An intelligent assessment method and system for carbon emissions in mining areas integrating multi-source data

Abstract

The present invention discloses an intelligent assessment method and system for carbon emissions from mining areas that integrates multi-source data, and specifically relates to the technical field of environmental monitoring. The method and system are based on obtaining historical record data of geological stress redistribution in the mining area and extracting historical escape characteristic data. The method collects real-time monitoring data of mining activities in the mining area, identifies potential escape areas based on the historical escape characteristic data, and generates a distribution map. The carbon-based gas concentration in the potential escape area is sampled, and the escape flux of the carbon-based gas in the potential escape area is predicted based on the changing trend of the geological stress in the mining area. The escape paths of the carbon-based gas in the potential escape area are analyzed based on the escape flux prediction value, and a potential escape path set is generated. The overall carbon emissions of the mining area are assessed by combining the potential escape path set of the carbon-based gas and the escape flux prediction value, and the assessment results are output, thereby effectively identifying and quantifying the hidden carbon-based gas escape induced by geological stress changes, and improving the accuracy of the mining area carbon emission assessment.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring technology, and more specifically, to a method and system for intelligently assessing carbon emissions in mining areas by integrating multi-source data. Background Art

[0002] As one of the important sources of carbon emissions, the accurate accounting and effective control of carbon emissions from the mining industry are of vital importance. Traditional methods for carbon emission assessment in mining areas, especially when dealing with fugitive carbon-based gas emissions, mainly rely on direct measurements of known and concentrated major gas sources or estimates based on empirical emission factors. The complex impact of the dynamic redistribution of geological stress fields caused by mining activities on the fugitive behavior of carbon-based gases in surrounding rocks and residual ore bodies is generally ignored. Existing technologies lack the ability to effectively identify, monitor and accurately quantify "enhanced fugitives" indirectly induced by geological stress changes, resulting in an underestimate of the total carbon emissions in mining areas, which restricts the level of carbon emission management and the effectiveness of emission reduction strategies.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for intelligent assessment of carbon emissions in mining areas by integrating multi-source data to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An intelligent assessment method for carbon emissions in mining areas by integrating multi-source data includes the following steps:

[0007] S1: Obtain historical records of geological stress redistribution in the mining area, analyze the characteristic parameters of the dissipation response in the historical records, and extract historical dissipation characteristic data;

[0008] S2: Collect real-time monitoring data of mining activities in the mining area, identify potential emission areas induced by mining activities based on historical emission characteristic data, and generate a distribution map of potential emission areas;

[0009] S3: Based on the potential emission area distribution map, sample the carbon-based gas concentration in the potential emission area and construct a gas concentration distribution matrix;

[0010] S4: Based on the gas concentration distribution matrix and combined with the changing trend of geological stress in the mining area, the emission flux of carbon-based gases in the potential emission area is predicted;

[0011] S5: Analyze the escape paths of the carbon-based gas in the potential escape area based on the escape flux prediction value, and generate a set of potential escape paths of the carbon-based gas;

[0012] S6: Combine the potential escape pathways of carbon-based gases and the predicted escape flux to evaluate the overall carbon emissions of the mining area and output the mining area carbon emissions assessment results.

[0013] In a preferred embodiment, S1 is specifically:

[0014] The pore structure change parameters of the surrounding rock at multiple acquisition moments in the historical record data are calculated according to the time series to obtain the pore structure change rate;

[0015] The difference between the gas permeability change parameters at multiple acquisition moments in the historical record data is calculated in time series to obtain the permeability change rate;

[0016] Calculate the fluctuation amplitude of the carbon-based gas desorption equilibrium change parameters across multiple acquisition moments in the historical record data to obtain the desorption equilibrium change amplitude;

[0017] Based on preset thresholds, the pore structure change rate, permeability change rate and equilibrium change amplitude are screened to generate historical escape characteristic data.

[0018] In a preferred embodiment, S2 is specifically:

[0019] Based on multiple monitoring points preset within the mining area, real-time monitoring data of mining activities in the mining area is collected;

[0020] Perform synchronous alignment and noise filtering on real-time monitoring data to generate calibrated real-time monitoring data;

[0021] Compare the calibrated real-time monitoring data with the historical emission characteristic data at the same monitoring point to determine whether the real-time monitoring data at each monitoring point exceeds the corresponding historical emission characteristic data;

[0022] A distribution map of potential emission areas is generated based on the spatial coordinates of monitoring points that exceed the corresponding historical emission characteristic data.

[0023] In a preferred embodiment, S3 is specifically:

[0024] The potential escape area distribution map is divided into several sampling units according to equidistant spatial grids;

[0025] Collect the carbon-based gas concentration at the center of the sampling unit according to the preset sampling time;

[0026] Generate a carbon-based gas concentration time series list for each sampling unit according to the sampling time, and arrange the carbon-based gas concentration time series list according to the row and column coordinate order of the spatial grid;

[0027] A gas concentration distribution matrix is ​​constructed based on the arranged carbon-based gas concentration time series list.

