An intelligent dust reduction system for open-pit coal mines based on multi-source data fusion

Abstract

This invention discloses an intelligent dust suppression system for open-pit coal mines based on multi-source data fusion, belonging to the field of open-pit coal mine dust control technology. It includes: a data acquisition system for collecting multi-source data from various dust-generating processes in the open-pit coal mine, including space-based, air-based, and ground-based observation data; a dust mass concentration calculation and prediction system for calculating the dust generation intensity of each production operation in the open-pit coal mine based on the multi-source data collected by the data acquisition system, predicting the dust mass concentration, and generating a control strategy based on the calculation and prediction results of the dust mass concentration calculation and prediction system; and a dust suppression system for realizing intelligent dust suppression in the open-pit coal mine according to the control strategy. The system of this invention can not only predict the dust diffusion range during open-pit coal mining in real time, but also realize remote intelligent linkage control, which helps protect the ecological environment and the health of workers.

Description

Technical Field

[0001] This invention relates to the field of open-pit coal mine dust control technology, and in particular to an intelligent dust suppression system for open-pit coal mines based on multi-source data fusion. Background Technology

[0002] During coal mining, operations such as tunneling, transportation, and loading generate a large amount of coal dust and silica dust. If the dust concentration exceeds the safety threshold, it will not only seriously endanger the health of workers and induce occupational diseases such as pneumoconiosis, but may also increase the risk of major safety accidents such as gas explosions and coal dust explosions.

[0003] The existing dust suppression spray equipment commonly equipped in mines mostly adopts traditional control methods based on fixed time intervals or fixed areas. It lacks the ability to dynamically respond to actual working conditions and cannot dynamically adjust according to the dust diffusion trend, resulting in low dust suppression efficiency, serious waste of water resources, and even local overspray or spray blind spots. It is difficult to meet the needs of modern coal mines for green, safe and efficient governance. Summary of the Invention

[0004] This invention provides an intelligent dust suppression system for open-pit coal mines based on multi-source data fusion, which solves the technical problems of existing technologies lacking dynamic response capability to actual working conditions, being unable to dynamically adjust according to dust diffusion trends, resulting in low dust suppression efficiency, serious water waste, and even local overspraying or spray blind spots, making it difficult to meet the technical needs of modern coal mines for green, safe, and efficient governance.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: An intelligent dust suppression system for open-pit coal mines based on multi-source data fusion includes: The data acquisition system is used to collect multi-source data from various dust-generating processes in open-pit coal mines; wherein, the multi-source data includes space-based observation data, air-based observation data, and ground-based observation data; The dust mass concentration calculation and prediction system is used to calculate the dust generation intensity of each production operation in an open-pit coal mine based on multi-source data collected by the data acquisition system, and to predict the dust mass concentration; and to generate control strategies based on the dust generation intensity calculation results and the dust mass concentration prediction results. The dust suppression system is used in conjunction with the dust mass concentration calculation and prediction system to achieve intelligent dust suppression in open-pit coal mines based on the control strategy generated by the dust mass concentration calculation and prediction system.

[0006] Furthermore, the data acquisition system includes a space-based observation module, an air-based observation module, and a ground-based observation module; The space-based observation module includes a meteorological satellite data receiving and parsing module, used to acquire space-based observation data covering the mining area; The airborne observation module includes a lidar, particle measurement sensor, and high-definition visible light and infrared imaging equipment carried by the UAV, used to acquire airborne observation data; The ground-based observation module includes environmental monitoring stations deployed in the dust source area and a fixed sensor array; wherein, the fixed sensor array includes wind speed sensors, temperature sensors, humidity sensors, and air pressure sensors. Sensors, C Sensors, CO sensors, CO2 sensors, NO x Sensors, including PM2.5 / PM10 dust concentration sensors, are used to acquire ground-based observation data.

[0007] Furthermore, the space-based observation data includes macro-meteorological parameters of the mine provided by meteorological satellites; wherein, the macro-meteorological parameters include temperature, humidity, wind speed, wind direction, and solar radiation intensity; The airborne observation data includes local dust concentration distribution, wind field and flow field changes, and operational dynamics information in the mining area, obtained by using dust sensors and aerial remote sensing equipment mounted on UAVs. The ground-based observation data includes microscopic operating conditions and emission data acquired by sensor arrays deployed in the dust source area and mechanical equipment operation status acquisition devices.

