Control method for linkage of multi-source dust treatment and dust removal in closed underground space

Abstract

The invention relates to a control method for linkage of multi-source dust treatment and dust removal in a closed underground space. The control method is applied to the dust removal system. The method comprises the following steps: firstly, performing three-dimensional dynamic simulation on an airflow field in a closed underground space based on a computational fluid mechanics model to obtain flow field data output by the three-dimensional dynamic simulation; then, based on the flow field data, isolation and sealing control is performed on the source of dust in the underground space. And then the operation area is divided into a plurality of functional areas, and the air volume of the independent air draft pipeline system of each functional area is determined. Then, the dust is guided to be concentrated towards a suction area in the multiple functional areas through an air flow organizing device. Finally, multiple pieces of sensor data of the multiple functional areas are collected according to the sensors of all the functional areas, and big data analysis is carried out based on the multiple pieces of sensor data so as to control the dust removal system. According to the invention, dust in the closed underground space can be efficiently and reliably controlled and treated.

Description

Technical Field

[0001] The present application relates to the technical field of dust control, and in particular to a control method for the linkage of multi-source dust control and dust removal in a closed underground space. Background Art

[0002] Against the backdrop of today's advancing urban construction and infrastructure development, underground spaces are increasingly being used. Multi-source dust control and integrated dust removal within these spaces are crucial environmental protection and industrial safety issues, impacting multiple sectors, including industrial production, construction, and mining operations. However, the resulting dust pollution poses serious challenges to worker health, a safe working environment, and facility reliability.

[0003] The design concept of traditional dust removal systems is still at the stage of "local governance and rigid control", which fails to meet the comprehensive governance needs under closed underground multi-source and high-frequency dust-producing conditions. Specifically, it manifests as: "fragmented" governance, "high load" energy consumption, "passive" control, and "non-systematic" effects; it brings continuous risks to the working environment safety, equipment stability and health of workers in closed underground spaces. Summary of the Invention

[0004] The purpose of this application is to provide a control method for multi-source dust control and dust removal linkage in a closed underground space, so as to solve the problem that traditional dust removal systems pose risks to closed underground space operations.

[0005] To achieve the above objectives, the present application provides, in a first aspect, a control method for multi-source dust control and dust removal linkage in a closed underground space, which is applied to a dust removal system. The control method includes: Performing a three-dimensional dynamic simulation of the airflow field in the closed underground space based on a computational fluid dynamics model to obtain flow field data output by the three-dimensional dynamic simulation; Based on the flow field data, the source of dust in the underground space is isolated and sealed; Based on the flow field data, the operating area of ​​the enclosed underground space is divided into multiple functional areas, and the air volume of the independent exhaust duct system of each functional area is determined; Based on the flow field data, the dust is guided to concentrate toward the suction areas in the plurality of functional areas through an airflow organization device; Multiple sensor data of multiple functional areas are collected according to the sensors of each functional area, and big data analysis is performed based on the multiple sensor data to control the dust removal system.

[0006] A second aspect of the present application provides a computer-readable storage medium, in which a program is stored. The program can be loaded by a processor and executed by the above-mentioned control method for multi-source dust control and dust removal linkage in a closed underground space.

[0007] The beneficial effects of this application are: This application first performs a three-dimensional dynamic simulation of the airflow field in the closed underground space based on the computational fluid dynamics model, and obtains the flow field data output by the three-dimensional dynamic simulation. Then, a source isolation operation is performed, that is, the source of the dust in the underground space is isolated and sealed based on the flow field data to reduce the spread of dust to the working space. Next, a regional suction operation is performed, and the working area of ​​the closed underground space is divided into multiple functional areas based on the flow field data, and the air volume of the independent exhaust duct system is configured for each functional area, so as to achieve precise regional management and enhance the reliability of the dust removal system. Furthermore, an airflow induction operation is performed, and based on the flow field data, the dust is guided to concentrate in the suction areas of multiple functional areas through airflow organization, thereby reducing the situation where dust does not need to spread. In addition, intelligent linkage control can also be performed, and multiple sensor data of multiple functional areas are collected according to the sensors of each functional area, and big data analysis is performed based on the multiple sensor data to control the dust removal system. This application integrates the above-mentioned multiple operations into the dust removal system to form a unified control platform, which can effectively solve the risk problems caused by energy efficiency imbalance, path loss of control and insufficient adaptability in underground space dust control, and can achieve efficient and reliable control and management of dust in closed underground spaces.

[0008] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flow chart of a method for controlling multi-source dust control and dust removal linkage in a closed underground space provided in an embodiment of the present application; Figure 2 This is a schematic structural diagram of a dust removal system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0011] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly specifying the number of the technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically qualified. In this application, the word "exemplary" is used to mean "serving as an example, illustration, or illustration." Any embodiment described in this application as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is provided to enable anyone skilled in the art to implement and use the present application. In the following description, details are listed for illustrative purposes. It should be understood that one of ordinary skill in the art will recognize that the present application can be implemented without these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0012] Although various dust control technologies have been widely adopted in current projects, traditional technologies generally have the following significant limitations in the face of complex working conditions with multiple sources of dust generation, such as sand and gravel crushing and concrete production in underground spaces.

[0013] 1. Localized dust removal has limitations. Traditional ventilation and dust removal methods typically employ localized negative pressure suction, with suction hoods or ducts installed only at a single device or point (such as a crusher outlet or a mixing inlet). This approach fails to uniformly manage the flow field across the entire space and can easily create blind spots for dust removal. If dust is not captured promptly, it can easily spread to surrounding areas, resulting in good localized dust removal results but still significant overall environmental pollution.

