Strip mine mining and loading dust dissipation simulation method and system

Abstract

The invention discloses a simulation method and system for dust dissipation during mining and loading of a strip mine. The method specifically comprises the following steps: collecting dust concentration data, micro-meteorological parameter data and equipment operation parameter data; performing space-time alignment and quality verification on the dust concentration data, the micrometeorological parameter data and the equipment operation parameter data to form a time-synchronous fusion data set; based on the fused data set, a dynamic source intensity model is adopted to calculate real-time dust yield parameters; and based on the real-time dust production parameter and the three-dimensional terrain model, dust diffusion simulation is carried out by adopting a computational fluid mechanics method, and the simulation considers field topographic fluctuation, equipment and obstacle influences of the strip mine. Through deep fusion of multi-source data driving, full-process dynamic source intensity calculation and high-fidelity environment simulation, the accuracy of dust dissipation simulation is remarkably improved, and a reliable scientific basis is provided for formulating accurate and efficient strip mine dust prevention measures.

Description

Technical Field

[0001] This invention relates to the field of multi-source data simulation technology, and in particular to a method and system for simulating dust dispersion during open-pit mining. Background Technology

[0002] During open-pit mining operations, the mechanical actions involved in loading, transporting, and unloading ore generate significant amounts of dust pollutants. This dust not only reduces workplace visibility and impacts production efficiency but also poses serious threats to the respiratory health of workers, with long-term exposure potentially leading to occupational diseases such as pneumoconiosis. Furthermore, dust emissions into the surrounding environment cause air pollution and negatively impact the quality of the ecological environment. With increasingly stringent environmental protection requirements, effectively controlling dust pollution from open-pit mining has become a critical issue that the industry urgently needs to address.

[0003] Currently, research on dust emission simulation in open-pit mines has been conducted to some extent both domestically and internationally, and various simulation methods have been proposed. However, significant shortcomings still exist in practical applications, mainly reflected in insufficient simulation accuracy. Specifically: First, existing methods mostly use static parameters and empirical formulas to calculate dust generation; second, in the diffusion simulation process, the complex terrain undulations of the mining area, the obstruction and disturbance effects of large mobile equipment (such as electric shovels and mining trucks), and the influence of local microclimates are often ignored, simplifying the boundary conditions and causing the simulation results to deviate significantly from the actual situation.

[0004] The aforementioned shortcomings make it difficult for simulation results to accurately reflect the spatiotemporal distribution patterns of dust. Consequently, dust control measures developed based on these results lack specificity, leading to problems such as unreasonable placement of dust suppression equipment, unscientific distribution of spray water, and poor optimization of operational procedures. This results in both resource waste and an inability to effectively control dust pollution. Therefore, providing a method that can accurately simulate the dust dispersion process during open-pit mining is of significant practical importance for improving the precision and effectiveness of dust control measures and protecting human health and the ecological environment. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for simulating dust emission during open-pit mining, which effectively solves the problems existing in traditional open-pit mining dust emission simulation methods, improves the accuracy of open-pit mining dust emission simulation, and solves at least one of the aforementioned prior art problems.

[0006] In a first aspect, the present invention provides a method for simulating dust dispersion during open-pit mining, the method specifically comprising: Collect dust concentration data, micro-meteorological parameter data, and equipment operating parameter data; perform spatiotemporal alignment and quality verification on the dust concentration data, micro-meteorological parameter data, and equipment operating parameter data to form a time-synchronized fused dataset; Based on the fused dataset, the real-time dust generation parameters are calculated using a dynamic source strength model; Based on the real-time dust generation parameters and the three-dimensional terrain model, a computational fluid dynamics method is used to simulate dust diffusion. The simulation takes into account the topographic undulations, equipment, and obstacles in the open-pit mine.

[0007] Secondly, the present invention provides a dust emission simulation system for open-pit mining, the system specifically comprising: The first module is used to collect dust concentration data, micro-meteorological parameter data, and equipment operation parameter data; and to perform spatiotemporal alignment and quality verification on the dust concentration data, micro-meteorological parameter data, and equipment operation parameter data to form a time-synchronized fused dataset. The second module is used to calculate the real-time dust generation parameters based on the fused dataset using a dynamic source strength model; The third module is used to simulate dust diffusion using computational fluid dynamics based on the real-time dust generation parameters and the three-dimensional terrain model. The simulation takes into account the topographic undulations, equipment, and obstacles in the open-pit mine.

[0008] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, and a computer program stored in the memory, wherein when the computer program is executed on the processor, it implements the open-pit mine dust emission simulation method as described in any of the above methods.

