Section-block-strip high-strength low-carbon mining method and system suitable for high and cold high-altitude mining area

Abstract

The invention discloses a section-block section-strip high-strength low-carbon mining method and system suitable for a high and cold high-altitude mining area. The method comprises the steps that a strip mine is divided into N independent mining sections in the advancing direction; on the basis of a mine multi-source multi-level three-dimensional geological refinement model, ore block sections and rock block sections are divided in each independent mining section; dividing a grade strip and a rock strip; carrying out differential process chain matching according to grade strip and rock strip division results; and dynamically updating the boundary of the strip through real-time geological data feedback, cooperatively scheduling mining equipment to execute a mining task, acquiring mining surface grade data in real time, and updating the boundary of the ore block section and the rock block section. The method has the remarkable effects that a grade feedback mechanism is designed to update the mining mode in real time, and high-strength low-carbon mining of mineral resources in the high-cold and high-altitude mining area is realized.

Description

Technical Field

[0001] This invention relates to the field of mineral resource mining technology, specifically to a high-intensity, low-carbon mining method and system for "section-block-strip" mining in high-altitude and cold regions. Background Technology

[0002] High-altitude areas are characterized by cold and harsh conditions, resulting in low levels of intensive and green low-carbon development of mineral resources. Existing technologies, which use fixed step divisions, have not addressed the problem of low energy efficiency caused by the heterogeneity of ore bodies. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the purpose of this invention is to provide a high-intensity, low-carbon mining method and system for "section-block-strip" mining in high-altitude and cold regions, so as to achieve high-intensity, low-carbon mining in these areas.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] Firstly, this invention proposes a high-intensity, low-carbon mining method for "section-block-strip" mining in high-altitude and cold regions, the key of which includes the following steps:

[0006] Step 1: Based on the mining and stripping advance direction and the spatial distribution of the ore deposit, the open-pit mine is divided into N independent mining sections along the advance direction, where N is an integer not less than 2. Each independent mining section covers a complete ore-rock unit and is equipped with an independent transportation channel.

[0007] Step 2: Based on the multi-source, multi-level, three-dimensional geological refinement model of the mine, divide the ore block segment and rock block segment into each independent mining section;

[0008] Step 3: Dynamically divide grade bands in the ore block segment and divide rock bands according to the abrasiveness index in the rock block segment;

[0009] Step 4: Perform differentiated process chain matching based on the grade banding results and the rock banding results;

[0010] Step 5: Dynamically update the strip boundary through real-time geological data feedback, coordinate and schedule mining equipment to execute mining tasks, and collect mining face grade data in real time. When the measured grade data deviates from the preset threshold, return to step 2 to update the boundary between ore block segment and rock block segment.

[0011] Furthermore, in step 2, the ore blocks are classified according to mineral type, and the rock blocks are graded according to rock strength.

[0012] Furthermore, in step 3, when dynamically dividing the grade strips in the ore block segment, the boundary value is calculated through the real-time economic critical grade model, and the strips are divided into high-grade strips, economic grade strips and low-grade strips according to the boundary value calculation results.

[0013] When dividing rock blocks into zones based on abrasiveness index, the abrasiveness index is determined by in-situ load tests, and the rock blocks are divided into hard rock zones, soft rock zones, and topsoil zones based on the abrasiveness index.

[0014] Furthermore, the mathematical expression for the real-time economic critical grade model is:

[0015]

[0016] Among them, C cut For economic critical grade; M c Unit mining cost; P c R is the unit cost of mineral processing; P is the mineral processing recovery rate; con Price of iron concentrate; P slag α represents the tailings processing cost; α is the price fluctuation coefficient.

[0017] Furthermore, the price volatility coefficient α is dynamically updated using an LSTM price prediction model.

[0018] Furthermore, in step 4, when performing differentiated process chain matching based on the grade banding results and the rock banding results, the specific matching method is as follows:

[0019] For high-grade strips: pre-splitting blasting with an aperture of 76-102mm + hydraulic shovel loading with a bucket capacity of ≤10m³ is used. The transportation is carried out using enclosed dump trucks, and the cargo box is equipped with RFID tags.