[0014] 2. Insufficient control rigidity and dynamic response. Most dust removal systems rely on constant air volume operation and lack the ability to coordinate control with actual dust production. This results in excess air volume during certain periods, wasting energy, while insufficient air volume during peak dust production periods renders the dust removal system ineffective. Furthermore, under conditions where the equipment is frequently started and stopped, the dust removal system's response lags, making it unable to synchronize adjustments in a timely manner.

[0015] 3. Improper air flow leads to dust backflow. Enclosed spaces are often complex and have limited ventilation channels. Without proper airflow guidance, dust can easily form a floating backflow, causing it to become immobile or settle in areas far from the suction port. Components within the space (such as columns, walls, and equipment) can obstruct airflow, creating quiet zones and corners where dust can accumulate. Long-term accumulation can easily lead to fire and explosion risks.

[0016] 4. Lack of coordination between ventilation and dust removal systems. In traditional projects, ventilation and dust removal systems are often designed and controlled independently. The ventilation system primarily focuses on air exchange, while the dust removal system primarily focuses on dust capture, lacking the necessary coordination mechanisms. This design often leads to the problem of "supply air picking up dust, while exhaust air fails to capture it in time," which in turn exacerbates dust dispersion.

[0017] 5. Spraying has limitations. While atomized water spraying can be effective in certain areas, its applicability is limited in enclosed environments. For example, high humidity can affect equipment insulation and corrosion, and spraying has low adsorption efficiency for dusts with poor hydrophilicity (such as mineral powder and dry materials). Furthermore, spraying cannot actively control the direction and path of dust and is typically used as a supplementary measure.

[0018] 6. Low system intelligence. Most traditional dust removal systems operate in a passive mode, lacking monitoring feedback, air volume adjustment, and automatic logic switching. Lacking the integration of intelligent controllers, sensors, and actuators, the systems are unable to identify areas with excessive dust concentrations in real time, and their ability to prioritize treatment is insufficient. They are highly reliant on manual operation and have slow response times, making them difficult to adapt to the demands of modern industrial intelligence.

[0019] Therefore, traditional dust removal system design concepts remain largely stuck in the "localized treatment, rigid control" stage, failing to meet the comprehensive treatment needs of modern underground multi-source, high-frequency dust generation conditions. This manifests itself in fragmented treatment, high energy consumption, passive control, and non-systematic results. This poses ongoing risks to underground working environment safety, equipment stability, and worker health. Therefore, it is imperative to propose a new dust removal method that combines, zones, flows, and controls, and possesses linked intelligent control capabilities.

[0020] Figure 1 This is a flow chart of a method for controlling the linkage of multi-source dust control and dust removal in a closed underground space provided in an embodiment of the present application. Figure 1 As shown, the method may include steps 101-105, which are described in detail below.

[0021] Step 101: Perform a three-dimensional dynamic simulation of the airflow field within the enclosed underground space based on a computational fluid dynamics (CFD) model to obtain flow field data. Flow field data is a set of numerical values ​​describing physical quantities such as the motion state, pressure distribution, temperature field, and particle trajectories of the fluid (air) within three-dimensional space. This data is used to guide the design of the dust removal system.

[0022] As an example, 3D scanning can be used to capture the structure of an enclosed underground space and measure dust characteristics and initial airflow. A CFD model can then be built to simulate dust dispersion and optimize meshing and boundary conditions. Dynamic simulation of equipment operation and sudden dust emission can then be used to verify the model's accuracy. Finally, flow field data can be exported to guide dust removal system design, improving the scientific nature of the solution and reducing commissioning costs. By establishing a digital twin model of dust transport in underground spaces, airflow prediction and optimization can be used to inform subsequent control measures.

[0023] Step 102: Based on the flow field data, isolate and seal the source of dust in the underground space.

[0024] In the embodiments of the present application, the source of dust can be isolated and sealed through point source sealing. Point source sealing combines physical barriers and energy control for dust control, and its necessity is reflected in the following aspects. First, it can block diffusion paths. Since the kinetic energy of dust is lowest during the initial stages of release, sealing can block over 95% of the diffusion path. Second, it can interfere with the energy field. The enclosed structure creates a confined space, allowing negative pressure suction energy to act more efficiently on the dust, increasing energy density by 5-8 times. Third, particle size screening can be performed. Flexible sealing materials can selectively block dust particles of different particle sizes (for example, the diffusion characteristics of PM2.5 and PM10 differ by up to 3 times). Furthermore, vibration coupling can be controlled. The metal-flexible composite structure can absorb equipment vibration energy (reducing it by over 60%), reducing the risk of seal failure. Point source sealing is also an economically optimal solution, with the cost of point source control being only 1 / 7 of that of spatial diffusion control. Point source sealing can also lock dust into a minimal area. Combined with negative pressure centralized treatment, it is more efficient and energy-efficient than allowing it to spread and then being treated, much like drinking through a straw is less labor-intensive than wiping up spilled water. Therefore, this step uses flow field data to locate the dust release source (such as the crusher inlet and outlet, or the conveyor belt transition). Sealing structures such as metal casings, flexible curtains, or airflow barriers are designed, and combined with a negative pressure extraction system (local negative pressure environment) to block the initial dust diffusion path.

[0025] Step 103: Based on the flow field data, the operating area of ​​the closed underground space is divided into multiple functional areas, and the air volume of the independent exhaust duct system of each functional area is determined.

[0026] In an embodiment of the present application, it is also possible to divide the closed underground space into operating areas to obtain multiple functional areas to achieve regional suction. Among them, the functional areas are areas divided according to different functional types. As an example, the underground space can be divided into functional areas such as crushing area, screening area, and conveying area according to the flow field data. At the same time, an independent exhaust duct system can be set up for each functional area, and then based on the dust characteristics (concentration, particle size) and airflow state of each functional area, the optimal air volume of the independent exhaust duct system is calculated as the air volume of the independent exhaust duct system of each functional area (such as 8000m³ / h in the crushing area and 5000m³ / h in the screening area). In this way, the traditional extensive mode of "whole-area unified suction" can be changed to achieve "precise air supply by zone", with an air volume distribution error of ≤5%, thereby reducing energy waste.

