An open-pit mine mining efficiency simulation optimization method

Abstract

The application discloses a kind of open-pit mining efficiency simulation optimization method, belong to the technical field related to digital management of mine exploitation.The method includes: using unmanned aerial vehicle to collect open-pit surface data, generates mining status plan;Import 3D mining software to construct geological model and design mining boundary;Based on VISSIM microcosmic traffic simulation software, construct transport simulation model, set traffic rules and establish vehicle dynamic numerical model;Calculate the speed and acceleration of mine truck under different climate conditions, embed simulation model operation, obtain the output quantity of vehicle per unit time;Predict the maximum excavation volume of period, optimize mining location and update mining boundary;Through multi-period iterative simulation, aggregate annual total mining volume, compare with design target, dynamically optimize vehicle and equipment resource allocation.The application realizes the dynamic simulation of mining and transport system and the quantitative analysis of climate influence, overcomes the limitations of traditional static model and experience management, significantly improves the accuracy of mining efficiency evaluation and the scientificity of resource allocation.

Description

Technical Field

[0001] This invention belongs to the technical field of digital management of mining operations, specifically relating to a simulation optimization method for open-pit mine mining efficiency. Background Technology

[0002] Open-pit mining is a complex systems engineering project encompassing multiple stages, including drilling, blasting, loading, and transportation. Among these, loading and transportation, as key processes in material flow, directly determine the overall production capacity of the mine and have a direct impact on the company's operating profits. Currently, the calculation of mining efficiency and production management mainly rely on traditional mathematical models based on simplified assumptions, empirical formulas, and two-dimensional engineering schedule management software. While these methods have played a role in macro-level planning, they have significant limitations.

[0003] First, existing methods mostly employ static and idealized models, making it difficult to simulate the complex dynamic interactions between equipment, roads, and loading / unloading points in a mining production system. They also fail to effectively reflect random phenomena such as traffic congestion and queuing in actual production. This leads to biases in the assessment of mining efficiency, preventing managers from quantitatively confirming during the planning phase whether allocated production resources can meet actual mining demands or accurately determine whether expected mining efficiency targets can be achieved. Second, open-pit mining operations are completely exposed to the natural environment. Severe weather conditions such as rain, snow, freezing, and fog significantly reduce road capacity, vehicle speed, and the efficiency of mining equipment, even causing production interruptions. Traditional management methods do not incorporate these climatic factors into quantitative analysis models, failing to accurately quantify the dynamic impact of real-world weather conditions on mining efficiency. This results in only extensive qualitative management, leading to frequent inaccuracies in production plans in climate-volatile regions, causing significant output and economic losses for enterprises. Furthermore, due to the lack of precise simulation of dynamic processes and climate impacts, current resource allocation schemes largely rely on historical experience, posing a dual risk of under-allocation leading to production shortfalls or over-allocation causing resource waste. Meanwhile, traditional two-dimensional progress management lacks an effective feedback mechanism and cannot dynamically and adaptively optimize and adjust subsequent production plans and resource allocation based on the mining process (such as changes in mining boundaries and extended transportation distances) and seasonal climate changes.

[0004] Therefore, there is an urgent need in this field for a method and system for optimizing open-pit mining efficiency that can overcome the above-mentioned shortcomings, especially one that can quantitatively consider the impact of climate and accurately and dynamically simulate and optimize mining efficiency. Summary of the Invention

[0005] In view of the above-mentioned problems in the existing technology, the present invention proposes a simulation optimization method for open-pit mine mining efficiency, aiming to solve at least one of the above problems.

[0006] To achieve the above technical objectives, the present invention adopts the following technical solution:

[0007] A simulation optimization method for open-pit mine mining efficiency includes the following steps:

[0008] Step S1: Use a surveying drone to scan the surface of the open-pit mine, collect surface data of the open-pit mine, and generate a plan view of the current mining status of the open-pit mine.

[0009] Step S2: Import the current open-pit mining plan into 3D mining software to construct a 3D geological excavation model of the open-pit mine, and design the final mining boundary based on the model;

[0010] Step S3: Import the current open-pit mine mining plan as the background map into the VISSIM micro-traffic simulation software, construct the transportation road network based on the final mining boundary, and arrange the work points and path framework to realize the construction of the mine transportation simulation model.

[0011] Step S4: Based on the mine transportation simulation model constructed in step S3, set transportation rules and establish a dynamic numerical model for vehicle transportation.