[0008] Furthermore, the dust mass concentration calculation and prediction system includes a data fusion module, a prediction analysis module, and a control execution module; wherein, The data fusion module is used to perform multi-scale and multi-dimensional collaborative fusion of multi-source data; The predictive analysis module is used to construct a dust mass concentration prediction model based on a hybrid modeling framework that combines physical and data-driven approaches. It uses each dust-generating process and its physical processes in an open-pit coal mine as a physical entity model, and combines a preset machine learning algorithm to construct a dust mass concentration prediction model. By introducing the preset model, it describes the dust diffusion, settling and secondary dust emission processes. It trains the dust mass concentration prediction model with multi-source data monitored in history, so that it learns the nonlinear relationship between multi-source data and dust mass concentration, and predicts the dust mass concentration based on multi-source data. The control execution module is used to generate an optimal control strategy based on the dust mass concentration output by the predictive analysis module, combined with preset environmental thresholds, pollution warning standards, and operational safety requirements. The optimal control strategy is then transmitted to the dust suppression system in real time using wireless communication and industrial Internet of Things technologies. The module also monitors the implementation effect of the control strategy in real time through a closed-loop feedback mechanism and dynamically adjusts and optimizes the control strategy.

[0009] Furthermore, the multi-scale, multi-dimensional collaborative fusion of multi-source data includes: The data is standardized through spatiotemporal registration technology, and noisy data is removed and data integrity is ensured through data cleaning and outlier detection methods. Key factors related to dust concentration changes are identified through multivariate feature extraction and dimensionality reduction methods. Then, Bayesian inference or Kalman filtering data assimilation technology is used to achieve optimal integration of multi-source data, so as to output a fusion dataset that can be used to drive the dust mass concentration prediction model.

[0010] Furthermore, the preset machine learning algorithm is random forest, support vector machine or deep neural network.

[0011] Furthermore, by introducing a pre-defined model, the dust diffusion, settling, and secondary dust re-entrainment processes are described, including: The dust diffusion, settling, and secondary dust re-entrainment process is described using a Gaussian diffusion model, specifically as follows: Calculate the dust generation intensity during drilling operations G 1. The formula is: ; In the formula, K a This is a correction factor used to correct for influencing factors under different operating conditions; K 1 represents the drilling rig type coefficient, reflecting the impact of different drilling rigs on dust generation intensity; N 1 represents the amount of drilling work per unit time; Calculate the dust generation intensity of blasting operations G 2. The formula is: ; In the formula, C This refers to the concentration of dust from blasting plumes in mines. V 2 represents the total volume of smoke plumes produced daily by explosions; Calculate the dust generation intensity of mining and loading operations G 3. The formula is: ; In the formula, P 3 represents the dust emission coefficient per truck; N 3 represents the number of vehicles used for mining and loading operations; Calculate the dust generation intensity of transportation operations G 4. The formula is: ; In the formula, V 4 represents the car's speed; P 4 represents the amount of dust on the road surface; M 4 represents the weight of the vehicle during transportation operations; N 4 represents the number of vehicles; Calculate the dust generation intensity of soil dumping operations G 5. The formula is: ; In the formula, V 5 represents the wind speed during the soil removal operation; H 5 represents the unloading height difference; W 5 represents the moisture content of the coal during the spoil heap operation; S 5 represents the daily discharge volume; The formula for calculating pollutant concentration is: ; In the formula, Downwind x Lateral offset y ,high z The concentration of pollutants at the location; Q The emission rate of the pollution source; Q Depend on G 1. G 2. G 3. G 4. G 5. Decision; u Wind speed; H For effective emission height; These are the lateral and vertical diffusion coefficients; is the vertical diffusion coefficient.

[0012] Furthermore, based on the dust mass concentration output by the predictive analysis module, and combined with preset environmental thresholds, pollution early warning standards, and operational safety requirements, an optimal control strategy is generated, including: Based on dust mass concentration, combined with preset environmental thresholds, pollution early warning standards, and operational safety requirements, the optimal control strategy is generated using expert systems, fuzzy control, or reinforcement learning algorithms. This includes optimizing spray volume, watering frequency, and / or operational plans to ensure that dust concentration in the mining area is maintained within the set range.

[0013] Furthermore, the dust suppression system includes a water pump and a spray device.

[0014] Furthermore, when the dust concentration at any monitoring point reaches or exceeds the preset warning threshold, the dust mass concentration calculation and prediction system automatically adjusts the output signal to increase the working intensity of the corresponding dust suppression system. If the maximum adjustment still cannot meet the environmental requirements, the system will automatically trigger the early warning and alarm system to notify on-site management personnel to intervene. Conversely, when the dust concentration decreases, the dust mass concentration calculation and prediction system will intelligently reduce the output level of the corresponding dust suppression system, gradually reduce energy consumption, and achieve a balance between mining production and environmental safety.

