Coal-based solid waste recycling full-process intelligent management and control platform and digital twin system

Abstract

The invention relates to the technical field of process data analysis and processing, and discloses a coal-based solid waste recycling full-process intelligent management and control platform and a digital twin system. The platform is characterized in that an identification module identifies a coal-based solid waste mineral phase through a ray diffractometer to obtain mineral composition classification data; the construction module constructs a digital twinborn model based on the classification data, simulates a dissolution reaction, and obtains weathering soil formation process data; the establishment module establishes a pollutant migration prediction model according to the process data to obtain environmental risk data; the optimization module optimizes the risk data through an improved particle swarm-genetic hybrid algorithm to obtain a resource path scheme; and the adjusting module adaptively adjusts the process parameters based on the path scheme to obtain a whole-process management and control instruction. The problems that accurate mineral phase recognition and intelligent classification are lacked in the coal-based solid waste recycling process, and a dynamic prediction model cannot be constructed and a weathering soil forming process cannot be simulated in real time based on mineral composition data are solved.

Description

Technical Field

[0001] The present application relates to the technical field of process data analysis and processing, and in particular to a full-process intelligent management and control platform and digital twin system for coal-based solid waste resource utilization. Background Art

[0002] Existing technologies primarily utilize traditional building materials manufacturing, combustion-based power generation, and valuable metal recovery methods for resource recovery. These methods typically rely on manual experience to set process parameters and employ regular sampling and testing to monitor the treatment process. These methods lack real-time tracking and intelligent control of changes in the physical and chemical properties of coal-based solid waste. Traditional coal gangue and fly ash processing technologies primarily focus on the production of a single product. They lack systematic methods for classifying and treating coal-based solid waste from different sources and properties, making large-scale, efficient resource utilization difficult.

[0003] The existing technology has the following shortcomings: First, the level of intelligence is limited. Most existing systems remain at the stage of data collection and simple analysis, lack deep learning and predictive maintenance capabilities, and cannot achieve true adaptive control; second, the environmental risk assessment model is not perfect, especially for the long-term prediction capabilities of complex processes such as pollutant migration and transformation, and the evolution of geological disaster risks in the ecological utilization of coal-based solid waste; third, the cost-benefit analysis and optimization algorithms are not mature enough, making it difficult to achieve the global optimal configuration of resource utilization paths under multi-objective constraints.

[0004] Based on the aforementioned technical deficiencies, further analysis revealed that existing technologies present even deeper challenges: how to establish a quantitative relationship model between the mineral phases of coal-based solid waste and its resource-recycling properties, how to dynamically simulate and predict the weathering and soil-forming process of coal-based solid waste, how to construct a multi-factor coupled environmental risk assessment system, and how to design intelligent decision-making algorithms for multi-objective collaborative optimization. Solving these problems requires the organic integration of physical and chemical mechanism modeling, digital twin technology, intelligent optimization algorithms, and adaptive control theory to form a comprehensive intelligent management and control technology system for the entire coal-based solid waste resource recovery process. Summary of the Invention

[0005] This application provides an intelligent management and control platform and digital twin system for the entire process of coal-based solid waste resource utilization. It first solves the basic technical problem of the lack of accurate mineral phase identification and intelligent classification in the process of coal-based solid waste resource utilization, and then solves the problem of being unable to build a dynamic prediction model and simulate the weathering and soil formation process in real time based on mineral composition data. Finally, it solves the problem of being unable to achieve multi-objective collaborative optimization and full-process adaptive management and control under environmental risk constraints.

[0006] In a first aspect, the present application provides a full-process intelligent management and control platform for coal-based solid waste resource utilization, which includes: An identification module is used to perform mineral phase identification processing on coal-based solid waste using a X-ray diffractometer to obtain classification data on the mineral composition of the coal-based solid waste; A construction module is used to construct a digital twin model of mineral phase transformation based on the classification data of the coal-based solid waste mineral composition, and simulate the dissolution reaction process of silicon and aluminum minerals to obtain data on the weathering process of coal gangue into soil; Establishing a module for establishing a pollutant migration prediction model based on the coal gangue weathering and soil formation process data to obtain site environmental risk data; An optimization module is used to perform multi-objective optimization processing on the site environmental risk data through an improved particle swarm-genetic hybrid algorithm to obtain a resource utilization path plan; The adjustment module is used to adaptively adjust the process parameters of coal-based solid waste treatment based on the resource recovery path plan to obtain full-process control instructions for coal-based solid waste resource recovery.

[0007] Secondly, this application provides a digital twin system that realizes intelligent management and control of the entire process of coal-based solid waste resource utilization based on an intelligent management and control platform for the entire process of coal-based solid waste resource utilization.

[0008] On the third aspect, a full-process intelligent management and control device for coal-based solid waste resource utilization is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory to enable the full-process intelligent management and control device for coal-based solid waste resource utilization to execute the above-mentioned full-process intelligent management and control platform for coal-based solid waste resource utilization.

[0009] In a fourth aspect, a computer-readable storage medium is provided, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the above-mentioned coal-based solid waste resource utilization full-process intelligent management and control platform.

[0010] In the technical solution provided by this application, the present invention uses a ray diffractometer to perform mineral phase identification processing on coal-based solid waste to obtain coal-based solid waste mineral composition classification data. Compared with traditional manual sampling and empirical judgment methods, ray diffraction technology can accurately identify the content ratios of mineral phases such as quartz, kaolinite, and illite, and realize quantitative classification of coal-based solid waste based on the molar ratio of silica to alumina, thereby providing accurate basic data support for subsequent resource processing, effectively solving the problems of inaccurate identification of coal-based solid waste properties and inconsistent classification standards in the existing technology. Based on the coal-based solid waste mineral composition classification data, a mineral phase transformation digital twin model is constructed and the dissolution reaction process of silica and aluminum minerals is simulated to obtain coal gangue weathering and soil formation process data. Digital twin technology can reflect the microscopic change process of physical minerals in real time by establishing a virtual mineral lattice and a three-dimensional bonded network structure. Compared with traditional static analysis methods, the digital twin model has the advantages of dynamic prediction and real-time synchronization, and can accurately simulate the mineral dissolution behavior under different temperature and pH conditions, providing scientific theoretical guidance and parameter optimization basis for the rapid weathering of coal gangue into soil. A pollutant migration prediction model is established based on the data of coal gangue weathering and soil formation process to obtain site environmental risk data. By constructing a three-dimensional groundwater flow numerical model and using the convection-diffusion equation to calculate pollutant transport, the migration path and concentration distribution of heavy metal ions in soil and groundwater can be accurately predicted. Compared with traditional empirical formulas and simplified models, this method takes into account multiple factors such as soil stratification characteristics, hydrogeological conditions and pollutant form transformation, significantly improving the accuracy and reliability of environmental risk assessment.