[0027] Step 104: Based on the flow field data, the airflow organizing device is used to guide the dust to concentrate in the suction areas of the multiple functional areas.

[0028] In the embodiment of the present application, the dust can also be guided to concentrate in the suction area through flow guidance and reasonable airflow organization, thereby reducing the situation where unnecessary diffusion is required.

[0029] As an example, a directional airflow channel can be constructed through devices such as induced air ducts, guide grilles, and exhaust air curtains. Based on flow field data, fluid mechanics principles such as the Bernoulli effect and pressure gradient can be used to transform dust from random diffusion to controlled migration and guide it to the suction area (such as a negative pressure hood or the inlet of dust removal equipment). The suction area refers to the core area of ​​negative pressure capture set up in the system, which may include but is not limited to fixed suction ports (the inlet negative pressure hood of dust removal equipment such as a cartridge dust collector), mobile capture points (i.e., dynamic suction monitoring equipment that follows the movement of dust-generating equipment), and path control nodes (such as the V-shaped guide suction groove at the conveyor belt transition).

[0030] In this way, flow field design transforms dust movement from random diffusion to controlled, directional migration, much like using ditches to guide floodwaters toward a reservoir. For example, through flow field reconstruction (such as installing 45-degree diversion grids to accelerate dust migration) and velocity field control (maintaining a pressure differential of ≥200 Pa between the suction and non-suction zones), dust migration time can be shortened to less than 10 seconds, with an escape rate of less than 5%.

[0031] Step 105 : collecting multiple sensor data of multiple functional areas according to the sensors of each functional area, and performing big data analysis based on the multiple sensor data to control the dust removal system.

[0032] In the implementation of this application, an intelligent linkage control system can be constructed with functions such as data collection, automatic air volume adjustment, regional priority treatment determination, dynamic energy consumption analysis, and fault alarms to achieve intelligent control of the dust removal system and improve its operational efficiency and reliability. Specifically, multiple sensors can be deployed at key nodes in each functional area. Key nodes are locations in the dust removal system that directly affect dust control efficiency. For example, these may include core dust-generating nodes (such as the crusher feed inlet and the vibrating screen working surface), airflow control nodes (such as the main ventilation tunnel inflection point and areas with sudden airflow changes such as the dust collector inlet and outlet), and safety protection control nodes (such as crowded areas and escape routes). By deploying sensors for dust concentration, wind speed, negative pressure, and other indicators in each functional area, real-time data can be collected and then dynamically adjusted through big data analysis (such as LSTM neural network prediction and fuzzy PID algorithm) to adjust dust removal strategies (such as fan power and damper opening). In this way, through an intelligent closed loop, an automated process of "perception (sensor - analysis (big data - decision-making (algorithm - execution (equipment control)" is formed, with a response time of ≤30 seconds, enabling adaptive optimization of the dust removal system, automatically adjusting operating parameters according to environmental changes, and maintaining optimal working conditions.

[0033] The embodiments of this application organically integrate various subsystems to form a complete dust control solution, which is promoted and applied in related fields. It integrates subsystems such as source isolation, regional suction, airflow induction, and intelligent control to form a unified control platform, namely the dust removal system. It can be promoted and applied in underground sand and gravel processing, concrete mixing plants, tunnel construction, underground mining and other fields to improve the level of dust control in the industry. In this way, it can effectively solve the problems of system dispersion, slow response, and low efficiency in dust control in closed underground spaces, and achieve efficient control and treatment of dust.

[0034] The embodiment of the present application first performs a three-dimensional dynamic simulation of the airflow field in the closed underground space based on the computational fluid dynamics model, and obtains the flow field data output by the three-dimensional dynamic simulation. Then, a source isolation operation is performed, that is, the source of the dust in the underground space is isolated and sealed based on the flow field data to reduce the spread of dust to the working space. Next, a regional suction operation is performed, and the working area of ​​the closed underground space is divided into multiple functional areas based on the flow field data, and the air volume of the independent exhaust duct system is configured for each functional area, so as to achieve precise regional management and enhance the reliability of the dust removal system. Furthermore, an airflow induction operation is performed, and the dust is guided to concentrate in the suction areas of multiple functional areas through airflow organization, thereby reducing the situation where dust does not need to spread. In addition, intelligent linkage control can also be performed, and multiple sensor data of multiple functional areas are collected according to the sensors of each functional area, and big data analysis is performed based on the multiple sensor data to control the dust removal system. The embodiment of the present application integrates the above-mentioned multiple operations into the dust removal system to form a unified control platform, which can effectively solve the risk problems caused by energy efficiency imbalance, path loss of control and insufficient adaptability in the dust control of underground spaces, and can realize efficient and reliable control and treatment of dust in closed underground spaces.

[0035] This embodiment of the application uses CFD to map physical and digital spaces, utilizes zoning strategies and airflow organization to form a multi-level control network, and ultimately achieves dynamic optimization of control efficiency through big data analysis. This method can be widely applied in enclosed environments such as underground mines, tunnel construction, and underground factories, promoting the transition of dust control from "extensive" to "precise and intelligent," providing technical support for occupational health and production safety.

[0036] In step 101, 3D scanning and modeling are first performed. A 3D stereolithography (STL) model of the enclosed underground space is obtained through 3D scanning. The physical properties of the dust and initial airflow parameters in the enclosed underground space are then measured. For example, a laser scanning device can be used to obtain the 3D STL model of the enclosed underground space, fully recreating geometric features such as the equipment layout and space dimensions. Then, dust particle size detection equipment is used to measure the physical properties of the dust, such as particle size distribution and density. Airflow detection equipment can also be used to measure initial airflow parameters. This provides boundary conditions for subsequent 3D simulations.