[0012] Step S5: Based on the traffic rules set in Step S4 and the established vehicle transportation dynamic numerical model, calculate the speed v' and acceleration a' of the mining truck under different climatic conditions;

[0013] Step S6: Configure the mining truck speed v' and acceleration a' obtained in Step S5 under different climatic conditions into the mine transportation simulation model of the VISSIM microscopic traffic simulation software, run the simulation, and obtain the number of vehicles output per unit time under the corresponding climatic conditions. , where i represents the number of typical climate events throughout the year;

[0014] Step S7: Calculate the number of vehicles output per unit time under the corresponding climatic conditions based on the simulation obtained in Step S6. Predict the maximum excavation volume of the open-pit mine;

[0015] Step S8: Based on the maximum excavation volume predicted by the simulation in step S7, determine the optimal mining location in the three-dimensional geological excavation model, update the mining boundary, generate a mining boundary plan, and complete the optimization of one simulation calculation cycle.

[0016] Compared with the prior art, the technical solution of the present invention has the following advantages:

[0017] 1. This invention integrates surveying drones, 3D mining software, and microscopic traffic simulation software to construct a complete technology chain from surface data acquisition and 3D geological modeling to transportation system simulation. It can accurately simulate the dynamic interaction process in the mine transportation system, including random phenomena such as vehicle following, road congestion, and loading and unloading queues, thereby significantly improving the accuracy and reliability of mining efficiency assessment and realizing refined and dynamic simulation and optimization of open-pit mine mining efficiency.

[0018] 2. By establishing a quantitative correction relationship between climate conditions and mining truck driving parameters (such as speed and acceleration), typical climate types throughout the year are incorporated into the simulation system, realizing dynamic evaluation of transportation capacity under different climate scenarios, effectively solving the problem of inaccurate planning in climate-variable regions using traditional methods.

[0019] 3. In view of the problem that existing resource allocation relies on historical experience and is prone to under-allocation or over-allocation, this invention compares the annual mining volume obtained by simulation with the design target, and combines it with the established vehicle transportation capacity model to accurately calculate the optimal equipment configuration scheme, thereby ensuring output while avoiding resource waste and improving investment efficiency.

[0020] Overall, this invention constructs a full-process, digital simulation and optimization method for mining efficiency, which significantly improves the intelligence level and scientific decision-making of mine production management, and provides reliable technical support for efficient and low-carbon mining in mining enterprises. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0022] Figure 2 This is a schematic diagram illustrating the process of generating the current mining status plan of this invention;

[0023] Figure 3 This is a schematic diagram of the DTM surface geological model generated by the present invention;

[0024] Figure 4 This is a schematic diagram of the three-dimensional geological excavation model of an open-pit mine generated by the present invention;

[0025] Figure 5 This is a schematic diagram of the transportation network and loading point layout of the present invention;

[0026] Figure 6 This is a schematic diagram illustrating the simulation and prediction of excavation locations over a period of time (e.g., a quarter) using the Dimine 3D mining software according to the present invention.

[0027] Figure 7This is a schematic diagram of the open-pit mine boundary at the end of a period (e.g., a quarter) obtained by the plotting tool in the Dimine 3D mining software according to the present invention. Detailed Implementation

[0028] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0029] like Figures 1 to 7 As shown, this invention provides a simulation optimization method for open-pit mine mining efficiency, which includes the following steps:

[0030] Step S1: Data collection of open-pit mine surface and generation of current mining status plan; specifically including:

[0031] S11. Use surveying drones to conduct a full-coverage scan of the entire surface of the open-pit mine, ensuring that the scan range covers the current mining area, transportation road network, and the planned mining boundaries, and ensuring that there are no missing data areas; during the scanning process, record the spatial coordinates and elevation information of each point on the surface in real time to form the original surface point cloud data;

[0032] S12. Import the original point cloud data into professional point cloud data processing software (such as ContextCapture, Pix4D, etc.), and through a series of processes such as aerial triangulation calculation, point cloud filtering and classification, separate ground points and non-ground points (such as vegetation, buildings, vehicles, etc.), and finally extract ground point cloud data that purely represents the surface morphology from the original surface point cloud data.

