A method and system for dynamic planning of open-pit mining and stripping transportation and road reinforcement

Abstract

This invention belongs to the field of mining road planning technology, and discloses a method and system for dynamic planning and road reinforcement of open-pit mining stripping and transportation. The method completes dynamic planning of open-pit mining stripping and transportation through geological dynamic modeling and mineability assessment, dynamic decomposition and assignment of transportation tasks, and collaborative scheduling of transportation vehicles; it completes intelligent road reinforcement through road state perception, gradient enhancement material design, adaptive material injection, and structural strengthening; and it completes digital twin decision-making through three-dimensional geological-transport vehicle coupled data and dynamic simulation and early warning based on a digital twin platform, providing early warning of landslide risks on transportation roads. This invention solves the core contradiction between open-pit mine transportation efficiency and road maintenance costs by combining spatiotemporal coupling optimization algorithms with intelligent responsive reinforcement technology, providing technical support for green mine construction.

Description

Technical Field

[0001] This invention belongs to the field of mining road planning technology, and particularly relates to a dynamic planning method and system for open-pit mining stripping and transportation, as well as road reinforcement. Background Technology

[0002] Transportation route planning research is typically used to comprehensively evaluate several alternative routes based on multiple factors to select the most suitable route. Previously, when solving multi-objective vehicle routing problems, ensemble methods were used to transform multiple objectives into single objectives. However, the weight parameters in ensemble methods are often subjectively set, and the weighted objective functions are mutually constrained through decision variables, resulting in a complex topological structure. Furthermore, the dimensions of the multi-objective functions are inconsistent, easily leading to one function dominating others. Therefore, in recent years, multi-objective evolutionary algorithms have been commonly used to solve transportation route problems. However, most current research focuses on the transportation of hazardous materials, and the factors considered are mostly social and environmental indicators, with relatively little analysis and research on the crucial aspect of transportation routes in open-pit mining and stripping.

[0003] The routes for last-mile transportation are complex and currently rely mainly on manual labor. In the design of transportation routes, little consideration has been given to the issues, resulting in overlapping and repetitive routes that lead to long transportation times. At present, the planning of transportation tasks mainly relies on manual calculations, which can only handle a small number of transportation tasks and cannot plan for a large number of transportation tasks, resulting in low planning efficiency.

[0004] Furthermore, existing technologies cannot accurately identify the condition of roads during transportation or take effective reinforcement measures. Summary of the Invention

[0005] To overcome the problems existing in related technologies, the present invention discloses an embodiment of a method and system for dynamic planning of open-pit mining and stripping transportation and road reinforcement.

[0006] The technical solution is as follows: a dynamic planning method for open-pit mining and stripping transportation and a road reinforcement method, including the following steps:

[0007] S1, through geological dynamic modeling and exploitability assessment, dynamic decomposition and assignment of transportation tasks, and collaborative scheduling of transportation vehicles, completes the dynamic planning of open-pit mining and stripping transportation.

[0008] S2, based on the dynamic planning results of open-pit mining and transportation, performs road condition perception, gradient enhancement material design, adaptive material injection, and structural reinforcement to complete intelligent road reinforcement;

[0009] S3, based on the results of intelligent road reinforcement, uses a digital twin platform to perform three-dimensional geological-transport vehicle coupled data and dynamic simulation and early warning, completes digital twin decision-making, and provides early warning of landslide risks on transport roads.

[0010] In step S1, geological dynamic modeling and mineability assessment include: constructing a three-dimensional geological dynamic model through a geological stability index, and dynamically correcting the mineable area of ​​the ore rock mass;

[0011] The geological stability index is:

[0012]

[0013] In the formula, These are three-dimensional geological dynamic values. It is a geological stability index. For a moment, This is the equivalent hardness coefficient of the rock mass;

[0014] The equivalent hardness coefficient of the rock mass is:

[0015]

[0016] In the formula, This represents the energy required for blasting the current rock mass. The blasting energy is based on the blastability of the benchmark rock mass. The current density of the rock mass, The base rock mass density.