[0015] The beneficial effects of the technical solution provided by this invention include at least the following: This invention focuses on open-pit coal mine dust prediction systems, using multi-source dust concentration data from the sky, ground, and air, along with remotely monitored physical entities, as the control objects. Driven by multi-source data from the sky and ground, it integrates spatiotemporal distribution data of dust concentration from space-based, air-based, and ground-based observations, combined with remotely monitored physical entities, to construct an intelligent dust suppression system for open-pit coal mines. Through a Gaussian model of dust diffusion, the dust generation intensity at each production stage in the open-pit coal mine is quantitatively determined, and machine learning algorithms are used to intelligently predict dust concentration and optimize dust suppression parameters. This system not only predicts the dust diffusion range during open-pit coal mining in real time but also enables remote intelligent linkage control, achieving intelligent dust suppression and contributing to the protection of the ecological environment and the health of workers. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a block diagram of the dust mass concentration prediction software system provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of an open-pit coal mine structure provided in an embodiment of the present invention; Figure 3 This is a layout logic diagram of an open-pit coal mine provided in an embodiment of the present invention; Figure 4 This is a flowchart of open-pit coal mine dust prediction based on multi-source data fusion from the sky and ground, provided in an embodiment of the present invention. Figure 5 This is a cloud map showing the mass concentration distribution of dust in an open-pit coal mine, provided in an embodiment of the present invention.

[0018] Explanation of reference numerals in the attached figures: 01. Group Company / Management Center (responsible for decision-making and remote supervision); 02. Corporate Headquarters (responsible for scheduling, safety and environmental management); 03. Local government / community management departments (responsible for receiving environmental data); 04. Wireless transmission link (data uplink path); 05. Communication base stations / wireless signal towers; 06. Cloud Platform / Internet; 07. Firewall / Security Gateway (responsible for network security protection); 08. Enterprise Host Workstations / Servers; 81. Office terminal equipment (PC, used to view monitoring and prediction results); 82. Data storage devices (which may be database servers or storage arrays); 83. Display terminal (can be a projection screen or conference display device); 84. Data backup device (can be an external hard drive or cold backup storage). 09. Data Center / Integrated Server (Mining Area Data Processing Node); 10. Satellite communication link; 11. Mobile communication base stations (4G / 5G / NB-IoT); 12. User mobile terminal (can be a mobile phone / tablet); 101. Satellite positioning system (which can be GPS / BeiDou); 102. Unmanned Aerial Vehicle (UAV) monitoring system (for dust / temperature / 3D terrain data acquisition); 103. Fixed remote sensing equipment (which may be radar or visual monitoring); 104. Dust and meteorological monitoring stations (including temperature inversion monitoring); 105. Communication Node / Ground Gateway; 106. Main transport roads (located in areas with high dust pollution rates); 107. Stockyard / loading / unloading point (a strong dust source); 108. Excavators / loading equipment (belonging to the category of strong disturbance and dust sources); 109. Mining trucks (responsible for transporting dust sources). Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0020] First, it should be noted that in the embodiments of the present invention, the words "exemplarily," "for example," etc., are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the term "exemplarily" is intended to present the concept in a specific manner. Furthermore, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either one or the other.

[0021] This embodiment provides an intelligent dust suppression system for open-pit coal mines based on multi-source data fusion, used for intelligent dust suppression in open-pit coal mines; wherein, as... Figure 2As shown, the basic overview of an open-pit coal mine includes the pit structure, mine layout, ventilation structures, transportation system, and spoil heap layout. The pit structure refers to the overall mining space structure formed by the layered mining method, consisting of benches, working walls, slopes, platforms, and stopes. The width, height, and slope angle of the benches are designed and determined according to geological conditions and production needs. The mine layout includes the overall layout of the mining operation area, coal mine and waste rock dumps, production and living facilities areas, road network, drainage system, and power and water supply systems. Ventilation structures include wind deflectors, wind screens, and other devices used to regulate the local wind field and airflow distribution in the working area; their layout and structural design must meet the safety and environmental standards of the mining area. The transportation system includes material transportation facilities such as mining dump trucks, unloading stations, crushing stations, dedicated railway lines, and coal bunkers, used to efficiently transfer ore, overburden, and waste rock. The spoil heap layout involves the storage sites and methods for waste rock, overburden, and coal gangue, and must consider storage stability, land optimization, and environmental impact control requirements. By systematically modeling and parametrically describing the basic conditions of open-pit coal mines, the necessary physical basis and structural constraints can be provided for subsequent dust source analysis, diffusion path simulation, and mass concentration prediction.

[0022] The intelligent dust suppression system for open-pit coal mines based on multi-source data fusion includes: The data acquisition system is used to collect multi-source data from various dust-generating processes in open-pit coal mines; wherein, the multi-source data includes space-based observation data, air-based observation data, and ground-based observation data; The dust mass concentration calculation and prediction system is used to calculate the dust generation intensity of each production operation in an open-pit coal mine based on multi-source data collected by the data acquisition system, and to predict the dust mass concentration; and to generate control strategies based on the dust generation intensity calculation results and the dust mass concentration prediction results. The dust suppression system is used in conjunction with the dust mass concentration calculation and prediction system to achieve intelligent dust suppression in open-pit coal mines based on the control strategy generated by the dust mass concentration calculation and prediction system.