[0020] For economic grade strips: pre-splitting blasting with a bore diameter of 60-76mm + hydraulic shovel loading with a bucket capacity of ≤8m³, and transportation using electric enclosed trucks;

[0021] For low-grade strips: use non-explosive shovel loading + hydraulic shovel with a bucket capacity ≤6m³, and transport to the heap leaching site using tracked transfer vehicles;

[0022] For hard rock strips: pre-splitting blasting with a bore diameter of 89-115mm is used, followed by loading with a hydraulic shovel with a bucket capacity of ≥12m³, and heavy-duty trucks are used for transportation.

[0023] For soft rock strips: use non-explosive shovel loading + tracked transportation;

[0024] For topsoil strips: implement stripping-closed transportation-ecological stockpiling, equipped with a multispectral soil analyzer to detect organic matter content in real time and generate stockpiling recommendations.

[0025] Furthermore, in step 5, when dynamically updating the boundaries of grade bands and rock bands through real-time geological data feedback, an identification component needs to be established. This identification component includes:

[0026] The laser mapping robot has fluorescent QR codes containing encrypted parameters sprayed on the facade of its steps;

[0027] Intelligent boundary identification includes storing 3D coordinates, strip type codes, mining process parameters, and real-time slope safety thresholds;

[0028] The slope monitoring array includes tilt sensors, pore water pressure gauges, and microseismic monitoring nodes deployed in soft rock strips.

[0029] Furthermore, in step 5, when coordinating the scheduling of mining equipment to perform mining tasks, if the deviation between the product code and the detection result of the laser-induced breakdown spectrometer set at the unloading port of the mining equipment is ≥10%, the scheduling command is frozen.

[0030] Furthermore, the preset threshold mentioned in step 5 is 15% of the calculated value of the multi-source, multi-level, three-dimensional geological refinement model of the mine.

[0031] Secondly, this invention proposes a high-intensity, low-carbon mining system of "section-block-strip" suitable for high-altitude and cold mining areas, comprising:

[0032] A geological modeling server is used to integrate mineralization data stored on the blockchain and to build a three-dimensional model of the mining area.

[0033] The independent mining section division module is used to divide the open-pit mine into N independent mining sections along the advance direction according to the mining and stripping direction and the spatial distribution of the ore deposit based on the three-dimensional model of the mining area. N is an integer not less than 2. Each independent mining section covers a complete ore and rock unit and is equipped with an independent transportation channel.

[0034] The block segmentation module is used to divide ore blocks and rock blocks within each independent mining section based on the multi-source, multi-level, three-dimensional geological refinement model of the mine.

[0035] The strip division module is used to dynamically divide grade strips in the ore block segment and to divide rock strips according to the abrasive index in the rock block segment.

[0036] The equipment scheduling engine is used to dynamically allocate mining equipment based on stripe type;

[0037] The quality feedback module is used to dynamically update the strip boundaries through real-time geological data feedback, coordinate the scheduling of mining equipment to execute mining tasks, and collect the grade data of the mining face in real time. When the measured grade data deviates from the preset threshold, the boundaries between the ore block segment and the rock block segment are automatically updated.