[0009] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the open-pit mine dust emission simulation method as described in any of the above methods.

[0010] Compared with the prior art, the present invention has at least one of the following technical effects: 1. This invention solves to some extent the problems existing in the traditional open-pit mine dust emission simulation method and improves the accuracy of dust emission simulation.

[0011] 2. This invention obtains a highly reliable and timely fused dataset by acquiring multi-source data and implementing spatiotemporal alignment and quality verification. This step ensures the accuracy and completeness of the input data required for subsequent simulations, avoiding simulation deviations caused by missing or distorted data from the source, and laying a solid foundation for accurate simulations.

[0012] 3. The dynamic source strength model of this invention incorporates factors such as the nonlinear influence index of material moisture content, height attenuation coefficient, and wind direction angle, which is more in line with actual scenarios and improves the accuracy of simulation.

[0013] 4. This invention acquires data using various modern surveying technologies such as LiDAR and UAVs, ensuring the timeliness and high accuracy of terrain data, which is the foundation for accurate simulation. The simulation content not only includes natural terrain (digital elevation model) but also explicitly includes all key obstacles (such as equipment and material piles). By transforming the computational domain of CFD simulation from an "empty field" to a "real field," the simulation can realistically reproduce physical phenomena such as airflow encountering equipment and experiencing flow around, deceleration, and vortex generation. Unstructured meshes and local refinement strategies, while ensuring computational efficiency, accurately capture the initial diffusion conditions near dust generation points, the complex flow field structure around equipment and obstacles, and airflow deformation at terrain changes, providing a high-quality mesh foundation for subsequent high-precision CFD calculations.

[0014] 5. The dynamic source strength model for loading operations in this invention breaks through the traditional rough calculation that relies solely on bucket volume, and introduces the influence of material moisture content, which is more in line with reality; it also introduces insertion speed, bucket cross-sectional area and key raw ore particle size parameters, which more accurately describe the dust mechanism generated by mechanical crushing.

[0015] 6. The dynamic source strength model for unloading operations of the present invention is based on the theory of gravity impact, calculates the drop in real time, and directly correlates the dust source strength with the drop and unloading flow rate to improve the calculation accuracy; it also considers the influence of wind direction angle to improve the simulation accuracy.

[0016] 7. This invention uses the calculated high-precision real-time dust generation parameters as the key dust source term boundary conditions, and employs computational fluid dynamics methods for simulation based on a three-dimensional terrain model. It fully considers the actual terrain undulations of the mining area and the obstruction, turbulence, and accumulation effects of equipment obstacles on the airflow field and dust diffusion path, overcoming the shortcomings of traditional models that simplify the environment to uniform or ideal conditions, thereby greatly improving the accuracy of dust emission prediction. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of a method for simulating dust dispersion during open-pit mining, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of an open-pit mine dust emission simulation system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0019] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0020] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0021] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0022] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0023] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0025] Existing research on dust emission simulation methods in open-pit mines has significant shortcomings in practical applications, primarily in its insufficient simulation accuracy. First, existing methods mostly rely on static parameters and empirical formulas to calculate dust generation, failing to fully consider the dynamic changes in material moisture content, ore and rock physical properties, and the real-time impact of equipment operating parameters (such as unloading height and speed), leading to large errors in source strength calculation. Second, during diffusion simulations, the complex terrain undulations of the mining area, the obstruction and disturbance effects of large mobile equipment (such as electric shovels and mining trucks), and the influence of local microclimates are often ignored, simplifying boundary conditions and causing significant deviations between simulation results and actual conditions. Furthermore, existing models often lack effective fusion and feedback correction mechanisms for multi-source monitoring data, making adaptive parameter optimization impossible.

[0026] The aforementioned deficiencies make it difficult for simulation results to accurately reflect the spatiotemporal distribution patterns of dust. Consequently, dust control measures developed based on these results lack specificity, leading to problems such as unreasonable placement of dust suppression equipment, unscientific distribution of spray water, and poor optimization of operational procedures. This results in both resource waste and an inability to effectively control dust pollution. Therefore, this application provides a method for simulating dust dispersion in open-pit mines, aiming to solve at least one of the aforementioned technical problems and improve the accuracy of the simulation method.

[0027] In this embodiment, the entity executing the process includes a terminal device. This terminal device includes, but is not limited to, servers, computers, and other devices capable of executing the methods disclosed in this application. Figure 1 A flowchart illustrating a method for simulating dust dispersion in open-pit mining, according to an embodiment of the present invention, is shown below in detail: S100: Collect dust concentration data, micro-meteorological parameter data, and equipment operation parameter data; perform spatiotemporal alignment and quality verification on the dust concentration data, micro-meteorological parameter data, and equipment operation parameter data to form a time-synchronized fused dataset.

