waste characteristic spectrum through multi-dimensional physical and chemical characteristic detection, analyze key collaborative factors, and generate an interaction relationship matrix; a solution generation module, used to establish a matching relationship between the coal-based solid waste combination and the resource utilization technology chain through a multi-objective optimization algorithm based on the interaction relationship matrix and the collaborative conversion characteristic data set, and output a cluster of configurable technology solutions; a priority evaluation module, used to generate a solution priority sequence based on the cluster of configurable technology solutions through dynamic influencing factor analysis and policy response mechanism; a strategy optimization module, used to generate a set of configuration strategies through a constraint optimization method based on the solution priority sequence and regional spatio-temporal constraints; a dynamic adjustment module, used to establish a closed-loop feedback engine, and trigger the dynamic update of the set of configuration strategies when external conditions changes are detected.

[0089] In summary, by accurately identifying the synergy between the components of coal-based solid waste, the present invention improves the conversion efficiency of traditional single-component treatment, optimizes the material conversion process, and enhances the level of comprehensive resource utilization; the combination of a precisely matched technical path and a multi-objective optimization algorithm reduces ineffective conversion and resource waste, improves the input-output ratio, and enhances the economic efficiency of solid waste treatment; through the optimization of process parameters and the collaborative configuration of the technology chain, harmful substance emissions and energy consumption are reduced, and the environmental friendliness of the solid waste treatment process is improved; the application of multi-dimensional physical and chemical property analysis and a synergy model enables the system to handle more complex and diverse combinations of coal-based solid waste, overcoming the limitations of traditional methods in dealing with complex components.

[0090] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended 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 recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A decision-making method for the resource utilization direction of multi-source coal-based solid waste, characterized in that: include: Construct a synergistic characteristic model of coal-based solid waste, construct a solid waste characteristic spectrum through multi-dimensional physical and chemical characteristic detection, analyze key synergistic factors, and generate an interactive relationship matrix; Based on the interactive relationship matrix, a matching relationship between coal-based solid waste combination and resource technology chain is established through a multi-objective optimization algorithm, and a technology solution cluster is output; Based on the technology solution cluster, a solution priority sequence is generated through dynamic influencing factor analysis and policy response mechanism; Based on the scheme priority sequence and regional spatiotemporal constraints, a configuration strategy set is generated by a constrained optimization method; A closed-loop feedback engine is established to trigger dynamic updating of the configuration strategy set when changes in external conditions are detected.

2. The decision-making method for resource utilization direction of multi-source coal-based solid waste according to claim 1 is characterized in that: The construction of the coal-based solid waste synergistic characteristic model includes: Pre-process the original samples of multi-source coal-based solid waste to obtain a standardized sample set; Performing multi-dimensional physical and chemical property detection on the standardized sample set to obtain physical and chemical property data; Constructing a feature space based on the physical and chemical property data and performing dimensionality reduction processing to generate a solid waste feature spectrum; Based on the physicochemical property data, a set of key synergistic factors is calculated and generated; Based on the key synergistic factor set and the solid waste characteristic spectrum, an interaction relationship matrix between coal-based solid waste components is constructed, and a synergistic transformation characteristic data set is generated.

3. The decision-making method for resource utilization direction of multi-source coal-based solid waste according to claim 1 is characterized in that: The matching relationship between coal-based solid waste combination and resource technology chain established by multi-objective optimization algorithm includes: Constructing a resource technology chain knowledge base, wherein the resource technology chain knowledge base includes a resource technology node set and a technology chain connection rule; Determine the coal-based solid waste combination based on the interaction relationship matrix and the synergistic transformation characteristic data set, calculate the synergistic matching degree of different coal-based solid waste combinations, and generate a solid waste combination solution set through an incremental clustering algorithm; Using the key synergistic factor set, establish the adaptability evaluation model of coal-based solid waste combination and resource technology nodes; Based on the output results of the adaptability evaluation model, a bipartite graph model between the solid waste combination scheme set and the resource technology node set is constructed; Applying a multi-objective evolutionary algorithm to the bipartite graph model to perform path optimization and generate a Pareto optimal solution set; A hierarchical clustering analysis is performed on the Pareto optimal solution set to obtain a family of technical solutions. The process parameters of the family of technical solutions are optimized and configured in combination with the collaborative transformation characteristic data set to form a technical solution cluster.