[0017] In step S1, the dynamic decomposition and assignment of transportation tasks includes: being completed through a spatiotemporal conflict prediction model and an elastic ore grade-energy consumption coupling model;

[0018] The spatiotemporal conflict prediction model is as follows:

[0019]

[0020] In the formula, This represents the probability of a collision involving transport vehicles. Let be the probability of transport vehicles moving in opposite directions. For the first Vehicles and the Collision distance of vehicle transport vehicles Relative running speed To maintain a safe distance;

[0021] The elastic ore grade-energy consumption coupling model is as follows:

[0022]

[0023] In the formula, For ore loading, This refers to the test weight of lithium ore. This represents the standard energy consumption during transportation. This represents the lithium content in the current ore grade. The target lithium content in the ore. For vehicle transportation energy consumption, The energy consumption for transportation as specified for the vehicle.

[0024] Furthermore, after the transportation task is dynamically decomposed and assigned, the transportation route is optimized in time and space by using a dynamic transportation cost function.

[0025] The dynamic transportation cost function is:

[0026]

[0027] In the formula, For dynamic transportation costs, For the first The speed of vehicle transportation, This is a real-time traffic condition coefficient, with a value ranging from 0.8 to 1.2. This refers to the tire wear coefficient. This refers to the tire wear factor.

[0028] In step S1, the coordinated scheduling of transport vehicles includes: integrating the energy consumption, waiting time, and ore grade fluctuations of transport vehicles through a multi-agent reinforcement learning framework to complete the global optimization of mining, transportation, and disposal;

[0029] The multi-agent reinforcement learning framework is as follows:

[0030]

[0031] In the formula, For the coordinated scheduling of transport vehicles, For the waiting time, This is the ore grade fluctuation coefficient. For fluctuating dynamic transportation costs, The waiting time for fluctuations and changes. Energy consumption of transport vehicles under transportation costs and waiting time. This represents the maximum energy consumption fluctuation value under fluctuating waiting time.

[0032] In step S2, road condition perception includes: reconstructing dynamic load stress distribution through an embedded piezoelectric sensor array and outputting a stress distribution matrix to monitor the bearing capacity status of different locations on the road in real time;

[0033] The formula for reconstructing the dynamic stress distribution is:

[0034]

[0035] In the formula, This represents the dynamic stress distribution value. This is the dynamic load value. For Renault, The material attenuation coefficient, The area of ​​the dynamic load zone. The road deformation angle under dynamic load;

[0036] The stress distribution matrix is:

[0037]

[0038] In the formula, represents the number of piezoelectric sensors in the horizontal direction, and represents the number of piezoelectric sensors in the vertical direction.

[0039] In step S2, the gradient reinforcement material design includes: reinforcement material formulation: 40-60% tailings sand, 30-40% slag-based cementitious agent, and 10-20% carbon fiber;

[0040] Strength optimization formula:

[0041]

[0042] In the formula, To enhance the compressive strength of the reinforced material, The initial compressive strength of the material before optimization. The carbon fiber reinforcement factor, The density of carbon fiber, The length of the carbon fiber. The diameter of the carbon fiber;

[0043] Adaptive material injection includes: optimizing the compressive strength and settlement under load based on the reinforcement material mix design function. Dynamically adjust the proportion of reinforcement materials;

[0044] Reinforcement material mix proportion function:

[0045]

[0046] In the formula, Screening values ​​are added to the material mix ratio for reinforcement. To determine the threshold by which a component in a reinforcing material affects its compressive strength, To determine the threshold at which a certain component in the reinforcement material affects the settlement, This represents the settlement under load.

[0047] In step S2, structural reinforcement includes: dispersing the concentrated load of the heavy mining truck through a gradient stiffness anchoring layer to reduce the rutting depth; the gradient stiffness anchoring layer from top to bottom is:

[0048] Surface layer: nano-silica modified concrete, 2-3cm thick;

[0049] Base layer: High-elasticity polymer mesh; 1-3 layers;

[0050] Load-bearing layer, reinforced with steel cage filling material.