[0028] Step S100 enables real-time acquisition and fusion of multi-dimensional data. By acquiring multi-source data and implementing spatiotemporal alignment and quality verification, a highly reliable and timely fused dataset is obtained. This step ensures the accuracy and completeness of the input data required for subsequent simulations, avoiding simulation deviations caused by missing or distorted data from the source, and laying a solid foundation for accurate simulations. Specifically, multi-sensor arrays can be deployed at key locations on equipment such as loading and transportation equipment to facilitate the acquisition of multi-source data from the field. For example, multiple monitoring points can be deployed on the top of the electric shovel bucket, unloading point, and cab, with each monitoring point integrating dust concentration, micro-meteorological (wind speed, wind direction, temperature and humidity), and vibration sensors. The types of sensors can be selected as 5G intrinsically safe mining transmission equipment with a data latency of less than 100ms. This multi-point synchronous monitoring eliminates the random errors of single-point measurements. The equipment's operating parameters / operating status (loading, rotation, unloading, transportation) provide a time-series reference for source strength calculation.

[0029] For example, the sensor array includes: An explosion-proof dust concentration sensor is installed at the bucket of an electric shovel. Vibration sensors installed on the boom of the electric shovel are used to identify the operating status; A miniature weather station mounted on top of the electric shovel measures wind speed, wind direction, temperature, and humidity. UWB positioning devices can track device locations in real time.

[0030] Other equipment in open-pit mines, such as mining trucks, can also employ explosion-proof dust concentration sensors, vibration sensors, and miniature weather stations, as well as UWB positioning devices, as shown in this example. The specific locations can be flexibly determined based on the equipment; this embodiment does not impose any restrictions.

[0031] In this embodiment, when selecting various sensors, appropriate explosion-proof designs can be chosen to match the application environment of open-pit mines; at the same time, appropriate seismic-resistant designs can be selected to adapt to the operating vibrations of the equipment, such as selecting a seismic resistance level of 8 or higher. For example, the operating parameters of the equipment can be used to identify the operating state, such as using vibration data to identify whether the operating state is loading, rotating, or unloading.

[0032] S200, based on the fused dataset, the real-time dust generation parameters are calculated using a dynamic source strength model.

[0033] Step S200 calculates the dynamic dust source strength, obtaining the real-time dust generation. Because a dynamic source strength model is used, considering multiple influencing factors, and combined with the fusion dataset provided in step S100, the calculated real-time dust generation parameters can more accurately reflect the actual dust generation patterns and mechanisms.

[0034] For example, the dynamic source strength model incorporates factors such as the nonlinear influence index of material moisture content, the height attenuation coefficient, and the wind direction angle, which is more in line with the actual scenario and improves the accuracy of the simulation.

[0035] S300, based on the real-time dust generation parameters and the three-dimensional terrain model, uses computational fluid dynamics to simulate dust diffusion. The simulation takes into account the undulations of the open-pit mine terrain, the influence of equipment and obstacles.

[0036] Step S300 combines a 3D terrain model to adapt to complex terrain conditions and uses computational fluid dynamics (CFD) methods for simulation. This step fully considers the influence of the actual terrain undulations and equipment obstacles on the airflow field and dust diffusion path (e.g., obstruction, turbulence, and accumulation effects), overcoming the drawbacks of traditional models that simplify the environment to uniform or ideal conditions, thereby greatly improving the accuracy of dust emission simulation.

[0037] For example, the dust emission simulation results may include predictions of dust diffusion paths, ranges, and spatial distribution of concentrations.

[0038] In this embodiment, step S100 provides accurate input to S200, and S200 provides accurate source terms to S300, systematically improving the accuracy of dust emission simulation from three levels: data foundation, model mechanism, and environmental reproduction.

[0039] In some embodiments, the step of performing spatiotemporal alignment and quality verification on the dust concentration data, micrometeorological parameter data, and equipment operating parameter data to form a time-synchronized fused dataset includes: Kalman filtering and time series alignment algorithm are used to fuse the dust concentration data, micrometeorological parameter data, and equipment operation parameter data; A data quality assessment model is established to automatically identify and remove outliers, thereby achieving data fusion and cleaning to obtain the fused dataset.

[0040] In this embodiment, Kalman filtering and time series alignment algorithms are used to fuse sensor data from different frequencies and sources. A dust data quality assessment model can also be established to automatically identify and remove outliers, thereby achieving data fusion and cleaning to obtain a time-synchronized fused dataset.

