Metallic ore adaptive mining method based on geological-ground pressure response dynamic feedback

Abstract

The application relates to the field of metal mine mining design, and particularly discloses a metal mine self-adaptive mining method based on geological-ground pressure response dynamic feedback, which comprises the following steps: obtaining geological exploration core and geophysical prospecting data of a metal mine area to be mined, determining target data according to the data, constructing a three-dimensional geological disaster model according to the target data, determining the ore body mining environment of each target point according to the model, and then obtaining a plurality of mining units; determining a target mining unit and a plurality of self-adaptive mining technical schemes from the units, calculating the mining ground pressure response and mining cost of surrounding rock in each scheme according to the model, determining a final mining scheme, mining the target mining unit based on the final mining scheme, monitoring the mining ground pressure response index of the surrounding rock, supporting the target mining unit according to the monitoring result, updating the model based on the target data indicated by the mining site after the mining is completed, returning to the step of determining the target mining unit until the mining is completed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of metal mine mining design, in particular to a metal mine adaptive mining method based on geological-ground pressure response dynamic feedback. BACKGROUND

[0002] With the development of mineral resources to the deep, the development of deep complex and difficult-to-mine ore bodies has become a key task in the field of mining. Deep ore bodies exist in complex geological environments and are affected by multiple factors such as geological structure movement and high ground stress, and their occurrence conditions are extremely complex. This complexity brings great challenges to the mining of deep ore bodies. Traditional mining techniques and methods gradually reveal many limitations when dealing with deep complex ore bodies, and are difficult to meet the needs of modern mining safety, efficiency and sustainable development.

[0003] Traditional mining techniques have a series of significant problems when dealing with deep complex and difficult-to-mine ore bodies. First, when designing the mining scheme, traditional mining techniques mainly rely on the static geological model at the exploration stage, which pays insufficient attention to the spatial distribution of rock mass quality and rock mass mechanical parameters. However, deep complex and difficult-to-mine ore bodies exhibit characteristics such as variable ore body morphology, uncertain hydrogeology, and dynamic changes in rock mass mechanical parameters and rock mass quality, which leads to potential safety and economic issues in the technical scheme based on the static model. Second, traditional mining techniques design mining schemes and parameters based on in-situ stress (mining depth), ignoring the dynamic evolution of mining-induced stress and ground pressure response characteristics during the mining process, making the mining scheme and parameters too rigid to adapt to the dynamic changes in the stress environment of the stope. Third, traditional mining technique scheme adjustment lacks initiative, and often only changes mining technical parameters when there are obvious ground pressure problems in the mine, such as severe stope roof caving and frequent rock burst disasters, which pose a great threat to production safety. This not only seriously disrupts the production plan of the mine, but also affects the production capacity of the mine. Finally, mining scheme design relies on "experience method", "engineering analogy method" and "reference to manual", and fails to fully consider the geological characteristics of deep complex and difficult-to-mine ore bodies and the mining-induced ground pressure response, lacking theoretical basis, which leads to problems such as blindness, inefficiency and poor safety in mining activities, seriously restricting the safe and efficient mining of deep complex and difficult-to-mine ore bodies. SUMMARY

[0004] In view of this, the application provides a metal mine adaptive mining method and device based on geological-ground pressure response dynamic feedback, a storage medium and a computer device. By obtaining multiple geological exploration cores and geophysical data of a to-be-mined metal mine area, a three-dimensional geological disaster model is constructed, which can accurately reflect the geological characteristics and mining stress variation of deep complex and difficult-to-mine ore bodies, and provide comprehensive and accurate basic data for the development of a mining plan. According to the three-dimensional geological disaster model, the mining area is divided to obtain multiple mining units, and multiple adaptive mining technical schemes are determined for each target mining unit. By calculating the surrounding rock mining ground pressure response and mining cost of each scheme, the optimal scheme is selected to realize dynamic optimization and adaptive adjustment of the mining plan, which can better adapt to the dynamic changes of the stress environment of the stope. In the mining process, the mining ground pressure response index of the surrounding rock is monitored in real time, and the corresponding support mode is selected according to the monitoring results to further improve the safety of mining. After the target mining unit is mined, the three-dimensional geological disaster model is updated based on the actual situation of the mining site, so that the model always remains consistent with the actual geological conditions, providing a reliable basis for subsequent mining. This method overcomes the blindness and inefficiency of traditional mining technology, and significantly improves the mining efficiency and safety of deep complex and difficult-to-mine ore bodies.

[0005] According to one aspect of the application, a metal mine adaptive mining method based on geological-ground pressure response dynamic feedback is provided, comprising:

[0006] determining a to-be-mined metal mine area, obtaining multiple geological exploration cores and geophysical data corresponding to the to-be-mined metal mine area, and determining the stratum, ore body, rock physical and mechanical parameters, rock mass quality grade and mining stress corresponding to each geological exploration core according to each geological exploration core and the geophysical data; constructing a three-dimensional geological disaster model of the to-be-mined metal mine area through three-dimensional space interpolation according to the stratum, ore body, rock physical and mechanical parameters, rock mass quality grade and mining stress corresponding to each geological exploration core;

[0007] determining the ore body mining environment corresponding to each target point of the to-be-mined metal mine area according to the three-dimensional geological disaster model, and regionally dividing the to-be-mined metal mine area to obtain multiple mining units according to the ore body mining environment corresponding to each target point;

[0008] determining a target mining unit from the multiple mining units, determining multiple adaptive mining technical schemes corresponding to the target mining unit, and respectively calculating the mining ground pressure response and mining cost of the surrounding rock in each adaptive mining technical scheme according to the three-dimensional geological disaster model, and determining the final mining technical scheme from the multiple adaptive mining technical schemes according to the mining ground pressure response and the mining cost;

[0009] Based on the final mining technical scheme, the target mining unit is subjected to metal ore mining, and the mining-induced ground pressure response index of the surrounding rock is monitored. According to the monitoring result, a corresponding supporting mode is selected to support the target mining unit. After the target mining unit is mined, the three-dimensional geological disaster model is updated based on the stratum, ore body, ore rock physical and mechanical parameters, rock mass quality grade and mining stress indicated by the mining site.

[0010] Returning to the step of determining the target mining unit from the plurality of mining units, until the target mining unit is mined.

[0011] According to another aspect of the present application, a metal mine adaptive mining device based on geological-ground pressure response dynamic feedback is provided, comprising:

[0012] The model construction module is configured to determine a target mining unit, obtain a plurality of geological exploration cores and geophysical data corresponding to the target mining unit, and determine the stratum, ore body, ore rock physical and mechanical parameters, rock mass quality grade and mining stress corresponding to each geological exploration core based on the geological exploration core and the geophysical data. The three-dimensional geological disaster model of the target mining unit is constructed by three-dimensional space interpolation based on the stratum, ore body, ore rock physical and mechanical parameters, rock mass quality grade and mining stress corresponding to each geological exploration core.

[0013] The region division module is configured to determine the ore body mining environment corresponding to each target point of the target mining unit based on the three-dimensional geological disaster model, divide the target mining unit into a plurality of mining units based on the ore body mining environment corresponding to each target point, and obtain the plurality of mining units.

[0014] The scheme determination module is configured to determine a target mining unit from the plurality of mining units, determine a plurality of adaptive mining technical schemes corresponding to the target mining unit, and calculate the mining-induced ground pressure response and mining cost of the surrounding rock in each adaptive mining technical scheme based on the three-dimensional geological disaster model. The final mining technical scheme is determined from the plurality of adaptive mining technical schemes based on the mining-induced ground pressure response and the mining cost.

[0015] The model update module is configured to mine the target mining unit based on the final mining technical scheme, monitor the mining-induced ground pressure response index of the surrounding rock, select a corresponding supporting mode to support the target mining unit based on the monitoring result, and update the three-dimensional geological disaster model based on the stratum, ore body, ore rock physical and mechanical parameters, rock mass quality grade and mining stress indicated by the mining site after the target mining unit is mined.

[0016] The return module is used to return to the step of determining the target mining unit from the plurality of mining units until the mining of the metal ore area is completed.

[0017] According to another aspect of this application, a storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the above-described adaptive mining method for metal mines based on dynamic feedback of geological-ground pressure response.

[0018] According to another aspect of this application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described adaptive mining method for metal mines based on dynamic feedback of geological-ground pressure response.