[0051] In step S3, the three-dimensional geological-transport vehicle coupling includes: integrating InSAR surface deformation data, transport vehicle OBD information, and meteorological data through data fusion in a digital twin platform;

[0052] Dynamic simulation and early warning include: using risk prediction models to warn of road landslide risks to transport vehicles;

[0053] The risk prediction model is as follows:

[0054]

[0055] In the formula, This is the predicted risk value. For time, For divergence difference, For load capacity, For load-bearing capacity, This is the current time.

[0056] Another objective of this invention is to provide a dynamic planning and road reinforcement system for open-pit mining stripping and transportation. This system implements the aforementioned dynamic planning and road reinforcement method for open-pit mining stripping and transportation. The system includes:

[0057] The mining and stripping transportation dynamic planning module completes the dynamic planning of open-pit mining and stripping transportation through geological dynamic modeling and exploitability assessment, dynamic decomposition and assignment of transportation tasks, and collaborative scheduling of transportation vehicles.

[0058] The intelligent road reinforcement module, based on the dynamic planning results of open-pit mining and transportation, performs road condition perception, gradient enhancement material design, adaptive material injection, and structural reinforcement to complete intelligent road reinforcement.

[0059] The digital twin decision-making module, based on the results of intelligent road reinforcement, uses a digital twin platform to perform three-dimensional geological-transport vehicle coupled data and dynamic simulation and early warning to complete digital twin decision-making and provide early warning of landslide risks on transport roads.

[0060] Combining all the above technical solutions, the beneficial effects of this invention are as follows: This invention offers short transportation times, can handle large-scale transportation tasks with high planning efficiency, and saves transportation costs. It identifies the road's condition and provides a method for reinforcing the road with reinforcement materials, ensuring transportation safety. This invention, through the combination of a spatiotemporal coupling optimization algorithm and intelligent responsive reinforcement technology, solves the core contradiction between open-pit mine transportation efficiency and road maintenance costs, providing technical support for green mine construction. Attached Figure Description

[0061] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure;

[0062] Figure 1 This is a flowchart of the dynamic planning and road reinforcement method for open-pit mining, stripping, and transportation provided in this embodiment of the invention;

[0063] Figure 2 This is a schematic diagram of the dynamic planning and road reinforcement system for open-pit mining, stripping, and transportation provided in this embodiment of the invention. Detailed Implementation

[0064] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0065] Example 1: The innovation of the open-pit mine stripping and transportation dynamic planning and road reinforcement method provided in this embodiment of the invention lies in:

[0066] A spatiotemporal collaborative dynamic transportation planning model: integrating geological change prediction with real-time feedback on the status of transport vehicles to achieve dynamic optimization of transportation routes;

[0067] Road condition-load adaptive reinforcement technology: Real-time sensing of road stress distribution through sensor networks and dynamic adjustment of reinforcement parameters;

[0068] Digital twin-driven decision-making system: Construct a coupled 3D geological model of the mine, transportation vehicles, and roads to achieve closed-loop optimization of the entire process.

[0069] Specifically, such as Figure 1 As shown, it includes:

[0070] S1 completes the dynamic planning of open-pit mining and stripping transportation through geological dynamic modeling and exploitability assessment, dynamic decomposition and assignment of transportation tasks, and collaborative scheduling of transportation vehicles.

[0071] S2, based on the dynamic planning results of open-pit mining and transportation, performs road condition perception, gradient enhancement material design, adaptive material injection, and structural reinforcement to complete intelligent road reinforcement;

[0072] S3, based on the results of intelligent road reinforcement, completes digital twin decision-making by using three-dimensional geological-transport vehicle coupled data and dynamic simulation and early warning based on a digital twin platform, and provides early warning of landslide risks on transport roads.

[0073] Example 2, as Figure 2 As shown, the open-pit mining stripping and transportation dynamic planning and road reinforcement system provided in this embodiment of the invention includes:

[0074] The mining and stripping transportation dynamic planning module is used to complete the dynamic planning of open-pit mining and stripping transportation through geological dynamic modeling and mineability assessment, dynamic decomposition and assignment of transportation tasks, and collaborative scheduling of transportation vehicles.

