A "section-block-strip" high-strength low-carbon mining method and system suitable for high-cold high-altitude mining areas

Abstract

The application discloses a "section-block-section" high-strength low-carbon mining method and system suitable for high-cold high-altitude mining areas, wherein the method comprises the following steps: dividing an open-pit mine into N independent mining sections along a pushing direction; based on a mine multi-source multi-level three-dimensional geological refinement model, dividing ore blocks and rock blocks in each independent mining section; performing grade strip and rock strip division; performing differential process chain matching according to the grade strip and rock strip division results; dynamically updating the strip boundary through real-time geological data feedback, and coordinating and scheduling mining equipment to perform a mining task, and collecting real-time mining face grade data, and updating the boundaries of the ore blocks and the rock blocks. The significant effect is 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 high-cold high-altitude mining areas is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mineral resources mining, and particularly relates to a "section-block-strip" high-strength low-carbon mining method and system suitable for high-cold high-altitude mining areas. BACKGROUND

[0002] In high-altitude areas, due to the high-cold environment, the intensive development and green low-carbon development level of mineral resources in the area are low, and the existing technology adopts fixed step division, which does not solve the problem of low energy efficiency caused by the heterogeneity of ore bodies. SUMMARY

[0003] In view of the deficiencies of the prior art, the present application aims to provide a "section-block-strip" high-strength low-carbon mining method and system suitable for high-cold high-altitude mining areas, so as to realize high-strength low-carbon mining in high-cold high-altitude areas.

[0004] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows:

[0005] In a first aspect, the present application provides a "section-block-strip" high-strength low-carbon mining method suitable for high-cold high-altitude mining areas, which is characterized by comprising the following steps:

[0006] Step 1, according to the advancing direction of mining and stripping and the spatial distribution of the ore deposit, the open pit is divided into N independent mining sections along the advancing direction, 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 a mine multi-source multi-level three-dimensional geological refinement model, ore blocks and rock blocks are divided in each independent mining section;

[0008] Step 3, grade strips are dynamically divided in the ore blocks, and rock strips are divided in the rock blocks according to the abrasion index;

[0009] Step 4, differential process chain matching is performed according to the grade strip division results and the rock strip division results;

[0010] Step 5, the strip boundaries are dynamically updated through real-time geological data feedback, and mining equipment is cooperatively scheduled to perform the mining task, and real-time mining face grade data is collected, and when the measured grade data deviates from the preset threshold, the boundaries of the ore blocks and the rock blocks are updated in step 2.

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

[0012] Further, in step 3, when the grade strip is dynamically divided in the ore block section, the boundary value is calculated by the real-time economic critical grade model, and the high-grade strip, the economic grade strip and the low-grade strip are divided according to the calculation result of the boundary value.

[0013] When the rock strip is divided according to the abrasion index in the rock block section, the abrasion index is determined by the in-situ point load test, and the rock block section is divided into hard rock strip, soft rock strip and topsoil strip according to the abrasion index.

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

[0015] ;

[0016] Wherein, C cut The economic critical grade is; M c The unit mining cost is; P c The unit beneficiation cost is; R The beneficiation recovery rate is; P con The iron concentrate price is; P slag The tailings treatment cost is; α The price fluctuation coefficient is.

[0017] Further, the price fluctuation coefficient α is dynamically updated by the LSTM price prediction model.

[0018] Further, in step 4, the differential process chain matching is performed according to the grade strip division result and the rock strip division result, and the specific matching mode is as follows:

[0019] For high-grade strip: adopt 76-102mm pre-splitting blasting + ≤10m³ hydraulic shovel with bucket capacity, and adopt closed self-unloading truck for transportation, and the cargo box is equipped with RFID tag;

[0020] For economic grade strip: adopt 60-76mm pre-splitting blasting + ≤8m³ hydraulic shovel with bucket capacity, and adopt electric closed truck for transportation;

[0021] For low-grade strip: adopt non-blasting shovel + ≤6m³ hydraulic shovel with bucket capacity, and adopt crawler-type transfer vehicle to the heap leaching field for transportation;

[0022] For hard rock strip: adopt 89-115mm pre-splitting blasting + ≥12m³ hydraulic shovel with bucket capacity, and adopt heavy truck for transportation;

[0023] For soft rock strip: adopt non-blasting shovel loading + crawler transport;

[0024] For topsoil strip: implement stripping-closed transport-ecological stacking, equipped with multi-spectral soil analyzer, real-time detection of organic matter content and generation of stacking recommendations.