[0038] The significant advantages of this invention are as follows: First, based on the mining and stripping advance direction and the spatial distribution of the ore deposit, the open-pit mine is divided into N independent mining sections along the advance direction. Then, based on a multi-source, multi-level, three-dimensional geological refinement model of the mine, ore block sections and rock block sections are divided within each independent mining section. Next, grade bands are dynamically divided within the ore block sections, and rock bands are divided within the rock block sections according to the abrasive index. Then, differentiated process chain matching is performed based on the grade band division results and the rock band division results. Finally, the band boundaries are dynamically updated through real-time geological data feedback, and mining equipment is coordinated and scheduled to execute mining tasks. Grade data of the mining face is collected in real time, and the boundaries of the ore block sections and rock block sections are updated when the measured grade data deviates from the preset threshold. Based on the above process, this invention establishes a multi-source, multi-level, three-dimensional geological refinement model of the mine, based on multi-source data fusion, covering attributes such as ore type, weight, grade, price, cost, and engineering quality. The model is then used to divide ore blocks and rock blocks within each independent mining section, forming a mineral mining method determination step based on "section-block-strip". Furthermore, a grade feedback mechanism is designed based on real-time feedback and dynamic updates of mining data to adjust the mining method in real time. This effectively overcomes the problem of low energy efficiency caused by the heterogeneity of ore bodies in existing technologies that use fixed step division, thus realizing high-intensity, low-carbon mining of mineral resources in high-altitude and cold regions. Attached Figure Description

[0039] Figure 1 This is a flowchart of the method described in this invention;

[0040] Figure 2 This is a schematic diagram of the mining area division;

[0041] Figure 3 This is a schematic diagram of the system described in this invention. Detailed Implementation

[0042] The specific embodiments and working principles of the present invention will be further described in detail below with reference to the accompanying drawings.

[0043] Example 1:

[0044] like Figure 1 As shown in the figure, this invention proposes a high-intensity, low-carbon mining method based on the "section-block-strip" approach suitable for high-altitude and cold mining areas. The specific steps are as follows:

[0045] Step 1: Divide into independent mining sections: Based on the mining and stripping advance direction and the spatial distribution of the ore deposit, the open-pit mine is divided into N independent mining sections along the advance direction, where N is an integer not less than 2, i.e., N≥2. Each independent mining section covers a complete ore-rock unit and is equipped with an independent transportation channel.

[0046] This step divides the open-pit mine into N sections (N≥2), each equipped with an independent transport channel, supporting parallel mining and improving operational efficiency (reducing equipment idle time by more than 20%), thereby helping to achieve high-intensity mining. Simultaneously, optimizing transport routes, shortening transport distances, reducing fuel consumption and carbon emissions (a 20% reduction in transport distance can reduce carbon emissions by 15%), and minimizing ineffective stripping, thus helping to achieve low-carbon mining.

[0047] Step 2: Divide the ore blocks and rock blocks: Based on the multi-source, multi-level, three-dimensional geological refinement model of the mine, divide the mining area into ore blocks and rock blocks. Specifically:

[0048] The ore blocks are classified according to the type of mineral, such as magnetite and hematite blocks;

[0049] The rock segments are classified according to rock strength, such as RQD≥70% being hard rock and RQD<50% being soft rock.

[0050] The core of this invention is the multi-source, multi-level, three-dimensional geological refinement model of the mine. It is a digital block model updated in real time based on geological data, production exploration data, and identification equipment (such as vehicle-mounted gamma detectors and laser mapping robots). Through multi-source data fusion and a multi-level structure, this model achieves a refined expression of ore body attributes, providing dynamic guidance for mining decisions. The following is a detailed description:

[0051] (a) Mathematical Model

[0052] The model adopts a three-dimensional block modeling framework, which discretizes the ore body space into regular grid units (blocks). Each unit is defined as V_{i,j,k} (i,j,k represent three-dimensional coordinate indices). The unit size is dynamically adjusted according to the heterogeneity of the ore body and the accuracy requirements (e.g., 5m×5m×5m).

[0053] Each block unit stores a multidimensional attribute vector A. Elements in the vector include ore type, density, grade information, product price, production cost, ore engineering quality (such as RQD value, abrasion index Ai), and structural attributes. The mathematical expression for the attribute vector is: A = [Type, ρ, Grade, P_con, M_C, P_C, RQD, A_i, ...], where attribute values ​​are initialized using geostatistical methods (such as Kriging interpolation) and updated using real-time data.

[0054] The update mechanism includes:

[0055] Data input integrates historical geological data, production exploration data, and real-time equipment feedback;

[0056] Real-time calibration: When the deviation between the measured data and the model value is ≥15%, the boundary recalculation algorithm is triggered, and the formula is:

[0057] New_Value = α × Model_Value + (1-α) × Measured_Value

[0058] Where α is the weighting coefficient.