[0019] By employing the aforementioned technical solution, this application provides a method, apparatus, storage medium, and computer equipment for adaptive mining of metal ores based on dynamic feedback of geological-ground pressure response. By acquiring multiple geological exploration cores and geophysical data from the metal ore area to be mined, a three-dimensional geological hazard model is constructed. This model accurately reflects the geological characteristics and mining-induced stress changes of deep, complex, and difficult-to-mine bodies, providing comprehensive and accurate basic data for the formulation of mining plans. The mining area is divided into multiple mining units based on the three-dimensional geological hazard model, and multiple adaptive mining technical schemes are determined for each target mining unit. By calculating the surrounding rock mining-induced ground pressure response and mining cost of each scheme, the optimal scheme is selected, achieving dynamic optimization and adaptive adjustment of the mining plan, which can better adapt to the dynamic changes in the stress environment of the mining site. During the mining process, the mining-induced ground pressure response index of the surrounding rock is monitored in real time, and corresponding support methods are selected based on the monitoring results, further improving the safety of mining. After the target mining unit is completed, the three-dimensional geological hazard model is updated based on the actual conditions of the mining site, ensuring that the model always remains consistent with the actual geological conditions, providing a reliable basis for subsequent mining. This method overcomes the blindness and inefficiency of traditional mining techniques, and significantly improves the mining efficiency and safety of deep, complex and difficult-to-mine bodies.

[0020] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 Fig. 1 shows a flow diagram of a metal mine adaptive mining method based on geological-ground pressure response dynamic feedback provided by an embodiment of the present application;

[0023] Figure 2 Fig. 2 shows a mine stratum and ore body distribution characteristic diagram provided by an embodiment of the present application;

[0024] Figure 3 Fig. 3 shows a mine stress evolution law diagram provided by an embodiment of the present application;

[0025] Figure 4 Fig. 4 shows a structure diagram of a model feature extraction module provided by an embodiment of the present application;

[0026] Figure 5 Fig. 5 shows a three-dimensional geological disaster model diagram provided by an embodiment of the present application;

[0027] Figure 6 Fig. 6 shows a local enlarged diagram of a three-dimensional geological disaster model A part provided by an embodiment of the present application;

[0028] Figure 7 Fig. 7 shows a two-dimensional cross-sectional image provided by an embodiment of the present application;

[0029] Figure 8 Fig. 8 shows a structure diagram of a metal mine adaptive mining device based on geological-ground pressure response dynamic feedback provided by an embodiment of the present application;

[0030] Figure 9 Fig. 9 shows a device structure diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0031] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0032] In the present embodiment, a metal mine adaptive mining method based on geological-ground pressure response dynamic feedback is provided, as shown in the figure, the method comprises: Figure 1

[0033] ​In step 101, a metal ore area to be mined is determined, a plurality of geological exploration cores corresponding to the metal ore area to be mined and geophysical data are obtained, and a stratum, ore body, physical and mechanical parameter of ore rock, rock mass quality grade, and mining stress corresponding to each geological exploration core are determined according to each geological exploration core and the geophysical data. According to the stratum, ore body, physical and mechanical parameter of ore rock, rock mass quality grade, and mining stress corresponding to each geological exploration core, a three-dimensional geological disaster model of the metal ore area to be mined is constructed by three-dimensional space interpolation.

[0034] In step 102, according to the three-dimensional geological disaster model, a mining environment of an ore body corresponding to each target point in the metal ore area to be mined is determined, the metal ore area to be mined is regionally divided according to the mining environment of the ore body corresponding to each target point, and a plurality of mining units are obtained.

[0035] In step 103, a target mining unit is determined from the plurality of mining units, a plurality of adaptive mining technical schemes corresponding to the target mining unit are determined, and the mining-induced ground pressure response and mining cost of surrounding rock in each adaptive mining technical scheme are calculated according to the three-dimensional geological disaster model. According to the mining-induced ground pressure response and the mining cost, a final mining technical scheme is determined from the plurality of adaptive mining technical schemes.

[0036] In step 104, based on the final mining technical scheme, the target mining unit is subjected to metal ore mining, and the mining-induced ground pressure response index of surrounding rock is monitored. According to the monitoring result, a corresponding supporting mode is selected to support the target mining unit. After the target mining unit is mined, the three-dimensional geological disaster model is updated based on the stratum, ore body, physical and mechanical parameter of ore rock, rock mass quality grade, and mining stress indicated by the mining site.

[0037] In step 105, the step of determining the target mining unit from the plurality of mining units is returned to until the metal ore area to be mined is completely mined.

[0038] The embodiment of the application provides a metal mine adaptive mining method based on geological-ground pressure response dynamic feedback. First, the target object of the metal mine area to be mined is determined. Then, multi-aspect data collection work is carried out to obtain a plurality of geological exploration cores and geophysical data corresponding to the area. The geological exploration core is a rock sample collected directly from the underground, which can directly reflect the rock characteristics at different depths of the underground; the geophysical data is underground geological information obtained through physical exploration methods, such as seismic exploration and electrical exploration. Then, based on each collected geological exploration core and geophysical data, professional geological analysis methods and technical means can be used to determine key information such as stratum corresponding to each geological exploration core, ore body, ore rock physical and mechanical parameters, rock mass quality grade and mining stress. Among them, the stratum information helps to understand the geological age and environment of the ore body; the ore body information determines the object and scale of mining; the ore rock physical and mechanical parameters reflect the mechanical properties such as strength and hardness of the rock; the rock mass quality grade is an evaluation of the overall quality of the rock mass; and the mining stress reflects the stress influence of the mining activity on the surrounding rock mass. After obtaining these detailed information, the three-dimensional space interpolation technology is used to expand the discrete point data into continuous three-dimensional space data, so as to construct a three-dimensional geological disaster model of the metal mine area to be mined. The model can intuitively and comprehensively show the underground geological structure and possible disaster risks, and provide an important basis for subsequent mining decisions.

[0039] After successfully constructing the three-dimensional geological disaster model, according to the rich information provided by the model, the ore body mining environment corresponding to each target point of the metal mine area to be mined is determined. The ore body mining environment here covers multiple aspects, such as mining stress, rock mass quality grade, ore rock physical and mechanical parameters, and ore body occurrence. Through detailed analysis of the ore body mining environment of each target point, scientific and reasonable regional division of the metal mine area to be mined is carried out according to the similarity and difference of the ore body mining environment, and finally a plurality of mining units are obtained. Each mining unit has relatively consistent ore body mining environment characteristics, and such division helps to formulate more accurate and effective mining schemes according to the characteristics of different units, and improves the mining efficiency and safety.

[0040] Afterwards, from the divided multiple mining units, the target mining unit is determined according to the mining plan, resource distribution and other factors. For the determined target mining unit, multiple adaptive mining technical schemes can be formulated. These schemes differ in mining methods, equipment selection, mining sequence, etc. Then, with the aid of the three-dimensional geological disaster model, numerical simulation, theoretical calculation and other methods are used to calculate the mining-induced ground pressure response of surrounding rock and the mining cost in each adaptive mining technical scheme. The mining-induced ground pressure response of surrounding rock reflects the influence of mining activities on the stress state of the surrounding rock mass, and is directly related to the safety during mining; the mining cost covers equipment purchase, personnel wages, energy consumption and other aspects, and is an important indicator for measuring the economy of the mining scheme. By comprehensively considering the two key factors of mining-induced ground pressure response and mining cost, through the establishment of a scientific evaluation standard and decision-making model, the optimal scheme is selected from multiple adaptive mining technical schemes as the final mining technical scheme.

[0041] Subsequently, based on the determined final mining technical scheme, actual metal mining operations are carried out on the target mining unit. In the process of mining, in order to ensure the safety and stability of mining, the mining-induced ground pressure response indicators of surrounding rock need to be monitored in real time. The monitoring indicators can include stress changes, displacement conditions, etc. of rock mass. Through the installation of professional monitoring equipment such as stress sensors, displacement meters, etc., the dynamic information of surrounding rock is obtained in time. According to the monitoring results, professional analysis methods and experience are used to select the corresponding support method to support the target mining unit. Common support methods include anchor support, anchor cable support, shotcrete support, etc., and different support methods are suitable for different geological conditions and stress states. After the target mining unit is mined, based on the actual information of strata, ore bodies, ore rock physical and mechanical parameters, rock mass quality grade and mining-induced stress in the mining site, the three-dimensional geological disaster model constructed previously is updated. Because mining activities can change the stress state and geological structure of underground rock mass, or deviate from the previous interpolation estimation, timely updating of the model can make it more accurately reflect the current geological conditions, providing a reliable basis for subsequent mining.