[0011] The site environmental risk data is subjected to multi-objective optimization processing through an improved particle swarm-genetic hybrid algorithm to obtain a resource recovery path plan. In the application of the improved particle swarm-genetic hybrid algorithm in the field of coal-based solid waste resource recovery, by taking the silicon-aluminum activity index, heavy metal leaching toxicity and soil formation efficiency as optimization targets, the improved particle swarm-genetic hybrid algorithm can quickly search for the optimal solution in a complex multi-dimensional parameter space. The global search capability of the particle swarm algorithm is combined with the local optimization characteristics of the genetic algorithm, effectively avoiding the defect of traditional optimization methods that are prone to falling into local optimality. The adaptive characteristics of the algorithm enable it to dynamically adjust the optimization strategy according to the characteristics of different coal-based solid wastes and environmental constraints. Compared with manual experience decision-making, the algorithm can comprehensively consider multiple objectives such as economic benefits, environmental safety and technical feasibility to achieve the global optimal configuration of the resource recovery path, significantly improving the scientificity and efficiency of decision-making. Based on the resource utilization path plan, the process parameters of coal-based solid waste treatment are adaptively adjusted to obtain the full-process control instructions for coal-based solid waste resource utilization. The adaptive adjustment mechanism monitors the deviation between the process parameters and the target set values ​​in real time, and uses the proportional integral differential control algorithm to generate accurate equipment control signals. Compared with traditional open-loop control and manual adjustment methods, this method has the advantages of fast response speed, high control accuracy and strong anti-interference ability. It can automatically adjust the process parameters according to changes in the properties of coal-based solid waste and fluctuations in environmental conditions, ensuring that the entire resource utilization process always operates in the optimal state. It effectively solves the problems of process parameter adjustment lag, insufficient control accuracy and poor system stability in the existing technology, and realizes intelligent, automated and refined management and control of coal-based solid waste resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 This is a schematic diagram of an embodiment of the intelligent management and control platform for the entire process of coal-based solid waste resource utilization in the embodiment of this application; Figure 2 It is a schematic block diagram of the structure of the full-process intelligent management and control equipment for coal-based solid waste resource utilization in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The embodiments of the present application provide a full-process intelligent management and control platform and digital twin system for coal-based solid waste resource utilization. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or apparatus.

[0015] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the full-process intelligent management and control platform for coal-based solid waste resource utilization includes: Identification module 101, used to perform mineral phase identification processing on coal-based solid waste using a X-ray diffractometer to obtain classification data of the mineral composition of the coal-based solid waste; A construction module 102 is used to construct a digital twin model of mineral phase transformation based on the classification data of the coal-based solid waste mineral composition, and simulate the dissolution reaction process of silicon and aluminum minerals to obtain data on the weathering and soil formation process of coal gangue; Establishing module 103, for establishing a pollutant migration prediction model based on the coal gangue weathering soil formation process data to obtain site environmental risk data; Optimization module 104, configured to perform multi-objective optimization processing on the site environmental risk data through an improved particle swarm-genetic hybrid algorithm to obtain a resource utilization path plan; The adjustment module 105 is used to adaptively adjust the coal-based solid waste treatment process parameters based on the resource recovery path solution to obtain full-process control instructions for coal-based solid waste resource recovery.

[0016] It is understandable that the execution subject of this application can be a digital twin system, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0017] Specifically, the identification module 101 uses a X-ray diffractometer to perform two-dimensional scanning detection on the coal-based solid waste sample, obtains X-ray diffraction spectrum data with a diffraction angle range of 5° to 80°, extracts the characteristic diffraction peak intensity values ​​of quartz, kaolinite, and illite through peak position recognition processing, calculates the mass percentage of silicon dioxide and aluminum oxide and obtains the molar ratio, and classifies the coal-based solid waste into three categories of low activity, medium activity, or high activity according to the numerical range, forming specific mineral composition classification data. The construction module 102 is based on the mineral composition classification data output by the identification module, and allocates the spatial positions of silicon atoms and aluminum atoms through molecular coordinate initialization processing, establishes the basic tetrahedral units of silicon-oxygen tetrahedron and aluminum-oxygen tetrahedron, and forms a three-dimensional bonded network topology structure by bridging assembly through sharing oxygen atoms. It is mapped to the digital space to establish a virtual mineral lattice containing atomic coordinates, bonding relationships and physical properties, and a real-time data synchronization interface and status update algorithm are set to establish a digital twin model of mineral phase transformation. At the same time, the reaction temperature and pH boundary condition parameters are set, the activation energy parameters of silicon-aluminum minerals are calculated, the dissolution reaction kinetic parameters are substituted into the silicate dissolution rate equation for iterative calculation, and the release process of silicon ions and aluminum ions is simulated according to the time step. The coal gangue weathering process data is generated by combining the microbial metabolism coupling calculation and the soil pH change.

[0018] Establishment module 103 analyzes the soil stratification characteristics of the coal gangue weathering process data, extracts porosity and permeability parameters to form site hydrogeological parameters, establishes a three-dimensional groundwater flow numerical model to calculate the head distribution at each node, uses the convection-diffusion equation to calculate pollutant transport, simulates the migration path of heavy metals in the soil, and calculates the heavy metal exceedance multiples through risk assessment and quantification to generate site environmental risk data. Optimization module 104 constructs a multi-objective function expression based on the site environmental risk data, including the silica-alumina activity index, heavy metal leaching toxicity, and soil formation efficiency. It initializes a particle swarm and assigns a position vector and velocity vector to each particle, where the position vector represents the combination of coal-based solid waste treatment process parameters. Through fitness evaluation and velocity update iterative processing, a genetic algorithm crossover mutation operation is initiated when particle fitness stagnates to generate new candidate solutions. Pareto front screening is used to select the process parameter configuration with the best overall performance from the non-dominated solution set to form a resource recovery path plan.