[0059] The economic model is integrated and updates economic parameters every 2 hours, such as the economic critical grade formula:

[0060]

[0061] Among them, C cut Economic critical grade (%); M c Unit mining cost (yuan / ton); P c R represents the unit cost of mineral processing (RMB / ton); R represents the mineral processing recovery rate (%); P represents the unit cost of mineral processing (RMB / ton). con Price of iron ore concentrate (USD / ton); P slag α represents the tailings processing cost (USD / ton); α is the price volatility coefficient (0.5≤α≤2.5), which is dynamically updated using an LSTM price forecasting model.

[0062] (II) Detailed introduction

[0063] The multi-source, multi-level, three-dimensional geological refinement model of the mine has the following characteristics:

[0064] Multi-source, multi-level structure: Integrates geological databases, production exploration data, and real-time sensor data, and adopts a three-level division (section level, block level, and strip level) to support refined decision-making.

[0065] Function: It serves as a "digital twin" to guide mining, outputs a real-time 3D visualization interface, and ensures data transparency and security through blockchain notarization.

[0066] Advantages: It solves the problem of low energy efficiency caused by the heterogeneity of ore bodies in high-altitude and cold mining areas, improves resource utilization, and supports high-intensity low-carbon goals.

[0067] This step, based on a multi-source, multi-level, three-dimensional geological refinement model of the mine, enables precise classification of ore (by mineral type) and rock (by RQD value), facilitating separate mining and transportation, improving resource recovery rate (avoiding 10% loss due to ore mixing), and thus helping to achieve high-intensity mining. Rock classification according to hard / soft rock can guide differentiated blasting (non-blasting loading of soft rock reduces energy consumption by 30%), and cost control is optimized through integrated economic parameters, thereby helping to achieve low-carbon mining.

[0068] Step 3: Dynamically divide grade bands and abrasion grading bands: Dynamically divide grade bands in the ore blocks, and divide rock bands according to the abrasion index in the rock blocks. Specifically:

[0069] In this example, when dynamically dividing grade strips within the ore block, boundary values ​​are calculated using a real-time economic critical grade model, and strip division is performed based on the boundary value calculation results:

[0070] 1) High-grade bands: TFe ≥ 55% and located in the core area of ​​the ore body;

[0071] 2) Economic grade band: 40% ≤ TFe < 55%;

[0072] 3) Low-grade bands: Boundary grade ≤ TFe < 40%;

[0073] In this example, the mathematical expression of the real-time economic critical grade model is:

[0074]

[0075] Among them, C cut Economic critical grade (%); M c Unit mining cost (yuan / ton); P c R represents the unit cost of mineral processing (RMB / ton); R represents the mineral processing recovery rate (%); P represents the unit cost of mineral processing (RMB / ton). con Price of iron ore concentrate (USD / ton); P slag α represents the tailings processing cost (USD / ton); α is the price volatility coefficient (0.5≤α≤2.5), which is dynamically updated using an LSTM price forecasting model.

[0076] In this example, when dividing rock bands according to the abrasion index within a rock block segment, the abrasion index (Ai) is determined through an in-situ load test, and the rock block segment is divided based on the abrasion index:

[0077] 1) Hard rock bands: Ai≥0.4;

[0078] 2) Soft rock bands: 0.1≤Ai<0.4;

[0079] 3) Topsoil strips: Ai < 0.1;

[0080] This step dynamically divides the ore blocks into grade bands, enabling a real-time economic critical grade model to classify high, medium, and low-grade bands, achieving "mining rich areas while protecting poor areas," thereby effectively increasing yield per unit time (high-intensity core) and realizing high-intensity mining. Low-grade bands are treated with non-blasting processes and heap leaching, reducing beneficiation energy consumption; high-grade bands are precisely mined to reduce waste rock contamination and tailings carbon emissions, thus achieving low-carbon mining.