[0037] Next, the 3D structural STL model was repaired and divided into multiple computational domains. These domains consist of a core dust-generating area and a peripheral diffusion area. The core dust-generating area is the primary source of dust release, including areas such as the crusher discharge port and screening surface, and requires detailed simulation of initial dust diffusion. The peripheral diffusion area, located outside the core dust-generating area within the enclosed underground space, is used to simulate long-distance dust migration and sedimentation.

[0038] An unstructured tetrahedral mesh is then used for the core dust-generating area, adapting to complex geometric features and capturing the local flow field details of the underground enclosed space. A structured hexahedral mesh is used for the peripheral diffusion area to reduce computational effort while ensuring smooth flow transitions. A computational fluid dynamics model is constructed based on the set boundary conditions and fluid governing equations. For example, based on the Navier-Stokes equations and the k-ε turbulence model, boundary conditions are set to construct a three-dimensional CFD model capable of simulating dust diffusion and airflow fishholes. Dividing the core dust-generating area and the peripheral diffusion area into different meshes effectively reduces computational effort, lowers modeling costs, and accelerates computational efficiency.

[0039] Next, dynamic simulation equipment was used to simulate sudden dust emission conditions, and the accuracy of the computational fluid dynamics model was adjusted based on the simulation results. By simulating scenarios such as equipment startup and shutdown and dust leakage, concentration and velocity field data at different times were output. Based on the simulation results and field measurement data, the parameters of the computational fluid dynamics model were adjusted to improve the accuracy of the model. Through full-scenario simulation of various working conditions, the requirements of different scenarios such as enclosed underground spaces such as underground mines, tunnels, and underground factories can be met.

[0040] Finally, a numerical solution is performed based on the adjusted computational fluid dynamics model to obtain flow field data. In one example, parallel computing can be used to accelerate the solution, effectively shortening the simulation time and outputting the flow field data. Combining the CFD model with a 3D STL model can form a digital twin of the enclosed underground space, supporting fusion mapping with sensor data. This enables the computability and predictability of flow field data in the enclosed underground space, providing a data foundation for source control, airflow organization, and intelligent regulation of dust removal.

[0041] As an example, 3D laser scanning can be used to accurately capture underground spatial structural data (including tunnel outlines, equipment layout, and ventilation vent locations). A refined geometric model can be constructed, encompassing dust sources like crushers and conveyor belts, as well as obstacles like wall supports. Local mesh refinement is then performed in key dust release areas. Based on this, a discrete phase model is coupled with a Realizable k-ε turbulence model, with settings for inlet velocity, pressure outlet, and dust source particle parameters. "Trap" capture conditions are then applied to the wall surface to simulate dynamic operating conditions.

[0042] In step 102, key dust-generating points within the enclosed underground space are first identified based on flow field data. Key dust-generating points are locations within the enclosed underground space that have a significant impact on dust generation. For example, these points may include, but are not limited to, typical dust release sources such as crusher inlets and outlets, vibrating screening surfaces, conveyor belt junctions, and concrete mixing stations.

[0043] In one example, particle trajectories can be output from a discrete phase model to calculate the dust residence time and concentration in multiple subspaces of a closed underground space. Subspaces with concentrations greater than a set concentration threshold are then marked as potential dust-generating points. The set concentration threshold refers to a pre-set threshold for determining the concentration conditions in a subspace. The velocity field of the potential dust-generating point is then determined based on the dust residence time, and the initial kinetic energy of the dust at the potential dust-generating point, determined based on the velocity field, is determined. These potential dust-generating points are candidates for key dust-generating points. Next, dust sensors are deployed at each potential dust-generating point, and their priorities are verified across multiple dimensions. Finally, potential dust-generating points with priorities within a set range are designated as key dust-generating points. For example, potential dust-generating points can be ranked according to the magnitude of the data collected by the dust sensors, resulting in an order in which potential dust-generating points with higher dust production are given higher priorities. Based on a pre-set range, potential dust-generating points within the range are then selected as key dust-generating points. For example, if the set range is the top five priority points, the top five priority points can be selected as key dust-generating points.

[0044] Then, based on the physical characteristics of dust at key dust-generating points, determine and install physical barriers at these points. Physical barriers are a form of isolation achieved through physical means. The physical characteristics of dust at key dust-generating points form the basis for determining the selection of physical barriers at these points.

[0045] In one example, if the key dust-generating point is a high-frequency vibration equipment area, a composite structure of a metal frame and an elastic buffer layer is used as the physical barrier structure. If the key dust-generating point is a static equipment area, a fixed metal shell with a silicone sealing strip is used as the physical barrier structure. If the key dust-generating point is an area with frequent start-stop equipment, a retractable sealing cover is used as the physical barrier structure. If the key dust-generating point is an area with continuously running equipment, a detachable flexible curtain is used as the physical barrier structure. It should be noted that the setting of the physical barrier structure is not limited to the above-mentioned method, and different methods can be selected for physical isolation according to actual applications.

[0046] Next, a negative pressure hood is installed within the sealed cavity of the physical barrier structure and connected to an exhaust device to create a local negative pressure environment. Negative pressure extraction is the energy-driven core of dust capture. Its necessity stems from: 1. Momentum cancellation: Dust has initial kinetic energy when released, and the negative pressure generates a reverse velocity field that directly offsets the diffusion force; 2. Gas-solid separation: Through the Bernoulli effect, a velocity gradient is formed within the sealed cavity, prompting the directional migration of dust toward the suction port; 3. Flow field constraint: The negative pressure field compresses turbulent vorticity, reducing the probability of random dust movement; 4. Energy efficiency: The energy consumption of point-source extraction is only 1 / 10 of that of full-space ventilation (at the same dust removal efficiency).