[0042] After the mining, supporting and model updating of a target mining unit are completed, the step of determining the target mining unit from the plurality of mining units can be returned to. That is, the next target mining unit is selected according to the established mining sequence and strategy, and the above-mentioned series of operations of determining the mining scheme, performing the mining operation, monitoring the support and updating the model are repeated. This cycle continues until all the mining units of the entire metal ore region to be mined are mined, and the metal ore in the region is fully and efficiently mined. It should be noted that if the target mining unit covers a large area, the three-dimensional geological disaster model previously constructed can also be updated based on the information of the stratum, ore body, ore rock physical and mechanical parameters, rock mass quality grade and mining stress and the like actually indicated by the mining site, and the final mining technical scheme of the target mining unit is determined again according to the updated three-dimensional geological disaster model, and the newly determined final mining technical scheme is executed. That is, during the mining of the target mining unit, a plurality of series of operations of determining the mining scheme, performing the mining operation, monitoring the support and updating the model are executed.

[0043] By applying the technical scheme of the embodiment, many problems existing in the prior art can be effectively solved. The method can accurately reflect the geological characteristics and mining stress change of deep complex and difficult-to-mine ore bodies by obtaining a plurality of geological exploration cores and geophysical data of a metal ore region to be mined, constructing a three-dimensional geological disaster model, and providing comprehensive and accurate basic data for the development of a mining scheme; the mining area is divided according to the three-dimensional geological disaster model to obtain a plurality of mining units, and a plurality of adaptive mining technical schemes are determined for each target mining unit, the optimal scheme is selected by calculating the surrounding rock mining ground pressure response and mining cost of each scheme, and the dynamic optimization and adaptive adjustment of the mining scheme are realized, which can better adapt to the dynamic changes of the stress environment of the stope; in the mining process, the mining ground pressure response index of the surrounding rock is monitored in real time, and the corresponding supporting mode is selected according to the monitoring result, which further improves the safety of the mining; after the mining of the target mining unit is completed, the three-dimensional geological disaster model is updated based on the actual situation of the mining site, so that the model always keeps consistent with the actual geological conditions, and provides a reliable basis for subsequent mining. The method overcomes the blindness and inefficiency of traditional mining technology, and significantly improves the mining efficiency and safety of deep complex and difficult-to-mine ore bodies.

[0044] In the embodiments of the present application, the "determining the stratum, ore body, rock mass physical and mechanical parameters, rock mass quality grade and mining stress corresponding to each geological exploration core according to each geological exploration core and the geophysical data" in step 101 comprises: determining the stratum and ore body corresponding to each geological exploration core according to each geological exploration core and the geophysical data; respectively performing rock mass physical and mechanical tests on each geological exploration core to determine the rock mass physical and mechanical parameters and rock mass quality grade of each geological exploration core; and obtaining the in-situ rock stress corresponding to each geological exploration core, and taking the in-situ rock stress as the mining stress of the geological exploration core.

[0045] In this embodiment, the stratum and ore body corresponding to each geological exploration core can be determined according to each geological exploration core and geophysical data. The geological exploration core is a rock sample drilled from a specific position underground, which directly carries the geological information of the position; the geophysical data is the underground geological information obtained by physical exploration methods such as seismic exploration, electrical exploration, magnetic exploration, etc., which can reflect the electrical properties, magnetic properties, elasticity and other characteristics of the underground rock mass from different angles. In processing, the intuitive features of the geological exploration core, such as the color, texture, mineral composition of the rock, etc., can be combined with the underground structure features presented by the geophysical data for comprehensive comparative analysis. For example, through the seismic exploration data, the reflection interface and wave velocity characteristics of different strata underground can be understood, which are corresponding to the stratum boundaries and rock types observed in the core, so that the stratum corresponding to each geological exploration core can be accurately determined. For the determination of the ore body, the mineral composition, content, structure and other characteristics of the ore in the core are also used, combined with the abnormal areas (such as the resistivity anomaly in electrical exploration, which may correspond to the ore body) reflected in the geophysical data, to accurately determine whether each core position is within the ore body range and the specific boundary of the ore body, to provide a basic geological framework for subsequent mining planning. For example, as shown in FIG. 1, a stratum and ore body distribution characteristic schematic diagram of a metal mine area to be mined is shown. Figure 2

[0046] ​After identifying the strata and ore bodies corresponding to each geological exploration core, a series of ore-rock physical and mechanical tests can be conducted on each core to gain a deeper understanding of its mechanical properties. Common tests include uniaxial compressive strength tests, which apply axial pressure to the core on a testing machine until it fails, measure the maximum pressure at failure, and then calculate the uniaxial compressive strength of the rock. This parameter reflects the rock's ability to resist axial pressure. Triaxial compression tests simulate the mechanical behavior of rock under triaxial stress underground, obtaining the rock's strength and deformation characteristics under different confining pressures. Through these tests, ore-rock physical and mechanical parameters of each geological exploration core can be obtained, such as elastic modulus, Poisson's ratio, internal friction angle, and cohesion. Simultaneously, based on these physical and mechanical parameters and relevant rock mass quality grading standards, such as RMR (Rock Mass Rating System) and Q (Rock Mass Quality Index), the rock mass quality corresponding to each geological exploration core can be classified. These grading standards comprehensively consider factors such as rock strength, integrity, and the degree of joint and fracture development, classifying rock mass quality into different grades, such as excellent, medium, and poor, providing a basis for assessing rock mass stability and selecting appropriate mining methods.

[0047] The original rock stress corresponding to each geological exploration core can also be obtained and used as the mining-induced stress of that core. Original rock stress refers to the stress existing in natural rock masses unaffected by engineering excavation, primarily caused by factors such as the gravity of overlying strata and tectonic movements. Various methods can be used to obtain the original rock stress corresponding to each geological exploration core, such as the stress relief method, which involves drilling holes in the rock mass and installing stress gauges, then releasing the rock mass constraints within a certain range around the borehole, measuring the changes in the stress gauges during the constraint release process, and thus calculating the magnitude and direction of the original rock stress. Another method is the hydraulic fracturing method, which involves injecting high-pressure water into the borehole to create cracks in the borehole wall, and determining the original rock stress based on the crack propagation pressure and direction. In this technical solution, the original rock stress corresponding to each geological exploration core is used as the mining-induced stress for that core. This is because before mining activities begin, the original rock stress represents the initial stress state of the rock mass, while during mining, the stress state of the rock mass changes with excavation, but the original rock stress serves as the basis and reference for this change. Using the original rock stress as the initial value of the mining-induced stress helps to analyze the dynamic evolution of rock stress during the mining process, and provides an important basis for assessing the impact of mining on rock mass stability and formulating reasonable support measures.

[0048] It should be noted that when updating the three-dimensional geological hazard model based on the geological strata, ore body, ore-rock physical and mechanical parameters, rock mass quality grade, and mining-induced stress indicated at the mining site, the mining-induced stress can be obtained using numerical calculation methods. Figure 3 This paper illustrates the evolution characteristics of mining stress in a mining unit during the mining process.

[0049] In the embodiment of the present application, step 102 comprises: dividing the metal mining area to be exploited into a plurality of grids according to a preset strategy; for each grid, determining at least one target point based on the grid, and respectively positioning each target point in the three-dimensional geological disaster model to obtain a positioning location corresponding to each target point, and constructing a mining environment vector of each target point according to the type of mining equipment, mining stress, rock mass quality grade, physical and mechanical parameters of the rock mass, and occurrence of the ore body corresponding to each positioning location in the three-dimensional geological disaster model; and respectively calculating the similarity between the mining environment vectors of each two target points, and attributing target points with a similarity greater than a preset similarity threshold to the same mining unit.

[0050] In this embodiment, the preset strategy is the key basis when performing grid division. The preset strategy can comprehensively consider various factors, such as grid size, shape, and complexity of geological structure, etc. If the metal mining area to be exploited is large and the geological structure is relatively simple, a larger size grid can be used for division to improve the calculation efficiency and the macroscopic nature of the overall planning; while for the area with complex geological structure, multiple faults or folds, a smaller size grid can be used to capture the changes in geological information more accurately. Through such a preset strategy, the entire metal mining area to be exploited is divided into a plurality of regular or irregular grids, which provides a basic spatial framework for subsequent determination of target points and analysis of the mining environment of the ore body, so that the analysis of the metal mining area to be exploited can be more detailed and targeted.