[0075] The intelligent road reinforcement module is used to complete intelligent road reinforcement based on the dynamic planning results of open-pit mining and transportation, including road condition perception, gradient enhancement material design, adaptive material injection, and structural strengthening.

[0076] The digital twin decision-making module is used to make digital twin decisions based on the results of intelligent road reinforcement, through three-dimensional geological-transport vehicle coupled data and dynamic simulation and early warning based on the digital twin platform, and to provide early warning of landslide risks on transport roads.

[0077] For example, the mining and transportation dynamic planning module includes:

[0078] The geological dynamic modeling and mineability assessment module is used to construct a three-dimensional geological dynamic model through geological stability index and dynamically correct the mineable area of ​​ore and rock mass.

[0079] The geological stability index is:

[0080]

[0081] In the formula, These are three-dimensional geological dynamic values. It is a geological stability index. For a moment, This is the equivalent hardness coefficient of the rock mass;

[0082] Combining the equivalent hardness coefficient of the rock mass With the rate of change of displacement Dynamically adjust the harvestable area;

[0083] The equivalent hardness coefficient of the rock mass is:

[0084]

[0085] In the formula, This represents the energy required for blasting the current rock mass. The blasting energy is based on the blastability of the benchmark rock mass. The current density of the rock mass, The base rock mass density is used to quantify the blastability of the rock mass, dynamically adjust blasting parameters (such as charge quantity), and reduce unexpected adjustments to subsequent transportation routes.

[0086] Among them, after obtaining the three-dimensional geological dynamic model, a UAV three-dimensional scanning plus microseismic monitoring system can be used to update the three-dimensional geological dynamic model every 4 hours;

[0087] The dynamic decomposition and assignment module for transportation tasks is used to complete the dynamic decomposition and assignment of transportation tasks through a spatiotemporal conflict prediction model and an elastic ore grade-energy consumption coupling model.

[0088] The spatiotemporal conflict prediction model is as follows:

[0089]

[0090] In the formula, This represents the probability of a collision involving transport vehicles. Let be the probability of transport vehicles moving in opposite directions. For the first Vehicles and the Collision distance of vehicle transport vehicles Relative running speed To maintain a safe distance;

[0091] Predicting the probability of collisions with transport vehicles To reduce delays caused by emergency avoidance, the route is replanned 10 minutes in advance.

[0092] Elastic ore grade-energy consumption coupling model:

[0093]

[0094] In the formula, For ore loading, This refers to the test weight of lithium ore. This represents the standard energy consumption during transportation. This represents the lithium content in the current ore grade. The target lithium content in the ore. For vehicle transportation energy consumption, The energy consumption for transportation as specified for the vehicle.

[0095] By optimizing ore loading Balancing quality control with truck energy consumption.

[0096] The transportation route spatiotemporal optimization module is used to optimize transportation routes through a dynamic transportation cost function;

[0097] The dynamic transportation cost function is:

[0098]

[0099] In the formula, For dynamic transportation costs, For the first The speed of vehicle transportation, This is a real-time traffic condition coefficient, with a value ranging from 0.8 to 1.2. This refers to the tire wear coefficient. This is the tire wear factor. Real-time road condition coefficients are introduced. and tire wear factor Optimize the traditional shortest path algorithm;

[0100] Implementation steps: Based on vehicle GPS and road sensor data, the mining truck route is replanned every 15 minutes;

[0101] The transport vehicle collaborative scheduling module is used to integrate transport vehicle energy consumption, waiting time, and ore grade fluctuations through a multi-agent reinforcement learning framework to achieve global optimization of mining, transportation, and disposal.

[0102] The multi-agent reinforcement learning framework is as follows:

[0103]

[0104] In the formula, For the coordinated scheduling of transport vehicles, For the waiting time, This is the ore grade fluctuation coefficient. For fluctuating dynamic transportation costs, The waiting time for fluctuations and changes. Energy consumption of transport vehicles under transportation costs and waiting time. This represents the maximum energy consumption fluctuation value under fluctuating waiting time.