[0081] Furthermore, classifying abrasive zones according to the abrasiveness index (Ai) allows for matching corresponding equipment, reducing equipment wear, maintaining operational continuity, and facilitating high-intensity mining. Simultaneously, soft rock zones utilize non-blasting and tracked transport (ground clearance ≥80cm) to minimize ground disturbance; topsoil zones utilize ecological stockpiling to improve soil reclamation rates and enhance carbon sequestration, thus contributing to low-carbon mining.

[0082] Step 4: Differentiated process chain matching: Based on the grade banding results and the rock banding results, differentiated process chains are matched. The specific matching method is as follows:

[0083] For high-grade strip: pre-splitting blasting with an aperture of 76-102mm + hydraulic shovel loading with a bucket capacity of ≤10m³, and transportation using enclosed dump trucks (cargo boxes equipped with RFID tags), with a unit energy consumption of ≤15kWh / ton;

[0084] For economic grade strips: pre-splitting blasting with a bore diameter of 60-76mm + hydraulic shovel loading with a bucket capacity of ≤8m³, transportation using electric enclosed trucks (load capacity 30-40t), unit carbon emissions ≤10kg CO2 / ton, and integration of grade feedback mechanism from step 5;

[0085] For low-grade strips: use non-blasting shovel loading + hydraulic shovel with a bucket capacity ≤6m³, and transport by tracked transfer vehicle (ground clearance ≥60cm) to the heap leaching site, with a tailings recovery rate ≥70%;

[0086] For hard rock strips: pre-splitting blasting with an aperture of 89-115mm is used, along with loading using a hydraulic shovel with a bucket capacity of ≥12m³. Transportation is carried out using heavy-duty electric trucks (load capacity ≥60t), with a unit explosive consumption of ≤0.5kg / m³. 3 ;;

[0087] For soft rock strips: use non-explosive shovel loading + tracked transportation (ground clearance ≥ 80cm) and biodiesel power;

[0088] For topsoil strips: implement stripping-closed transportation-ecological stockpiling, equipped with a multispectral soil analyzer to detect organic matter content in real time and generate stockpiling recommendations, with an organic matter recovery rate of ≥80%.

[0089] Based on the differentiated process chain matching in this step, not only can customized processes be implemented for strip mining (such as using large-capacity hydraulic shovels for high-grade strip mining), optimizing equipment utilization (matching degree ≥90%) and helping to achieve high strength, but also: process differentiation can significantly reduce energy consumption (e.g., electric trucks used for economic-grade strip mining reduce carbon emissions by 40% compared to diesel trucks; topsoil strip mining has an organic matter recovery rate ≥85%), and non-blasting processes reduce carbon emissions, helping to achieve low-carbon mining.

[0090] Step 5, Real-time Feedback and Dynamic Updates: The strip boundaries are dynamically updated based on real-time geological data feedback. Mining equipment is coordinated and scheduled to execute mining tasks. Real-time grade data of the mining face is collected. When the measured grade data deviates from a preset threshold, the process returns to Step 2 to update the boundaries between ore and rock segments. Specifically:

[0091] When dynamically updating the boundaries of grade bands and rock bands using real-time geological data feedback in this step, an identification component needs to be established. This identification component includes:

[0092] The laser mapping robot has fluorescent QR codes containing encrypted parameters sprayed on the facade of its steps, with a positioning error of ≤3cm;

[0093] Intelligent boundary identification includes storing 3D coordinates, strip type codes (HP / EP / LP / HR / SR / TS), mining process parameters, and real-time slope safety thresholds;

[0094] The slope monitoring array includes tilt sensors (accuracy 0.1) deployed in soft rock strips, pore water pressure gauges (range 0-1MPa), and microseismic monitoring nodes (frequency response 5-500Hz).

[0095] In the specific implementation process, this step is designed with a scheduling instruction freezing mechanism. That is, when coordinating the scheduling of mining equipment to perform mining tasks, if the deviation between the product code and the detection result of the laser-induced breakdown spectrometer (LIBS) set at the unloading port of the mining equipment is ≥10%, the scheduling instruction will be frozen.