[0051] Then, for each grid, one or more target points can be determined based on the grid. The target point can be the centroid of the grid, the endpoint of any one or more edges of the grid, etc., which is not limited herein. Further, each target point is respectively positioned in the three-dimensional geological disaster model to obtain a positioning location corresponding to each target point. The three-dimensional geological disaster model is a digital model containing rich geological information, which shows the distribution of strata, ore bodies, faults and other geological elements in the metal mining area to be exploited in the form of three-dimensional space. The determined target points can be positioned in the three-dimensional geological disaster model with the help of professional three-dimensional modeling software. By inputting the coordinate information of the target point, the software can accurately find the position of the target point in the three-dimensional model. This positioning location not only contains the coordinates of the target point in the horizontal direction, but also contains the height information in the vertical direction, thereby comprehensively determining the specific position of the target point in the three-dimensional space. For example, in a three-dimensional mine model, by inputting the coordinate value of the target point, the software can accurately display the target point on the corresponding stratum or ore body position in the model.

[0052] In the three-dimensional geological disaster model, each positioning position is associated with multiple information closely related to the mining environment of the ore body, such as mining equipment type, mining stress, rock mass quality grade, physical and mechanical parameters of ore rock, and occurrence of ore body. Among them, the mining equipment type reflects the mechanical equipment used when mining at this position. Different equipment is suitable for different geological conditions and mining methods, for example, large drilling equipment is needed in hard rock mass, while more attention should be paid to the selection of supporting equipment in soft rock mass. The mining equipment type can be added after the three-dimensional geological disaster model is constructed by three-dimensional space interpolation; the occurrence of the ore body describes the spatial position and form of the ore body, including the strike, tendency and dip angle of the ore body. Understanding the occurrence of the ore body helps to determine the direction and sequence of mining and optimize the mining scheme. By comprehensively analyzing the above information, the mining environment vector of each target point can be determined comprehensively and accurately, providing a reliable basis for subsequent mining design and safety measures. In a specific embodiment, the mining equipment type, mining stress, rock mass quality grade, physical and mechanical parameters of ore rock and occurrence of ore body can be represented by specific numerical values, and then these numerical values can be spliced together to form the mining environment vector of the target point.

[0053] Finally, the similarity between the mining environment vectors of each two target points is calculated respectively. By calculating the similarity (such as cosine similarity or Euclidean distance reciprocal) between all target points, and setting a preset similarity threshold, the target points with high similarity in geological and engineering conditions (i.e. similarity exceeding the preset similarity threshold) are automatically merged into the same mining unit, thereby realizing intelligent partitioning based on similarity of geological properties, ensuring uniform mining environment within the same unit, and laying a solid foundation for subsequent development of unified and efficient adaptive mining scheme.

[0054] In the embodiment of the present application, optionally, the "determining a plurality of adaptive mining technical schemes corresponding to the target mining unit" in step 103 comprises: calculating a mining body mining environment vector corresponding to the target mining unit according to a mining body mining environment vector of a target point contained in the target mining unit; obtaining a mining equipment type, a mining-induced stress, a rock mass quality grade, a mining rock physical and mechanical parameter and a ore body occurrence of the target mining unit according to the mining body mining environment vector of the target mining unit; calling a preset large language model, taking the mining equipment type, the ore body occurrence and the rock mass quality grade as target guide words, and outputting a plurality of mining methods through the preset large language model; intercepting a three-dimensional geological disaster sub-model corresponding to the target mining unit from the three-dimensional geological disaster model, and determining a model feature vector of the target mining unit through a model feature extraction module based on the three-dimensional geological disaster sub-model; for each mining method, generating a basic attribute feature vector of the target mining unit based on the mining method, the ore body occurrence, the mining-induced stress, the rock mass quality grade and the mining rock physical and mechanical parameter, and obtaining a fusion feature vector through a feature fusion module based on the model feature vector and the basic attribute feature vector; and inputting the fusion feature vector corresponding to each mining method into a mining scheme generation module respectively to obtain an adaptive mining technical scheme corresponding to each mining method.

[0055] In this embodiment, when determining a plurality of adaptive mining technical schemes corresponding to the target mining unit, first, a mining body mining environment vector corresponding to the target mining unit can be calculated according to a mining body mining environment vector of a target point contained in the target mining unit. Then, a mining equipment type, a mining-induced stress, a rock mass quality grade, a mining rock physical and mechanical parameter and an ore body occurrence corresponding to the target mining unit can be analyzed reversely according to the mining body mining environment vector of the target mining unit.

[0056] Next, a pre-set large language model can be used to generate multiple possible mining methods. Specifically, the mining equipment type, ore body occurrence, and rock mass quality grade can be input into the large language model as target guiding words. Based on its vast training data and strong semantic understanding capabilities, the large language model can perform in-depth analysis and processing of these input information. It can combine various mining knowledge, practical cases, and the adaptation relationship between different equipment types and geological conditions learned by itself to output multiple mining methods that match the input conditions. For example, for the case of low rock mass quality grade and small mining equipment, the model may output the drift filling mining method, as this method can effectively control ground pressure and reduce rock mass deformation; if the ore body occurrence is thick and large equipment is used, the stage open stope and subsequent filling mining method may be recommended. This process fully utilizes the intelligent generation capabilities of the large language model, quickly expands the candidate range of mining methods, and provides more possibilities for subsequent scheme optimization. It is worth noting that the large language model can output one or more mining methods.

[0057] To better understand the geological conditions of the target mining unit, a three-dimensional geological disaster sub-model corresponding to the target mining unit can be extracted from the existing three-dimensional geological disaster model. This process is similar to "cutting" the target area in the overall model to analyze it separately. After extraction, the three-dimensional geological disaster sub-model can be processed using a model feature extraction module. This module can use advanced image processing techniques and algorithms, such as convolutional neural networks, to extract and analyze key information such as geological structures and fault distribution in the three-dimensional geological disaster sub-model. Through these processes, the complex geological information in the three-dimensional geological disaster sub-model is converted into a numerical model feature vector. This feature vector can fully and accurately represent the geological characteristics of the target mining unit, providing an important risk assessment basis for subsequent scheme development.

[0058] For each mining method output by the large language model, a basic attribute feature vector can be further generated in combination with various parameters of the target mining unit. These parameters include the characteristics of the mining method itself, the occurrence of the ore body, the mining conditions, the mining stress, the rock mass quality grade, and the physical and mechanical parameters of the ore and rock, etc. Specifically, each parameter can be encoded, and then the encoding results are spliced together to obtain the basic attribute feature vector. After generating the basic attribute feature vector, the model feature vector obtained before is fused with the basic attribute feature vector through a feature fusion module. Feature fusion can be performed in various ways, such as weighted summation, feature splicing, etc. The fused feature vector can comprehensively reflect the geological characteristics of the target mining unit, the characteristics of the mining method, and the influence of various geological and mining parameters, forming a more comprehensive and rich feature representation. The purpose of this step is to organically combine geological information and mining method information, providing more accurate and comprehensive input data for subsequent generation of adaptive mining technical schemes.

[0059] Further, the fusion feature vector corresponding to each mining method is input into a mining scheme generation module. The mining scheme generation module can analyze and process the input fusion feature vector based on pre-set algorithms and rules, considering various information in the fusion feature vector, such as geological risk, applicability of the mining method, mining efficiency, etc. Through a series of calculations and optimization processes, adaptive mining technical schemes corresponding to each mining method are generated, which can fully adapt to the actual conditions of the target mining unit and achieve safe, efficient, and economic mining goals. In a specific embodiment, the mining scheme generation module can be a BP neural network (Back Propagation Neural Network).

[0060] In a specific embodiment, the mining scheme generation module generates three adaptive mining technical methods for the target mining unit as follows:

[0061] Scheme 1: Stage empty field subsequent filling mining method; the stope structure parameters are 40m x 16m x 50m (length x width x height), the blast hole diameter is 165mm, the blast hole hole pattern parameters are 3m x 3m, the air interval charging is adopted, the recovery sequence of every other one is adopted, the first step stope is mined, after filling is completed, the second step stope is mined, the stope production capacity is 1400 tons / day, the ore loss rate is 8%, and the dilution rate is 4%.