[0019] The adjustment module 105 performs process parameter analysis on the resource recovery path plan, extracts key control parameters such as geopolymer preparation temperature, stirring speed, solid-liquid ratio and activator addition amount, calculates the deviation between the current detection value and the target set value through real-time process parameter comparison processing, and uses proportional integral differential control calculation processing to generate corresponding equipment control signals. After execution priority sorting and timing coordination processing, they are sent to each control device in the order of the coal-based solid waste treatment process, forming a full-process management and control instruction for coal-based solid waste resource recovery.

[0020] For example, X-ray diffractometer testing revealed quartz as the primary component of coal gangue samples. Peak position identification and extraction of characteristic diffraction peak intensities revealed a molar ratio of silicon dioxide to aluminum oxide of 3.2, classifying it as a medium-active solid waste. The digital twin model established a corresponding silicon-oxygen tetrahedral network structure based on this classification data. Simulations revealed that the release rate of silicon ions gradually accelerated in an alkaline environment. Coupled microbial metabolism calculations indicated that the soil pH gradually neutralized from an initial 4.5 to 6.8, with organic matter content accumulating over time. A pollutant migration prediction model calculated groundwater flow distribution based on weathering and soil formation data. Convection-diffusion equations indicated that heavy metal ions were primarily concentrated in the surface soil, with excess concentrations within safe limits. An improved particle swarm-genetic hybrid algorithm, taking into account three objective functions, determined through multiple rounds of iterative optimization the optimal stirring speed of 280 rpm, a solid-to-liquid ratio of 1:2.5, and an activator dosage of 8% of the total weight. Based on these results, the adaptive control module generated specific equipment control instructions, ensuring that the entire coal-based solid waste recycling process operates within optimal process parameters.

[0021] In a specific embodiment, the identification module 101 is configured to: The coal-based solid waste sample is subjected to two-dimensional scanning detection by a X-ray diffractometer to obtain X-ray diffraction spectrum data with a diffraction angle range of 5° to 80°; Performing peak position identification processing on the X-ray diffraction spectrum data, extracting characteristic diffraction peak intensity values ​​of quartz, kaolinite, and illite, and obtaining qualitative identification results of mineral phases; Calculating the mass percentages of silicon dioxide and aluminum oxide based on the mineral phase qualitative identification results, and obtaining the molar ratio to obtain a quantitative parameter of the silicon-aluminum ratio; The quantitative parameter of the silicon-aluminum ratio is classified according to the numerical range. When the molar ratio is greater than 4.0, it is classified as low-activity solid waste; when the molar ratio is between 2.0 and 4.0, it is classified as medium-activity solid waste; when the molar ratio is less than 2.0, it is classified as high-activity solid waste, and the classification data of the mineral composition of coal-based solid waste is obtained.

[0022] Specifically, when the identification module 101 performs a two-dimensional scanning detection on the coal-based solid waste sample through a ray diffractometer, the ray diffractometer emits X-rays of a specific wavelength to irradiate the surface of the coal-based solid waste sample. When the rays encounter the crystal structure in the sample, diffraction occurs. The diffraction angle range is set to 5° to 80°, covering the characteristic diffraction angles of the main mineral phases in the coal-based solid waste. During the scanning process, the detector records the diffraction intensity values ​​at different angle positions according to a fixed step size, forming ray diffraction spectrum data with the diffraction angle as the horizontal coordinate and the diffraction intensity as the vertical coordinate.

[0023] In the peak position identification and processing stage, digital signal processing technology is used to analyze the X-ray diffraction spectrum data. First, the background noise and baseline drift in the spectrum are removed by a filtering algorithm, and then the peak detection algorithm is used to identify the significant peaks in the spectrum. The characteristic diffraction peak of quartz is located at 26.6°, the characteristic diffraction peak of kaolinite is located at 12.3°, and the characteristic diffraction peak of illite is located at 8.8°. The peak position identification algorithm determines the existence of the mineral phase by comparing the deviation between the measured peak position and the standard peak position, and records the intensity value of each characteristic peak at the same time. The intensity value reflects the relative content of the corresponding mineral phase in the sample, forming a qualitative identification result of the mineral phase.

[0024] When calculating the mass percentage of silica and alumina, the reference intensity method is used for quantitative calculation based on the intensity value of each mineral phase in the mineral phase qualitative identification results. Quartz minerals are completely composed of silica, kaolinite minerals contain silica and alumina components, and illite minerals also contain silica and alumina components. The total content of silica and alumina is obtained by multiplying the intensity value of each mineral phase by the corresponding chemical composition coefficient and summing them. When calculating the mass percentage, the mass of each component is divided by the total mass of the sample and multiplied by 100. The molar ratio calculation requires dividing the mass percentage by the corresponding molecular weight. The molecular weight of silica is 60.08 grams per mole, and the molecular weight of alumina is 101.96 grams per mole. The molar ratio is equal to the number of moles of silica divided by the number of moles of alumina.

[0025] The classification and processing of the quantitative parameters of the silicon-aluminum ratio is based on the relationship between the activity of coal-based solid waste and the molar ratio. When the molar ratio is greater than 4.0, it indicates that the silicon dioxide content is relatively high and the aluminum oxide content is low. Such coal-based solid waste has low reaction activity in an alkaline environment and is classified as low-activity solid waste. When the molar ratio is between 2.0 and 4.0, the ratio of silicon dioxide to aluminum oxide is relatively balanced, and it has a moderate degree of reaction activity, and is classified as medium-activity solid waste. When the molar ratio is less than 2.0, the aluminum oxide content is relatively high. Such coal-based solid waste has a strong reaction activity and is classified as high-activity solid waste. The classification results directly affect the selection and addition amount of the activator in the subsequent geopolymer preparation process.

[0026] In a specific embodiment, the construction module 102 includes: A processing unit is used to input the classification data of the coal-based solid waste mineral composition into a three-dimensional network structure modeling algorithm for spatial configuration processing, establish a bonding network model of silicon-oxygen tetrahedron and aluminum-oxygen tetrahedron, and obtain a digital twin model of mineral phase transformation; A calculation unit is used to set boundary condition parameters of a reaction temperature of 20° C. to 80° C. and a pH value of 8.0 to 14.0 based on the mineral phase transformation digital twin model, and calculate the activation energy parameters of silica-alumina minerals in an alkaline environment to obtain dissolution reaction kinetic parameters; An iterative unit is used to substitute the dissolution reaction kinetic parameters into the silicate dissolution rate equation for iterative calculation processing, simulate the release process of silicon ions and aluminum ions at intervals of a time step of 1 hour, and obtain a mineral dissolution progress curve; The coupling unit is used to perform microbial metabolism coupling calculation processing on the mineral dissolution process curve, and obtain the coal gangue weathering soil process data by combining the soil pH change and organic matter accumulation rate.