[0096] This step utilizes gamma detectors and other equipment to detect quality data in real time, triggering boundary recalculations and scheduling to ensure precise execution (recalculation for deviations ≥15%). This helps reduce downtime and facilitates high-intensity mining. Simultaneously, real-time model updates and scheduling optimization (such as freezing instructions with deviations ≥10% in the quality feedback module) prevent resource waste and achieve low-carbon control throughout the process. Furthermore, each step, centered on a three-dimensional geological model, forms a data-driven closed loop, improving energy efficiency in high-altitude and cold environments (increasing high-intensity production by 20-30% and reducing the carbon footprint per unit ore by 25-40%), demonstrating strong overall synergy.

[0097] Based on the above methodological steps, real-time model updates and dynamic zoning (such as grade striping) ensure mining efficiency (increasing production capacity by 20%), and combined with differentiated processes (such as large-capacity bucket equipment), optimize equipment utilization. Simultaneously, integrated economic and environmental parameters guide low-carbon processes (such as electric trucks reducing carbon emissions by 40%), real-time feedback mechanisms reduce resource waste, and slope optimization is based on mineral and rock engineering quality attributes (such as RQD), effectively reducing accident risks (such as trigger mechanisms to suspend operations). Furthermore, in high-altitude and cold environments, multi-source data fusion addresses the shortcomings of existing technologies, achieving intensive (high-intensity) and green (low-carbon) development. Model attributes (such as structurally relevant attributes) enhance reliability (such as slope monitoring arrays combined with model data), ensuring safe mining in high-altitude and cold regions and preventing a surge in carbon emissions.

[0098] Example 2:

[0099] like Figure 3 As shown, this embodiment of the invention proposes a system for implementing the method described in Embodiment 1, comprising:

[0100] A geological modeling server is used to integrate mineralization data stored on the blockchain and to build a three-dimensional model of the mining area. The real-time economic critical grade model is updated every 2 hours.

[0101] The independent mining section division module is used to divide the open-pit mine into N independent mining sections along the advance direction according to the mining and stripping direction and the spatial distribution of the ore deposit based on the three-dimensional model of the mining area. N is an integer not less than 2. Each independent mining section covers a complete ore and rock unit and is equipped with an independent transportation channel.

[0102] The block segmentation module is used to divide ore blocks and rock blocks within each independent mining section based on the multi-source, multi-level, three-dimensional geological refinement model of the mine.

[0103] The strip division module is used to dynamically divide grade strips in the ore block segment and to divide rock strips according to the abrasive index in the rock block segment.

[0104] The equipment scheduling engine is used to dynamically allocate mining equipment according to strip type, that is, to perform differentiated process chain matching based on the grade strip division results and rock strip division results: blasting unit → hard rock / high grade strip (matching degree ≥90%); electric hydraulic shovel → high grade ore area (energy consumption ≤15kWh / ton).

[0105] The quality feedback module is used to dynamically update the strip boundary through real-time geological data feedback, coordinate the scheduling of mining equipment to execute mining tasks, and collect the grade data of the mining face in real time. When the measured grade data deviates from the preset threshold, the boundary between the ore block segment and the rock block segment is automatically updated.

[0106] Edge computing nodes: Deployed on mining equipment, with a response latency of ≤50ms, to provide edge computing capabilities, improve the system's output processing capacity, and reduce the system's task latency;

[0107] The quality feedback module is also used to install a laser-induced breakdown spectrometer (LIBS) at the unloading port, and freeze the scheduling command when the deviation between the product code and the detection result is ≥10%.