[0041] In some embodiments, in step S200 above, the dynamic source strength model includes a loading operation dynamic source strength model, a slewing operation dynamic source strength model, an unloading operation dynamic source strength model, and a transportation operation dynamic source strength model. The parameters introduced in the dynamic source strength model of the loading operation include: loading dust generation coefficient, material moisture content, moisture content influence index, material density, bucket cross-sectional area, bucket insertion speed, initial particle size and particle size attenuation coefficient, wherein the particle size attenuation coefficient is used to characterize the difficulty of particle refinement. The parameters introduced in the slewing operation dynamic source strength model include: slewing stripping coefficient, material volume in bucket, bucket slewing linear velocity, wind speed perpendicular to the bucket sidewall, angle between wind direction and bucket sidewall normal, slewing duration and surface material depletion time constant, wherein the surface material depletion time constant is used to characterize the reduction rate of surface fine particles that can be blown off. The parameters introduced in the dynamic source strength model for unloading operations include: dust generation coefficient during unloading, moisture content influence index during unloading, unloading drop, unloading flow rate, wind direction influence coefficient, and the angle between the wind direction and the normal to the unloading impact surface. The parameters introduced in the dynamic source strength model for transportation operations include: road dust coefficient, moisture content influence index in the transportation process, vehicle speed, vehicle load, dust threshold wind speed, and wind speed at road surface height.

[0042] In this embodiment, the dynamic source strength model for loading operations breaks through the traditional coarse calculation relying solely on bucket volume. It introduces insertion speed, bucket cross-sectional area, and key raw ore particle size parameters, thus more accurately describing the dust mechanism generated by mechanical crushing. The dust generation coefficient during loading is related to the hardness and brittleness of the rock and ore. Furthermore, the dust generation coefficient can be set to be dynamically updated, achieved through machine learning using historical data, thereby adapting to different ore and rock regions. Additionally, the material moisture content can be set to be measured in real-time by a near-infrared sensor to obtain accurate real-time data.

[0043] In this embodiment, the dynamic source strength model for slewing operations quantifies the coupling effect between slewing speed and relative wind speed, and introduces a time decay factor to simulate the gradual reduction of dust-bearing surface material during slewing, thus avoiding the error of constant source strength throughout the entire slewing path.

[0044] In this embodiment, the dynamic source strength model for unloading operations integrates parameters such as unloading drop and unloading flow rate, thereby improving the accuracy of dust generation calculation.

[0045] In this embodiment, the dynamic source intensity model for transportation operations incorporates a dust-generating threshold wind speed parameter. Dust is generated only when the wind speed exceeds the threshold, which is more in line with physical reality. Simultaneously considering vehicle speed and load, two key operational parameters, it can simulate moving line sources directly related to operational intensity, providing a possibility for accurately simulating dust diffusion on transportation routes.

[0046] In some embodiments, the dynamic source strength model for loading operations is: , in, Let t be the dust generation rate during shoveling (kg / s); The dust generation coefficient during loading; The moisture content of the material has a value range of (0-1). The moisture content influence index; Material density (kg / m³) 3 ); The cross-sectional area of ​​the bucket (m²) 2 ); The bucket insertion speed (m / s); The initial average particle size of the material (m); The particle size attenuation coefficient characterizes the difficulty of particle refinement.

[0047] It is related to the hardness and brittleness of the ore and rock, and can be dynamically updated through machine learning using historical data; Dynamic calibration is possible; It can be determined through experiments.

[0048] This model is used to calculate the dust generated when the bucket of an electric shovel or hydraulic shovel is inserted into and excavates materials. It breaks through the traditional rough calculation that only relies on the bucket volume and introduces the nonlinear effect of the material moisture content (α exponent), which is more in line with reality. It also introduces insertion speed, bucket cross-sectional area and key raw ore particle size parameters to more accurately describe the dust mechanism generated by mechanical crushing. Its dynamic updates enable it to adapt to different mineral and rock regions.

[0049] In some embodiments, the dynamic source strength model for slewing operations is: , in, Let t be the dust generation rate at time t (kg / s); The volume of material in the bucket (m³) 3 ); The linear velocity of the bucket rotation (m / s); The wind speed perpendicular to the side wall of the bucket (m / s); The angle (°) between the wind direction and the normal to the side wall of the bucket; The duration of the rotation (s); The surface material depletion time constant represents the rate at which the surface fine particles that can be blown off decrease.

[0050] in, It can be extracted from micro-weather station data.

[0051] This model is used to calculate the dust generated by wind stripping during the process of a fully loaded bucket rotating to the unloading point. It quantifies the coupling effect between rotation speed and relative wind speed and introduces a time decay factor to simulate the process of the dust-bearing surface material gradually decreasing during rotation. This avoids the error of constant source strength throughout the entire rotation path, i.e., it avoids uniformity error.

