Intelligent spray control method, system and device for dust suppression of stockyard

Abstract

The application discloses an intelligent spray control method, system and device for dust suppression of a stockyard. The method comprises the following steps: collecting field environment, dust and operation data in real time, and identifying instantaneous dust production of each dust production point; combining with a three-dimensional topography of a stockpile to correct a dust diffusion model, and predicting a space-time distribution of dust diffusion; calculating an optimal droplet particle size of each area based on topography partition; completing space-time matching of a fog field and priority replacement of a coverage blind area in linkage; executing spray and online calibration of model parameters through effect feedback; and the system comprises perception, three-dimensional scanning, diffusion prediction, particle size calculation, cooperative scheduling, execution and closed-loop correction modules. The application breaks through the limitation of traditional passive threshold control, realizes deep cooperation of dust feedforward interception, accurate particle size matching and dynamic replacement of blind areas, has high dust suppression efficiency and low water consumption, and is suitable for dust control of various stockyards.

Description

Technical Field

[0001] This invention relates to the field of dust control technology in material yards, specifically to an intelligent spray control method, system, and device for dust suppression in material yards. Background Technology

[0002] Open-air material yards generate significant amounts of fugitive dust during material storage, loading, unloading, and transfer, causing material loss and polluting the surrounding atmosphere. Spray dust suppression is currently the most widely used dust control method due to its flexible deployment and moderate cost. Existing intelligent spray systems generally employ concentration-triggered control logic: dust sensors are deployed on-site, and the spray system in the corresponding area is activated only when the dust concentration exceeds a set threshold. This approach is essentially reactive, requiring the dust to disperse and trigger the sensors before it responds, inherently exhibiting a lag. Especially in windy weather, dust can quickly drift outside the yard, causing emissions to exceed standards by the time the spray system is activated.

[0003] Meanwhile, most existing spray systems use fixed-size water mist particles, and a few solutions can only switch between two or three coarse settings, which cannot match the constantly changing characteristics of dust particles on site. For example, during operation, there are many coarse particles, and in calm weather, there are many fine particles. A fixed-size water mist either fails to capture fine dust, or wastes water and easily wets and clumps the material, affecting subsequent use.

[0004] In addition, the material pile will continue to deform as the material is piled and removed, and the top of the pile will sink, the slope angle will change, and the overall height will rise or fall, which will turn the area that could originally be covered into a spray dead zone; and the angle of most existing nozzles is difficult to adjust to adapt to the changes in the shape of the material pile. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent spray control method, system, and device for dust suppression in material yards, solving the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent spray control method for dust suppression in material yards, comprising the following steps: S1: Real-time collection of environmental parameters, dust monitoring data and operation status data of the entire material yard, identification of the location and type of dust generation points, and dynamic calculation of the instantaneous dust generation at each dust generation point; S2: Obtain the three-dimensional topographic data of the current material pile, perform terrain correction on the dust diffusion model based on the three-dimensional topographic data, and calculate the spatial distribution and temporal pattern of dust diffusion within a set time period in the future by combining the instantaneous dust generation and real-time wind field parameters, and obtain the arrival time, expected concentration and dust transmission distance of dust at each location. S3: Based on the three-dimensional topography data of the material pile, the material yard is divided into multiple feature regions. Combining the real-time dust particle size distribution data and the dust transmission distance of the corresponding region, the optimal droplet size for each region is calculated according to the physical mechanism of dust capture and converted into the corresponding spray operation parameters. S4: Match the corresponding spraying device to the spatiotemporal distribution of dust diffusion, and adjust the operating parameters and start-up sequence of each spraying device by correcting the droplet offset caused by the wind field in combination with the optimal droplet size; at the same time, identify blind areas that cannot be covered by spraying based on the three-dimensional topography data of the material pile, prioritize the blind areas in combination with the dust diffusion path, and perform supplementary spraying scheduling for high-priority blind areas. S5: Issue operating instructions to each spraying device to complete the dust suppression spraying operation; S6: Collect dust monitoring data after spraying, compare the actual dust suppression effect with the predicted results, and automatically calibrate the parameters of the relevant models online.

[0007] Furthermore, in step S1, the types of dust-generating points include concentrated point sources formed by unloading points, continuous line sources formed by vehicle travel routes, and large-area surface sources formed by wind erosion on the surface of material piles; the instantaneous dust generation is dynamically calculated by combining the type of operation, the dust generation characteristics of the material, and the real-time wind speed.

[0008] Furthermore, in step S2, the terrain correction specifically includes three types of correction methods: adding a material pile height lifting term to correct the initial dust diffusion height, adding a leeward slope eddy correction term to correct the diffusion path of the leeward area of ​​the material pile, and adding a slope slip diffusion term to correct the secondary dust diffusion trajectory generated by particles rolling down the slope.

[0009] Furthermore, in step S3, the material yard is divided into four characteristic areas based on the three-dimensional morphology of the material pile: the top working area, the slope area, the ground driving area, and the boundary buffer zone. The physical mechanism of dust collection comprehensively considers three collection mechanisms: inertial collision, interception, and Brownian diffusion. The curve of the overall collection efficiency as a function of droplet size is calculated, and the droplet size corresponding to the peak efficiency is taken as the optimal droplet size.

[0010] Furthermore, in step S4, the specific method for correcting the droplet offset caused by the wind field is as follows: based on the current optimal droplet size and real-time wind field parameters, calculate the lateral offset distance of the droplets within the effective range, and reverse-calibrate the spray angle of the spray device to counteract the influence of the wind field on the spatial position of the fog field.