[0047] Sealing control and negative pressure suction form a dual defense system of "physical barrier + energy control." First, they achieve synergy in the spatial dimension. Sealing control limits the dust diffusion space (compressing three-dimensional diffusion into two-dimensional motion), while negative pressure suction creates a directional velocity field within the confined space. For example, a flexible curtain confines dust to a 0.5 m³ volume, and the negative pressure system generates a high-speed airflow of 5 m / s within this small space, ten times faster than in open space. Second, they optimize energy transfer. Sealing control reduces energy loss, and the enclosed structure minimizes airflow outflow, allowing over 90% of the suction energy to be applied to the dust. Negative pressure suction can compensate for sealing defects by actively filling leaks in the sealing gap through a pressure differential. Furthermore, dynamic response coupling can be achieved. Sealing control provides stable boundary conditions, facilitating convergence of the negative pressure system's control equations. Negative pressure suction can also be adjusted in real time to compensate for vibration and deformation of the sealing structure.

[0048] In an embodiment of the present application, each functional area is provided with an independent exhaust duct system, and each exhaust duct system communicates with the main control system separately. The main control system can control the exhaust duct system of each functional area separately. In step 103, first, based on the flow field data, the dust parameters and airflow characteristics of the enclosed underground space are measured. Then, based on the dust parameters and airflow characteristics, the operating area of ​​the enclosed underground space is divided into a primary treatment area, a secondary linkage area, and an independent control area. The primary treatment area can correspond to the dust-producing core area, for example, it can include a crushing area, a feeding area, and a screening area. The primary treatment area is equipped with an independent controller and a dedicated suction unit, and undertakes most of the dust removal work. The secondary linkage area can cover the dust diffusion path, for example, it can include a conveying area and a channel area. The secondary linkage area can share a controller to form a collaborative network to handle the remaining escaping dust. Each primary area of ​​the independent control area is equipped with a dedicated controller to monitor and provide feedback on the work content and results in real time. Finally, the air volume of the exhaust duct system of each functional area is adjusted by the main control system.

[0049] The air volume of the exhaust duct system can be calculated using the following formula: ; in, For the The air volume of the exhaust duct system in each working area, For the The dust concentration in the working area The total air volume of the main control system.

[0050] The embodiment of the present application installs dust concentration sensors to monitor the dust concentration in each functional area in real time, dynamically adjust the air volume distribution, achieve zoning management, and dust removal on demand.

[0051] In step 104, based on the flow field data, the negative pressure capture core areas within the multiple functional zones are first identified as suction zones. Suction zones can include fixed suction ports, mobile capture points, and path control nodes. Fixed suction ports can include the inlet negative pressure hood of dust removal equipment (such as a cartridge dust collector), mobile capture points can include dynamic suction monitoring equipment that follows the movement of dust-generating equipment, and path control nodes can include V-shaped flow guide suction troughs at conveyor belt junctions.

[0052] Then, a drainage fan is installed upstream of the dust diffusion path to create a directional airflow corridor, and a guide grille with a set inclination angle (e.g., 45°) is installed to reconstruct the airflow field. Next, the pressure gradient between the suction and non-suction areas in multiple functional areas is maintained. An air curtain is installed above the conveyor belt to form an airflow guardrail, and a vortex generator is installed in the dust accumulation area to break up large vortices into small ones. Finally, the inertia of the dust is used to achieve gas-solid separation. A sudden expansion pipe section is installed in front of the suction port in the suction area, and spiral guide vanes are used to generate a cyclonic flow field, extending the residence time of the dust in the suction area and improving the capture efficiency.

[0053] The embodiment of the present application transforms the dust movement from random diffusion to controlled directional migration through flow field design. The various airflow organizing devices need to operate in a coordinated manner to form a unified airflow direction and speed, thereby reducing situations where diffusion is not required.

[0054] In step 105, multiple sensors are first deployed at key dust-generating points, airflow paths, and spatial environments in multiple functional areas, and multiple sensor data from the multiple sensors are acquired at a set frequency, wherein the set frequency is determined based on the dynamic change characteristics of dust.

[0055] Key dust-generating points are strategic locations in the system that directly impact dust control effectiveness. They can be categorized into three categories: core dust-generating nodes: direct dust-generating points such as the crusher feed inlet and the vibrating screen working surface; airflow control nodes: areas with sudden airflow changes, such as inflection points in the main ventilation tunnel and the dust collector inlet and outlet; and safety protection nodes: sensitive areas such as crowded areas and escape routes. Each node can be equipped with a dedicated sensor combination based on functional requirements, forming a "one-node, one-solution" monitoring system. For example, the crusher feed inlet can be equipped with a laser dust concentration meter, vibration sensor, and infrared thermal imager to monitor crushing intensity and dust emission in real time. The dust collector inlet can be equipped with a differential pressure sensor, anemometer, and humidity sensor to assess filter cartridge clogging. The operator's console can be equipped with a PM2.5 sensor, oxygen concentration meter, and noise meter to ensure a healthy workplace environment. Ventilation inflection points can be equipped with a three-dimensional ultrasonic anemometer and turbulence intensity sensor to detect vortex generation.

[0056] In one example, full-parameter sensors can be deployed at primary nodes, such as critical dust-generating areas (e.g., crushing zones), while only critical sensors can be deployed at secondary nodes (e.g., return air ducts) to reduce sensor costs. Data fusion mechanisms can also be used to group sensors from adjacent nodes into a monitoring network, such as using a Kalman filter algorithm to eliminate single-point errors. Furthermore, fault-tolerant design can be implemented, with redundant sensors deployed at critical nodes.