[0062] Scheme 2 is a sublevel open stoping method; the sublevel height is 60 m, the stope width is 15-20 m, the pillar width is 6-8 m, the sublevel height is 10-15 m, the bottom pillar height is 10 m, no top pillar is left, the blast hole diameter is 76 mm, the blast hole hole pattern parameters are 1.8 m x 2.3 m, continuous charging is adopted, the recovery sequence is one out of two, the first step stope is mined first, and the second step stope is mined after filling, the stope production capacity is 850 tons / day, the ore loss rate is 8%, and the dilution rate is 5%.

[0063] Scheme 3 is a point pillar (strip pillar) upward horizontal slicing filling mining method; the middle sublevel height is 60 m, a 10 m top pillar is left, the sublevel height is 12.5 m, the slicing height is 3.3 m, the designed point pillar size is 4 m x 4 m, and the strip pillar width is 3-4 m; the blast hole diameter is 42 mm, the blast hole hole pattern parameters are 1.2 m x 1.2 m, continuous charging is adopted, the recovery sequence is one out of two, the first step stope is mined first, and the second step stope is mined after filling, the stope production capacity is 180 tons / day, the ore loss rate is 2%, and the dilution rate is 2%.

[0064] The ground pressure response and mining cost economic indicators of the above three adaptive mining technical schemes are shown in the following table:

[0065]

[0066] After comprehensive consideration, scheme 1 is determined as the final mining technical scheme of the target mining unit.

[0067] In the embodiment of the application, optionally, the "calculating the ore body mining environment vector corresponding to the target mining unit according to the ore body mining environment vectors of the target points contained in the target mining unit" comprises: for each target point in the target mining unit, assigning a corresponding weight coefficient to the ore body mining environment vector of the target point according to the shortest distance between the position of the target point and the position of each geological exploration core sampling point, the geophysical prospecting data confidence of the target point, and the distance between the target point and the geometric center of the target mining unit; and calculating the ore body mining environment vector corresponding to the target mining unit according to the ore body mining environment vectors and weight coefficients of each target point in the target mining unit.

[0068] In this embodiment, for each target point in the target mining unit, a weighting coefficient can first be assigned. The assignment of the weighting coefficient is based on three key factors: the nearest distance between the location of the target point and the locations of the geological exploration core sampling points, the confidence level of the geophysical data corresponding to the target point, and the distance between the target point and the geometric center of the target mining unit. Among these factors, the closest distance reflects the proximity of the target point to known geological information. Within a mining unit, the closer a point is to the geological core sampling point, the closer its geological attributes are to the core data, and the higher its reliability. Geophysical data confidence directly reflects the reliability of the geophysical data at the target point; higher confidence indicates more credible data, and its weight can be increased accordingly. As for the distance between the target point and the geometric center of the target mining unit, the comprehensive attributes of a mining unit should ideally reflect the characteristics of its main body. Data points at the edges or corners of the unit may be "contaminated" by different geological conditions in adjacent mining units. Therefore, the closer the distance to the geometric center of the target mining unit, the higher its weight coefficient can be assigned. This strengthens the influence of the core area data within the target mining unit, making the calculated comprehensive vector more representative of the overall situation of the target mining unit and reducing boundary effects. By comprehensively considering these three factors, an appropriate weight coefficient is assigned to each target point.

[0069] After assigning the weighting coefficients, the mining environment vector of each target point can be multiplied by its corresponding weighting coefficient. Then, all the products are summed to obtain the mining environment vector corresponding to the target mining unit. This vector integrates the mining environment information of all target points within the target mining unit and is weighted according to the importance of each target point, thus more accurately reflecting the mining environment status of the entire target mining unit.

[0070] Optionally, in this embodiment, the step of "determining the model feature vector of the target mining unit based on the three-dimensional geological hazard sub-model and through the model feature extraction module" includes: using the slicing direction determination unit of the model feature extraction module to identify a first direction corresponding to a geological fault and a second direction corresponding to the ore body strike from the three-dimensional geological hazard sub-model, and determining a first slicing direction based on the first direction and a second slicing direction based on the second direction; using the slicing unit of the model feature extraction module to slice the three-dimensional geological hazard sub-model according to the first slicing direction to obtain a first set of two-dimensional cross-sectional images, and using the slicing unit of the model feature extraction module to slice the three-dimensional geological hazard sub-model according to the second slicing direction to obtain a second set of two-dimensional cross-sectional images; using the feature extraction unit of the model feature extraction module to extract features from the first set of two-dimensional cross-sectional images and the second set of two-dimensional cross-sectional images respectively to obtain a first image feature and a second image feature; using the feature fusion unit of the model feature extraction module to fuse the first image feature and the second image feature to obtain an image fusion feature; and using the average pooling layer of the model feature extraction module to convert the image fusion feature into a feature vector of a preset length to obtain the model feature vector of the target mining unit.

[0071] In this embodiment, the structure of the model feature extraction module is as follows: Figure 4 As shown, firstly, the directional information of key geological features is extracted from the 3D geological hazard sub-model through the slicing direction determination unit of the model feature extraction module. Specifically, by analyzing the geological faults and orebody strikes in the 3D geological hazard sub-model, their main extension directions in space are determined. For example, a fault may extend along a specific angle, while the orebody strike may have different directions. Based on this directional information, two main slicing directions are automatically determined: the first slicing direction is used to capture fault features, and the second slicing direction is used to capture orebody strike features. This directional slicing ensures that subsequent processing focuses on key information related to the geological hazard and orebody boundaries, avoiding interference from irrelevant information.

[0072] Next, based on the slicing direction determined in the previous step, the 3D geological hazard sub-model is sliced ​​using the slicing unit of the model feature extraction module. For the first slicing direction, a series of evenly spaced two-dimensional cross-sectional images are generated along this direction. These images clearly show the spatial distribution and morphological characteristics of the faults. Similarly, for the second slicing direction, another set of two-dimensional cross-sectional images is generated to show the structural information of the ore body's strike. During the slicing process, it is necessary to ensure that the image resolution and size are consistent for subsequent processing. Through multi-directional slicing, the geological structure can be observed from different perspectives, providing a foundation for comprehensive feature extraction. Figure 5As shown, a three-dimensional geological hazard model of a metal ore area to be mined is presented. A partial enlarged view of part A of the three-dimensional geological hazard model is shown below. Figure 6 As shown, Figure 7 A two-dimensional cross-sectional image of the three-dimensional geological hazard model is shown.

[0073] Furthermore, features are extracted using the feature extraction unit of the model feature extraction module for the first and second sets of two-dimensional cross-sectional images, respectively. Through the feature extraction unit, features can be extracted from the first and second sets of two-dimensional cross-sectional images to obtain first image features and second image features. Specifically, for the first set of two-dimensional cross-sectional images, the feature extraction unit can extract multi-scale features from each two-dimensional cross-sectional image (e.g., fault lines at a fine scale, fault zones at a coarse scale). The multi-scale features of each two-dimensional cross-sectional image are fused to obtain the first image features, which are used to characterize fault geometric parameters (e.g., dip angle, displacement) and degree of fragmentation. Similarly, for the second set of two-dimensional cross-sectional images, the feature extraction unit can extract multi-scale features from each two-dimensional cross-sectional image (e.g., orebody boundaries at a fine scale, orebody morphology at a coarse scale). The multi-scale features of each two-dimensional cross-sectional image are fused to obtain the second image features, which are used to characterize the spatial distribution (e.g., thickness, continuity) and internal structure of the orebody.

[0074] Since the first and second sets of images focus on fault and orebody strike features respectively, directly fusing these features can provide more comprehensive geological information. Therefore, feature fusion units in the model's feature extraction module can integrate feature information from different slice directions. The fusion process can employ simple concatenation or weighted averaging to combine the two sets of features into a unified feature representation. This fusion strategy helps the model utilize information from both fault and orebody strikes simultaneously, improving its understanding of complex geological structures. For example, orebody boundaries near faults may have unique characteristics, and the fused features can better reflect this correlation.

[0075] Finally, the fused high-dimensional feature map is converted into a fixed-length feature vector through the global average pooling layer of the model's feature extraction module. Global average pooling compresses spatial information into vector form by calculating the average value of each channel in the feature map. This step not only reduces the dimensionality of the features but also enhances their translation invariance, making them more suitable for subsequent tasks (such as classification or regression). Through this standardization process, the extracted features can be efficiently utilized to support practical decision-making.