[0028] In a preferred embodiment, the rows of the gas concentration distribution matrix represent the row coordinates of the spatial grid, the columns of the gas concentration distribution matrix represent the column coordinates of the spatial grid, and the elements of the gas concentration distribution matrix correspond to the carbon-based gas concentration at the center of the sampling unit.

[0029] In a preferred embodiment, S4 is specifically:

[0030] The carbon-based gas concentration at the center of the sampling unit in the gas concentration distribution matrix is ​​matched with the corresponding sampling time to obtain a carbon-based gas concentration change curve;

[0031] Obtain geological stress field change trend data covering potential escape areas;

[0032] Based on the carbon-based gas concentration change curve and geological stress field change trend data, a carbon-based gas escape flux prediction model is constructed;

[0033] The predicted value of the carbon-based gas leakage flux in the potential leakage area according to the carbon-based gas leakage flux prediction model.

[0034] In a preferred embodiment, the geological stress field change trend data includes the surrounding rock stress evolution curve and structural unit displacement data within the mining cycle.

[0035] In a preferred embodiment, S5 is specifically:

[0036] Mapping the adjacency relationships between sampling units and sampling units in the potential escape area into a three-dimensional node network;

[0037] Assign the predicted value of the fugitive flux of the corresponding sampling unit to each node in the three-dimensional node network as the node weight;

[0038] Connect high-weight nodes to low-weight nodes in sequence to form a set of potential escape paths for carbon-based gases.

[0039] In a preferred embodiment, S6 is specifically:

[0040] Traverse each potential escape path in the set of potential escape paths of carbon-based gases, accumulate the escape flux prediction values ​​of all sampling units, and obtain the flux accumulation value of each potential escape path;

[0041] The potential escape path with the largest flux accumulation value is selected as the main escape path, and the three-dimensional coordinates of each node in the main escape path are extracted to form the spatial trajectory dataset of the main escape path;

[0042] The spatial trajectory dataset of the main emission path is combined with the emission flux prediction values ​​of each sampling unit in the potential emission area to construct an overall carbon emission assessment model for the mining area.

[0043] Based on the overall carbon emission assessment model of the mining area, the total amount of carbon-based gas emissions in the potential escape area is estimated, and the mining area carbon emission assessment results are output.

[0044] In another aspect, the present invention provides an intelligent assessment system for carbon emissions in mining areas that integrates multi-source data, comprising:

[0045] Historical analysis module: obtains historical records of geological stress redistribution in the mining area, analyzes the characteristic parameters of the dissipation response in the historical records, and extracts historical dissipation characteristic data;

[0046] Real-time monitoring module: collects real-time monitoring data of mining activities in the mining area, identifies potential emission areas induced by mining activities based on historical emission characteristic data, and generates a distribution map of potential emission areas;

[0047] Concentration sampling module: Based on the potential emission area distribution map, the concentration of carbon-based gases in the potential emission area is sampled and a gas concentration distribution matrix is ​​constructed;

[0048] Flux prediction module: Based on the gas concentration distribution matrix and combined with the changing trend of geological stress in the mining area, it predicts the escape flux of carbon-based gases in the potential escape area;

[0049] Path analysis module: Analyzes the escape paths of carbon-based gases in potential escape areas based on the predicted escape flux value, and generates a set of potential escape paths for carbon-based gases;

[0050] Emission assessment module: Combines the potential escape pathways of carbon-based gases and the predicted escape flux to assess the overall carbon emissions of the mining area and outputs the mining area carbon emission assessment results.

[0051] The technical effects and advantages of the method and system for intelligently assessing carbon emissions in mining areas by integrating multi-source data are as follows:

[0052] By analyzing historical data on geological stress redistribution and combining it with real-time monitoring data from mining activities, we can identify potential emission zones induced by mining activities within the mining area, improving the comprehensiveness of emission source identification. By sampling carbon-based gas concentrations within potential emission zones and predicting emission fluxes based on the changing trends of geological stress in the mining area, we can improve the accuracy of carbon emission accounting. By analyzing a set of potential emission pathways and combining them with emission flux predictions, we can assess the overall carbon emissions of the mining area, providing a scientific basis for developing more targeted emission reduction measures. This makes the overall carbon emission assessment of the mining area more accurate, dynamic, and intelligent, effectively avoiding underestimation of actual carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic diagram of an intelligent assessment method for carbon emissions in mining areas that integrates multi-source data according to the present invention;

[0054] Figure 2 This is a structural schematic diagram of an intelligent assessment system for carbon emissions in mining areas that integrates multi-source data according to the present invention. DETAILED DESCRIPTION