[0057] Next, a filtering algorithm is used to remove outliers from the sensor data. The data is then spatially and temporally aligned and stored in a time series database to perform data preprocessing. Next, a prediction model is constructed based on dust diffusion patterns, such as an LSTM model. This model, combined with historical data from the time series database, predicts the dust concentration after a set time. Finally, a PID control algorithm is used to adjust the dynamic parameters of the dust removal system based on the predicted dust concentration and the target concentration. This allows the system to automatically adjust operating parameters based on environmental changes, maintaining optimal operating conditions. This enables intelligent control of the dust removal system and improves system efficiency and reliability.

[0058] The subsystems are organically integrated to form a complete dust control solution, which is promoted and applied in related fields. For example, subsystems such as source isolation, regional suction, airflow induction, and intelligent control are integrated to form a unified control platform. This solution is promoted and applied in underground sand and gravel processing, concrete mixing plants, tunnel construction, underground mines and other fields to improve the level of dust control in the industry. By formulating unified design standards and specifications, the adaptability of the system in different application scenarios is ensured. In addition, operators can be systematically trained to ensure the correct use and maintenance of the system. Through the above implementation methods, the problems of system dispersion, slow response, low efficiency, etc. in dust control in underground spaces can be effectively solved, and efficient control and treatment of dust can be achieved.

[0059] The embodiments of the present application break through the limitations of traditional dust control in many key aspects, forming the following major innovations.

[0060] 1. Multi-dimensional collaborative governance model. The embodiment of this application breaks through the single exhaust governance mode and adopts a multi-level collaborative dust removal strategy of "point source control + regional suction + flow guidance" to achieve comprehensive dust control and eliminate the blind spots and limitations of traditional local governance.

[0061] 2. Modular Zoned Suction Structure. This embodiment of the application features a modular structure with independently controllable air ducts and fans in each zone. This design is suitable for complex, multi-source dust-generating environments, ensuring comprehensive treatment coverage and flexible deployment, capable of adjustment based on site needs.

[0062] 3. Embedded flow guidance mechanism. The embodiment of the present application not only focuses on the dust capture point, but also emphasizes the dust migration path. By actively intervening in the airflow, a directional dust removal effect is formed, effectively reducing the diffusion and backflow of dust.

[0063] 4. Intelligent linkage control platform. The dust removal system of the embodiment of the present application has adaptive response capabilities. It can drive system operation through sensor data and timely adjust air volume and suction strategy to meet the treatment needs under dynamic and variable working conditions, thereby improving the efficiency and effectiveness of treatment.

[0064] 5. Adapting to the structural characteristics of underground spaces. The system structure and control logic designed in this embodiment are optimized for underground engineering environments with high humidity, confinement, and minimal air disturbance, improving the system's adaptability and maintainability, ensuring stable operation under various complex conditions.

[0065] To sum up, the innovation of the embodiments of the present application lies in the formation of an efficient, flexible and intelligent dust control solution through the comprehensive use of multi-dimensional collaborative management, modular design, flow guidance and control, intelligent control platform and structural design adapted to the characteristics of underground space. It effectively responds to the control needs of multi-source dust in closed underground spaces and provides strong support for improving the safety of the working environment and the health of workers.

[0066] The following example uses the three-level technical framework of "modeling, planning, hardware deployment, and intelligent optimization" to systematically implement dust control. In one specific embodiment, a method for controlling multi-source dust control and dust removal linkage in a closed underground space can include the following steps.

[0067] First, a 3D CFD model was constructed using ANSYS Fluent to simulate the dust diffusion path, identifying the crusher discharge and conveyor belt turning points as key treatment targets. Based on the simulation results, the space was divided into a primary treatment area (jaw crusher and cone crusher), a secondary linkage area (screening machine and conveyor belt), and an independent control area (mixing station). A main induction air duct with a 30° inclination was designed to utilize gravity settling to reduce energy consumption.

[0068] Secondly, dynamic blocking is performed. Pneumatic telescopic enclosures are installed at the crusher inlet and outlet, an annular negative pressure chamber is set up around the screening machine, and a silicone fiberglass double-layer curtain structure is used at the conveyor belt drop point. Every 1m increase in drop triggers a 20% increase in air volume.

[0069] Then, staged suction is performed. Equipped with one 75kW master fan (global negative pressure 120Pa) and 18 slave fans (7.522kW), air volume is redistributed across zones via Venturi reducers (with a 0.6:1 contraction ratio), improving efficiency by 85%. The pulse cleaning system is triggered when filter cartridge resistance reaches 1200Pa, with a cleaning cycle of 30 seconds.

[0070] Next, path intervention was performed. Three longitudinal main air ducts (1.2m x 0.8m) and 24 lateral adjustable branch ducts were deployed, and a laser particle counter (TSI 3330) was used to track dust movement in real time. When a lateral diffusion velocity > 0.3m / s was detected, the pressure was instantly increased to 8m / s to intercept the dust, keeping the path deviation coefficient below 1.18.

[0071] Finally, the system integrates 12 types of sensor data, including dust concentration, equipment current, and vibration spectrum, and uses an LSTM neural network to predict dust distribution trends over the next five minutes. The system dynamically adjusts fan frequency and activates sleep mode during non-production periods. In the event of a sensor failure, the system infers dust concentration based on motor current harmonics. This AR operation and maintenance system improves filter cartridge maintenance efficiency by 60%.

[0072] The feasibility verification and effectiveness analysis of the above-mentioned embodiment are as follows. A 30-day continuous operation test compared the data before and after the transformation: Dust removal efficiency: The average PM10 concentration decreased, the capture efficiency increased, and the secondary capture rate of fugitive dust in key areas increased. Energy consumption optimization: Energy consumption per unit of output decreased. Environmental improvement: Humidity stabilized from over 85% to below 65%, equipment corrosion rate decreased, and visibility distance of the working surface increased. Path control: The dust migration path deviation coefficient was reduced, and trajectory stability was improved.