[0033] S13. Based on the ground point cloud data, use the point cloud processing software to generate a surface model containing terrain outlines and initial contour lines, and output it as a dxf format graphic file;

[0034] S14. Assign precise elevation values ​​to the initial contour lines in the graphic file, that is, based on the precise three-dimensional coordinates of each point in the ground point cloud data, assign a true and precise elevation attribute value to each initial contour line through a spatial interpolation algorithm (this process is as follows). Figure 2 (As shown). After elevation assignment, the DXF format graphic file becomes a digital map file containing complete and accurate digital elevation information, namely, a plan view of the current mining status of the open-pit mine. This map accurately records the topography of the mine at the current moment, accurately reflects the surface undulations of the mining area, and provides a unified and accurate spatial reference for all subsequent simulation work.

[0035] Step S2, the construction of a three-dimensional geological excavation model and the design of the mining boundary, specifically includes:

[0036] S21. Import the current open-pit mining plan (DXF format) generated in step S1 with completed elevation assignment into Dimine 3D mining software; use Dimine 3D mining software to read the contour lines and elevation information in the DXF file, convert them into DEM (Digital Elevation Model) data, and further generate a digital terrain model of the mining area, i.e., a surface DTM geological model (such as...). Figure 3 (as shown)

[0037] S22. In Dimine software, further import geological borehole data and exploration line profiles. The geological borehole data includes information such as borehole coordinates, inclination, lithological stratification, and ore grade. Then, using Dimine's solid modeling function, create a 3D wireframe model of the strata and ore body based on the geological borehole data. Finally, through Boolean operations, merge the surface DTM geological model with the 3D wireframe model to construct a 3D geological excavation model of the open-pit mine containing the strata and ore body (e.g., Figure 4 (as shown)

[0038] S23. Based on the three-dimensional geological excavation model of the open-pit mine, input the design parameters such as the final slope angle and stage height, and complete the optimization design of the final mining boundary in the three-dimensional mining software.

[0039] Step S3: Construction of a mine transportation simulation model based on VISSIM microscopic traffic simulation software, specifically including:

[0040] S31. Use the current open-pit mine mining plan (i.e., the dxf format file with elevation assignment) as the background map, import it into the VISSIM micro traffic simulation software, and perform coordinate system one and scale calibration to ensure that the geometric spatial relationship in the simulation environment completely corresponds to the geographic spatial information of the real-world open-pit mine.

[0041] S32. Based on the final mining boundary designed in step S2, construct the transportation road network in the VISSIM microscopic traffic simulation software. The core of road network construction is to create a series of well-defined links and connectors in the simulation environment to accurately simulate real roads. Specifically, this includes: 1) using the "Link" tool of the VISSIM microscopic traffic simulation software to draw the main transportation roads on the base map, connecting the mining face with each ore transport exit (e.g., Figure 5 and Figure 71) Access Ditch 1, Access Ditch 2, and Access Ditch 3 are shown. 2) A road branch network extends from the main road to each working face of the steps. 3) In the software, each link (including the main transport road and the road branch network) is assigned a unique identifier when it is created, and a descriptive name is assigned to its attributes, such as Main Road_West Section, Exit Ramp_Access Ditch 2, etc. 4) Set key attributes for each defined link, including the number of lanes, lane width, gradient, and speed limit.

[0042] S33. Arrange the work points and path framework, including: arranging excavator loading points at corresponding locations based on the number of benches being mined simultaneously and the working face parameters; setting unloading points at the end of transportation; and creating a framework for empty driving paths (from vehicle entrance to loading point) and heavy driving paths (from loading point to unloading point) in the VISSIM micro-traffic simulation software, and associating them through "decision points" to form the logical basis for closed-loop transportation.

[0043] Step S4: Based on the mine transportation simulation model constructed in Step S3, set the transportation rules and establish a dynamic numerical model of vehicle transportation; wherein, the setting of transportation rules includes the initial vehicle configuration setting, vehicle driving rule setting and road traffic priority rule setting.

[0044] The initial vehicle configuration serves as the foundational input for the simulation, including the specific model of the mining trucks (referred to as mining trucks) selected for transportation, the total number of mining trucks planned for transportation operations, and the physical parameters of the mining trucks. Taking the BZK D45 mining dump truck as an example, its core physical parameters are as follows:

[0045] Table 1. Physical Parameters of BZK D45 Mining Dump Truck

[0046]

[0047] The vehicle driving rules setting mainly involves setting the basic driving speed v and the basic driving acceleration a, which are used to provide a benchmark for subsequent climate impact correction. The set basic driving speed v is the basic operating speed range under normal weather conditions, which can be determined through open-pit mining design manuals and mine design specifications; the basic driving acceleration a is a typical value under normal weather conditions determined based on vehicle dynamics calculations.