[0027] Specifically, when the processing unit of the construction module 102 inputs the classification data of the coal-based solid waste mineral composition into the three-dimensional network structure modeling algorithm, it first performs molecular coordinate initialization processing on the classification data, and allocates the position coordinates of silicon atoms and aluminum atoms in three-dimensional space according to the mineral phase content ratio output by the identification module. The three-dimensional network structure modeling algorithm uses crystallographic principles to establish the spatial relationship between atoms. Each silicon atom is connected with four oxygen atoms according to a tetrahedral geometric structure to form a silicon-oxygen tetrahedron, and each aluminum atom is also connected with four oxygen atoms to form an aluminum-oxygen tetrahedron. The basic tetrahedral units are bridged and assembled by sharing oxygen atoms to form a three-dimensional bonded system with a network topology. The three-dimensional bonded network topology is then mapped into the digital space to establish a virtual mineral lattice containing atomic coordinates, bonding relationships and physical properties. The digital mineral structure model establishes a dynamic correspondence between physical minerals and virtual models by setting a real-time data synchronization interface and a status update algorithm to form a digital twin model of mineral phase transformation.

[0028] When the calculation unit sets the boundary condition parameters based on the digital twin model of mineral phase transformation, the reaction temperature range is set to 20°C to 80°C, covering the temperature change range of coal-based solid waste under natural environment and industrial treatment conditions. The pH value is set to 8.0 to 14.0, which corresponds to the chemical environment of coal-based solid waste under the action of alkaline activators. The boundary condition parameters directly affect the reactivity and dissolution behavior of silica-alumina minerals. The activation energy parameter calculation is based on the principle of Arrhenius equation. The activation energy value of the reaction is determined by analyzing the change law of the reaction rate constant under different temperature conditions. The activation energy parameters of silica-alumina minerals in an alkaline environment reflect the energy threshold required for the destruction of the mineral lattice structure and ion release. The higher the activation energy value, the more difficult it is for the mineral to be dissolved by alkaline solution, and the lower the activation energy value, the more likely the mineral is to undergo dissolution reaction. The dissolution reaction kinetic parameters include key values ​​such as reaction rate constant, activation energy, and reaction order.

[0029] When the iterative unit substitutes the dissolution reaction kinetic parameters into the silicate dissolution rate equation for iterative calculation, the silicate dissolution rate equation describes the relationship between the rate and time of release of silicon ions and aluminum ions from the mineral lattice. The time step is set to 1 hour to ensure a balance between calculation accuracy and efficiency. The iterative calculation process repeats the dissolution rate calculation at a fixed time interval. Each iteration updates the dissolution rate at the current moment based on the ion concentration and mineral surface state at the previous moment. The silicon ion release process is affected by the mineral surface area, solution concentration gradient and temperature conditions. The aluminum ion release process also follows similar kinetic laws but has different reaction parameters. The iterative calculation continues until the preset simulation time length is reached. The simulation results are output in the form of a curve showing the change of ion concentration over time, forming a mineral dissolution process curve.

[0030] When the coupling unit performs microbial metabolism coupling calculation on the mineral dissolution process curve, the organic acids produced by the microbial metabolism process will accelerate the chemical weathering of minerals. At the same time, microbial respiration consumes oxygen and produces carbon dioxide, changing the chemical environment of the soil. The microbial metabolism coupling calculation introduces biological factors into the original chemical dissolution model and reflects the influence of microbial activity by modifying the parameters of the dissolution rate equation. The soil pH change data comes from the combined effect of microbial metabolic acid production and mineral dissolution releasing alkaline ions. The organic matter accumulation rate reflects the process of microbial death and decomposition and the accumulation of metabolic products in the soil. The coupling calculation unifies the chemical weathering process and the biological weathering process in the same mathematical model. The generated coal gangue weathering process data contains information on multiple dimensions such as mineral composition changes, ion concentration evolution, pH regulation and organic matter content growth.

[0031] In a specific embodiment, the processing unit is configured to: Performing molecular coordinate initialization processing on the coal-based solid waste mineral composition classification data, allocating the spatial positions of silicon atoms and aluminum atoms according to the mineral phase content ratio, and obtaining atomic space coordinate parameters; Establishing a tetrahedral geometric structure based on the atomic space coordinate parameters, connecting each silicon atom with four oxygen atoms to form a silicon-oxygen tetrahedron, and connecting each aluminum atom with four oxygen atoms to form an aluminum-oxygen tetrahedron, to obtain a basic tetrahedral unit; Performing a network connection process on the basic tetrahedral units, bridging and assembling silicon-oxygen tetrahedrons and aluminum-oxygen tetrahedrons by sharing oxygen atoms, thereby obtaining a three-dimensional bonded network topology structure; Mapping the three-dimensional bond network topology into digital space, establishing a virtual mineral lattice containing atomic coordinates, bonding relationships and physical property parameters, and obtaining a digital mineral structure model; A real-time data synchronization interface and a status update algorithm are set for the digital mineral structure model, a dynamic correspondence between physical minerals and virtual models is established, and a digital twin model of mineral phase transformation is obtained.