[0055] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] Example 1

[0057] Figure 1 The present invention provides an intelligent assessment method for carbon emissions in mining areas by integrating multi-source data, which includes the following steps:

[0058] S1: Obtain historical records of geological stress redistribution in the mining area, analyze the characteristic parameters of the dissipation response in the historical records, and extract historical dissipation characteristic data;

[0059] S2: Collect real-time monitoring data of mining activities in the mining area, identify potential emission areas induced by mining activities based on historical emission characteristic data, and generate a distribution map of potential emission areas;

[0060] S3: Based on the potential emission area distribution map, sample the carbon-based gas concentration in the potential emission area and construct a gas concentration distribution matrix;

[0061] S4: Based on the gas concentration distribution matrix and combined with the changing trend of geological stress in the mining area, the emission flux of carbon-based gases in the potential emission area is predicted;

[0062] S5: Analyze the escape paths of the carbon-based gas in the potential escape area based on the escape flux prediction value, and generate a set of potential escape paths of the carbon-based gas;

[0063] S6: Combine the potential escape pathways of carbon-based gases and the predicted escape flux to evaluate the overall carbon emissions of the mining area and output the mining area carbon emissions assessment results.

[0064] S1: Obtain historical records of geological stress redistribution in the mining area, analyze the characteristic parameters of the dissipation response in the historical records, and extract historical dissipation characteristic data, including:

[0065] Multiple geological monitoring devices are installed in the mining area. The density of the monitoring devices is determined by the complexity of the mining area's geological structure. For example, the number of monitoring devices is increased in areas with more complex geological structures. The geological monitoring devices can collect real-time stress change parameters of the mining area's geological structure as mining activities occur. These parameters include the degree of stress concentration within the rock mass, the changing characteristics of the surrounding rock's stability, the generation and expansion of cracks within the surrounding rock, and changes in surrounding rock pore structure parameters, gas permeability, and carbon-based gas desorption equilibrium parameters. This generates historical data on the redistribution of geological stress in the mining area.

[0066] The characteristic parameters of the escape response specifically include parameters for the change in surrounding rock pore structure, gas permeability, and carbon-based gas desorption equilibrium. The parameters for the change in surrounding rock pore structure include the change in effective porosity per unit volume, the trend in the change in the width of the main fracture, and the evolution of the fracture penetration rate. The gas permeability parameter is the rate of change in the escaped gas volume per unit area per unit time under unit pressure differential, as obtained through directional mercury intrusion or downhole pumpback testing. The carbon-based gas desorption equilibrium parameter is the pressure curve required for the adsorbed gas on the surrounding rock surface to reach stable desorption under different stress states.

[0067] The pore structure change parameters of the surrounding rock at multiple acquisition moments in the historical record data are calculated according to the time series to obtain the pore structure change rate;

[0068] Specifically, for the surrounding rock pore structure change parameter, the effective porosity data point at each historical monitoring moment was selected. The effective porosity values ​​at two adjacent moments were then subtracted in chronological order. This difference was divided by the time interval between the previous and next moments to obtain the porosity change rate per unit time, recorded as the pore structure change rate. The pore structure change rate is expressed as the pore volume fraction that increases or decreases per cubic meter of surrounding rock per unit time, in units of volume fraction per hour. The spatial position of the geological unit corresponding to the pore structure change rate is also recorded.

[0069] The difference between the gas permeability change parameters at multiple acquisition moments in the historical record data is calculated in time series to obtain the permeability change rate;

[0070] Specifically, the permeability change parameter is extracted by taking the gas escape rate per unit volume of surrounding rock under unit pressure differential during two consecutive historical monitoring periods. The difference is then divided by the duration difference between the first and second time periods to obtain the permeability change rate. The permeability change rate represents the increase or decrease in gas escape capacity per unit time at a spatial location.

[0071] Calculate the fluctuation amplitude of the carbon-based gas desorption equilibrium change parameters across multiple acquisition moments in the historical record data to obtain the desorption equilibrium change amplitude;

[0072] Specifically, the desorption equilibrium pressure values ​​at different stress levels are extracted, and the difference between the maximum and minimum pressure values ​​at consecutive sampling points represents the magnitude of the desorption equilibrium change for the unit. A larger magnitude of desorption equilibrium change indicates more severe desorption instability under stress perturbation and greater gas release potential.

[0073] The pore structure change rate, permeability change rate and equilibrium change amplitude are screened based on preset thresholds to generate historical emission characteristic data;

[0074] Specifically, thresholds are preset for the surrounding rock pore structure change rate, gas permeability change rate, and carbon-based gas desorption equilibrium change range. These thresholds are determined based on the actual geological conditions of the mining area, historical statistical analysis data, and expert evaluation. The surrounding rock pore structure change rate, gas permeability change rate, and carbon-based gas desorption equilibrium change range are screened for the corresponding preset thresholds. Those values ​​that are greater than or equal to the preset thresholds are marked as historical escape characteristic data.