[0076] Through the above steps, the automatic conversion from the three-dimensional geological hazard sub-model to the model feature vector was achieved, providing a data foundation for subsequent geological analysis and mining optimization.

[0077] Optionally, after obtaining the adaptive mining technology scheme corresponding to each mining method in this application embodiment, the method further includes: for each adaptive mining technology scheme, simulating the mining activity corresponding to the adaptive mining technology scheme in the three-dimensional geological hazard sub-model corresponding to the target mining unit using the finite element method, and recording the stress redistribution of the rock mass in the target mining unit after excavation, identifying high stress concentration areas based on the stress redistribution, calculating the safety factor of the high stress concentration area through stress value and rock mass strength value, judging whether the rock mass of the target mining unit has reached the critical state of instability based on the safety factor, and determining the first risk coefficient based on the judgment result; extracting the shape of the rock mass in the target mining unit after excavation from the three-dimensional geological hazard sub-model corresponding to the target mining unit. The adaptive mining technology scheme is further developed by: identifying high-deformation zones based on the deformation distribution; calculating the stability coefficient of the high-deformation zones based on the deformation value and critical deformation value of the rock mass; determining a second risk coefficient based on the stability coefficient; identifying potential plastic zones in the target mining unit after excavation in the three-dimensional geological hazard sub-model corresponding to the target mining unit; extracting the volume of potential plastic zones; calculating the ratio of the volume of potential plastic zones to the exposed area of ​​the rock mass; determining a third risk coefficient based on the ratio; determining the final risk coefficient of the adaptive mining technology scheme based on the first risk coefficient, the second risk coefficient, and the third risk coefficient; and eliminating adaptive mining technology schemes with a final risk coefficient greater than a preset coefficient based on the final risk coefficient of each adaptive mining technology scheme.

[0078] In this embodiment, after obtaining the adaptive mining technology solutions corresponding to each mining method, for each solution, simulation can be performed in a three-dimensional geological hazard sub-model corresponding to the target mining unit using the finite element method. The finite element method is a numerical calculation method that can discretize complex geometric structures into a finite number of elements and simulate the response of the overall structure by solving the mechanical behavior of each element. Here, mining activities involved in the adaptive mining technology solution, such as excavation and support operations, are simulated in the three-dimensional geological hazard sub-model. During the simulation, the stress redistribution of the rock mass in the target mining unit after excavation is recorded. Stress redistribution refers to the phenomenon where stress is redistributed in the rock mass due to changes in the original stress state caused by mining activities. By analyzing the stress redistribution, high stress concentration areas can be identified, which are often potential danger zones for rock mass instability. Next, the stress values ​​and rock mass strength values ​​corresponding to the high stress concentration areas are identified, and the safety factor of the high stress concentration areas is calculated using these values. Based on the calculated safety factor, it is determined whether the rock mass of the target mining unit has reached the critical state of instability. If the safety factor is less than a certain critical value, it indicates that the rock mass is in an unstable state and is prone to instability and failure. Based on this judgment, a first risk factor is determined, which reflects the degree of risk of rock mass instability.

[0079] Next, during the simulation, the deformation distribution of the rock mass in the target mining unit after excavation can be extracted from the 3D geological hazard sub-model. Deformation distribution is another important response of the rock mass under the influence of mining activities. By analyzing the deformation distribution, high-deformation zones can be identified, which may also be potential locations for rock mass instability. Based on the comparison between the actual deformation value and the critical deformation value of the rock mass in the high-deformation zone, the stability coefficient of the high-deformation zone can be calculated. The stability coefficient provides a quantitative indicator of the rock mass deformation stability. Based on this coefficient, a second risk coefficient can be determined, which reflects the degree of risk of instability caused by rock mass deformation.

[0080] Furthermore, during the simulation process, potential plastic zones can be extracted from the three-dimensional geological hazard sub-model within the target mining unit after excavation. Potential plastic zones are areas of irreversible deformation in the rock mass under stress; the ratio of their volume to the exposed area of ​​the rock mass reflects the overall stability of the rock mass. By extracting the volume of the potential plastic zone and calculating its ratio to the exposed area of ​​the rock mass, a third risk coefficient can be determined. This coefficient considers the impact of rock mass plastic deformation on stability and is an important component in assessing the safety of mining technology schemes.

[0081] Next, based on the first, second, and third risk coefficients calculated previously, the final risk coefficient of the adaptive mining technology scheme is determined. The final risk coefficient can be obtained by weighted summation of these three risk coefficients or other mathematical operations. Finally, based on the final risk coefficient of each adaptive mining technology scheme, adaptive mining technology schemes with a final risk coefficient greater than a preset coefficient are eliminated. The preset coefficient can be set based on actual engineering experience and safety standards. This embodiment of the application, by eliminating high-risk schemes, can screen out relatively safe and reliable adaptive mining technology schemes, providing a guarantee for subsequent mining operations.

[0082] In this embodiment of the application, optionally, the mining-induced ground pressure response index includes stress value, deformation value, and plastic zone volume; step 104, "monitoring the mining-induced ground pressure response index of the surrounding rock and selecting the corresponding support method to support the target mining unit according to the monitoring results," includes: using a stress gauge, a deformation meter, and a sonic instrument to monitor the stress value, deformation value, and plastic zone volume in the surrounding rock of the target mining unit, and constructing a mining-induced ground pressure response index vector based on the stress value, the deformation value, and the plastic zone volume; calculating the similarity between the mining-induced ground pressure response index vector and each preset response index vector in the preset database, and using the support method corresponding to the preset response index vector with the highest similarity and greater than the preset similarity threshold as the support method corresponding to the monitoring results to support the target mining unit.

[0083] In this embodiment, the mining-induced ground pressure response of the surrounding rock is crucial for ensuring mining safety during the mining process of the target mining unit. Therefore, various specialized instruments can be used to monitor relevant indicators of the surrounding rock. A stress gauge, a device capable of measuring the magnitude of internal stress in rock mass, is installed in the surrounding rock of the target mining unit to obtain real-time stress values, reflecting changes in the stress state of the rock mass after mining activities. A deformation meter is used to monitor the deformation of the surrounding rock, measuring displacement changes in different directions to obtain deformation values, which reflect the degree of deformation of the surrounding rock under stress. A sonic logging instrument emits sound waves into the surrounding rock and receives reflected waves, inferring the volume of the plastic zone based on the propagation characteristics of sound waves in the rock mass. The volume of the plastic zone reflects the range of plastic deformation of the rock mass under stress. After monitoring the stress values, deformation values, and plastic zone volume using these instruments, these data are combined in a specific order and format to construct a mining-induced ground pressure response index vector. This vector is a multidimensional dataset that comprehensively contains key response information of the surrounding rock under mining-induced ground pressure, providing a data foundation for the selection of subsequent support methods.

[0084] After constructing the mining-induced ground pressure response index vector, its similarity can be calculated with each preset response index vector in the pre-set database. The pre-set database stores mining-induced ground pressure response index vectors for various working conditions and their corresponding support methods. This data is based on extensive engineering practice and theoretical analysis. Similarity calculation can employ various methods, such as Euclidean distance and cosine similarity. By calculating the distance or angular relationship between the mining-induced ground pressure response index vector and each preset response index vector, their similarity can be obtained.

[0085] After calculating all similarities, the preset response index vector with the highest similarity is selected. Simultaneously, to ensure sufficient reliability of the selected support method, it is necessary to determine whether this highest similarity exceeds a preset similarity threshold. The preset similarity threshold for 3D geological hazards is set based on engineering experience and safety requirements; only when the similarity exceeds this threshold is the monitoring result considered to match a certain working condition in the preset database. If a preset response index vector with the highest similarity exceeding the preset similarity threshold exists, then the support method corresponding to that vector is used as the support method corresponding to the monitoring result, and this method is adopted to support the target mining unit. In this way, a suitable support method can be quickly and accurately selected from the existing experience database based on the actual mining-induced ground pressure response of the surrounding rock, improving support effectiveness and mining safety.