[0032] Specifically, when the processing unit performs molecular coordinate initialization processing, it receives the coal-based solid waste mineral composition classification data output by the identification module. The data contains the content ratio information of mineral phases such as quartz, kaolinite, and illite. The molecular coordinate initialization processing first determines the total number of silicon atoms and aluminum atoms according to the chemical composition of each mineral phase. Quartz minerals are completely composed of silicon-oxygen bonds and therefore contribute to the number of silicon atoms. Kaolinite and illite minerals contain both silicon atoms and aluminum atoms. The processing unit calculates the number ratio of silicon atoms to aluminum atoms according to the stoichiometric relationship, and then uses a random distribution algorithm to assign an initial coordinate position to each atom in three-dimensional space. The spatial coordinate parameters are represented by a Cartesian coordinate system. The position of each atom is determined by three values ​​of x, y, and z. The minimum distance constraint between atoms ensures that adjacent atoms do not overlap and collide. After the allocation is completed, an atomic spatial coordinate parameter data set containing the spatial position information of all silicon atoms and aluminum atoms is formed. When establishing the tetrahedral geometric structure, the processing unit starts to construct the basic structural unit based on the atomic space coordinate parameters. During the formation of the silicon-oxygen tetrahedron, each silicon atom acts as the central atom to establish a covalent bond with the four surrounding oxygen atoms. The position selection of the oxygen atom follows the tetrahedral geometric constraints. The silicon-oxygen bond length is set to a fixed value, and the bond angle follows the 109.5-degree standard angle of the tetrahedron. The formation process of the aluminum-oxygen tetrahedron is similar to that of the silicon-oxygen tetrahedron but uses different bond length parameters. The aluminum-oxygen bond length is slightly larger than the silicon-oxygen bond length, reflecting the ionic radius characteristics of the aluminum atom. The process of establishing the tetrahedral geometric structure determines the exact position of each oxygen atom relative to the central atom through geometric calculations. After completion, a basic tetrahedral unit set consisting of independent silicon-oxygen tetrahedral units and aluminum-oxygen tetrahedral units is obtained. In the network connection processing stage, the processing unit performs bridging assembly operations on the basic tetrahedron units. The bridging process realizes the connection between different tetrahedrons by sharing oxygen atoms. The shared oxygen atom becomes a bridge connecting two tetrahedrons. One oxygen atom belongs to two adjacent tetrahedral structures at the same time. The network connection algorithm first identifies tetrahedron pairs with connection conditions. The connection conditions include spatial distance constraints and geometric angle constraints. The tetrahedrons that meet the conditions are bridged by adjusting the position of oxygen atoms. The basic geometric shape of the tetrahedron remains unchanged during the bridging process. After the connection is completed, a network structure composed of multiple tetrahedrons connected by shared oxygen atoms is formed. The three-dimensional bonded network topology has branching and ring connection characteristics. The topological properties of the network reflect the crystal structure characteristics of the mineral. The processing unit records each connection relationship and network node information to form topological structure data.During the digital space mapping process, the processing unit converts the three-dimensional bond network topology into a computer-processable digital representation. The mapping process includes the digital storage of coordinate data, the graph theory representation of the bond relationship, and the database association of physical property parameters. The virtual mineral lattice exists in the digital space as a data structure, which contains an atomic coordinate matrix to record the three-dimensional position of each atom, a bond relationship matrix to describe the connection between atoms, and a physical property parameter database to store the physical and chemical properties related to each atom and bond. The digital mineral structure model fully describes the microstructural characteristics of the virtual mineral through the combination of these data structures.

[0033] The setting of real-time data synchronization interface and status update algorithm enables the digital mineral structure model to have dynamic response capabilities. The real-time data synchronization interface is responsible for receiving real-time data from physical detection equipment, including data input from temperature sensors, pH detectors, ion concentration monitors and other equipment. The status update algorithm adjusts the relevant parameters in the virtual model according to the received real-time data. Temperature changes will affect the atomic vibration amplitude and bonding stability, pH changes will affect the charge state and reaction activity of ions, and changes in ion concentration will affect chemical equilibrium and reaction direction. The algorithm converts these external changes into adjustments to model parameters through preset physical and chemical laws. The dynamic correspondence ensures that the virtual model always reflects the current state of the physical minerals. The digital twin model of mineral phase transformation achieves consistency between virtual and reality through this synchronization mechanism.

[0034] In one embodiment, the module 103 is established to: Perform soil stratification characteristic analysis on the coal gangue weathering process data, extract porosity and permeability coefficient parameters, and obtain site hydrogeological parameters; Establishing a three-dimensional groundwater flow numerical model based on the site hydrogeological parameters, calculating the water head distribution of each node, and obtaining groundwater flow field distribution data; The groundwater flow field distribution data is processed by using the convection-diffusion equation to calculate the transport of pollutants and obtain the pollutant concentration distribution result; The pollutant concentration distribution results are subjected to risk assessment and quantification processing, and the multiples of heavy metal exceeding the standard are calculated to obtain the site environmental risk data.

[0035] Specifically, when the establishment module 103 performs soil stratification characteristic analysis and processing, it receives the gangue weathering process data output by the construction module. The data includes information on changes in mineral composition, particle size distribution, and chemical composition evolution of soil layers at different depths. The soil stratification characteristic analysis and processing first divides the weathering process data into layers according to the depth dimension. The surface soil is most strongly affected by weathering, the middle soil is in a weathering transition state, and the bottom soil retains the original gangue characteristics. The porosity parameter is extracted by analyzing the proportional relationship between solid particles and void volume in each soil layer. The soil layer with a higher degree of weathering forms more pore space due to the fragmentation and rearrangement of mineral particles, and the porosity value increases accordingly. The permeability coefficient parameter reflects the ability of the soil layer to conduct water. The permeability coefficient is calculated by analyzing the soil particle size distribution and pore connectivity. The soil layer with a high fine particle content has a smaller permeability coefficient, while the soil layer dominated by coarse particles has a larger permeability coefficient. The site hydrogeological parameter set includes key values ​​such as the porosity, permeability coefficient, moisture content, and bulk density of each soil layer. When establishing a three-dimensional groundwater flow numerical model, a module is established to construct a mathematical model describing the law of groundwater movement based on the site hydrogeological parameters. The three-dimensional groundwater flow numerical model divides the study area into three-dimensional grid units, and each grid unit is assigned corresponding hydrogeological parameters. The groundwater flow follows Darcy's law. The head distribution calculation is achieved by solving the groundwater flow equations. The head distribution of each node reflects the potential energy distribution state of groundwater in three-dimensional space. The head gradient drives the groundwater to flow from the high head area to the low head area. The numerical model solves the partial differential equations through the finite difference method or the finite element method. The calculation process takes into account the constraints of boundary conditions and initial conditions. The groundwater flow field distribution data represents the flow direction and flow velocity of each point in the form of a velocity vector field.