[0023] Specifically, in this embodiment, such as Figure 3As shown, the physical entities of the data acquisition system include multi-source sensing devices for space-based, air-based, and ground-based observations, physical sensor elements, remote sensing satellite receiving terminals, UAV-mounted monitoring platforms, ground-based monitoring networks, signal transmission devices, optical transceivers, programmable logic controllers (PLCs), frequency converters, and a host computer system. The space-based observation module includes a meteorological satellite data receiving and analysis module, used to acquire macro-meteorological information covering the mining area, such as atmospheric temperature, humidity, wind speed, wind direction, and solar radiation intensity, and interacts with the host computer system via the satellite data receiving terminal. The air-based observation module includes UAV-mounted lidar, particle measurement sensors, and high-definition visible light and infrared imaging equipment, used to acquire dust concentration distribution at different altitudes in the mining area, local wind field characteristics, and dynamic images of the working face, and transmits the data back to the host computer system in real time via a wireless communication module. The ground-based observation module includes environmental monitoring stations and fixed sensor arrays deployed in key dust-generating areas such as mining areas, transportation roads, and spoil heaps. The ground-based sensor elements include, but are not limited to, wind speed sensors, temperature sensors, humidity sensors, and air pressure sensors. Sensors, C Sensors, CO sensors, CO2 sensors, NO x Sensors, including PM2.5 / PM10 dust concentration sensors, transmit monitoring data to a programmable logic controller (PLC) via signal transmission devices. The PLC connects to a frequency converter, optical transceiver, and host computer system via signal transmission devices, responsible for real-time data acquisition, transmission scheduling, and preliminary processing. The optical transceiver transmits information collected by the distributed sensor network to the PLC and host computer system. The host computer system, including a host computer, monitor, data storage unit, and printer, is located in the central monitoring room of the mine and is used for data integration, virtual simulation, predictive analysis, and visualization. Through these physical entities and their synergistic effects, comprehensive acquisition, transmission, processing, and application of multi-source observation data from space, air, and ground are achieved, providing complete physical support and data assurance for the open-pit coal mine dust mass concentration prediction system.

[0024] The dust mass concentration calculation and prediction system integrates space-based, air-based, and ground-based observation data from various dust-generating processes in open-pit coal mines, and combines this with physical entity information acquired through remote monitoring. Using a Gaussian dust diffusion model, it quantitatively assesses the dust generation intensity of each production operation. Based on this, it utilizes machine learning algorithms to intelligently predict dust mass concentration and simultaneously optimize and control dustfall parameters, providing scientific decision support for dust pollution prevention and control in open-pit coal mines. It includes a data acquisition module, a data fusion module, a prediction and analysis module, and a control execution module. Its control logic is as follows: driven by multi-source data from space-based, air-based, and ground-based observations, using the dust-generating processes in each production operation of the open-pit coal mine as physical entity models, a virtual model for predicting dust mass concentration is established based on machine learning algorithms. The prediction system uses multi-source sensors to perceive the operating status of the physical entities in real time and utilizes virtual simulation technology to construct dust mass concentration prediction software, achieving dynamic parallel synchronization and interactive mapping between the physical entities and the visualized prediction virtual model.

[0025] The data acquisition module obtains basic data on the working environment of open-pit coal mines through the data acquisition system, including but not limited to space-based observation data, air-based observation data, and ground-based observation data. Among them, space-based observation data includes macro-meteorological parameters such as temperature, humidity, wind speed, wind direction, and solar radiation intensity provided by meteorological satellites; air-based observation data includes local dust concentration distribution, wind field and flow field changes, and operational dynamics information in the mining area obtained by dust sensors and aerial remote sensing equipment carried by UAVs; and ground-based observation data includes micro-operating conditions and emission data obtained by online particulate matter concentration monitoring instruments, wind speed and direction sensors, and mechanical equipment operation status acquisition devices deployed in mine pits, transportation roads, spoil heaps, and other key dust-generating links.

[0026] The data fusion module is used to perform multi-scale and multi-dimensional collaborative fusion of the above-mentioned multi-source heterogeneous data. Specifically, it includes standardizing the data through spatiotemporal registration technology, removing noisy data and ensuring data integrity through data cleaning and outlier detection methods, identifying key factors related to dust concentration changes through multivariate feature extraction and dimensionality reduction methods, and then using data assimilation techniques such as Bayesian inference and Kalman filtering to achieve optimal integration of multi-source observation information, so as to output a fused dataset that can be used to drive predictive analysis.

[0027] The predictive analysis module is based on a hybrid modeling framework that combines physics-driven and data-driven approaches. It uses the various dust-generating processes and their physical processes in open-pit coal mines as physical entity models. It constructs a dust mass concentration prediction model by combining machine learning algorithms such as random forest, support vector machine, and deep neural network. It introduces computational fluid dynamics model, particle transport model, and local meteorological model to describe dust diffusion, sedimentation, and secondary dust emission processes. It trains the machine learning model with historical monitoring data and real-time input data to learn complex nonlinear relationships. It improves the model's prediction accuracy and generalization ability through optimization methods such as cross-validation and hyperparameter tuning. At the same time, it performs sensitivity analysis and uncertainty quantification to evaluate the robustness of the prediction results.