4. The decision-making method for resource utilization direction of multi-source coal-based solid waste according to claim 1 is characterized in that: The generation of the scheme priority sequence through dynamic influencing factor analysis and policy response mechanism includes: Build a dynamic influencing factor evaluation system to conduct a time-series evaluation of each solution in the technical solution cluster; Establish a policy impact matrix, quantify the impact coefficient of policy factors on each evaluation indicator, and calculate policy sensitivity; Analyze the synergy relationship between solutions within the technical solution cluster, build a synergy effect evaluation model, and generate a synergy effect score; Design a dynamic coupling evaluation function, integrate dynamic scoring, policy sensitivity and synergy effect scoring, and generate a comprehensive evaluation value for the scheme; Based on the comprehensive evaluation value of the scheme, the time series TOPSIS method is applied to generate a scheme priority sequence.

5. The decision-making method for resource utilization direction of multi-source coal-based solid waste according to claim 1 is characterized in that: Generating a configuration strategy set by using a constraint optimization method includes: Construct a dynamic spatiotemporal constraint system to quantify geographic distribution, resource flows, seasonal fluctuations, and infrastructure conditions into sets of constraint parameters; Based on the priority sequence of the solutions, establish a technical solution deployment time sequence diagram and set the implementation cycle and dependency relationship of each solution; Design a multi-stage constrained optimization model, taking the comprehensive benefits of regional resources as the objective function and the set of constraint parameters as boundary conditions; Applying piecewise linear programming method to solve the multi-stage constrained optimization model and generate a configuration scheme; Perform robustness analysis on configuration schemes, simulate constraint fluctuation scenarios, and generate configuration strategy sets.

6. The decision-making method for resource utilization direction of multi-source coal-based solid waste according to claim 1 is characterized in that: Triggering the dynamic update of the configuration policy set includes: Real-time monitoring of key components of solid waste, environmental policy information and regional carbon emission data; Calculate the fluctuation value of key components of solid waste, the update mark of environmental protection policy standards and the deviation value of regional carbon emission quota; Establish an event handling mechanism to monitor external conditions and determine whether the triggering criteria have been met; Based on the bipartite graph model reconstruction algorithm, the affected resource technology chain nodes and solid waste combination relationships are located according to the triggering event type, and the matching relationship is locally reconstructed and parameters are re-optimized.

7. The decision-making method for resource utilization direction of multi-source coal-based solid waste according to claim 2 is characterized in that: The physicochemical property data include element composition data, specific surface area data, pore structure parameters, and pyrolysis property parameters; The key synergistic factor set includes material transformation synergistic factors, element migration synergistic factors and reaction kinetics synergistic factors.

8. A decision-making system for the resource utilization direction of multi-source coal-based solid waste, characterized in that: include: The characteristic analysis module is used to build a synergistic characteristic model of coal-based solid waste, construct a solid waste characteristic spectrum through multi-dimensional physical and chemical characteristic detection, analyze key synergistic factors, and generate an interactive relationship matrix; A solution generation module is used to establish a matching relationship between coal-based solid waste combination and resource technology chain through a multi-objective optimization algorithm based on the interaction relationship matrix and the collaborative transformation characteristic data set, and output a technology solution cluster; Priority assessment module, which is used to generate a priority sequence of solutions based on a cluster of technical solutions through dynamic influencing factor analysis and policy response mechanism; A strategy optimization module, used to generate a configuration strategy set through a constraint optimization method based on the scheme priority sequence and regional spatiotemporal constraints; The dynamic adjustment module is used to establish a closed-loop feedback engine, and when changes in external conditions are detected, it triggers the dynamic update of the configuration strategy set.

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