[0036] When the convection-diffusion equation is used to calculate pollutant transport, a module is established with groundwater flow field distribution data as input conditions. The convection-diffusion equation describes the migration process of pollutants in groundwater. The convection term reflects the transmission process of pollutants with the flow of groundwater, and the diffusion term reflects the molecular diffusion and mechanical dispersion of pollutants due to the concentration gradient. The pollutant transport calculation process requires setting the location, intensity and duration of the pollution source. Heavy metal ions in coal-based solid waste, such as cadmium, lead, and arsenic, are tracked and calculated as the main pollutants. The calculation process is carried out in a time-stepping manner, and the pollutant concentration distribution is updated within each time step. The concentration distribution is affected by the groundwater flow velocity, diffusion coefficient and reaction parameters. Different heavy metal ions have different migration characteristics and environmental behaviors. The pollutant concentration distribution results present the change pattern of pollutant concentration at each monitoring point over time in the form of a three-dimensional concentration field. During the risk assessment and quantification stage, a module is established to conduct a quantitative analysis of the environmental risks of the pollutant concentration distribution results. The heavy metal exceedance multiples are calculated by comparing the actual concentration with the environmental standard limit. The groundwater quality standard stipulates the maximum allowable concentration of various heavy metal ions. The exceedance multiples are equal to the actual concentration divided by the standard limit. An exceedance multiple greater than 1 indicates an environmental risk. The greater the exceedance multiple, the higher the risk level. The risk assessment and quantification process also considers the toxicity weight and exposure pathway of the pollutants, and comprehensively assesses the human health risks and ecological environmental risks. The site environmental risk data includes information such as the risk level, exceedance multiples and impact range of each monitoring point.

[0037] In one embodiment, the optimization module 104 is configured to: Performing objective function construction processing on the site environmental risk data to obtain a multi-objective function expression; Initializing a particle swarm population based on the multi-objective function expression, assigning a position vector and a velocity vector to each particle, wherein the position vector represents a combination of process parameters for coal-based solid waste treatment, and obtaining an initial particle swarm population; The initial particle swarm population is subjected to fitness evaluation and speed update iterative processing, and when the particle fitness stagnates, a genetic algorithm crossover mutation operation is initiated to generate a new candidate solution and update the global optimal position to obtain an optimization iterative result; The optimization iteration results are subjected to Pareto front screening processing, and the process parameter configuration with the best comprehensive performance is selected from the non-dominated solution set to obtain a resource path solution.

[0038] Specifically, when the optimization module 104 performs the objective function construction process, it receives the site environmental risk data output by the establishment module, which contains key information such as the heavy metal exceedance multiples, pollution impact range and environmental risk level. The objective function construction process converts multiple optimization objectives in the coal-based solid waste resource utilization process into mathematical expressions. The silicon-aluminum activity index is the first optimization objective to reflect the reaction activity of coal-based solid waste in the geopolymer preparation process. The higher the activity index, the better the resource utilization efficiency. The heavy metal leaching toxicity is the second optimization objective to reflect the environmental safety in the ecological utilization process of coal-based solid waste. The lower the leaching toxicity, the smaller the environmental risk. The soil formation efficiency is the third optimization objective to reflect the speed of coal gangue weathering into usable soil. The higher the soil formation efficiency, the shorter the ecological restoration cycle. The multi-objective function expression linearly or nonlinearly combines the three single objective functions through weight coefficients, and the weight coefficients are dynamically adjusted according to actual application needs and environmental risk levels. During the particle swarm initialization process, the optimization module creates the initial population of the particle swarm algorithm based on the multi-objective function expression. Each particle represents a combination of process parameters for coal-based solid waste treatment. The position vector contains key process parameters such as geopolymer preparation temperature, stirring speed, solid-liquid ratio, activator addition amount, and coal gangue pretreatment time. Each dimension of the position vector corresponds to a specific process parameter value. The velocity vector represents the movement direction and speed of the particle in the parameter space. The dimension of the velocity vector is consistent with the position vector. The initial particle swarm population randomly allocates position vectors and velocity vectors within the preset parameter range through a random number generator. The population size is determined according to the complexity of the optimization problem and the limitation of computing resources.

[0039] During the iterative processing phase of fitness evaluation and speed update, the optimization module calculates the fitness of each particle in the initial particle swarm. Fitness evaluation is performed by substituting the particle's position vector into the multi-objective function expression to obtain a fitness value. The fitness value reflects the comprehensive performance of the current process parameter combination. Speed ​​update follows the standard update formula of the particle swarm algorithm. The speed of each particle is attracted by the individual historical optimal position and the global historical optimal position. Inertia weight and learning factor parameters are introduced during the speed update process to adjust the particle's exploration ability and convergence speed. Position update is achieved by adding the current position to the updated speed. The iterative process repeats the fitness evaluation, speed update, and position update operations. If the global optimal fitness does not improve significantly over several consecutive generations, the particle's fitness is considered stagnant. Stagnation detection triggers the start conditions of the genetic algorithm's crossover and mutation operations. The crossover operation randomly selects two particles and exchanges some dimensions of their position vectors to generate a new candidate solution. The mutation operation generates a new candidate solution by adding random perturbations to the random dimensions of the particle position vector. The new candidate solution replaces the particle with poor fitness in the swarm and updates the global optimal position. The optimization iteration results include the particle swarm population and the corresponding fitness distribution after multiple rounds of iterations.

[0040] During the Pareto front screening phase, the optimization module screens the optimization iteration results for multi-objective optimization solutions. The Pareto front is the set of non-dominated solutions in a multi-objective optimization problem. Non-dominated solutions are solutions that are not completely dominated by other solutions on all objective functions. The screening process first determines the dominance relationship of all particles in the population. This is determined by comparing the performance of two solutions on various objective functions. If solution A is not inferior to solution B on all objective functions and is superior to solution B on at least one objective function, then solution A dominates solution B. The non-dominated solution set contains all solutions that are not dominated by other solutions. Pareto front screening selects evenly distributed and representative solutions from the non-dominated solution set as candidate solutions. The selection process considers the crowding distance and diversity index between solutions. The crowding distance reflects the density of solutions in the objective space, and the diversity index ensures that the selected solutions cover the entire Pareto front. The process parameter configuration with the best overall performance is determined from the Pareto front by comprehensively considering the importance of each objective function and the actual constraints. The resource utilization path solution contains the optimal process parameter configuration and the corresponding expected performance indicators.

[0041] In a specific embodiment, the adjustment module 105 is configured to: Performing process parameter analysis on the resource recovery pathway, extracting key control parameters such as geopolymer preparation temperature, stirring speed, solid-liquid ratio, and activator addition amount, and obtaining target process parameter setting values; Performing real-time process parameter comparison processing based on the target process parameter setting value, calculating the deviation between the current detection value and the target setting value, and obtaining the parameter adjustment deviation; The parameter adjustment deviation is processed by proportional integral differential control calculation to generate a corresponding equipment control signal to obtain a process parameter adjustment instruction; The process parameter adjustment instructions are prioritized and time-coordinated, and sent to each control device in the order of the coal-based solid waste treatment process to obtain full-process management and control instructions for coal-based solid waste resource utilization.