[0028] Based on the concentration distribution results output by the predictive analysis module, the control execution module combines preset environmental thresholds, pollution early warning standards, and operational safety requirements. It uses algorithms such as expert systems, fuzzy control, and reinforcement learning to generate optimal control strategies, including measures such as spray dust suppression, water spraying dust reduction, and operational plan optimization. Utilizing wireless communication and industrial IoT technologies, control commands are transmitted in real time to various execution terminals of the dust suppression system, including water spraying vehicles, spraying devices, and the production scheduling center. Through a closed-loop feedback mechanism, the module monitors the effectiveness of control measures in real time, dynamically adjusts and optimizes control strategies, and achieves synchronous interaction and coordinated regulation between physical entities and virtual predictive models. This ensures the high efficiency, accuracy, and real-time nature of dust concentration prediction and control.

[0029] Specifically, the method for calculating dust mass concentration in open-pit coal mines integrates and quantifies the dust emission characteristics of each major dust-generating stage in the open-pit coal mine production process using a dust diffusion model. This includes dust mass concentrations from drilling, blasting, mining and loading, transportation, and spoil disposal. The method combines macroscopic meteorological conditions provided by space-based observation data, local three-dimensional dust distribution characteristics obtained from space-based observation data, and real-time emission data from each operational stage monitored by ground-based observation data. Through multi-source information synergy driving the dust diffusion model, it achieves high-precision calculation and spatiotemporal distribution prediction of dust mass concentrations in any horizontal direction and vertical height within the open-pit coal mine operating area. This method not only captures the multi-source dust transport and diffusion processes under complex operating conditions but also possesses multi-scale, multi-dimensional comprehensive simulation and quantitative analysis capabilities, providing scientific, systematic, and visualized technical support for dust pollution prevention and control, operational optimization, and environmental management in open-pit coal mines. The specific calculation method is as follows: Dust concentration during drilling operations G 1. Calculated using Formula 1: (1) In the formula, G1 represents the dust generation intensity during drilling operations, expressed in g / s; K a This is a correction factor used to correct for influencing factors under different operating conditions; K 1 represents the drilling rig type coefficient, reflecting the impact of different drilling rigs on dust generation intensity; N 1 represents the drilling operation volume per unit time, in m³ / h or m / h.

[0030] Dust concentration during blasting operations G 2. Calculated using Formula 2: (2) In the formula: G 2 indicates the dust generation intensity of blasting operations, expressed in kg / d; C This refers to the dust concentration of smoke plumes from mine blasting, expressed in kg / m³. 3 ; V 2 represents the total volume of smoke plumes produced daily by explosions, in meters (m). 3 / d.

[0031] Dust concentration during mining and loading operations G 3 is calculated using formula 3: (3) In the formula: G Table 3 shows the dust generation intensity of the mining and loading operations, in kg / h. P 3 represents the dust emission coefficient per truck, expressed in kg (truckload·h). -1 ; N 3 represents the number of vehicles used for mining and loading operations, in units of units.

[0032] Dust concentration during transportation operations G 4. Calculated using Formula 4: (4) In the formula: G 4 represents the dust generation intensity during transportation operations, expressed in g / s; V 4 represents the vehicle speed, measured in km / h; P 4 represents the amount of dust on the road surface, in kg / m³. 2 ; M 4. Vehicle weight during transportation operations, in tons (t); N 4 represents the number of vehicles, in units of vehicles.

[0033] Dust concentration during soil dumping operations G 5 is calculated using formula 5: (5) In the formula: G 5 represents the dust generation intensity during soil removal operations, expressed in kg / s. V5 represents the wind speed during the soil removal operation, in m / s; H 5 represents the unloading elevation difference, in meters (m). W 5 represents the moisture content of coal during spoil heap operations, expressed in %; S 5 represents the daily discharge volume, in cubic meters. 3 / s.

[0034] This model assumes that pollutants are transported convectively along the wind direction and diffuse randomly laterally and vertically, with the concentration distribution following a normal (Gaussian) distribution. It is suitable for continuous point, line, or area source emissions, especially under conditions of stable wind speeds, flat terrain, and no strong thermal disturbances. The Gaussian diffusion model is simple, requires fewer parameters, and is computationally efficient, making it one of the most widely used atmospheric diffusion models. The calculation is based on Equation 6.

[0035] (6)

[0036] In the formula: C ( x , y , z (Downwind) x Lateral offset y ,high z The concentration of pollutants at the location is expressed in g / m³. Q The emission rate of the pollution source is expressed in g / s. Q Depend on G 1. G 2. G 3. G 4. G 5. Decision (through the decision on) G 1. G 2. G 3. G 4. G 5 are accumulated to obtain Q ); u Wind speed, in m / s; H Effective emission height, in meters (m); σy , σz These are the lateral and vertical diffusion coefficients, respectively, in meters (m).