[0048] The setting of road traffic priority rules mainly includes, based on the road network constructed in step S3, setting clear road priorities and specifying vehicle yielding rules at all intersections, merging areas and other locations where traffic conflicts may occur, through the "priority rule" function of VISSIM micro traffic simulation software, in order to simulate real traffic conflicts and traffic management logic.

[0049] Furthermore, the vehicle transportation dynamic numerical model is mainly designed to achieve high-precision simulation of the microscopic car-following behavior of mining trucks. This scheme preferentially adopts the Wiedemann 74 model car-following behavior to establish the vehicle transportation dynamic numerical model, as follows:

[0050]

[0051] In the formula, AX is the expected stopping distance for a stationary vehicle; BX is the expected stopping distance for a moving vehicle; EX is the perception threshold coefficient; ABX is the minimum expected driving distance for the vehicles in front and behind; V is the minimum speed of the vehicles in front and behind; SDX is the distance between the vehicles in front and behind when the driver of the following vehicle begins to pay attention to the low-speed vehicle in front; L is the length of the vehicle in front (m); AX add AX mult BX add BX mult EX add EX mult To adjust the parameters; RND1(I), RND(I), and RND2(I) are three independent random number parameters, and the values ​​of all three are limited to the interval between 0 and 1; NRND is a normally distributed random number.

[0052] Step S5: Based on the traffic rules set in Step S4 and the established vehicle transportation dynamic numerical model, calculate the speed v' and acceleration a' of the mining truck under different climatic conditions. The specific steps are as follows:

[0053] S51. Obtain climate data (such as rainfall intensity, snow thickness, visibility, etc.) for the target simulation period. Based on this data, calculate the influence factors of climate on road surface adhesion coefficient, rolling resistance coefficient and driver visibility distance through a predetermined influence function or lookup table.

[0054] S52. Using the influence factors calculated in step S51, the basic driving speed v and basic driving acceleration a set in step S4 are corrected. For example, a multiplication correction factor can be used to calculate the corrected parameters.

[0055] The following is a simple example of correcting for the base vehicle speed v and the base vehicle acceleration a:

[0056] Based on the "Open-Pit Mining Manual," the basic operating speed range (10~30 km / h) under normal weather conditions is determined, and a benchmark value (e.g., 24 km / h) is established. For adverse weather conditions such as rain and snow, a reduction factor of 20%-40% is applied to the benchmark value to calculate the corrected operating speed (e.g., 24 km / h × 0.75 = 18 km / h). Acceleration is calculated based on vehicle dynamics principles by adjusting the rolling resistance coefficient. The theoretical calculation of acceleration uses the following formula:

[0057] ;

[0058] ;

[0059] ;

[0060] In the formula, This is the rolling resistance coefficient; G represents the road slope angle; G represents the vehicle's weight. ρ is the vehicle driving force; ρ is the rolling resistance; m is the vehicle mass; a is the vehicle acceleration;

[0061] In normal weather, using the standard rolling resistance coefficient Calculate the baseline acceleration 'a'. In rainy or snowy weather, use an increased rolling resistance coefficient, such as... Then, based on the formula of the above vehicle dynamics principle, the acceleration a' under the corresponding climate conditions can be obtained by calculation;

[0062] Step S6: Configure the mining truck speed v' and acceleration a' under different climatic conditions as corrected in Step S5 into the VISSIM microscopic traffic simulation software and run the simulation, collecting the output results. The collection of output results includes obtaining the number of vehicles per unit time under the corresponding climatic conditions. This first requires determining the vehicle length L and AX based on the selected mining truck model and mine road specifications. add AX mult BX add BX mult EX add EX mult , RND1 ( I Parameters such as speed and acceleration are used to calculate the minimum expected travel distance ABX between the front and rear vehicles during the operation of this type of mining truck, and the distance SDX between the front and rear vehicles when the rear driver begins to notice the low-speed vehicle ahead. Then, in the VISSIM microscopic traffic simulation software, simulation calculation parameters such as ABX, SDX, driving speed range, and acceleration are set according to different seasonal environments. At the entrance of the open-pit mine, the mining efficiency of the open-pit mine is simulated according to the designed vehicles. After the simulation calculation is completed, the number of vehicles output per unit time under the corresponding climatic conditions is monitored at the exit of the transportation road. i represents the number of typical climatic conditions throughout the year. It should be noted that the mining truck driving speed v' and acceleration a' obtained in step S5 under different climatic conditions are closely related to the smaller vehicle speed V of the front and rear vehicles. By configuring the mining truck driving speed v' and acceleration a' into the software and running the simulation, the climatic influence is accurately transmitted and embedded into the microscopic vehicle following behavior, thereby affecting the efficiency of the entire transportation system macroscopically.