[0086] Furthermore, as Figure 1 In terms of specific implementation, this application provides an adaptive mining device for metal mines based on dynamic feedback of geological-ground pressure response, such as... Figure 8 As shown, the device includes:

[0087] The model construction module is used to determine the metal ore area to be mined, acquire multiple geological exploration cores and geophysical data corresponding to the metal ore area to be mined, and determine the strata, ore body, ore-rock physical and mechanical parameters, rock mass quality grade and mining stress corresponding to each geological exploration core based on the geological exploration core and the geophysical data. Based on the strata, ore body, ore-rock physical and mechanical parameters, rock mass quality grade and mining stress corresponding to each geological exploration core, a three-dimensional geological hazard model of the metal ore area to be mined is constructed through three-dimensional spatial interpolation.

[0088] The region division module is used to determine the ore body mining environment corresponding to each target point in the metal ore area to be mined based on the three-dimensional geological disaster model, and to divide the metal ore area to be mined into multiple mining units based on the ore body mining environment corresponding to each target point.

[0089] The scheme determination module is used to determine a target mining unit from the plurality of mining units, determine a plurality of adaptive mining technology schemes corresponding to the target mining unit, and calculate the mining-induced ground pressure response and mining cost of the surrounding rock in each adaptive mining technology scheme according to the three-dimensional geological hazard model, and determine the final mining technology scheme from the plurality of adaptive mining technology schemes based on the mining-induced ground pressure response and the mining cost.

[0090] The model update module is used to carry out metal mining on the target mining unit based on the final mining technology scheme, monitor the mining-induced ground pressure response index of the surrounding rock, select the appropriate support method to support the target mining unit according to the monitoring results, and update the three-dimensional geological hazard model based on the strata, ore body, ore and rock physical and mechanical parameters, rock mass quality grade and mining-induced stress indicated at the mining site after the target mining unit is mined.

[0091] The return module is used to return to the step of determining the target mining unit from the plurality of mining units until the mining of the metal ore area is completed.

[0092] Optionally, the model building module is used for:

[0093] Based on each geological exploration core and the geophysical data, the corresponding strata and ore bodies for each geological exploration core are determined;

[0094] Each geological exploration core was subjected to ore and rock physical and mechanical tests to determine the ore and rock physical and mechanical parameters and rock mass quality grade of each geological exploration core.

[0095] Obtain the original rock stress corresponding to each geological exploration core, and use the original rock stress as the mining stress of the geological exploration core.

[0096] Optionally, the region division module is used for:

[0097] The area of ​​the metal ore to be mined is divided into multiple grids according to a preset strategy.

[0098] For each grid, at least one target point is determined based on the grid, and each target point is located in the three-dimensional geological hazard model to obtain the location corresponding to each target point. Based on the mining equipment type, mining stress, rock mass quality grade, ore-rock physical and mechanical parameters and ore body occurrence corresponding to each location in the three-dimensional geological hazard model, an ore body mining environment vector for each target point is constructed.

[0099] Calculate the similarity between the mining environment vectors of each pair of target points, and classify the target points with similarity greater than a preset similarity threshold as the same mining unit.

[0100] Optionally, the scheme determination module is used for:

[0101] Calculate the ore body mining environment vector corresponding to the target mining unit based on the ore body mining environment vector of the target point contained in the target mining unit;

[0102] Based on the ore body mining environment vector of the target mining unit, obtain the mining equipment type, mining stress, rock mass quality grade, ore-rock physical and mechanical parameters and ore body occurrence of the target mining unit;

[0103] A preset large language model is invoked, and the mining equipment type, the ore body occurrence, and the rock mass quality grade are used as target guide words. Multiple mining methods are then output through the preset large language model.

[0104] The three-dimensional geological hazard sub-model corresponding to the target mining unit is extracted from the three-dimensional geological hazard model. Based on the three-dimensional geological hazard sub-model, the model feature vector of the target mining unit is determined through the model feature extraction module.

[0105] For each mining method, a basic attribute feature vector of the target mining unit is generated based on the mining method, the occurrence of the ore body, the mining-induced stress, the rock mass quality grade, and the physical and mechanical parameters of the ore and rock. Based on the model feature vector and the basic attribute feature vector, a fused feature vector is obtained through a feature fusion module.

[0106] The fusion feature vector corresponding to each mining method is input into the mining scheme generation module to obtain the adaptive mining technology scheme corresponding to each mining method.

[0107] Optionally, the scheme determination module is further configured to:

[0108] For each target point in the target mining unit, a corresponding weighting coefficient is assigned to the ore body mining environment vector of the target point based on the nearest distance between the location of the target point and the location of the geological exploration core sampling points, the confidence level of the geophysical data corresponding to the target point, and the distance between the target point and the geometric center of the target mining unit.

[0109] The mining environment vector of the target mining unit is calculated based on the mining environment vector and weight coefficient of the ore body corresponding to each target point in the target mining unit.

[0110] Optionally, the device further includes a scheme selection module; the scheme selection module is used for:

[0111] After obtaining the adaptive mining technology solutions corresponding to each mining method, for each adaptive mining technology solution, the mining activities corresponding to the adaptive mining technology solution are simulated in the three-dimensional geological hazard sub-model corresponding to the target mining unit using the finite element method, and the stress redistribution of the rock mass in the target mining unit after excavation is recorded. Based on the stress redistribution, high stress concentration areas are identified, and the safety factor of the high stress concentration area is calculated by using the stress value and the rock mass strength value. Based on the safety factor, it is determined whether the rock mass of the target mining unit has reached the critical state of instability, and the first risk coefficient is determined based on the judgment result.

[0112] Extract the deformation distribution of the rock mass in the target mining unit after excavation from the three-dimensional geological hazard sub-model corresponding to the target mining unit, identify high deformation zones based on the deformation distribution, calculate the stability coefficient of the high deformation zones based on the deformation value and critical deformation value of the rock mass, and determine the second risk coefficient based on the stability coefficient.

[0113] In the three-dimensional geological hazard sub-model corresponding to the target mining unit, the potential plastic zone in the target mining unit after excavation is identified, the volume of the potential plastic zone is extracted, the ratio of the volume of the potential plastic zone to the exposed rock mass area is calculated, and the third risk coefficient is determined based on the ratio.

[0114] The final risk coefficient of the adaptive mining technology solution is determined based on the first risk coefficient, the second risk coefficient, and the third risk coefficient.

[0115] Based on the final risk coefficient of each adaptive mining technology scheme, adaptive mining technology schemes with a final risk coefficient greater than the preset coefficient are eliminated from the list of adaptive mining technology schemes.

[0116] Optionally, the mining-induced ground pressure response indicators include stress values, deformation values, and plastic zone volume; the model update module is used for:

[0117] The stress value, deformation value, and plastic zone volume in the surrounding rock of the target mining unit are monitored by stress gauge, deformation meter, and acoustic wave meter, respectively. Based on the stress value, deformation value, and plastic zone volume, a mining-induced ground pressure response index vector is constructed.

[0118] Based on the mining-induced ground pressure response index vector, the similarity between the vector and each preset response index vector in the preset database is calculated. The support method corresponding to the preset response index vector with the highest similarity and greater than the preset similarity threshold is taken as the support method corresponding to the monitoring result, and the target mining unit is supported.

[0119] It should be noted that other corresponding descriptions of the functional units involved in the adaptive mining device for metal mines based on dynamic feedback of geological-ground pressure response provided in this application embodiment can be found in the following references. Figures 1 to 7 The corresponding descriptions in the method will not be repeated here.

[0120] This application also provides a computer device, which may specifically be a personal computer, a server, a network device, etc. Figure 9 As shown, the computer device includes a bus, a processor, memory, and a communication interface, and may also include an input / output interface and a display device. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores location information. The network interface allows communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the various method embodiments.