[0037] Based on the above, this embodiment constructs a set as follows Figure 1The dust concentration prediction software system shown is based on digital twins and designed for multi-source data fusion scenarios in open-pit coal mines. It constructs a complete dust concentration prediction and intelligent control system. The system includes: a system security login module, a system early warning and alarm module, a space-based data monitoring module, an airborne data monitoring module, a ground-based data monitoring module, a model calculation and analysis module, an intelligent regulation and control module, a report and data management module, a system settings and maintenance module, a knowledge base, and decision support functions. Specifically, the system security login module is used for user authentication, permission hierarchy, and access control; the system early warning and alarm module is used to trigger multi-dimensional early warning and alarm mechanisms based on real-time monitoring and prediction results; the space-based data monitoring module is used to receive and analyze macro-environmental and meteorological parameters of the mining area obtained from remote sensing satellites and Earth observation systems; the airborne data monitoring module is used to manage the airborne monitoring system carried by UAVs to collect dust concentration and three-dimensional distribution information in the airspace above and surrounding areas of the mining area; and the ground-based data monitoring module is used to integrate real-time monitoring data from ground-based sensor networks deployed at open-pit coal mine working faces, transportation roads, and spoil heaps. The system comprises the following modules: a data measurement module; a model calculation and analysis module, used to drive a dust diffusion and mass concentration prediction model based on multi-source data; an intelligent regulation and control module, used to generate regulation strategies based on model calculation results and link them with on-site execution equipment (such as water pumps and spray devices); a report and data management module, used for systematic management of multi-source data, model results, alarm records, regulation logs, and other information; a system setting and maintenance module, used to configure system parameters and ensure stable system operation; and a knowledge base and decision support module, used to provide multi-dimensional knowledge support and intelligent decision assistance for the open-pit coal mine dust mass concentration prediction software system. The process of using this system to predict coal mine dust is as follows: Figure 4 As shown.

[0038] The aforementioned system enables the establishment of a multi-source data-driven dust diffusion calculation and control system by utilizing space-based, airborne, and ground-based observation data, combined with the emission characteristics of dust-generating processes, meteorological conditions, and diffusion mechanisms. This system, through monitoring, modeling, fusion, calculation, and control, forms a closed loop from data acquisition to intelligent decision-making, achieving dynamic tracking and efficient prediction of coal mine dust diffusion processes. Specific steps include: Step 1: By deploying various types of sensor elements in key areas such as open-pit coal mine drilling areas, blasting areas, mining and loading faces, transportation roads, and spoil heaps, the production and operation environment and dust emission characteristics of the mining area are continuously and in real time monitored. Sensor elements include, but are not limited to, wind speed sensors, wind direction sensors, temperature and humidity sensors, pressure sensors, dust concentration sensors, and particulate matter analyzers, to collect ground-based observation data of the mining area. At the same time, the three-dimensional distribution characteristics of dust in the air above the mining area are obtained through airborne monitoring equipment carried by UAVs, and combined with space-based atmospheric environmental parameters provided by remote sensing satellites, the complete acquisition of multi-source observation data is achieved.

[0039] Step 2: Based on the topographic features, wind conditions, and dust source distribution of the mining area, a dust transport and diffusion model is constructed using atmospheric dynamics principles and dust diffusion theory. Based on macroscopic meteorological boundary conditions provided by space-based observation data, local high-resolution concentration fields provided by air-based data, and real-time emission intensity provided by ground-based data, a high-precision calculation of dust mass concentration at any horizontal location and vertical height within the mining area is achieved through multi-source data fusion algorithms (such as Kalman filtering and Bayesian inference). Key variables involved in the dust mass concentration calculation include source strength parameters, transport coefficients, diffusion coefficients, settling velocity, and particulate density. Relevant calculation formulas and parameters can be derived from existing fluid dynamics and environmental dynamics models. The calculation results are as follows: Figure 5 As shown.

[0040] The third step involves introducing an intelligent scheduling module based on fuzzy control algorithms. This module comprehensively assesses dust concentration results calculated from multi-source data and, combined with mine operation processes, meteorological conditions, and pollution thresholds, formulates adaptive adjustment strategies. The fuzzy control algorithm utilizes an "IF-THEN" rule base and fuzzy inference mechanism to transform complex nonlinear relationships and uncertainties into specific, executable control strategies. It does not rely on complex optimization networks or large amounts of historical samples, and can quickly adapt to environmental changes, outputting control parameters such as spray volume and watering frequency to ensure that dust concentration within the mining area remains within a set range.

[0041] Step 4: Receive control commands from the intelligent scheduling module through the Distributed Control System (DCS). The DCS system has a modular and distributed architecture, enabling hierarchical control and real-time management of various work units and control equipment. The DCS system forms a closed-loop control system between monitoring points and actuators (such as water pumps, spray devices, and conveyor belts). Through redundant networks and real-time databases, it ensures that commands are transmitted quickly and accurately to each control unit, and achieves intelligent regulation and system status visualization based on multi-source data.