[0073] In summary, the embodiments of this application systematically address the challenges of adaptability, energy efficiency, and path controllability for dust control in enclosed underground spaces through the integrated technology chain of "modeling, sealing, suction, guidance, and optimization." Experimental data demonstrates that its dust removal efficiency, energy consumption, and reliability significantly outperform traditional methods, achieving both environmental and economic benefits. This provides a reusable benchmark solution for underground industrialized scenarios such as mines and tunnels.

[0074] The embodiments of the present application achieve multi-dimensional performance breakthroughs in dust control in closed underground spaces by integrating spatial airflow reconstruction, dust path intervention and intelligent collaborative control technologies. The specific technical effects are as follows.

[0075] Significantly Improved Dust Removal Efficiency: The system's overall dust collection efficiency has been enhanced, surpassing traditional single-suction methods. For example, at an underground sand and gravel processing plant, dust concentration at the crusher discharge port has been reduced, as has the average PM10 concentration, fully meeting the GBZ2.12019 occupational health standard. Through the synergistic effect of the induced air duct matrix and laser trajectory tracking, the dust migration path deviation coefficient has been optimized from 2.35 in the traditional model to 1.18, achieving a secondary capture rate of over 92% of fugitive dust in key areas.

[0076] Overall energy consumption has been significantly reduced: A graded negative pressure suction network, combined with Venturi duct coupling technology, achieves dynamic redistribution of air volume, reducing overall system energy consumption by 40% to 50%. The main fan consumes only 30% of its full load power in basic negative pressure mode, and sleep mode during non-production periods further reduces standby power consumption to below 5.5kW. For example, in an underground aggregate processing system with an annual output of one million tons, annual power consumption has been reduced from 580,000 kWh to 320,000 kWh, with energy consumption per unit of output as low as 0.68 kWh / ton, shortening the payback period to 2.3 years.

[0077] Comprehensively enhanced engineering adaptability: The modular design allows for the integration of new dust-generating equipment within 12 hours, maintaining a system capture efficiency exceeding 94% even after expansion. The induced air ducts can adapt to complex underground structures with elevation fluctuations ≤0.1m and slopes ≤15°. Even within a narrow 5m×5m area, a micro-duct network can be deployed, achieving a control coverage rate exceeding 98%. The dry dust removal solution strictly controls relative humidity in moisture-sensitive areas to below 65%, reducing equipment corrosion by 90% and completely eliminating the risk of electrical short circuits caused by spraying.

[0078] System reliability has been significantly improved: Using multimodal data fusion and an LSTM prediction model, dust dispersion trends can be predicted 510 minutes in advance, reducing response time to less than 200ms. Even in the event of a sensor failure, indirect monitoring based on motor current harmonic characteristics can maintain dust removal efficiency above 85%. Key component failure warning accuracy reaches 95%, and average annual unplanned downtime does not exceed 6.5 hours. An augmented reality operation and maintenance system increases filter cartridge replacement efficiency by 60%, and blockchain technology enables traceability management throughout the equipment lifecycle.

[0079] Significant environmental and social benefits: Work surface visibility has increased from less than 5 meters to over 20 meters, and the incidence of respiratory illnesses among workers has dropped by 83%. Dust concentration at the exhaust outlet has remained stable at ≤10 mg / m³, reducing dust emissions by over 120 tons annually. A case study at an underground iron mine shows annual equipment maintenance costs saving over 800,000 yuan, achieving significant environmental and economic benefits.

[0080] Through systematic innovation, this technology solves the long-standing problems of energy efficiency imbalance, path loss of control and lack of adaptability in dust control in closed spaces, providing an efficient, reliable and economical solution for underground industrial scenarios.

[0081] Figure 2 Schematic diagram of the structure of a dust removal system 200 provided in an embodiment of the present application. Figure 2As shown, the dust removal system 200 includes a memory 201 and a processor 202. The memory 201 is configured to store instructions. The processor 202 is configured to call instructions from the memory 201 and, when executing the instructions, implement any of the control methods for multi-source dust control and dust removal linkage in a closed underground space in the embodiments of the present application.

[0082] An embodiment of the present application also provides a computer-readable storage medium, which stores a program that can be loaded by a processor and execute any of the control methods for multi-source dust control and dust removal linkage in a closed underground space in the embodiments of the present application.

[0083] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above embodiments can be implemented by hardware or by computer program. When all or part of the functions in the above embodiments are implemented by computer program, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above functions can be implemented. In addition, when all or part of the functions in the above embodiments are implemented by computer program, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and saved in the memory of the local device by downloading or copying, or the system of the local device is updated. When the program in the memory is executed by the processor, all or part of the functions in the above embodiments can be implemented.

[0084] The above specific examples are used to illustrate the present application, which is only used to help understand the present application and is not intended to limit the present application. For those skilled in the art of the present application, based on the concept of the present application, they can also make some simple deductions, modifications or substitutions.

Claims

1. A control method for multi-source dust control and dust removal linkage in a closed underground space, characterized in that: Applied to a dust removal system, the control method includes: Performing a three-dimensional dynamic simulation of the airflow field in the closed underground space based on a computational fluid dynamics model to obtain flow field data output by the three-dimensional dynamic simulation; Based on the flow field data, the source of dust in the underground space is isolated and sealed; Based on the flow field data, the operating area of ​​the enclosed underground space is divided into multiple functional areas, and the air volume of the independent exhaust duct system of each functional area is determined; Based on the flow field data, the dust is guided to concentrate toward the suction areas in the plurality of functional areas through an airflow organization device; Multiple sensor data of multiple functional areas are collected according to the sensors of each functional area, and big data analysis is performed based on the multiple sensor data to control the dust removal system.