[0075] S2: Collect real-time monitoring data of mining activities in the mining area, identify potential emission areas induced by mining activities based on historical emission characteristic data, and generate a distribution map of potential emission areas, including:

[0076] Based on multiple monitoring points preset within the mining area, real-time monitoring data of mining activities in the mining area is collected;

[0077] Specifically, several real-time monitoring points will be established within the mining area. The locations of these points will be determined based on the mining area's geological structure, mining layout, and historical emission risk distribution. For example, more monitoring points will be set up in areas of concentrated geological stress, areas with frequent carbon-based gas emissions in the past, and areas with intensive mining activity, to effectively improve the spatial coverage of monitoring data. Each real-time monitoring point will be equipped with the same type of real-time data monitoring device, which is used to continuously collect various types of real-time monitoring data closely related to mining activities in the mining area, such as rock stress parameter data, gas concentration data, surrounding rock pore structure data, and gas permeability data.

[0078] Perform synchronous alignment and noise filtering on real-time monitoring data to generate calibrated real-time monitoring data;

[0079] Specifically, the synchronization and alignment process uses a unified timestamp marking method, such as the high-precision clock provided by the Global Positioning System for data synchronization. The data collected by each real-time monitoring point is time-series unified according to the unified timestamp, ensuring that the real-time monitoring data collected by each monitoring point strictly corresponds in time and eliminating the time lag problem between different monitoring devices. The real-time monitoring data that has undergone synchronization processing is subjected to a noise filtering algorithm, such as the Kalman filter algorithm or the wavelet filter algorithm. By setting the filtering parameters of the filtering algorithm, environmental noise and abnormal fluctuations in the real-time monitoring data are eliminated, and the calibrated real-time monitoring data is generated.

[0080] Compare the calibrated real-time monitoring data with the historical emission characteristic data at the same monitoring point to determine whether the real-time monitoring data at each monitoring point exceeds the corresponding historical emission characteristic data;

[0081] Specifically, for each real-time monitoring point, the corresponding monitoring parameters in the calibrated real-time monitoring data are compared, such as the real-time rate of change of surrounding rock pore structure, the real-time rate of change of gas permeability, and the real-time amplitude of change in carbon-based gas desorption equilibrium, to see whether these parameters exceed the historical emission characteristic data. During the comparison process, if the real-time monitoring parameters are greater than or equal to the corresponding historical emission characteristic data, the real-time monitoring point is marked as a potential emission risk point; otherwise, it is determined not to be a potential emission risk point.

[0082] Generate a potential emission area distribution map based on the spatial coordinates of monitoring points that exceed the corresponding historical emission characteristic data;

[0083] Specifically, for all real-time monitoring points marked as potential escape risk points, spatial data interpolation algorithms, such as Kriging, are used to perform spatial interpolation calculations based on the spatial coordinates of the monitoring points. This allows the boundaries and distribution of potential escape areas to be inferred and annotated. Spatial data processing techniques, such as geographic information system spatial analysis tools, are used to connect the identified potential escape risk points to form a potential escape area distribution map. This potential escape area distribution map can intuitively demonstrate the spatial locations of carbon-based gas escapes induced by mining activities in the mining area.

[0084] S3: Based on the potential emission area distribution map, sample the carbon-based gas concentration in the potential emission area and construct a gas concentration distribution matrix, including:

[0085] The potential escape area distribution map is divided into several sampling units according to equidistant spatial grids;

[0086] Specifically, based on the distribution map of potential escape areas, the spatial grid division method is used to divide the distribution map into equidistant spatial grids. The appropriate spatial grid size is selected, and the size of the grid is determined based on the actual area of ​​the mining area, the scope of mining activities, and the spatial characteristics of carbon-based gas escapes. Generally, larger grids are used in larger areas of the mining area to ensure that the sampling data has better spatial coverage; smaller grids are used in areas where carbon-based gas escapes are more concentrated to improve spatial resolution and sampling accuracy. Through equidistant spatial grid division, the distribution map of potential escape areas is divided into multiple equidistant sampling units, and each sampling unit represents the carbon-based gas concentration data within a certain area.

[0087] Collect the carbon-based gas concentration at the center of the sampling unit according to the preset sampling time;

[0088] Specifically, within each sampling unit, a central location is selected to collect carbon-based gas concentration. The sampling point is usually located at the geometric center of each sampling unit. For example, if a regular rectangular or square grid is used, the sampling point is located at the center of the grid. At each predetermined sampling time, the carbon-based gas concentration data at the center of each sampling unit is regularly collected by the gas concentration monitoring device installed at the monitoring point. The sampling time can be set according to the actual situation of the mining area, and usually a timed sampling or periodic sampling method is adopted, such as gas concentration sampling once every hour or every half hour.