[0042] Step 5: When the dust concentration at any monitoring point reaches or exceeds the preset warning threshold, the DCS system automatically adjusts the output signal and increases the workload of the corresponding control equipment (such as increasing the spray volume and optimizing the operation process). If the maximum adjustment still cannot meet the environmental requirements, the system will automatically trigger the early warning and alarm system to notify on-site management personnel to intervene. Conversely, when the dust concentration decreases, the system will intelligently reduce the output level and gradually reduce energy consumption, achieving a balance between mining production and environmental safety.

[0043] In summary, this embodiment focuses on an open-pit coal mine prediction system, using multi-source dust concentration data from the air, ground, and space, along with remotely monitored physical entities, as the control objects. Driven by multi-source data from the air and ground, it integrates spatiotemporal distribution data of dust concentration from space-based, airborne, and ground-based observations, combined with remotely monitored physical entities, to construct an intelligent dust suppression system for open-pit coal mines. Through a Gaussian model of dust diffusion, the dust generation intensity at each production stage in the open-pit coal mine is quantitatively determined, and machine learning algorithms are used to intelligently predict dust concentration and optimize dust suppression parameters. This system not only predicts the dust diffusion range during open-pit coal mining in real time but also enables remote intelligent linkage control, achieving intelligent dust suppression and contributing to the protection of the ecological environment and the health of workers.

[0044] Furthermore, it should be noted that the present invention can be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention can take the form of a completely or partially hardware embodiment, a completely or partially software embodiment, or an embodiment combining software and hardware aspects. Moreover, when implemented in software, embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any usable medium accessible to a computer or a data storage device such as a server or data center containing one or more sets of usable media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive (SSD).

[0045] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0046] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0047] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element. Furthermore, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. Additionally, the character " / " in this document generally indicates an "or" relationship between the preceding and following related objects, but it may also indicate an "and / or" relationship; please refer to the context for specific interpretation. "At least one" means one or more, and "more" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can be expressed as: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0048] Furthermore, it is understood that in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0049] 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 implementations should not be considered beyond the scope of this invention.

[0050] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of functional modules / 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 device, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., 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. Additionally, the functional units in the various embodiments of this invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0051] If the method 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, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0052] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments of the present invention have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make several improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. An intelligent dust suppression system for open-pit coal mines based on multi-source data fusion, characterized in that, include: The data acquisition system is used to collect multi-source data from various dust-generating processes in open-pit coal mines; wherein, the multi-source data includes space-based observation data, air-based observation data, and ground-based observation data; The dust mass concentration calculation and prediction system is used to calculate the dust generation intensity of each production operation in an open-pit coal mine based on multi-source data collected by the data acquisition system, and to predict the dust mass concentration; and to generate control strategies based on the dust generation intensity calculation results and the dust mass concentration prediction results. The dust suppression system is used in conjunction with the dust mass concentration calculation and prediction system to achieve intelligent dust suppression in open-pit coal mines based on the control strategy generated by the dust mass concentration calculation and prediction system.

2. The intelligent dust suppression system for open-pit coal mines based on multi-source data fusion as described in claim 1, characterized in that, The data acquisition system includes a space-based observation module, an air-based observation module, and a ground-based observation module; The space-based observation module includes a meteorological satellite data receiving and parsing module, used to acquire space-based observation data covering the mining area; The airborne observation module includes a lidar, particle measurement sensor, and high-definition visible light and infrared imaging equipment carried by the UAV, used to acquire airborne observation data; The ground-based observation module includes environmental monitoring stations deployed in the dust source area and a fixed sensor array; wherein, the fixed sensor array includes wind speed sensors, temperature sensors, humidity sensors, and air pressure sensors. Sensors, C Sensors, CO sensors, CO2 sensors, NO x Sensors, including PM2.5 / PM10 dust concentration sensors, are used to acquire ground-based observation data.

3. The intelligent dust suppression system for open-pit coal mines based on multi-source data fusion as described in claim 1, characterized in that, The space-based observation data includes macro-meteorological parameters of the mine provided by meteorological satellites; wherein, the macro-meteorological parameters include temperature, humidity, wind speed, wind direction and solar radiation intensity; The airborne observation data includes local dust concentration distribution, wind field and flow field changes, and operational dynamics information in the mining area, obtained by using dust sensors and aerial remote sensing equipment carried by UAVs. The ground-based observation data includes microscopic operating conditions and emission data acquired by sensor arrays deployed in the dust source area and mechanical equipment operation status acquisition devices.