2. The control method according to claim 1, characterized in that: The three-dimensional dynamic simulation of the airflow field in the closed underground space based on the computational fluid dynamics model is performed to obtain the flow field data output by the three-dimensional dynamic simulation, including: Acquire a three-dimensional structural stereolithography model of the enclosed underground space through three-dimensional scanning, and measure the physical properties of dust and initial airflow parameters in the enclosed underground space; Repairing the three-dimensional structure stereolithography model and dividing it into multiple calculation domains, wherein the calculation domain includes a core dust generation area and a peripheral diffusion area; An unstructured tetrahedral grid is used for the core dust-generating area, a structured hexahedral grid is used for the peripheral diffusion area, and a computational fluid dynamics model is constructed based on set boundary conditions and fluid control equations; Conducting a simulation experiment of sudden dust conditions using a dynamic simulation device, and adjusting the accuracy of the computational fluid dynamics model based on the simulation experiment results; A numerical solution is performed based on the adjusted computational fluid dynamics model to obtain the flow field data.

3. The control method according to claim 1, wherein: The dust removal system includes an exhaust device, which isolates and seals the source of dust in the underground space based on the flow field data, including: determining key dust-generating points in the enclosed underground space based on the flow field data; Determine and set up the physical barrier structure of the key dust generating point according to the physical characteristics of the dust at the key dust generating point; A negative pressure hood is arranged in the sealed cavity of the physical barrier structure and is connected to the exhaust device to form a local negative pressure environment.

4. The control method according to claim 3, characterized in that: Determining key dust-generating points in the enclosed underground space based on the flow field data includes: Based on the particle trajectories output by the discrete phase model, the dust residence time and concentration in multiple subspaces of the closed underground space are calculated; Marking the subspace with a concentration greater than a set concentration threshold as a potential dust generating point; determining a velocity field of the potential dust generating point based on the dust residence time, and determining an initial kinetic energy of the dust at the potential dust generating point based on the velocity field; Deploy a dust sensor at each of the potential dust generating points and verify the priority of the potential dust generating points through multiple dimensions; The potential dust generating points whose priorities are within a set range are regarded as the key dust generating points.

5. The control method according to claim 3, characterized in that: The determining and setting of the physical barrier structure of the key dust generating point according to the physical characteristics of the dust at the key dust generating point includes: If the key dust-generating point is a high-frequency vibration equipment area, a composite structure of a metal frame and an elastic buffer layer is used as the physical barrier structure; If the key dust-generating point is a static equipment area, a fixed metal shell with a silicone sealing strip is used as the physical barrier structure; If the key dust-generating point is an area where equipment is frequently started and stopped, a telescopic sealing cover will be activated as the physical barrier structure; If the key dust-generating point is an area where equipment is continuously in operation, a detachable flexible curtain is used as the physical barrier structure.

6. The control method according to claim 1, characterized in that: The exhaust duct system of each functional area communicates with the main control system respectively. Based on the flow field data, the operation area of ​​the closed underground space is divided into multiple functional areas, and the air volume of the independent exhaust duct system of each functional area is determined, including: Determining dust parameters and airflow characteristics of the enclosed underground space based on the flow field data; Based on the dust parameters and airflow characteristics, the operating area of ​​the enclosed underground space is divided into a primary treatment area, a secondary linkage area, and an independent control area, wherein the primary treatment area includes a crushing area and a screening area, and the secondary linkage area includes a conveying area and a passage area; The air volume of the exhaust duct system in each functional area is adjusted by the main control system.

7. The control method according to claim 6, characterized in that: The air volume of the exhaust duct system is calculated by the following formula: ； in, For the The air volume of the exhaust duct system in each working area, For the The dust concentration in the working area is the total air volume of the main control system.

8. The control method according to claim 1, characterized in that: The method of guiding the dust to concentrate in the suction areas of the plurality of functional areas by an airflow organizing device based on the flow field data includes: Based on the flow field data, a negative pressure capture core area in the plurality of functional areas is used as the suction area, wherein the suction area includes a fixed suction port, a movable capture point and a path control node; A drainage fan is set upstream of the dust diffusion path to construct a directional airflow corridor, and a guide grille with a set inclination angle is installed to achieve reconstruction of the airflow field; Maintaining a pressure gradient between the suction area and the non-suction area in the plurality of functional areas, setting an air curtain above the conveyor belt to form an airflow guardrail, and installing a vortex generator in the dust accumulation area to break up large vortices into small vortices; A sudden expansion pipe section is arranged in front of the suction port of the suction area, and a spiral guide vane is used to generate a cyclonic flow field, thereby extending the residence time of the dust in the suction area.

9. The control method according to claim 1, characterized in that: The collecting of a plurality of sensor data of a plurality of functional areas according to the sensors of the functional areas, and performing big data analysis based on the plurality of sensor data to control the dust removal system, includes: Deploy multiple sensors at key dust-generating points, airflow paths, and spatial environments in multiple functional areas, and acquire multiple sensor data from the multiple sensors at a set frequency, where the set frequency is determined based on the dynamic change characteristics of dust; Eliminate abnormal values ​​of the plurality of sensor data by a filtering algorithm, and perform a spatiotemporal alignment operation on the plurality of sensor data before storing them in a time series database; Constructing a prediction model based on the dust diffusion law, and combining the historical data in the time series database to predict the dust concentration after a set time; According to the predicted dust concentration and the target concentration, the dynamic parameters of the dust removal system are adjusted through a fuzzy proportional integral differential control algorithm.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which can be loaded by a processor and executed by the control method for multi-source dust control and dust removal linkage in a closed underground space as described in any one of claims 1 to 9.

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