[0052] In some embodiments, the dynamic source strength model for the unloading operation is: , in, Let t be the dust generation rate at time t (kg / s); The dust generation coefficient during unloading. The moisture content of the material is in the range of (0-1). The index representing the impact of moisture content during the unloading process; Material density (kg / m³) 3 ); Acceleration due to gravity (m / s²) 2 ); The discharge drop (m); Discharge flow rate (kg / s); This refers to the wind direction influence coefficient. The angle (°) is the angle between the wind direction and the normal to the unloading impact surface.

[0053] in, It can be updated dynamically. It can be calculated in real time using the positioning system and the stockpile elevation model. It can be calculated based on the bucket volume and the opening speed of the unloading gate.

[0054] This model is used to calculate the dust generated by the impact and fall of materials when the bucket unloads them onto the mine car. Based on the theory of gravity impact, the model calculates the drop height H in real time, directly linking the dust source strength with the drop height (H) and the unloading flow rate (W) to improve calculation accuracy; it also considers the influence of wind direction angle to improve simulation precision.

[0055] In some embodiments, the dynamic source strength model for transportation operations is: , in, The intensity of line source dust generation at location x on the road (kg / (s·m)); This refers to the road dust coefficient. The moisture content of the material; The moisture content impact index during transportation; The vehicle's speed (m / s); Vehicle load capacity (kg); The dust-generating threshold wind speed (m / s); The wind speed (m / s) at road surface height. Let be the Dirac function, indicating that the source strength is concentrated at the location of the vehicle. .

[0056] This model is used to calculate dust generated by loaded mining trucks traveling on roads due to road bumps and wind. It introduces a dust-generating threshold wind speed; dust is only generated when the wind speed exceeds this threshold, which is more in line with physical reality. Simultaneously considering two key operational parameters—vehicle speed and load—it can simulate moving line sources directly related to operational intensity, making it possible to accurately simulate dust dispersion on transportation roads.

[0057] In the above embodiments, the model parameters are flexibly adapted to different mineral and rock materials (coal, iron ore, copper ore, etc.), taking into account the common material moisture content changes (0%-25%) in open-pit mines and adapting to various unloading height (1.5-4.0m) conditions. At the same time, factors such as wind direction and angle are also taken into account, so it can better adapt to open-pit mine scenarios.

[0058] In some embodiments, the method for constructing the three-dimensional terrain model includes: High-precision topographic point cloud data of open-pit mines can be obtained through airborne lidar scanning, UAV photogrammetry, or total station measurement. The terrain point cloud data is preprocessed to generate a digital elevation model. The preprocessing includes denoising, filtering, and interpolation. Based on mining and stripping engineering plans or real-time positioning data, establish three-dimensional geometric models of equipment and obstacles; The digital elevation model is fused with the three-dimensional geometric model to construct a comprehensive three-dimensional scene model that includes terrain undulations and obstacles; The comprehensive 3D scene model is divided into computational meshes, and unstructured meshes are used for discretization. Local mesh refinement is performed near dust sources, around obstacles, and in areas with large terrain gradient changes.

[0059] In this embodiment, data is acquired using various modern surveying technologies such as LiDAR and UAVs, ensuring the timeliness and high accuracy of the terrain data, which is the foundation for accurate simulation. The simulation content not only includes natural terrain (digital elevation model) but also explicitly includes all key obstacles (such as equipment and material piles). This step transforms the computational domain of the CFD simulation from an "empty field" to a "real field," enabling the simulation to realistically reproduce physical phenomena such as airflow encountering equipment and causing flow around, deceleration, and vortex generation. Unstructured meshes and local refinement strategies are used to accurately capture the initial diffusion conditions near dust generation points, the complex flow field structure around equipment and obstacles, and airflow deformation at terrain changes, while ensuring computational efficiency. This provides a high-quality mesh foundation for subsequent high-precision CFD calculations.

[0060] In some embodiments, the dust diffusion simulation using computational fluid dynamics methods includes: The real-time dust generation parameter is set as a dust source term at the corresponding source strength position in the integrated three-dimensional scene model; A terrain surface was constructed using digital elevation model data, and no-slip boundary conditions were applied to the terrain surface. The wall function method was used to handle the near-surface boundary layer effect. Define the boundary conditions for the simulation, including: setting wind speed and direction boundary conditions at the inlet of the computational domain based on real-time meteorological data, setting the ground and equipment surfaces as non-slip wall boundary conditions, and setting the top and outlet of the computational domain as pressure outlet boundary conditions. Choose either the k-ε turbulence model or the Reynolds stress model to close the governing equations and solve the three-dimensional steady-state flow field based on the Reynolds-averaged Navier-Stokes equations; Based on the three-dimensional steady-state flow field, the discrete phase model or the Eulerian-Lagrange method is used to calculate the motion trajectory and diffusion process of dust particles under the influence of terrain undulations and obstacles defined by the integrated three-dimensional scene model, and finally output the spatiotemporal distribution results of dust concentration.