[0108] Example 3:

[0109] This embodiment also proposes a differentiated slope design method in the implementation process of Embodiment 1 or Embodiment 2, specifically including:

[0110] Hard rock strip: final slope angle 35-38° + 5m safety platform + prestressed anchor cable (pull-out force ≥500kN);

[0111] Soft rock strip: slope angle 25-28° + drainage holes (hole diameter ≥100mm, elevation angle 5°) every 10m elevation difference + geogrid reinforcement;

[0112] Triggering mechanism: When the displacement rate is ≥5mm / day or the daily frequency of microseismic events is ≥3 times, the operation of adjacent strips is suspended and the UAV slope scanning is started.

[0113] In summary, this invention first divides the open-pit mine into N independent mining sections along the mining and stripping direction and the spatial distribution of the ore deposit. Then, based on a multi-source, multi-level, three-dimensional geological refinement model of the mine, it divides ore blocks and rock blocks within each independent mining section. Next, it dynamically divides grade bands within the ore blocks and rock bands within the rock blocks according to the abrasion index. Then, it performs differentiated process chain matching based on the grade band division results and the rock band division results. Finally, it dynamically updates the band boundaries through real-time geological data feedback, coordinates the scheduling of mining equipment to execute mining tasks, and collects grade data from the mining face in real time. When the measured grade data deviates from a preset threshold, it updates the boundaries between the ore blocks and rock blocks. Based on the above process, a mineral mining method determination step based on "section-block-strip" was formed, and a grade feedback mechanism was designed to update the mining method in real time. This effectively overcomes the problem of low energy efficiency caused by the heterogeneity of ore bodies due to the use of fixed step division in existing technologies, and realizes high-intensity low-carbon mining of mineral resources in high-altitude and cold mining areas.

[0114] The technical solution provided by this invention has been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make several improvements and modifications to this invention without departing from the principles of this invention, and these improvements and modifications also fall within the protection scope of the claims of this invention.

Claims

1. A high-intensity, low-carbon mining method using a "section-block-strip" approach suitable for high-altitude, cold-climate mining areas, characterized in that... Includes the following steps: Step 1: Based on the mining and stripping advance direction and the spatial distribution of the ore deposit, the open-pit mine is divided into N independent mining sections along the advance direction, where N is an integer not less than 2. Each independent mining section covers a complete ore-rock unit and is equipped with an independent transportation channel. Step 2: Based on the multi-source, multi-level, three-dimensional geological refinement model of the mine, divide the ore block segment and rock block segment into each independent mining section; Step 3: Dynamically divide grade bands in the ore block segment and divide rock bands according to the abrasiveness index in the rock block segment; Step 4: Perform differentiated process chain matching based on the grade banding results and the rock banding results; Step 5: Dynamically update the strip boundary through real-time geological data feedback, coordinate and schedule mining equipment to execute mining tasks, and collect mining face grade data in real time. When the measured grade data deviates from the preset threshold, return to step 2 to update the boundary between ore block segment and rock block segment.

2. The high-intensity, low-carbon mining method of "section-block-strip" applicable to high-altitude and cold mining areas according to claim 1, characterized in that: In step 2, the ore blocks are classified according to the type of mineral, and the rock blocks are graded according to the rock strength.

3. The high-intensity, low-carbon mining method of "section-block-strip" applicable to high-altitude and cold mining areas according to claim 1, characterized in that: In step 3, when dynamically dividing the grade strips in the ore block, the boundary value is calculated by the real-time economic critical grade model, and the strips are divided into high-grade strips, economic grade strips and low-grade strips according to the boundary value calculation results. When dividing rock blocks into zones based on abrasiveness index, the abrasiveness index is determined by in-situ load tests, and the rock blocks are divided into hard rock zones, soft rock zones, and topsoil zones based on the abrasiveness index.

4. The "section-block-strip" high-intensity low-carbon mining method applicable to high-altitude and cold mining areas according to claim 3, characterized in that: The mathematical expression for the real-time economic critical grade model is: Among them, C cut For economic critical grade; M c Unit mining cost; P c R is the unit cost of mineral processing; P is the mineral processing recovery rate; con Price of iron concentrate; P slag α represents the tailings processing cost; α is the price fluctuation coefficient.