[0089] Generate a carbon-based gas concentration time series list for each sampling unit according to the sampling time, and arrange the carbon-based gas concentration time series list according to the row and column coordinate order of the spatial grid;

[0090] Specifically, the carbon-based gas concentration data collected at the center of each sampling unit will generate a carbon-based gas concentration time series table for each sampling unit at each sampling moment. The carbon-based gas concentration time series table lists the gas concentration data at the center of each sampling unit at different sampling moments in chronological order, reflecting the pattern of carbon-based gas concentration changes over time. The time series tables of all sampling units will be arranged according to the row and column coordinates of the spatial grid. For example, after the grid is divided into a number of rows and columns, the time series table of each sampling unit is sorted according to the row and column coordinates of the grid.

[0091] Constructing a gas concentration distribution matrix based on the arranged carbon-based gas concentration time series list;

[0092] Specifically, the rows of the gas concentration distribution matrix represent the row coordinates of the spatial grid, and the columns of the gas concentration distribution matrix represent the column coordinates of the spatial grid. The elements of the gas concentration distribution matrix correspond to the carbon-based gas concentration at the center of the sampling unit. For example, if the mining area is divided into a spatial grid with 5 rows and 4 columns, the gas concentration distribution matrix will have 5 rows and 4 columns.

[0093] S4: Based on the gas concentration distribution matrix and combined with the changing trend of geological stress in the mining area, the predicted value of the escape flux of carbon-based gases in the potential escape area is predicted, including:

[0094] The carbon-based gas concentration at the center of the sampling unit in the gas concentration distribution matrix is ​​matched with the corresponding sampling time to obtain a carbon-based gas concentration change curve;

[0095] Specifically, the carbon-based gas concentration values ​​corresponding to the center of each sampling unit in the gas concentration distribution matrix are paired with their corresponding sampling moments. This yields a continuous data set showing the carbon-based gas concentration at the center of each sampling unit over time, known as a carbon-based gas concentration variation curve. The horizontal axis of the carbon-based gas concentration variation curve represents the continuous sampling moments, while the vertical axis represents the carbon-based gas concentration at each moment. This fully captures the trend of gas concentration changes over time.

[0096] Obtain geological stress field change trend data covering potential escape areas;

[0097] Specifically, geological monitoring devices within the mining area are used to collect and obtain data on the changing trends of the geological stress field within the potential escape area. These data include the surrounding rock stress evolution curve during the mining cycle and the displacement data of the mining area's structural units. The surrounding rock stress evolution curve describes the temporal trend of the stress state within the surrounding rock caused by the removal of the ore body during mining activities; the structural unit displacement data specifically describes the spatial displacement characteristics of different structural units in the mining area (such as tunnel support structures, goaf boundaries, rock layer contact interfaces, etc.) under the influence of mining activities. All of the above data are acquired using high-precision geological stress sensors and displacement sensors to ensure the accuracy and reliability of the monitoring data.

[0098] Based on the carbon-based gas concentration change curve and geological stress field change trend data, a carbon-based gas escape flux prediction model is constructed;

[0099] Specifically, a carbon-based gas escape flux prediction model is established with the carbon-based gas concentration change curve and the geological stress field change trend data as input. For example, a neural network model based on time series can be selected, including a long short-term memory network model or a recurrent neural network model, to learn and determine the mapping relationship between the carbon-based gas escape flux and the gas concentration change and geological stress change through training with historical data. When establishing a prediction model, the model input uses the historical data of the carbon-based gas concentration change curve and the geological stress field data as input variables, and the output predicts the carbon-based gas escape flux value of each sampling unit. According to the geological structure characteristics and data characteristics of different areas in the mining area, the training data of the prediction model is subjected to necessary normalization or standardization to improve the model training effect and prediction accuracy.

[0100] The prediction model is trained and its parameters optimized. The training process is achieved by dividing the model into training and validation sets. The model training method utilizes a cross-validation mechanism and is optimized by gradually adjusting the model's structural parameters (such as the number of hidden layers and nodes) and training hyperparameters (such as the learning rate and number of iterations) until the model's prediction error meets the required accuracy for predicting mining area carbon emissions. The model optimization objective is typically to minimize the prediction error, for example, using mean absolute error or mean squared error as the objective function for model optimization. Iterative optimization is then repeated to ensure the high accuracy and stability of the prediction model.

[0101] The predicted value of the carbon-based gas emission flux in the potential emission area according to the carbon-based gas emission flux prediction model;

[0102] Specifically, the optimized carbon-based gas emission flux prediction model was used to predict potential emission areas, obtaining predicted carbon-based gas emission flux values ​​at the center of each spatial grid cell within the potential emission area. The predicted values ​​reflect the potential carbon-based gas emission flux levels at different locations during mining.