[0063] Step S7: Based on the simulation output obtained in step S6, predict the maximum excavation volume of the open-pit mine:

[0064] ;

[0065] In the formula, The maximum annual excavation volume of the open-pit mine during the j-th simulation calculation period; This refers to the vehicle load factor. is the time utilization coefficient; i is the number of typical climate events throughout the year; Let be the number of vehicles output per unit time under the i-th climate condition; Let i be the number of days in the i-th climate type; Let j represent the single vehicle's load capacity, and j be the number of simulation calculation cycles. For example, the number of days with the i-th climate type within the first simulation calculation cycle is denoted as . .

[0066] Step S8: Based on the maximum excavation volume predicted in step S7, determine the optimal mining location in the 3D geological excavation model and update the mining boundary to provide input for the next round of simulation. Specifically, the optimization of the mining location is performed as follows: In the Dimine 3D mining software, using the maximum periodic excavation volume predicted in S7 as the target, and based on parameters such as the location of the excavators, the number of benches being mined simultaneously, and the ore grade distribution, simulate and optimize the mining location sequence that yields the highest economic benefit or the optimal path when mining the required volume (see [link to relevant documentation]). Figure 6 The updating of the mining boundary includes, based on the determined optimal mining location, using the contour mapping function of Dimine 3D mining software to calculate the new pit bottom elevation and slope top line after the mining volume is reached, and generating a mining boundary plan after the mining volume is reached (see...). Figure 7 ), and export it as a dxf format file.

[0067] S9. Iterative Simulation and Calculation of Annual Total Mining Volume:

[0068] The mining boundary plan view generated in S8, representing the end-of-cycle state, is used as the mining status plan view for the next simulation calculation cycle. Then, using the updated mining status plan view as spatial input, steps S3 to S8 are repeated to complete the sequential simulation for m calculation cycles throughout the year. Finally, the predicted excavation volumes for the m calculation cycles throughout the year are summarized to calculate the simulated cumulative excavation volume of the open-pit mine for the entire year.

[0069]

[0070] In the formula, Y represents the total annual excavation volume of the open-pit mine calculated in the simulation.

[0071] S10. Compare the annual total mining volume obtained from the simulation with the design target to optimize resource allocation, specifically including:

[0072] Assuming the designed annual mining volume of the open-pit mine is ,when When the designed resource allocation can meet the mining needs, the relationship between the designed vehicle configuration quantity and the output quantity is compared, and the following formula is used to optimize the number of transport vehicles N:

[0073] ;

[0074] ;

[0075] In the formula, C represents the single-vehicle shift transport capacity; C represents the number of daily work shifts. This is the transportation imbalance coefficient; The vehicle availability rate is calculated to account for factors such as maintenance and drivers; H represents the number of working days per year, and T represents the round-trip time of a vehicle in the simulation model.

[0076] when At this point, the annual mining output of the open-pit mine cannot meet the requirements under the designed resource allocation conditions, necessitating optimization of the resource allocation. Optimization methods include increasing the number or capacity of excavators and shovels. Then, a new round of iterative simulations throughout the year is initiated using the new resource allocation scheme until the simulation result Y meets the Y0 requirement. It should be noted that the resource allocation here refers to the combination, quantity, and capacity of key production equipment required to achieve the predetermined annual mining output.