[0042] Specifically, when the adjustment module 105 performs process parameter analysis, it receives the resource recovery path plan output by the optimization module, which includes the optimal process parameter configuration and expected performance indicators determined after multi-objective optimization. The process parameter analysis first performs data structure analysis on the resource recovery path plan to identify the types and numerical ranges of the various process parameters contained therein. The geopolymer preparation temperature is a key control parameter that affects the dissolution rate and polymerization reaction activity of silicon-aluminum minerals. The temperature parameter is extracted from the plan and converted into a temperature setting value that can be recognized by the control equipment. The stirring speed parameter controls the mixing uniformity and mass transfer efficiency of the reaction system. After the speed parameter is extracted, the mechanical limitations and power constraints of the equipment need to be considered. The solid-liquid ratio parameter determines the ratio relationship between coal-based solid waste and liquid-phase activator. The solid-liquid ratio value directly affects the fluidity of the reaction slurry and the degree of reaction completeness. The activator addition amount parameter controls the intensity and duration of the alkaline environment. The addition amount parameter needs to be dynamically adjusted according to the silicon-aluminum ratio of the coal-based solid waste. The target process parameter setting value is obtained by converting these key control parameters into a standardized numerical format and assigning it to the corresponding control loop. The real-time process parameter comparison processing stage adjustment module establishes a parameter monitoring and comparison mechanism based on the target process parameter setting value. The real-time process parameter detection obtains the current process status through the temperature sensor, speed encoder, flow meter and concentration detector deployed at the production site. The current detection value is transmitted to the adjustment module in a standardized data format for processing. The comparison processing calculates the deviation by numerically comparing the current detection value with the target setting value. The deviation calculation adopts the difference operation, that is, the deviation is equal to the target setting value minus the current detection value. A positive deviation indicates that the current parameter is lower than the target value and needs to be increased. A negative deviation indicates that the current parameter is higher than the target value and needs to be reduced. The parameter adjustment deviation includes information such as the deviation value, deviation direction and deviation change rate. The deviation change rate is obtained by calculating the derivative of the deviation value of multiple consecutive sampling periods, reflecting the trend and speed of parameter change.

[0043] In the proportional-integral-differential control calculation and processing stage, the adjustment module inputs the parameter adjustment deviation into the proportional-integral-differential control algorithm to generate the control signal. The proportional-integral-differential control algorithm is a classic closed-loop control method. The proportional link produces a control effect proportional to the deviation according to the size of the current deviation. The proportional coefficient determines the sensitivity and response strength of the controller to the deviation. The integral link accumulates and calculates historical deviations to eliminate the steady-state error of the system. The integral time constant affects the strength of the integral effect and the stability of the system. The differential link produces a leading control effect according to the rate of change of the deviation to improve the dynamic response characteristics of the system. The differential time constant determines the influence of the differential effect. The control calculation processing performs a weighted summation on the outputs of the proportional, integral and differential links to obtain the total control output. The control output is converted into an equipment control signal after limiting and filtering. The equipment control signal contains elements such as control direction, control amplitude and execution time. The process parameter adjustment instruction is output to the on-site execution equipment in the form of a digital signal or an analog signal. The execution priority sorting and timing coordination processing stage adjustment module comprehensively manages and coordinates the process parameter adjustment instructions. The execution priority sorting determines the execution order according to the degree of influence of the process parameters on the overall performance and the urgency of the adjustment. Temperature control usually has a higher execution priority due to its large thermal inertia. The stirring speed adjustment has a faster response speed and a relatively lower priority. The adjustment of the stimulant addition involves chemical reaction balance and needs to be coordinated with other parameters. The timing coordination processing considers the mutual influence and constraint relationship between different control loops to avoid system disturbances caused by the simultaneous execution of multiple control actions. The timing coordination algorithm arranges the sending time of the control instructions according to the process sequence of the coal-based solid waste treatment process to ensure that each control device executes the adjustment action at a reasonable time interval and logical order. The full-process management and control instructions of coal-based solid waste resource utilization integrate the adjustment requirements and execution plans of all process parameters to form a complete equipment control solution.

[0044] An embodiment of the present application provides a digital twin system that realizes intelligent management and control of the entire process of coal-based solid waste resource utilization based on an intelligent management and control platform for the entire process of coal-based solid waste resource utilization.

[0045] Reference Figure 2 In the embodiment of the present invention, a full-process intelligent control device for coal-based solid waste resource utilization is also provided. The full-process intelligent control device for coal-based solid waste resource utilization can be a server, and its internal structure can be as follows: Figure 2As shown. The coal-based solid waste resource full-process intelligent management and control equipment includes a processor, memory, display screen, input device, network interface and database connected through a system bus. Among them, the computer-designed processor is used to provide computing and control capabilities. The memory of the coal-based solid waste resource full-process intelligent management and control equipment includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the coal-based solid waste resource full-process intelligent management and control equipment is used to store the corresponding data in this embodiment. The network interface of the coal-based solid waste resource full-process intelligent management and control equipment is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0046] Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present invention, and does not constitute a limitation on the full-process intelligent management and control equipment for coal-based solid waste resource utilization to which the scheme of the present invention is applied.

[0047] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are run on a computer, the computer executes the steps of the coal-based solid waste resource utilization full-process intelligent management and control platform.

[0048] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0049] If the integrated unit is implemented as 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, or the part that contributes to the existing technology, 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 for enabling a coal-based solid waste resource full-process intelligent management and control device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program code.

[0050] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A full-process intelligent management and control platform for coal-based solid waste resource utilization, characterized by: The platform includes: An identification module is used to perform mineral phase identification processing on coal-based solid waste using a X-ray diffractometer to obtain classification data on the mineral composition of the coal-based solid waste; A construction module is used to construct a digital twin model of mineral phase transformation based on the classification data of the coal-based solid waste mineral composition, and simulate the dissolution reaction process of silicon and aluminum minerals to obtain data on the weathering process of coal gangue into soil; Establishing a module for establishing a pollutant migration prediction model based on the coal gangue weathering and soil formation process data to obtain site environmental risk data; An optimization module is used to perform multi-objective optimization processing on the site environmental risk data through an improved particle swarm-genetic hybrid algorithm to obtain a resource utilization path plan; The adjustment module is used to adaptively adjust the process parameters of coal-based solid waste treatment based on the resource recovery path plan to obtain full-process control instructions for coal-based solid waste resource recovery.