[0103] S5: Analyze the escape paths of carbon-based gases in the potential escape area based on the predicted escape flux value, and generate a set of potential escape paths for carbon-based gases, including:

[0104] Mapping the adjacency relationships between sampling units and sampling units in the potential escape area into a three-dimensional node network;

[0105] Specifically, according to the potential escape area, all sampling units in the area and their spatial adjacency are mapped in detail to form a three-dimensional node network. The center position of each sampling unit after the spatial grid division corresponds to a node in the three-dimensional space. The position coordinates of each node are recorded based on the actual position of the sampling unit in the mining area space. Adjacent sampling units determine the connection relationship between corresponding nodes through spatial geometric adjacency, that is, if the boundary or vertex of any unit in the spatial grid has a common contact position with the boundary or vertex of the adjacent unit, it is regarded as an adjacency relationship, thereby constructing a three-dimensional node network structure in the three-dimensional space that represents the spatial position and mutual relationship of the sampling units.

[0106] Assign the predicted value of the fugitive flux of the corresponding sampling unit to each node in the three-dimensional node network as the node weight;

[0107] Specifically, based on the carbon-based gas escape flux prediction model, the predicted value of the carbon-based gas escape flux at the center of each sampling unit within the prediction period is calculated and determined. The escape flux prediction value of each node is the node weight in the node network, and the node weight represents the intensity level of possible escape of carbon-based gas in the corresponding sampling unit. The size of the node weight is expressed as the predicted value of the escape flux. For example, a node with a larger predicted value indicates a higher potential intensity of carbon-based gas escape. By assigning node weights to corresponding nodes, each node in the three-dimensional node network has the characteristics of escape flux prediction.

[0108] Connect high-weight nodes to low-weight nodes in sequence to form a set of potential escape paths for carbon-based gases;

[0109] Specifically, after constructing a complete node network and determining the weights of each node, a strategy of sequentially connecting nodes from high-weight to low-weight nodes is adopted based on the magnitude of the predicted leakage flux values ​​to form a set of potential leakage paths. Starting from the node with the highest predicted leakage flux value, connections are made step by step along the spatial adjacency relationship in the three-dimensional node network, moving towards the node with the lowest predicted leakage flux value. The connection process always maintains a directionality from nodes with high predicted leakage flux values ​​to nodes with low predicted leakage flux values.

[0110] S6: Combine the potential escape pathways of carbon-based gases and the predicted escape flux to assess the overall carbon emissions of the mining area and output the mining area carbon emissions assessment results, including:

[0111] Traverse each potential escape path in the set of potential escape paths of carbon-based gases, accumulate the escape flux prediction values ​​of all sampling units, and obtain the flux accumulation value of each potential escape path;

[0112] Specifically, based on a set of potential carbon-based gas escape paths, each specific potential escape path in the set is traversed. The path traversal process accumulates the predicted escape flux values ​​of the sampling units corresponding to each node on the path, sequentially following the connection order of spatially adjacent nodes. This involves adding the predicted escape flux value of the first node to the predicted escape flux value of the next node, and gradually summing the predicted escape flux values ​​of all nodes on the path to obtain the cumulative flux value corresponding to each potential escape path. This cumulative flux value can intuitively reflect the overall carbon-based gas escape intensity along that path.

[0113] The potential escape path with the largest flux accumulation value is selected as the main escape path, and the three-dimensional coordinates of each node in the main escape path are extracted to form the spatial trajectory dataset of the main escape path;

[0114] Specifically, the path with the largest cumulative flux value among all potential escape paths is selected as the primary escape path. This path is considered to be the most significant channel for carbon-based gas escape during mining, making it more representative. Once the primary escape path is determined, the three-dimensional coordinates of all nodes along the path are extracted to form a spatial trajectory dataset for the primary escape path. This spatial trajectory dataset represents the specific location and direction of the primary escape path within the mining area, providing a visual and accurate representation of the spatial trajectory of carbon-based gas escape.

[0115] The spatial trajectory dataset of the main emission path is combined with the emission flux prediction values ​​of each sampling unit in the potential emission area to construct an overall carbon emission assessment model for the mining area.

[0116] Specifically, based on the spatial trajectory dataset of the main evaporation path and combined with the predicted evaporation flux values ​​corresponding to each sampling unit in the potential evaporation area, an overall carbon emission assessment model for the mining area was constructed. The model construction process includes the following steps: spatially correlating the spatial trajectory coordinates of nodes on the main evaporation path with the corresponding evaporation flux predicted values ​​of the nodes; incorporating the evaporation flux predicted values ​​of other sampling units in the potential evaporation area outside the path into the model to form a spatial database of carbon emissions for the mining area; and using spatial interpolation methods and spatial statistical methods, such as spatial autocorrelation analysis, fusing the evaporation flux data of nodes on the path with the evaporation flux data of sampling units outside the path to form a complete assessment model that can characterize the spatial distribution of carbon emissions in the mining area as a whole. The overall carbon emission assessment model for the mining area not only reflects the high evaporation intensity of the main evaporation path, but also reflects the spatial distribution of evaporation flux in areas outside the main evaporation path.