4. The intelligent dust suppression system for open-pit coal mines based on multi-source data fusion as described in claim 1, characterized in that, The dust mass concentration calculation and prediction system includes a data fusion module, a prediction analysis module, and a control execution module; wherein... The data fusion module is used to perform multi-scale and multi-dimensional collaborative fusion of multi-source data; The predictive analysis module is used to construct a dust mass concentration prediction model based on a hybrid modeling framework that combines physical and data-driven approaches. It uses each dust-generating process and its physical processes in an open-pit coal mine as a physical entity model, and combines a preset machine learning algorithm to construct a dust mass concentration prediction model. By introducing the preset model, it describes the dust diffusion, settling and secondary dust emission processes. It trains the dust mass concentration prediction model with multi-source data monitored in history, so that it learns the nonlinear relationship between multi-source data and dust mass concentration, and predicts the dust mass concentration based on multi-source data. The control execution module is used to generate an optimal control strategy based on the dust mass concentration output by the predictive analysis module, combined with preset environmental thresholds, pollution warning standards, and operational safety requirements. The optimal control strategy is then transmitted to the dust suppression system in real time using wireless communication and industrial Internet of Things technologies. The module also monitors the implementation effect of the control strategy in real time through a closed-loop feedback mechanism and dynamically adjusts and optimizes the control strategy.

5. The intelligent dust suppression system for open-pit coal mines based on multi-source data fusion as described in claim 4, characterized in that, The multi-scale, multi-dimensional collaborative fusion of multi-source data includes: The data is standardized through spatiotemporal registration technology, and noisy data is removed and data integrity is ensured through data cleaning and outlier detection methods. Key factors related to dust concentration changes are identified through multivariate feature extraction and dimensionality reduction methods. Then, Bayesian inference or Kalman filtering data assimilation technology is used to achieve optimal integration of multi-source data, so as to output a fusion dataset that can be used to drive the dust mass concentration prediction model.

6. The intelligent dust suppression system for open-pit coal mines based on multi-source data fusion as described in claim 4, characterized in that, The preset machine learning algorithm is random forest, support vector machine or deep neural network.

7. The intelligent dust suppression system for open-pit coal mines based on multi-source data fusion as described in claim 4, characterized in that, The process of dust diffusion, settling, and secondary dust re-entrainment is described by introducing a preset model, including: The dust diffusion, settling, and secondary dust re-entrainment process is described using a Gaussian diffusion model, specifically as follows: Calculate the dust generation intensity during drilling operations G 1. The formula is: ； In the formula, K a This is a correction factor used to correct for influencing factors under different operating conditions; K 1 represents the drilling rig type coefficient, reflecting the impact of different drilling rigs on dust generation intensity; N 1 represents the amount of drilling work per unit time; Calculate the dust generation intensity of blasting operations G 2. The formula is: ； In the formula, C This refers to the concentration of dust from blasting plumes in mines. V 2 represents the total volume of smoke plumes produced by the explosions each day; Calculate the dust generation intensity of mining and loading operations G 3. The formula is: ； In the formula, P 3 represents the dust emission coefficient per truck; N 3 represents the number of vehicles used for mining and loading operations; Calculate the dust generation intensity of transportation operations G 4. The formula is: ； In the formula, V 4 represents the car's speed; P 4 represents the amount of dust on the road surface; M 4 represents the weight of the vehicle during transportation operations; N 4 represents the number of vehicles; Calculate the dust generation intensity of soil dumping operations G 5. The formula is: ； In the formula, V 5 represents the wind speed during the soil removal operation; H 5 represents the unloading height difference; W 5 represents the moisture content of the coal during the spoil heap operation; S 5 represents the daily discharge volume; The formula for calculating pollutant concentration is: ； In the formula, Downwind x Lateral offset y ,high z The concentration of pollutants at the location; Q The emission rate of the pollution source; Q Depend on G 1. G 2. G 3. G 4. G 5. Decision; u Wind speed; H For effective emission height; These are the lateral and vertical diffusion coefficients; is the vertical diffusion coefficient.

8. The intelligent dust suppression system for open-pit coal mines based on multi-source data fusion as described in claim 4, characterized in that, The optimal control strategy is generated based on the dust mass concentration output by the predictive analysis module, combined with preset environmental thresholds, pollution early warning standards, and operational safety requirements, including: Based on dust mass concentration, combined with preset environmental thresholds, pollution early warning standards, and operational safety requirements, the optimal control strategy is generated using expert systems, fuzzy control, or reinforcement learning algorithms. This includes optimizing spray volume, watering frequency, and / or operational plans to ensure that dust concentration in the mining area is maintained within the set range.

9. The intelligent dust suppression system for open-pit coal mines based on multi-source data fusion as described in claim 1, characterized in that, The dust suppression system includes a water pump and a spray device.

10. The intelligent dust suppression system for open-pit coal mines based on multi-source data fusion as described in claim 1, characterized in that, When the dust concentration at any monitoring point reaches or exceeds the preset warning threshold, the dust mass concentration calculation and prediction system automatically adjusts the output signal to increase the working intensity of the corresponding dust suppression system. If the maximum adjustment still cannot meet the environmental requirements, the system will automatically trigger the early warning and alarm system to notify on-site management personnel to intervene. Conversely, when the dust concentration decreases, the dust mass concentration calculation and prediction system will intelligently reduce the output level of the corresponding dust suppression system, gradually reduce energy consumption, and achieve a balance between mining production and environmental safety.

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