[0077] 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 equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for simulating and optimizing the efficiency of open-pit mining, characterized in that, The method includes the following steps: Step S1: Use a surveying drone to scan the surface of the open-pit mine, collect surface data of the open-pit mine, and generate a plan view of the current mining status of the open-pit mine. Step S2: Import the current open-pit mining plan into 3D mining software to construct a 3D geological excavation model of the open-pit mine, and design the final mining boundary based on the model; Step S3: Import the current open-pit mine mining plan as the background map into the VISSIM micro-traffic simulation software, construct the transportation road network based on the final mining boundary, and arrange the work points and path framework to realize the construction of the mine transportation simulation model. Step S4: Based on the mine transportation simulation model constructed in step S3, set transportation rules and establish a dynamic numerical model for vehicle transportation. Step S5: Based on the traffic rules set in Step S4 and the established vehicle transportation dynamic numerical model, calculate the speed v' and acceleration a' of the mining truck under different climatic conditions; Step S6: Configure the mining truck speed v' and acceleration a' obtained in Step S5 under different climatic conditions into the mine transportation simulation model of the VISSIM microscopic traffic simulation software, run the simulation, and obtain the number of vehicles output per unit time under the corresponding climatic conditions. Where i represents the number of typical climate events throughout the year; Step S7: Calculate the number of vehicles output per unit time under the corresponding climatic conditions based on the simulation obtained in Step S6. Predict the maximum excavation volume of the open-pit mine; Step S8: Based on the maximum excavation volume predicted by the simulation in step S7, determine the optimal mining location in the three-dimensional geological excavation model, update the mining boundary, generate a mining boundary plan, and complete the optimization of one simulation calculation cycle. Step S9: Use the mining boundary plan generated in step S8 as the mining status plan for the next simulation calculation cycle; then, using the updated mining status plan as spatial input, repeat steps S3 to S8 to complete the sequential simulation for m calculation cycles, and sum up the calculations to obtain the annual mining volume of the open-pit mine. Step S10: Compare the annual total mining volume obtained from the simulation with the design target to optimize resource allocation.

2. The open-pit mine mining efficiency simulation optimization method as described in claim 1, characterized in that, The open-pit mining status plan in step S1 is a digital map file containing complete and accurate digital elevation information.

3. The open-pit mine mining efficiency simulation optimization method as described in claim 1, characterized in that, The setting of traffic rules in step S4 includes initial vehicle configuration settings, vehicle driving rule settings, and road traffic priority rule settings. Among them, the setting of vehicle driving rules includes setting the basic driving speed v and the basic driving acceleration a.

4. The open-pit mine mining efficiency simulation optimization method as described in claim 1, characterized in that, The vehicle transportation dynamic numerical model established in step S4 includes: ； ； ； ； ； In the formula, AX is the expected stopping distance for a stationary vehicle; BX is the expected stopping distance for a moving vehicle; EX is the perception threshold coefficient; ABX is the minimum expected driving distance for the vehicles in front and behind; V is the minimum speed of the vehicles in front and behind; SDX is the distance between the vehicles in front and behind when the driver of the following vehicle begins to pay attention to the low-speed vehicle in front; L is the length of the vehicle in front; AX add AX mult BX add BX mult EX add EX mult To adjust the parameters; RND1(I), RND(I), and RND2(I) are three independent random number parameters, and the values ​​of all three are limited to the interval between 0 and 1; NRND is a normally distributed random number.

5. The open-pit mine mining efficiency simulation optimization method as described in claim 4, characterized in that, In step S7, the maximum excavation volume of the open-pit mine is predicted using the following formula: ； In the formula, The maximum annual excavation volume of the open-pit mine during the j-th simulation calculation period; This refers to the vehicle load factor. is the time utilization coefficient; i is the number of typical climate events throughout the year; Let i be the number of vehicles output per unit time under the i-th climate condition within the j-th simulation calculation cycle; Let i be the number of days with the i-th climate type within the j-th simulation calculation cycle; This refers to the load capacity of a single vehicle.

6. The open-pit mine mining efficiency simulation optimization method as described in claim 5, characterized in that, In step S9, the total annual mining volume of the open-pit mine obtained through simulation is denoted as Y. Step S10's optimization of resource allocation includes: assuming the designed annual mining volume of the open-pit mine is... ,when When the designed resource allocation can meet the mining needs, the relationship between the designed vehicle configuration quantity and the output quantity is compared, and the following formula is used to optimize the number of transport vehicles N: ； ； In the formula, C represents the single-vehicle shift transport capacity; C represents the number of daily work shifts. This is the coefficient for transport imbalance. To account for the impact of maintenance and driver factors on vehicle availability; H represents the number of working days per year, and T represents the turnaround time for a vehicle to make one round trip in the simulation model.

7. The open-pit mine mining efficiency simulation optimization method as described in claim 6, characterized in that, when At this point, the annual mining volume of the open-pit mine under the designed resource allocation conditions cannot meet the requirements, and the resource allocation needs to be optimized. The optimization methods include increasing the number of excavators or increasing the capacity of excavators. Then, a new round of annual iterative simulation is carried out using the new resource allocation scheme until the simulation result Y meets the requirements of Y0.

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