[0061] In this embodiment, the dust source term in the CFD simulation is directly derived from the previously calculated real-time dust generation parameters. This establishes a crucial data link from the dynamic source strength model to the diffusion simulation, ensuring that the source input for the simulation is accurate and dynamically changing, rather than a fixed estimate. The entire simulation process is conducted within the comprehensive 3D scene model established in the preceding steps. This comprehensive 3D scene model defines the geometric space for computation, in which terrain undulations directly affect the overall structure of the flow field (such as valley wind effects), and equipment and obstacles directly determine the local details of the flow field (such as flow around and wake). The flow field calculated by the solver on this model is a high-fidelity flow field that incorporates all these influences. The boundary condition setting of the ground and equipment surfaces as no-slip walls is a mathematical guarantee that the flow field can respond to terrain and obstacles. The dust particle tracking calculation is performed in this realistic flow field, so its diffusion path will naturally bypass equipment, move along the terrain slope, or accumulate on the leeward side of obstacles, thus the final output concentration distribution result truly reflects the impact of the complex environment of the open-pit mine on dust diffusion.

[0062] In some embodiments, setting the real-time dust generation parameter as a dust source term at the corresponding source strength location in the integrated 3D scene model includes: Determine the current operating status of the device based on the fused dataset; The corresponding dynamic source strength model is invoked based on the job status; Real-time dust generation parameters calculated based on the source strength model; Based on the real-time dust generation parameters and the corresponding physical state of the equipment, the dust source items are set at the corresponding source strength positions in the comprehensive three-dimensional scene model.

[0063] In this embodiment, the real-time dust generation parameter is not a single value, but a source strength distribution function that evolves in time and space. The core of its calculation lies in dynamically calling and fusing the sub-model outputs of the corresponding operating stage based on the real-time operating status of the equipment. Based on the fused dataset provided in the preceding steps (such as equipment vibration spectrum, GPS / BeiDou location, attitude angle, motor current, video image recognition results, etc.), a pre-trained classifier (such as a model based on logistic regression, support vector machine, or lightweight neural network) can determine in real time which operating stage the equipment is currently in (loading, rotation, unloading, transportation). Based on the identified operating status, the corresponding dynamic source strength model is called for calculation: If it is identified as a shovel-loaded state, then calculate... ; If it is identified as a rotation state, then calculate... ; If the status is identified as unloading, then calculate... ; If identified as being in transit, then calculate... , where x is the real-time position coordinate of the vehicle.

[0064] The calculated real-time dust generation parameters and their corresponding physical states (such as position, velocity, and direction) are used as source terms to provide accurate input for CFD simulation. For example: for electric shovels, the real-time dust generation parameters are assigned to the current position of the shovel bucket in the 3D model; for mining trucks, the real-time dust generation parameters... It is defined as a line source or point source that moves along the road and varies in intensity.

[0065] For complex scenarios where multiple devices operate simultaneously, the system independently performs the aforementioned identification and calculation for each device and superimposes the source strength contributions of all devices into the computational domain of the CFD simulation.

[0066] The steps involved in this embodiment serve as a bridge connecting the dynamic source strength model and high-precision CFD simulation. Through intelligent state recognition and dynamic model invocation, the outputs of the four sub-models are transformed into source term boundary conditions with high spatiotemporal resolution required by the CFD solver, thereby systematically and fundamentally improving the realism and accuracy of the final dust diffusion simulation results.

[0067] Reference Figure 2 An embodiment of the present invention provides a simulation system 2 for dust emission during open-pit mining, wherein the system 2 specifically includes: The first module is used to collect dust concentration data, micro-meteorological parameter data, and equipment operation parameter data; and to perform spatiotemporal alignment and quality verification on the dust concentration data, micro-meteorological parameter data, and equipment operation parameter data to form a time-synchronized fused dataset. The second module is used to calculate the real-time dust generation parameters based on the fused dataset using a dynamic source strength model; The third module is used to simulate dust diffusion using computational fluid dynamics based on the real-time dust generation parameters and the three-dimensional terrain model. The simulation takes into account the topographic undulations, equipment, and obstacles in the open-pit mine.