[0105] Through reward function By integrating energy consumption of transport vehicles, waiting time, and fluctuations in ore grade, the overall optimization of mining-transportation-discharge is achieved (breaking through the limitations of manual scheduling).

[0106] For example, a road intelligent reinforcement module includes:

[0107] The road condition sensing module is used to reconstruct the dynamic load stress distribution through an embedded piezoelectric sensor array and output a stress distribution matrix to monitor the bearing capacity status of different locations on the road in real time.

[0108] Formula for reconstructing dynamic stress distribution:

[0109]

[0110] In the formula, This represents the dynamic stress distribution value. This is the dynamic load value. For Renault, The material attenuation coefficient, The area of ​​the dynamic load zone. The road deformation angle under dynamic load; the dynamic load stress distribution reconstruction accurately locates high strain areas to guide targeted reinforcement.

[0111] The stress distribution matrix is:

[0112]

[0113] In the formula, represents the number of piezoelectric sensors in the lateral direction, and represents the number of piezoelectric sensors in the longitudinal direction. The stress distribution matrix monitors the bearing capacity at different locations on the road in real time, addressing the issue of excessive road surface elevation differences.

[0114] Gradient reinforcement material design module, reinforcement material formula: tailings sand 40-60%, slag-based cementitious agent 30-40%, and carbon fiber 10-20%;

[0115] Strength optimization formula:

[0116]

[0117] In the formula, To enhance the compressive strength of the reinforced material, The initial compressive strength of the material before optimization. The carbon fiber reinforcement factor, The density of carbon fiber, The length of the carbon fiber. The diameter of the carbon fiber is increased to improve compressive strength. The above can extend the service life of roads by 2-3 times;

[0118] The adaptive material injection module is used to optimize the compressive strength and settlement under load of the reinforcement material based on the reinforcement material mix ratio function. Dynamically adjust the proportion of reinforcement materials;

[0119] Reinforcement material mix proportion function:

[0120]

[0121] In the formula, Screening values ​​are added to the material mix ratio for reinforcement. To determine the threshold by which a component in a reinforcing material affects its compressive strength, To determine the threshold at which a certain component in the reinforcement material affects the settlement, This represents the settlement under load.

[0122] The structural reinforcement module is used to disperse the concentrated load of heavy mining trucks through a gradient stiffness anchoring layer, thereby reducing the rutting depth; the gradient stiffness anchoring layer, from top to bottom, consists of:

[0123] Surface layer: nano-silica modified concrete, 2-3cm thick;

[0124] Base layer: High-elasticity polymer mesh (tensile strength) ), 1-3 floors;

[0125] Load-bearing layer, reinforced steel cage filled with reinforcing material;

[0126] Technical benefits: Disperses the concentrated load of heavy mining trucks, reducing rut depth by up to 40% (compared to traditional compaction processes).

[0127] For example, a digital twin decision-making module includes:

[0128] The 3D geology-transport vehicle coupling module is used to integrate InSAR surface deformation data, transport vehicle OBD information, and meteorological data through data fusion in a digital twin-based platform.

[0129] The dynamic simulation and early warning module is used to provide early warnings of road landslide risks for transport vehicles through a risk prediction model.

[0130] The risk prediction model is as follows:

[0131]

[0132] In the formula, This is the predicted risk value. For time, For divergence difference, For load capacity, For load-bearing capacity, The current time is used. Landslide risk is predicted 30 minutes in advance based on the load-bearing capacity divergence difference.

[0133] Table 1 shows a comparison of the technical effects of the present invention with those of existing technologies.

[0134] Table 1 Comparison of Technical Effects

[0135]

[0136] Example 3, reinforcement material formulation: 40% tailings sand, 40% slag-based cementitious agent and 20% carbon fiber.

[0137] Example 4, reinforcement material formulation: 60% tailings sand, 30% slag-based cementitious agent and 10% carbon fiber.