5. The "section-block-strip" high-intensity low-carbon mining method applicable to high-altitude and cold mining areas according to claim 4, characterized in that: The price volatility coefficient α is dynamically updated using an LSTM price forecasting model.

6. The high-intensity, low-carbon mining method for "section-block-strip" mining in high-altitude and cold regions according to claim 3, characterized in that: In step 4, when performing differentiated process chain matching based on the grade banding results and the rock banding results, the specific matching method is as follows: For high-grade strips: pre-splitting blasting with an aperture of 76-102mm + hydraulic shovel loading with a bucket capacity of ≤10m³ is used. The transportation is carried out using enclosed dump trucks, and the cargo box is equipped with RFID tags. For economic grade strips: pre-splitting blasting with a bore diameter of 60-76mm + hydraulic shovel loading with a bucket capacity of ≤8m³, and transportation using electric enclosed trucks; For low-grade strips: use non-explosive shovel loading + hydraulic shovel with a bucket capacity ≤6m³, and transport to the heap leaching site using tracked transfer vehicles; For hard rock strips: pre-splitting blasting with a bore diameter of 89-115mm is used, followed by loading with a hydraulic shovel with a bucket capacity of ≥12m³, and heavy-duty trucks are used for transportation. For soft rock strips: use non-explosive shovel loading + tracked transportation; For topsoil strips: implement stripping-closed transportation-ecological stockpiling, equipped with a multispectral soil analyzer to detect organic matter content in real time and generate stockpiling recommendations.

7. The high-intensity, low-carbon mining method of "section-block-strip" applicable to high-altitude and cold mining areas according to claim 1, characterized in that: In step 5, when dynamically updating the boundaries of grade bands and rock bands using real-time geological data feedback, an identification component needs to be established. This identification component includes: The laser mapping robot has fluorescent QR codes containing encrypted parameters sprayed on the facade of its steps; Intelligent boundary identification includes storing 3D coordinates, strip type codes, mining process parameters, and real-time slope safety thresholds; The slope monitoring array includes tilt sensors, pore water pressure gauges, and microseismic monitoring nodes deployed in soft rock strips.

8. The high-intensity, low-carbon mining method of "section-block-strip" applicable to high-altitude and cold mining areas according to claim 1, characterized in that: In step 5, when coordinating the scheduling of mining equipment to perform mining tasks, if the deviation between the product code and the detection result of the laser-induced breakdown spectrometer set at the unloading port of the mining equipment is ≥10%, the scheduling command is frozen.

9. The high-intensity, low-carbon mining method for "section-block-strip" mining in high-altitude and cold regions according to claim 1, characterized in that: The preset threshold mentioned in step 5 is 15% of the calculated value of the multi-source, multi-level, three-dimensional geological refinement model of the mine.

10. A high-intensity, low-carbon mining system for "section-block-strip" mining suitable for high-altitude and cold-climate mining areas, used to implement the method as described in any one of claims 1 to 9, characterized in that, include: A geological modeling server is used to integrate mineralization data stored on the blockchain and to build a three-dimensional model of the mining area. The independent mining section division module is used to divide the open-pit mine into N independent mining sections along the advance direction according to the mining and stripping direction and the spatial distribution of the ore deposit based on the three-dimensional model of the mining area. N is an integer not less than 2. Each independent mining section covers a complete ore and rock unit and is equipped with an independent transportation channel. The block segmentation module is used to divide ore blocks and rock blocks within each independent mining section based on the multi-source, multi-level, three-dimensional geological refinement model of the mine. The strip division module is used to dynamically divide grade strips in the ore block segment and to divide rock strips according to the abrasive index in the rock block segment. The equipment scheduling engine is used to dynamically allocate mining equipment based on stripe type; The quality feedback module is used to dynamically update the strip boundaries through real-time geological data feedback, coordinate the scheduling of mining equipment to execute mining tasks, and collect the grade data of the mining face in real time. When the measured grade data deviates from the preset threshold, the boundaries between the ore block segment and the rock block segment are automatically updated.

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