[0068] It is understandable that, such as Figure 1 The content of the open-pit mine dust emission simulation method embodiments shown are all applicable to the embodiments of this system. The specific functions implemented in the embodiments of this system are the same as those shown in the figure. Figure 1 The simulation method for dust emission from open-pit mining shown is the same as the one described above, and the beneficial effects achieved are the same as those described above. Figure 1 The beneficial effects achieved by the open-pit mine dust emission simulation method shown in the embodiment are the same.

[0069] Another embodiment of the present invention provides a computer device, including: a memory and a processor, and a computer program stored in the memory, wherein when the computer program is executed on the processor, it implements the open-pit mine dust emission simulation method as described in any of the above methods.

[0070] It is understandable that, such as Figure 1 The content of the open-pit mine dust emission simulation method embodiment shown is applicable to this embodiment, and the specific functions implemented in this embodiment are the same as those shown in the example. Figure 1 The simulation method for dust emission from open-pit mining shown is the same as the one described above, and the beneficial effects achieved are the same as those described above. Figure 1 The beneficial effects achieved by the open-pit mine dust emission simulation method shown in the embodiment are the same.

[0071] Another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the open-pit mine dust emission simulation method as described in any of the above methods.

[0072] It is understandable that, such as Figure 1 The content of the open-pit mine dust emission simulation method embodiment shown is applicable to this embodiment, and the specific functions implemented in this embodiment are the same as those shown in the example. Figure 1 The simulation method for dust emission from open-pit mining shown is the same as the one described above, and the beneficial effects achieved are the same as those described above. Figure 1 The beneficial effects achieved by the open-pit mine dust emission simulation method shown in the embodiment are the same.

[0073] It should be noted that the information interaction and execution process between the above systems are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0074] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0075] Reference Figure 3 The present invention also provides a computer device 3, including: a memory 302 and a processor 301, and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, it implements the open-pit mine dust emission simulation method as described in any of the above methods.

[0076] The computer device 3 may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will understand that... Figure 3 The computer device 3 is merely an example and does not constitute a limitation on the computer device 3. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0077] The processor 301 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0078] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may be an external storage device of the computer device 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 3. Furthermore, the memory 302 may include both internal and external storage units of the computer device 3. The memory 302 is used to store the operating system, applications, boot loader, data, and other programs, such as the program code of the computer program. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0079] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the open-pit mine dust emission simulation method as described in any of the above methods.

[0080] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0081] 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.

[0082] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0083] In the embodiments disclosed in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0084] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

Claims

1. A method of simulating dust dispersion from a surface mine, characterised by, The method specifically comprises: Collecting dust concentration data, microclimate parameter data, and equipment operation parameter data; performing space-time alignment and quality checking on the dust concentration data, microclimate parameter data, and equipment operation parameter data to form a time-synchronized fusion data set; Based on the fusion data set, a dynamic source intensity model is used to calculate real-time dust production parameters; Based on the real-time dust production parameters and a three-dimensional terrain model, a computational fluid dynamics method is used to simulate dust diffusion, and the simulation takes into account the terrain undulation, equipment, and obstacles of the open-pit mine site.

2. The method of claim 1, wherein, The space-time alignment and quality checking on the dust concentration data, microclimate parameter data, and equipment operation parameter data to form a time-synchronized fusion data set comprises: Using Kalman filtering and time series alignment algorithm to fuse the dust concentration data, microclimate parameter data, and equipment operation parameter data; Establishing a data quality evaluation model to automatically identify and eliminate outliers to obtain the fusion data set.

3. The method of claim 1, wherein, The dynamic source intensity model comprises a shovel loading operation dynamic source intensity model, a swing operation dynamic source intensity model, a unloading operation dynamic source intensity model, and a transportation operation dynamic source intensity model.

4. The method according to claim 3, characterized in that, The parameters introduced in the shovel loading operation dynamic source intensity model include a shovel loading dust production coefficient, a material moisture content, a moisture content influence index, a material density, a shovel cross-sectional area, a shovel insertion speed, a material initial particle size, and a particle size attenuation coefficient for representing the difficulty of particle refinement; The parameters introduced in the swing operation dynamic source intensity model include a swing stripping coefficient, a material volume in the shovel, a shovel swing linear velocity, a wind speed perpendicular to the shovel side wall, an angle between the wind direction and the normal of the shovel side wall, a swing duration, and a surface material depletion time constant for representing the reduction rate of surface fine particle material that can be blown off; The parameters introduced in the unloading operation dynamic source intensity model include an unloading dust production coefficient, an unloading link moisture content influence index, an unloading drop, an unloading flow rate, a wind direction influence coefficient, and an angle between the wind direction and the normal of the unloading impact surface; The parameters introduced in the transportation operation dynamic source intensity model include a road dust raising coefficient, a transportation link moisture content influence index, a vehicle driving speed, a vehicle load, a dust raising threshold wind speed, and a wind speed at the road surface height.