2. The full-process intelligent management and control platform for coal-based solid waste resource utilization according to claim 1 is characterized in that: The identification module is used to: The coal-based solid waste sample is subjected to two-dimensional scanning detection by a X-ray diffractometer to obtain X-ray diffraction spectrum data with a diffraction angle range of 5° to 80°; Performing peak position identification processing on the X-ray diffraction spectrum data, extracting characteristic diffraction peak intensity values ​​of quartz, kaolinite, and illite, and obtaining qualitative identification results of mineral phases; Calculating the mass percentages of silicon dioxide and aluminum oxide based on the mineral phase qualitative identification results, and obtaining the molar ratio to obtain a quantitative parameter of the silicon-aluminum ratio; The quantitative parameter of the silicon-aluminum ratio is classified according to the numerical range. When the molar ratio is greater than 4.0, it is classified as low-activity solid waste; when the molar ratio is between 2.0 and 4.0, it is classified as medium-activity solid waste; when the molar ratio is less than 2.0, it is classified as high-activity solid waste, and the classification data of the mineral composition of coal-based solid waste is obtained.

3. The full-process intelligent management and control platform for coal-based solid waste resource utilization according to claim 1 is characterized in that: The building blocks include: A processing unit is used to input the classification data of the coal-based solid waste mineral composition into a three-dimensional network structure modeling algorithm for spatial configuration processing, establish a bonding network model of silicon-oxygen tetrahedron and aluminum-oxygen tetrahedron, and obtain a digital twin model of mineral phase transformation; A calculation unit is used to set boundary condition parameters of a reaction temperature of 20° C. to 80° C. and a pH value of 8.0 to 14.0 based on the mineral phase transformation digital twin model, and calculate the activation energy parameters of silica-alumina minerals in an alkaline environment to obtain dissolution reaction kinetic parameters; An iterative unit is used to substitute the dissolution reaction kinetic parameters into the silicate dissolution rate equation for iterative calculation processing, simulate the release process of silicon ions and aluminum ions at intervals of a time step of 1 hour, and obtain a mineral dissolution progress curve; The coupling unit is used to perform microbial metabolism coupling calculation processing on the mineral dissolution process curve, and obtain the coal gangue weathering soil process data by combining the soil pH change and organic matter accumulation rate.

4. The full-process intelligent management and control platform for coal-based solid waste resource utilization according to claim 3 is characterized in that: The processing unit is configured to: Performing molecular coordinate initialization processing on the coal-based solid waste mineral composition classification data, allocating the spatial positions of silicon atoms and aluminum atoms according to the mineral phase content ratio, and obtaining atomic space coordinate parameters; Establishing a tetrahedral geometric structure based on the atomic space coordinate parameters, connecting each silicon atom with four oxygen atoms to form a silicon-oxygen tetrahedron, and connecting each aluminum atom with four oxygen atoms to form an aluminum-oxygen tetrahedron, to obtain a basic tetrahedral unit; Performing a network connection process on the basic tetrahedral units, bridging and assembling silicon-oxygen tetrahedrons and aluminum-oxygen tetrahedrons by sharing oxygen atoms, thereby obtaining a three-dimensional bonded network topology structure; Mapping the three-dimensional bond network topology into digital space, establishing a virtual mineral lattice containing atomic coordinates, bonding relationships and physical property parameters, and obtaining a digital mineral structure model; A real-time data synchronization interface and a status update algorithm are set for the digital mineral structure model, a dynamic correspondence between physical minerals and virtual models is established, and a digital twin model of mineral phase transformation is obtained.

5. The full-process intelligent management and control platform for coal-based solid waste resource utilization according to claim 1 is characterized in that: The establishment module is used to: Perform soil stratification characteristic analysis on the coal gangue weathering process data, extract porosity and permeability coefficient parameters, and obtain site hydrogeological parameters; Establishing a three-dimensional groundwater flow numerical model based on the site hydrogeological parameters, calculating the water head distribution of each node, and obtaining groundwater flow field distribution data; The groundwater flow field distribution data is processed by using the convection-diffusion equation to calculate the transport of pollutants and obtain the pollutant concentration distribution result; The pollutant concentration distribution results are subjected to risk assessment and quantification processing, and the multiples of heavy metal exceeding the standard are calculated to obtain the site environmental risk data.

6. The full-process intelligent management and control platform for coal-based solid waste resource utilization according to claim 1 is characterized in that: The optimization module is used to: Performing objective function construction processing on the site environmental risk data to obtain a multi-objective function expression; Initializing a particle swarm population based on the multi-objective function expression, assigning a position vector and a velocity vector to each particle, wherein the position vector represents a combination of process parameters for coal-based solid waste treatment, and obtaining an initial particle swarm population; The initial particle swarm population is subjected to fitness evaluation and speed update iterative processing, and when the particle fitness stagnates, a genetic algorithm crossover mutation operation is initiated to generate a new candidate solution and update the global optimal position to obtain an optimization iterative result; The optimization iteration results are subjected to Pareto front screening processing, and the process parameter configuration with the best comprehensive performance is selected from the non-dominated solution set to obtain a resource path solution.

7. The full-process intelligent management and control platform for coal-based solid waste resource utilization according to claim 1 is characterized in that: The adjustment module is used to: Performing process parameter analysis on the resource recovery pathway, extracting key control parameters such as geopolymer preparation temperature, stirring speed, solid-liquid ratio, and activator addition amount, and obtaining target process parameter setting values; Performing real-time process parameter comparison processing based on the target process parameter setting value, calculating the deviation between the current detection value and the target setting value, and obtaining the parameter adjustment deviation; The parameter adjustment deviation is processed by proportional integral differential control calculation to generate a corresponding equipment control signal to obtain a process parameter adjustment instruction; The process parameter adjustment instructions are prioritized and time-coordinated, and sent to each control device in the order of the coal-based solid waste treatment process to obtain full-process management and control instructions for coal-based solid waste resource utilization.

8. A digital twin system, characterized in that: Based on the full-process intelligent management and control platform for coal-based solid waste resource utilization as described in any one of claims 1 to 7, intelligent management and control of the full-process of coal-based solid waste resource utilization is achieved.

9. A full-process intelligent management and control device for coal-based solid waste resource utilization, characterized in that: It includes a memory and a processor, the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it realizes the full-process intelligent management and control platform for coal-based solid waste resource utilization as described in 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 executed by a processor, the processor executes the full-process intelligent management and control platform for coal-based solid waste resource utilization as described in any one of claims 1 to 7.

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