[0117] Based on the overall carbon emission assessment model of the mining area, the total amount of carbon-based gas emissions in the potential escape area is estimated and the mining area carbon emission assessment results are output;

[0118] Specifically, the overall carbon emissions assessment model for the mining area is used to spatially integrate and estimate the total carbon-based gas emissions in the potential fugitive zone. Specifically, the predicted fugitive flux value for each sampling unit in the model is multiplied by its corresponding spatial area or volume, based on the predicted fugitive flux value for each sampling unit in the model. The total fugitive flux values ​​for all sampling units are then cumulatively summed to obtain an estimated total carbon-based gas emissions for the entire potential fugitive zone.

[0119] The estimated total carbon-based gas emissions for the entire mining area are explicitly output as a mining area carbon emissions assessment result. The assessment output is numerically expressed, such as annual carbon emissions, monthly carbon emissions, or total carbon emissions for a specific mining activity period. A spatial distribution map of the mining area's carbon emissions is also output to clearly and intuitively demonstrate the overall spatial distribution of carbon emissions within the mining area.

[0120] Example 2

[0121] The difference between Example 2 of the present invention and Example 1 is that this example introduces an intelligent assessment system for carbon emissions in mining areas that integrates multi-source data.

[0122] Figure 2 The present invention provides a structural schematic diagram of an intelligent assessment system for carbon emissions in mining areas that integrates multi-source data. The intelligent assessment system for carbon emissions in mining areas that integrates multi-source data includes:

[0123] Historical analysis module: obtains historical records of geological stress redistribution in the mining area, analyzes the characteristic parameters of the dissipation response in the historical records, and extracts historical dissipation characteristic data;

[0124] Real-time monitoring module: collects real-time monitoring data of mining activities in the mining area, identifies potential emission areas induced by mining activities based on historical emission characteristic data, and generates a distribution map of potential emission areas;

[0125] Concentration sampling module: Based on the potential emission area distribution map, the concentration of carbon-based gases in the potential emission area is sampled and a gas concentration distribution matrix is ​​constructed;

[0126] Flux prediction module: Based on the gas concentration distribution matrix and the changing trend of geological stress in the mining area, it predicts the emission flux of carbon-based gases in the potential emission area;

[0127] Path analysis module: Analyzes the escape paths of carbon-based gases in potential escape areas based on the predicted escape flux value, and generates a set of potential escape paths for carbon-based gases;

[0128] Emission assessment module: Combines the potential escape pathways of carbon-based gases and the predicted escape flux to assess the overall carbon emissions of the mining area and outputs the mining area carbon emission assessment results.

[0129] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0130] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0131] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0132] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0134] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0135] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0136] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the 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 computer 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 application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0137] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0138] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent assessment method for carbon emissions in mining areas by integrating multi-source data, characterized in that: The steps include: S1: Obtain historical records of geological stress redistribution in the mining area, analyze the characteristic parameters of the dissipation response in the historical records, and extract historical dissipation characteristic data; S2: Collect real-time monitoring data of mining activities in the mining area, identify potential emission areas induced by mining activities based on historical emission characteristic data, and generate a distribution map of potential emission areas; S3: Based on the potential emission area distribution map, sample the carbon-based gas concentration in the potential emission area and construct a gas concentration distribution matrix; S4: Based on the gas concentration distribution matrix and combined with the changing trend of geological stress in the mining area, the emission flux of carbon-based gases in the potential emission area is predicted; S5: Analyze the escape paths of the carbon-based gas in the potential escape area based on the escape flux prediction value, and generate a set of potential escape paths of the carbon-based gas; S6: Combine the potential escape pathways of carbon-based gases and the predicted escape flux to evaluate the overall carbon emissions of the mining area and output the mining area carbon emissions assessment results; Traverse each potential escape path in the set of potential escape paths of carbon-based gases, accumulate the escape flux prediction values ​​of all sampling units, and obtain the flux accumulation value of each potential escape path; The potential escape path with the largest flux accumulation value is selected as the main escape path, and the three-dimensional coordinates of each node in the main escape path are extracted to form the spatial trajectory dataset of the main escape path; The spatial trajectory dataset of the main emission path is combined with the emission flux prediction values ​​of each sampling unit in the potential emission area to construct an overall carbon emission assessment model for the mining area. Based on the overall carbon emission assessment model of the mining area, the total amount of carbon-based gas emissions in the potential escape area is estimated, and the mining area carbon emission assessment results are output.