[0121] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0122] In one embodiment, a computer-readable storage medium is provided, which may be non-volatile or volatile, having stored thereon a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0123] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0124] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0125] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0126] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0127] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A metal mine adaptive mining method based on geology-ground pressure response dynamic feedback, characterized in that, The method comprises the following steps: determining a metal ore area to be mined, obtaining a plurality of geological exploration cores and geophysical data corresponding to the metal ore area to be mined, and determining the stratum, ore body, rock mass physical and mechanical parameters, rock mass quality grade and mining stress corresponding to each geological exploration core according to the geological exploration core and the geophysical data; and constructing a three-dimensional geological disaster model of the metal ore area to be mined by three-dimensional space interpolation according to the stratum, ore body, rock mass physical and mechanical parameters, rock mass quality grade and mining stress corresponding to each geological exploration core; determining the ore body mining environment corresponding to each target point in the metal ore area to be mined according to the three-dimensional geological disaster model, and regionally dividing the metal ore area to be mined according to the ore body mining environment corresponding to each target point to obtain a plurality of mining units; determining a target mining unit from the plurality of mining units, determining a plurality of adaptive mining technical schemes corresponding to the target mining unit, and respectively calculating the mining-induced ground pressure response and mining cost of the surrounding rock in each adaptive mining technical scheme according to the three-dimensional geological disaster model, and determining a final mining technical scheme from the plurality of adaptive mining technical schemes according to the mining-induced ground pressure response and the mining cost; based on the final mining technical scheme, mining the target mining unit, monitoring the mining-induced ground pressure response index of the surrounding rock, selecting a corresponding support mode for the target mining unit according to the monitoring result, and updating the three-dimensional geological disaster model based on the stratum, ore body, rock mass physical and mechanical parameters, rock mass quality grade and mining stress indicated by the mining site after the target mining unit is mined; returning to the step of determining the target mining unit from the plurality of mining units until the metal ore area to be mined is mined.

2. The method of claim 1, wherein, The method comprises the following steps: determining the stratum and ore body corresponding to each geological exploration core according to the geological exploration core and the geophysical data; respectively performing rock mass physical and mechanical tests on each geological exploration core to determine the rock mass physical and mechanical parameters and rock mass quality grade of each geological exploration core; obtaining the original rock stress corresponding to each geological exploration core, and taking the original rock stress as the mining stress of the geological exploration core.

3. The method of claim 1, wherein, The method comprises the following steps: dividing the metal ore area to be mined into a plurality of grids according to a preset strategy; For each grid, at least one target point is determined based on the grid, and each target point is respectively positioned in the three-dimensional geological disaster model to obtain a positioning position corresponding to each target point, and a mining environment vector of each target point is constructed according to a mining equipment type, a mining stress, a rock mass quality grade, a physical and mechanical parameter of ore rock and an occurrence of ore body corresponding to each positioning position in the three-dimensional geological disaster model; The similarity between the mining environment vectors of each two target points is calculated respectively, and the target points with a similarity greater than a preset similarity threshold are attributed to a same mining unit.

4. The method of claim 1, wherein, The determination of the plurality of adaptive mining technical schemes corresponding to the target mining unit includes: The mining environment vector of the target mining unit is calculated according to the mining environment vector of the target point included in the target mining unit; The mining equipment type, the mining stress, the rock mass quality grade, the physical and mechanical parameter of ore rock and the occurrence of ore body corresponding to the target mining unit are obtained according to the mining environment vector of the target mining unit; The preset large language model is called, the mining equipment type, the occurrence of ore body and the rock mass quality grade are taken as target guide words, and a plurality of mining methods are output by the preset large language model; A three-dimensional geological disaster sub-model corresponding to the target mining unit is intercepted from the three-dimensional geological disaster model, and a model feature vector of the target mining unit is determined based on the three-dimensional geological disaster sub-model through a model feature extraction module; For each mining method, a basic attribute feature vector of the target mining unit is generated based on the mining method, the occurrence of ore body, the mining stress, the rock mass quality grade and the physical and mechanical parameter of ore rock, and a fusion feature vector is obtained based on the model feature vector and the basic attribute feature vector through a feature fusion module; Each fusion feature vector corresponding to each mining method is input into a mining scheme generation module to obtain an adaptive mining technical scheme corresponding to each mining method.

5. The method of claim 4, wherein, The mining environment vector of the target mining unit is calculated according to the mining environment vector of the target point included in the target mining unit, including: For each target point in the target mining unit, a corresponding weight coefficient is assigned to the mining environment vector of the target point according to the nearest distance between the position of the target point and the position of each geological exploration core sampling point, the geophysical data confidence corresponding to the target point, and the distance between the target point and the geometric center of the target mining unit; The mining environment vector of the target mining unit is calculated according to the mining environment vector and the weight coefficient corresponding to each target point in the target mining unit.

6. The method according to claim 4 or 5, characterized in that, After obtaining the adaptive mining technical scheme corresponding to each mining method, the method further includes: For each adaptive mining technology scheme, the mining activity corresponding to the adaptive mining technology scheme is simulated in the three-dimensional geological disaster sub-model corresponding to the target mining unit by a finite element method, and the stress redistribution of the rock mass in the target mining unit after excavation is recorded, a high stress concentration area is identified based on the stress redistribution, a safety factor of the high stress concentration area is calculated by a stress value and a rock mass strength value, whether the rock mass in the target mining unit reaches a critical instability state is judged according to the safety factor, and a first risk coefficient is determined according to the judgment result; a deformation distribution of the rock mass in the target mining unit after excavation is extracted in the three-dimensional geological disaster sub-model corresponding to the target mining unit, a high deformation area is identified based on the deformation distribution, a stability coefficient of the high deformation area is calculated according to a deformation value and a critical deformation value of the rock mass, and a second risk coefficient is determined according to the stability coefficient; a potential plastic zone in the target mining unit after excavation is identified in the three-dimensional geological disaster sub-model corresponding to the target mining unit, and a potential plastic zone volume is extracted, a ratio of the potential plastic zone volume to a rock mass exposed area is calculated, and a third risk coefficient is determined according to the ratio; a final risk coefficient of the adaptive mining technology scheme is determined according to the first risk coefficient, the second risk coefficient, and the third risk coefficient; adaptive mining technology schemes with a final risk coefficient greater than a preset coefficient are eliminated from each adaptive mining technology scheme according to the final risk coefficient of each adaptive mining technology scheme.

7. The method of claim 1, wherein, The mining-induced ground pressure response indicators include stress values, deformation values, and plastic zone volumes; the mining-induced ground pressure response indicators of the surrounding rock are monitored, and a corresponding support mode is selected according to the monitoring result to support the target mining unit, including: stress gauges, deformation meters, and acoustic wave meters are used to monitor the stress values, deformation values, and plastic zone volumes in the surrounding rock of the target mining unit, and a mining-induced ground pressure response indicator vector is constructed according to the stress values, deformation values, and plastic zone volumes; the similarity between each preset response indicator vector in a preset database and the mining-induced ground pressure response indicator vector is calculated, and the support mode corresponding to the preset response indicator vector with the highest similarity and greater than a preset similarity threshold is taken as the support mode corresponding to the monitoring result to support the target mining unit.

8. A metal mine adaptive mining device based on geology-ground pressure response dynamic feedback, characterized in that, including: a model construction module configured to determine a metal ore area to be mined, obtain a plurality of geological exploration cores and geophysical data corresponding to the metal ore area to be mined, and determine a stratum, ore body, ore rock physical and mechanical parameter, rock mass quality grade, and mining stress corresponding to each geological exploration core according to each geological exploration core and the geophysical data, and construct a three-dimensional geological disaster model of the metal ore area to be mined by three-dimensional space interpolation according to the stratum, ore body, ore rock physical and mechanical parameter, rock mass quality grade, and mining stress corresponding to each geological exploration core; The regional division module is configured to determine a mining environment of an ore body corresponding to each target point in the metal mine area to be mined according to the three-dimensional geological disaster model, divide the metal mine area to be mined into a plurality of mining units according to the mining environment of the ore body corresponding to each target point, and obtain the plurality of mining units. The scheme determination module is configured to determine a target mining unit from the plurality of mining units, determine a plurality of adaptive mining technical schemes corresponding to the target mining unit, calculate a mining-induced ground pressure response and a mining cost of surrounding rock in each adaptive mining technical scheme according to the three-dimensional geological disaster model, and determine a final mining technical scheme from the plurality of adaptive mining technical schemes according to the mining-induced ground pressure response and the mining cost. The model updating module is configured to mine the target mining unit based on the final mining technical scheme, monitor a mining-induced ground pressure response index of the surrounding rock, select a corresponding support mode for supporting the target mining unit according to a monitoring result, and update the three-dimensional geological disaster model based on stratum, ore body, ore rock physical and mechanical parameters, rock mass quality grade, and mining stress indicated by a mining site after the target mining unit is mined. The returning module is configured to return to the step of determining the target mining unit from the plurality of mining units until the metal mine area to be mined is completely mined.

9. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the method in any one of claims 1 to 7.

10. A computer device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, The processor executes the computer program to implement the method in any one of claims 1 to 7.

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