[0138] Example 5, Reinforcing material formulation: 50% tailings sand, 35% slag-based cementitious agent, and 15% carbon fiber.

[0139] Application example 1.

[0140] Taking an open-pit mine as an application scenario: a sensor network was deployed to collect strain data of the north slope, and a dynamic planning system optimized the transportation route from 14.2km to 9.8km. After adopting gradient anchoring layer technology, the cost of anti-slide pile engineering was reduced by 35% (compared to the treatment plan).

[0141] Application Example 2.

[0142] Taking a certain mine application as an example (daily stripping volume of 50,000 tons); Dynamic programming: through P c The model reduces the number of avoidance operations, increasing the daily effective transportation time by 2.5 hours; road reinforcement: using gradient materials to repair high-strain areas reduces maintenance costs by 45% year-on-year; this invention solves the pain points of response lag and blind reinforcement in traditional methods.

[0143] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0144] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for dynamic planning of stripping and haulage in an open pit mine and for road reinforcement, characterized in that, The method includes the following steps: S1, through geological dynamic modeling and exploitability assessment, dynamic decomposition and assignment of transportation tasks, and collaborative scheduling of transportation vehicles, completes the dynamic planning of open-pit mining and stripping transportation. S2, based on the dynamic planning results of open-pit mining and transportation, performs road condition perception, gradient enhancement material design, adaptive material injection, and structural reinforcement to complete intelligent road reinforcement; S3, based on the results of intelligent road reinforcement, uses a digital twin platform to perform three-dimensional geological-transport vehicle coupled data and dynamic simulation and early warning, completes digital twin decision-making, and provides early warning of landslide risks on transport roads; In step S1, geological dynamic modeling and mineability assessment include: constructing a three-dimensional geological dynamic model through a geological stability index, and dynamically correcting the mineable area of ​​the ore rock mass; The geological stability index is: ； In the formula, is a three-dimensional geological dynamic value, is a geological stability index, is a time, is an equivalent hardness coefficient of the rock mass. The equivalent hardness coefficient of the rock mass is: ； In the formula, is the current rock mass blastability blasting energy, is the reference rock mass blastability blasting energy, is the current rock mass density, is the reference rock mass density.

2. The method for dynamic planning of open-pit mining, stripping, and transportation, and road reinforcement according to claim 1, is characterized in that, In step S1, the dynamic decomposition and assignment of transportation tasks includes: being completed through a spatiotemporal conflict prediction model and an elastic ore grade-energy consumption coupling model; The spatiotemporal conflict prediction model is as follows: ； In the formula, This represents the probability of a collision involving transport vehicles. Let be the probability of transport vehicles moving in opposite directions. For the first Vehicles and the Collision distance of vehicle transport vehicles Relative running speed To maintain a safe distance; The elastic ore grade-energy consumption coupling model is as follows: ； In the formula, For ore loading, This refers to the test weight of lithium ore. This represents the standard energy consumption during transportation. This represents the lithium content in the current ore grade. The target lithium content in the ore. For vehicle transportation energy consumption, The energy consumption for transportation as specified for the vehicle.

3. The method for dynamic planning of open-pit mining, stripping, and transportation, and road reinforcement according to claim 2, is characterized in that, After the transportation task is dynamically decomposed and assigned, the transportation route is optimized in time and space by using a dynamic transportation cost function. The dynamic transportation cost function is: ； In the formula, For dynamic transportation costs, For the first The speed of vehicle transportation, This is a real-time traffic condition coefficient, with a value ranging from 0.8 to 1.

2. This refers to the tire wear coefficient. This refers to the tire wear factor.

4. The method for dynamic planning of open-pit mining, stripping, and transportation, and road reinforcement according to claim 3, is characterized in that, In step S1, the coordinated scheduling of transport vehicles includes: integrating the energy consumption, waiting time, and ore grade fluctuations of transport vehicles through a multi-agent reinforcement learning framework to complete the global optimization of mining, transportation, and disposal; The multi-agent reinforcement learning framework is as follows: ； In the formula, For the coordinated scheduling of transport vehicles, For the waiting time, This is the ore grade fluctuation coefficient. For fluctuating dynamic transportation costs, The waiting time for fluctuations and changes. Energy consumption of transport vehicles under transportation costs and waiting time. This represents the maximum energy consumption fluctuation value under fluctuating waiting time.