[0025] Further, in step 5, when dynamically updating the boundaries of grade strips and rock strips through real-time geological data feedback, an identification component is set up, which includes:

[0026] Laser mapping robot, with fluorescent two-dimensional code containing encrypted parameters sprayed on its step facade;

[0027] Intelligent boundary identification, including storage of three-dimensional coordinates, strip type code, mining process parameters, and real-time slope safety threshold;

[0028] Slope monitoring array, including inclination sensors, pore water pressure gauges, and microseismic monitoring nodes laid in soft rock strips.

[0029] Further, in step 5, when coordinating and scheduling 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 discharge port of the mining equipment is ≥10%, the scheduling instruction is frozen.

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

[0031] In the second aspect, the present application proposes a "section-block-strip" high-strength low-carbon mining system suitable for high-cold high-altitude mining areas, which includes:

[0032] Geological modeling server for integrating mineralization data stored in blockchain and establishing a three-dimensional model of the mining area;

[0033] Independent mining section division module for dividing the open pit mine into N independent mining sections along the advancing direction according to the three-dimensional model of the mining area and the spatial distribution of the ore deposit, N being an integer not less than 2, each independent mining section covering a complete ore rock unit and being equipped with an independent transport channel;

[0034] Block segment division module for dividing ore blocks and rock blocks in each independent mining section according to the mine multi-source multi-level three-dimensional geological refinement model;

[0035] Strip division module for dynamically dividing grade strips in the ore blocks and rock strips in the rock blocks according to the abrasion index;

[0036] Device scheduling engine for dynamically allocating mining equipment according to strip type;

[0037] A quality feedback module is configured to dynamically update the strip boundary based on real-time geological data feedback, coordinate and schedule mining equipment to perform mining tasks, and collect real-time mining face grade data, and automatically update the ore block segment and rock block segment boundary when the measured grade data deviates from the preset threshold.

[0038] The significant effect of the present application is that the present application first divides the open pit mine into N independent mining sections along the advancing direction and the spatial distribution of the ore deposit; then divides the ore block segment and the rock block segment in each independent mining section based on the mine multi-source multi-level three-dimensional geological refinement model; then dynamically divides the grade strip in the ore block segment and the rock strip in the rock block segment according to the abrasion index; then matches the differentiated process chain according to the grade strip division result and the rock strip division result; finally, dynamically updates the strip boundary based on real-time geological data feedback, coordinates and schedules mining equipment to perform mining tasks, and collects real-time mining face grade data, and updates the ore block segment and the rock block segment boundary when the measured grade data deviates from the preset threshold. Based on the above process, the present application establishes a multi-source data fusion based on the properties of ore and rock types, weight, grade, price, cost, and engineering quality based on the mine multi-source multi-level three-dimensional geological refinement model, and divides the ore block segment and the rock block segment in each independent mining section using the model, forms a "section-block-strip" based on the mining method determination steps, and designs a grade feedback mechanism based on real-time feedback and dynamic update of the mining data to adjust the mining method in real time, thereby effectively overcoming the low energy efficiency problem caused by the heterogeneity of the ore body caused by the fixed step division in the prior art, and realizing high-intensity low-carbon mining of mineral resources in high-cold high-altitude mining areas. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a flowchart of the method of the present application;

[0040] Figure 2 is a stope division schematic diagram;

[0041] Figure 3 is a principle block diagram of the system of the present application. DETAILED DESCRIPTION