2. The method for intelligent assessment of mining area carbon emissions by integrating multi-source data according to claim 1 is characterized in that: S1, specifically: The pore structure change parameters of the surrounding rock at multiple acquisition moments in the historical record data are calculated according to the time series to obtain the pore structure change rate; The difference between the gas permeability change parameters at multiple acquisition moments in the historical record data is calculated in time series to obtain the permeability change rate; Calculate the fluctuation amplitude of the carbon-based gas desorption equilibrium change parameters across multiple acquisition moments in the historical record data to obtain the desorption equilibrium change amplitude; Based on preset thresholds, the pore structure change rate, permeability change rate and equilibrium change amplitude are screened to generate historical escape characteristic data.

3. The method for intelligent assessment of carbon emissions from mining areas by integrating multi-source data according to claim 2 is characterized in that: S2, specifically: Based on multiple monitoring points preset within the mining area, real-time monitoring data of mining activities in the mining area is collected; Perform synchronous alignment and noise filtering on real-time monitoring data to generate calibrated real-time monitoring data; Compare the calibrated real-time monitoring data with the historical emission characteristic data at the same monitoring point to determine whether the real-time monitoring data at each monitoring point exceeds the corresponding historical emission characteristic data; A distribution map of potential emission areas is generated based on the spatial coordinates of monitoring points that exceed the corresponding historical emission characteristic data.

4. The method for intelligent assessment of carbon emissions from mining areas by integrating multi-source data according to claim 3 is characterized in that: S3, specifically: The potential escape area distribution map is divided into several sampling units according to equidistant spatial grids; Collect the carbon-based gas concentration at the center of the sampling unit according to the preset sampling time; Generate a carbon-based gas concentration time series list for each sampling unit according to the sampling time, and arrange the carbon-based gas concentration time series list according to the row and column coordinate order of the spatial grid; A gas concentration distribution matrix is ​​constructed based on the arranged carbon-based gas concentration time series list.

5. The method for intelligent assessment of carbon emissions from mining areas by integrating multi-source data according to claim 4 is characterized in that: The rows of the gas concentration distribution matrix represent the row coordinates of the spatial grid, the columns of the gas concentration distribution matrix represent the column coordinates of the spatial grid, and the elements of the gas concentration distribution matrix correspond to the carbon-based gas concentration at the center position of the sampling unit.

6. The method for intelligent assessment of mining area carbon emissions by integrating multi-source data according to claim 5 is characterized in that: S4, specifically: The carbon-based gas concentration at the center of the sampling unit in the gas concentration distribution matrix is ​​matched with the corresponding sampling time to obtain a carbon-based gas concentration change curve; Obtain geological stress field change trend data covering potential escape areas; Based on the carbon-based gas concentration change curve and geological stress field change trend data, a carbon-based gas escape flux prediction model is constructed; The predicted value of the carbon-based gas leakage flux in the potential leakage area according to the carbon-based gas leakage flux prediction model.

7. The method for intelligent assessment of mining area carbon emissions by integrating multi-source data according to claim 6 is characterized in that: The geological stress field change trend data include the surrounding rock stress evolution curve and structural unit displacement data during the mining cycle.

8. The method for intelligent assessment of carbon emissions from mining areas by integrating multi-source data according to claim 6 is characterized in that: S5, specifically: Mapping the adjacency relationships between sampling units and sampling units in the potential escape area into a three-dimensional node network; Assign the predicted value of the fugitive flux of the corresponding sampling unit to each node in the three-dimensional node network as the node weight; Connect high-weight nodes to low-weight nodes in sequence to form a set of potential escape paths for carbon-based gases.

9. A mining area carbon emission intelligent assessment system integrating multi-source data, used to implement the mining area carbon emission intelligent assessment method integrating multi-source data according to any one of claims 1 to 8, characterized in that: include: Historical analysis module: obtains historical records of geological stress redistribution in the mining area, analyzes the characteristic parameters of the dissipation response in the historical records, and extracts historical dissipation characteristic data; Real-time monitoring module: collects real-time monitoring data of mining activities in the mining area, identifies potential emission areas induced by mining activities based on historical emission characteristic data, and generates a distribution map of potential emission areas; Concentration sampling module: Based on the potential emission area distribution map, the concentration of carbon-based gases in the potential emission area is sampled and a gas concentration distribution matrix is ​​constructed; Flux prediction module: Based on the gas concentration distribution matrix and combined with the changing trend of geological stress in the mining area, it predicts the escape flux of carbon-based gases in the potential escape area; Path analysis module: Analyzes the escape paths of carbon-based gases in potential escape areas based on the predicted escape flux value, and generates a set of potential escape paths for carbon-based gases; Emission assessment module: Combines the potential escape pathways of carbon-based gases and the predicted escape flux to assess the overall carbon emissions of the mining area and outputs the mining area carbon emission assessment results.

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