5. The method for dynamic planning of open-pit mining, stripping, and transportation, and road reinforcement according to claim 1, is characterized in that, In step S2, road condition perception includes: reconstructing dynamic load stress distribution through an embedded piezoelectric sensor array and outputting a stress distribution matrix to monitor the bearing capacity status of different locations on the road in real time; The formula for reconstructing the dynamic stress distribution is: ； In the formula, This represents the dynamic stress distribution value. This is the dynamic load value. For Renault, The material attenuation coefficient, The area of ​​the dynamic load zone. The road deformation angle under dynamic load; The stress distribution matrix is: ； In the formula, The number of piezoelectric sensors in the lateral direction. This represents the number of piezoelectric sensors in the longitudinal direction.

6. The method for dynamic planning of open-pit mining, stripping, and transportation, and road reinforcement according to claim 5, is characterized in that, In step S2, the gradient reinforcement material design includes: reinforcement material formulation: 40-60% tailings sand, 30-40% slag-based cementitious agent, and 10-20% carbon fiber; Strength optimization formula: ； In the formula, To enhance the compressive strength of the reinforced material, The initial compressive strength of the material before optimization. The carbon fiber reinforcement factor, The density of carbon fiber, The length of the carbon fiber. The diameter of the carbon fiber; Adaptive material injection includes: optimizing the compressive strength and settlement under load based on the reinforcement material mix design function. Dynamically adjust the proportion of reinforcement materials; Reinforcement material mix proportion function: ； In the formula, Screening values ​​are added to the material mix ratio for reinforcement. To determine the threshold by which a component in a reinforcing material affects its compressive strength, To determine the threshold at which a certain component in the reinforcement material affects the settlement, This represents the settlement under load.

7. The method for dynamic planning of open-pit mining, stripping, and transportation, and road reinforcement according to claim 6, is characterized in that, Structural reinforcement includes: dispersing the concentrated load of heavy mining trucks through gradient stiffness anchoring layers to reduce rutting depth; the gradient stiffness anchoring layers from top to bottom are: Surface layer: nano-silica modified concrete, 2-3cm thick; Base layer: High-elasticity polymer mesh; 1-3 layers; Load-bearing layer, reinforced with steel cage filling material.

8. The method for dynamic planning of open-pit mining, stripping, and transportation, and road reinforcement according to claim 1, is characterized in that, In step S3, the three-dimensional geological-transport vehicle coupling includes: integrating InSAR surface deformation data, transport vehicle OBD information, and meteorological data through data fusion in a digital twin platform; Dynamic simulation and early warning include: using risk prediction models to warn of road landslide risks to transport vehicles; The risk prediction model is as follows: ； In the formula, This is the predicted risk value. For time, For divergence difference, For load capacity, For load-bearing capacity, This is the current time.

9. A dynamic planning and road reinforcement system for open-pit mining, stripping, and transportation, characterized in that, The system implements the dynamic planning and road reinforcement method for open-pit mining stripping and transportation as described in any one of claims 1-8, and the system includes: The mining and stripping transportation dynamic planning module completes the dynamic planning of open-pit mining and stripping transportation through geological dynamic modeling and exploitability assessment, dynamic decomposition and assignment of transportation tasks, and collaborative scheduling of transportation vehicles. The intelligent road reinforcement module, based on the dynamic planning results of open-pit mining and transportation, performs road condition perception, gradient enhancement material design, adaptive material injection, and structural reinforcement to complete intelligent road reinforcement. The digital twin decision-making module, based on the results of intelligent road reinforcement, uses a digital twin platform to perform three-dimensional geological-transport vehicle coupled data and dynamic simulation and early warning to complete digital twin decision-making and provide early warning of landslide risks on transport roads.

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