[0042] The specific embodiments and working principles of the present application will be further described in detail below with reference to the accompanying drawings. Example 1

[0043] As Figure 1 shown, the present application proposes a "section-block-strip" high-intensity low-carbon mining method suitable for high-cold high-altitude mining areas, and the specific steps are as follows:

[0044] Step 1, dividing independent mining sections: according to the mining and stripping direction and the spatial distribution of the deposit, the open-pit mine is divided into N independent mining sections along the advancing direction, 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;

[0045] This step helps to achieve high-intensity mining by dividing the open-pit mine into N sections (N≥2), each equipped with an independent transportation channel, supporting parallel mining and improving operational efficiency (reducing equipment idle time by more than 20%). At the same time, the transportation path is optimized, the haul distance is shortened, the fuel consumption and carbon emissions are reduced (a 20% reduction in haul distance can reduce carbon emissions by 15%), and invalid stripping is reduced, thereby helping to achieve low-carbon mining.

[0046] Step 2, dividing ore blocks and rock blocks: based on the mine multi-source multi-level three-dimensional geological refinement model, the ore blocks and rock blocks are divided within each independent mining section, specifically:

[0047] The ore blocks are classified by ore type, such as magnetite and hematite blocks;

[0048] The rock blocks are classified by rock strength, such as RQD≥70% for hard rock and RQD<50% for soft rock.

[0049] The mine multi-source multi-level three-dimensional geological refinement model is the core of the invention, which is a digital block model based on geological data and production exploration data and real-time updating of recognition equipment (such as vehicle-mounted gamma detection instrument, laser mapping robot). This model realizes the fine expression of ore body properties through multi-source data fusion and multi-level structure, and provides dynamic guidance for mining decision-making. The following is a detailed introduction:

[0050] (I) Mathematical model

[0051] This model uses a three-dimensional block modeling framework to discretize the ore body space into regular grid cells (blocks), each cell is defined as V_{i,j,k} (i, j, k represent three-dimensional coordinate indices), and the cell size is dynamically adjusted according to the heterogeneity of the ore body and the accuracy requirements (such as 5m×5m×5m).

[0052] Each block cell stores a multi-dimensional attribute vector A, the elements in the vector include ore-rock type, bulk weight (density), grade information, product price, production cost, ore-rock engineering quality (such as RQD value, abrasion index Ai), and structural attributes. The mathematical expression of the attribute vector is: A=[Type, ρ, Grade, P_con, M_C, P_C, RQD, A_i,...], where the attribute values are initialized by geostatistical methods (such as Kriging interpolation) and updated in real time combined with real-time data.

[0053] The update mechanism includes:

[0054] Data input, integration of historical geological data, production exploration data and real-time device feedback;

[0055] Real-time calibration, when the measured data deviates from the model value by ≥15%, trigger the boundary recalculation algorithm, formula:

[0056] New_Value = α × Model_Value + (1-α) × Measured_Value

[0057] Where, α is the weighting coefficient.

[0058] Economic model integration, update economic parameters every 2 hours, such as economic critical grade formula:

[0059] ;

[0060] Where, C cut is the economic critical grade (%); M c is the unit mining cost (yuan / ton); P c is the unit beneficiation cost (yuan / ton); R is the beneficiation recovery rate (%); P con is the iron concentrate price (US dollars / ton); P slag is the tailings disposal cost (US dollars / ton); α is the price fluctuation coefficient (0.5≤α≤2.5), dynamically updated by the LSTM price prediction model.

[0061] (II) Detailed introduction

[0062] The mine multi-source multi-level three-dimensional geological fine model has the following characteristics:

[0063] Multi-source multi-level structure: integrate geological database, production exploration data and real-time sensor data, adopt three-level division (section level, block level, strip level), support fine decision-making.

[0064] Function: as a "digital twin" to guide mining, real-time output three-dimensional visualization interface, and through blockchain notarization to ensure data transparency and security.