5. The method of claim 4, wherein, The shovel loading operation dynamic source intensity model is: ， wherein, is the instantaneous dust production rate of the shovel loading (kg / s), and is the real-time dust production parameter of the dynamic source strength model of the shovel loading operation; is the dust production coefficient of the shovel loading; is the moisture content of the material, with a value range of (0-1); is the moisture content influence index; is the material density (kg / m 3 ); is the cross-sectional area of the shovel (m 2 ); is the insertion speed of the shovel (m / s); is the initial average particle size of the material (m); is the particle size attenuation coefficient, representing the difficulty of particle refinement.

6. The method of claim 4, wherein, The unloading operation dynamic source intensity model is: ， wherein, is the unloading dust emission rate (kg / s) at time t, and is the real-time dust emission parameter of the unloading operation dynamic source strength model; is the unloading dust emission coefficient, is the moisture content of the material, ranging from (0-1); is the moisture content influence index of the unloading link; is the material density (kg / m 3 ); is the acceleration due to gravity (m / s 2 ); is the unloading drop (m); is the unloading flow (kg / s); is the wind direction influence coefficient; is the angle between the wind direction and the normal of the unloading impact surface (°).

7. The method of claim 3, wherein, The construction method of the three-dimensional terrain model comprises: Obtaining high-precision terrain point cloud data of the open-pit mine through airborne laser radar scanning, unmanned aerial vehicle photogrammetry, or total station measurement; Performing preprocessing on the terrain point cloud data and generating a digital elevation model, the preprocessing including denoising, filtering, and interpolation; Based on the mining and stripping engineering plan or real-time positioning data, a three-dimensional geometric model of the equipment and obstacles is established; Fusing the digital elevation model and the three-dimensional geometric model to construct a comprehensive three-dimensional scene model containing terrain undulation and obstacles; and The comprehensive three-dimensional scene model is subjected to computational grid division, is discretized by using a non-structured grid, and is subjected to local grid densification near a dust source, around an obstacle, and in a region with large terrain gradient variation.

8. The method of claim 7, wherein, The dust diffusion simulation by using the computational fluid dynamics method comprises: The real-time dust production amount parameter is set as a dust source term at a corresponding source intensity position in the comprehensive three-dimensional scene model. A terrain surface is constructed by using digital elevation model data, a no-slip boundary condition is applied to the terrain surface, and a wall function method is used to process a near-ground boundary layer effect. Boundary conditions for simulation are defined, including setting a wind speed and direction boundary condition at an inlet of a calculation domain based on real-time meteorological data, setting a no-slip wall surface boundary condition for a ground and a device surface, and setting a pressure outlet boundary condition for a top and an outlet of the calculation domain. A k-ε turbulent flow model or a Reynolds stress model is selected to close a control equation set, and a three-dimensional steady flow field based on a Reynolds-averaged Navier-Stokes equation is solved. Based on the three-dimensional steady flow field, a discrete phase model or an Euler-Lagrange method is used to calculate a motion trajectory and a diffusion process of dust particles under the influence of terrain undulations and obstacles defined in the comprehensive three-dimensional scene model, and finally a time-space distribution result of a dust concentration is output.

9. The method of claim 8, wherein, The real-time dust production amount parameter is set as a dust source term at a corresponding source intensity position in the comprehensive three-dimensional scene model. The current operation state of the device is determined according to the fusion data set. A corresponding dynamic source intensity model is called according to the operation state. A real-time dust production amount parameter is calculated according to the source intensity model. The real-time dust production amount parameter and a physical state corresponding to the device are set as a dust source term at a corresponding source intensity position in the comprehensive three-dimensional scene model.

10. An open pit mine extraction dust dispersion simulation system characterized by, The system specifically comprises: A first module is configured to collect dust concentration data, micro-meteorological parameter data, and device operation parameter data, perform time-space alignment and quality check on the dust concentration data, the micro-meteorological parameter data, and the device operation parameter data, and form a time-synchronized fusion data set. A second module is configured to calculate a real-time dust production amount parameter by using a dynamic source intensity model based on the fusion data set. A third module is configured to perform dust diffusion simulation by using a computational fluid dynamics method based on the real-time dust production amount parameter and a three-dimensional terrain model, and the simulation considers the influence of terrain undulations, devices, and obstacles in a surface mine.

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