[0065] Advantages: solve the problem of low energy efficiency caused by heterogeneity of ore body in high-cold and high-altitude mining areas, improve resource utilization, and support high-intensity and low-carbon goals.

[0066] 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.

[0067] 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:

[0068] 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:

[0069] 1) High-grade bands: TFe ≥ 55% and located in the core area of ​​the ore body;

[0070] 2) Economic grade band: 40% ≤ TFe < 55%;

[0071] 3) Low-grade bands: Boundary grade ≤ TFe < 40%;

[0072] In this example, the mathematical expression of the real-time economic critical grade model is:

[0073] ;

[0074] in, C cut Economic critical grade (%) M c Unit mining cost (yuan / ton); P c Unit beneficiation cost (RMB / ton); R Mineral processing recovery rate (%); P con Price of iron ore concentrate (USD / ton); P slag Cost of tailings processing (USD / ton); α The price volatility coefficient (0.5≤α≤2.5) is dynamically updated using an LSTM price forecasting model.

[0075] 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:

[0076] 1) Hard rock bands: Ai≥0.4;

[0077] 2) Soft rock bands: 0.1≤Ai<0.4;

[0078] 3) Topsoil strips: Ai < 0.1;

[0079] 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 to reduce beneficiation energy consumption; precise mining of high-grade bands reduces waste rock contamination and tailings carbon emissions, thus achieving low-carbon mining.

[0080] 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.

[0081] 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:

[0082] 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;

[0083] 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;

[0084] 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%;

[0085] 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 ;;

[0086] For soft rock strips: use non-explosive shovel loading + tracked transportation (ground clearance ≥ 80cm) and biodiesel power;

[0087] 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%.

[0088] 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.

[0089] 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:

[0090] 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:

[0091] The laser mapping robot has fluorescent QR codes containing encrypted parameters sprayed on the facade of its steps, with a positioning error of ≤3cm;

[0092] 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;

[0093] The slope monitoring array includes tilt sensors (accuracy 0.1), pore water pressure gauges (range 0-1MPa), and microseismic monitoring nodes (frequency response 5-500Hz) deployed on soft rock strips.

[0094] 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.

[0095] This step uses gamma detectors and other instruments to detect grade data in real time and provides feedback, 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.

[0096] 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. Example 2

[0097] like Figure 3 As shown, this embodiment of the invention proposes a system for implementing the method described in Embodiment 1, comprising:

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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;

[0105] 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%. Example 3

[0106] This embodiment also proposes a differentiated slope design method in the implementation process of Embodiment 1 or Embodiment 2, specifically including:

[0107] Hard rock strip: final slope angle 35-38° + 5m safety platform + prestressed anchor cable (pull-out force ≥500kN);

[0108] Soft rock strip: slope angle 25-28° + drainage holes (hole diameter ≥100mm, elevation angle 5°) every 10m elevation difference + geogrid reinforcement;

[0109] 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.

[0110] 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.

[0111] 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. 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 according to the abrasion index, the abrasion index is determined by in-situ load test, and the rock blocks are divided into hard rock zones, soft rock zones, and topsoil zones according to the abrasion index. 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.

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 "section-block-strip" high-intensity low-carbon mining method applicable to high-altitude and cold mining areas according to claim 1, characterized in that: The mathematical expression for the real-time economic critical grade model is: ； in, C cut It is the critical grade for economic development; M c Unit mining cost; P c Unit beneficiation cost; R For mineral processing recovery rate; P con For the price of iron concentrate; P slag Cost of tailings treatment; α This is the price volatility coefficient.

4. The high-intensity, low-carbon mining method of "section-block-strip" applicable to high-altitude and cold mining areas according to claim 3, characterized in that: The price volatility coefficient α The price forecasting model is dynamically updated.

5. The "section-block-strip" high-intensity low-carbon mining method 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 the soft rock strips.

6. 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.

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: 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.

8. A high-intensity, low-carbon mining system for "section-block-strip" mining in high-altitude, cold regions, suitable for implementing the method described in any one of claims 1 to 7, 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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