[0011] Furthermore, in step S4, the priority ranking rule for coverage blind spots is as follows: they are ranked from shortest to longest time when dust arrives in the blind spot, with the shorter the arrival time, the higher the priority. Blind spots without dust passing through are not subject to supplementary spraying. When performing supplementary spraying scheduling, the coverage range is expanded by adjusting the angle and pressure of the existing spraying device first. If effective coverage cannot be achieved, the backup supplementary spraying device is activated.

[0012] Furthermore, in step S6, the model parameters for online automatic calibration include the terrain correction coefficient of the dust diffusion model, the material adaptation coefficient of the dust capture mechanism model, and the effective range parameter of the spray device. The calibration process is based on the difference between dust monitoring data before and after spraying, and iterative fitting correction is performed using the least squares method.

[0013] Furthermore, it also includes a periodic morphology update step: triggering a full-area 3D scan at a set time after the end of each day's operation, reconstructing the latest 3D morphology data of the material pile, recalculating the effective coverage range of each spraying device, updating the coverage blind zone map, and simultaneously correcting the terrain parameters of the dust diffusion model and the regional boundaries that match the particle size of the zones.

[0014] The intelligent spray control system for dust suppression in material yards utilizes the aforementioned intelligent spray control method for dust suppression in material yards, including a sensing layer module, a three-dimensional morphology scanning module, a dust diffusion prediction module, a droplet size calculation module, a fog field collaborative scheduling module, a spray execution module, and a closed-loop correction module. The sensing layer module is connected to the dust diffusion prediction module, the droplet size calculation module and the closed-loop correction module respectively, and is used to collect and output environmental parameters, dust monitoring data and operation status data. The three-dimensional morphology scanning module is connected to the dust diffusion prediction module, the droplet size calculation module and the fog field collaborative scheduling module respectively, and is used to collect and output the three-dimensional morphology data of the material pile. The dust diffusion prediction module is signal-connected to the fog field collaborative scheduling module. It is used to receive dust generation data and three-dimensional topographic data of the material pile. After calculation by the topographically corrected diffusion model, it outputs the spatiotemporal distribution data of dust diffusion. The droplet size calculation module is signal-connected to the fog field collaborative scheduling module. It is used to receive dust particle size distribution data, dust transmission distance data and material yard zoning data, and output the optimal droplet size parameters for each region after calculation by the dust collection mechanism model. The fog field collaborative scheduling module is signal-connected to the spray execution module. It is used to receive dust diffusion spatiotemporal distribution data, optimal droplet size parameters and coverage blind zone data, and output the operating parameters and timing instructions of each spray device after comprehensive calculation. The closed-loop correction module is connected to the dust diffusion prediction module, the droplet size calculation module, and the fog field collaborative scheduling module, respectively, and is used to compare dust data before and after spraying and to calibrate the model parameters of each module online.

[0015] The intelligent spray control device for dust suppression in the material yard is equipped with the aforementioned intelligent spray control system for dust suppression in the material yard.

[0016] This invention provides an intelligent spray control method, system, and device for dust suppression in material yards, which has the following beneficial effects: 1. This intelligent spray control method, system, and device for dust suppression in material yards constructs an active and precise control system that deeply couples dust diffusion prediction, droplet size adaptation, and morphological blind spot compensation. It achieves spatiotemporal feedforward interception of dust through a terrain-corrected diffusion model, replacing the traditional post-event response mode; it replaces fixed parameter or coarse-level switching atomization control with dynamic particle size matching driven by the capture mechanism; and it replaces the static coverage scheme with a fixed angle with dynamic blind spot compensation based on morphological perception. It simultaneously eliminates the inherent defects of traditional schemes from the three dimensions of time, particle size, and space, fundamentally improving the accuracy and operational reliability of dust suppression control.

[0017] 2. The intelligent spray control method, system, and device for dust suppression in material yards also realizes the reuse of multi-dimensional data and system-level joint collaboration. The three-dimensional morphology data of the material pile simultaneously supports diffusion model correction, regional characteristic division, and coverage blind zone identification. The optimal particle size parameter simultaneously serves to improve collection efficiency and calibrate wind-induced offset. The dust prediction results are simultaneously used for fog field timing scheduling and blind zone priority judgment. This control system can simultaneously reduce water consumption, reduce material over-wetting, and maintain stable long-term operation while ensuring that emissions at the site boundary meet standards. It takes into account multiple engineering requirements such as environmental compliance, production process quality, and operation and maintenance costs. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the steps of the present invention. Detailed Implementation

[0019] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0020] Taking an open-air coal yard as an example, the yard is 200 meters long and 120 meters wide. The site is equipped with multiple sets of servo adjustable air-driven spray devices, variable frequency water pump sets, environmental monitoring equipment, dust monitoring equipment, visual acquisition equipment, and 3D scanning equipment. All equipment is connected to an industrial edge controller, which completes calculations and control locally without relying on cloud networks.

[0021] The servo-adjustable air-assisted spraying devices are evenly arranged along both sides of the material yard at 15-meter intervals. Each spraying device can automatically adjust its angle in both horizontal and vertical directions, with a horizontal adjustment range of 0 to 180 degrees and a vertical adjustment range of 0 to 60 degrees. The rated working pressure of the spraying devices ranges from 0.8 MPa to 1.2 MPa, corresponding to an effective horizontal range of 60 to 80 meters and an atomized particle size range of 20 to 120 micrometers. The dual-sided spraying can completely cover the entire 120-meter-wide material yard area. The variable frequency water pump set can continuously adjust the output pressure, with a pressure adjustment accuracy of not less than 0.02 MPa.

[0022] Environmental monitoring equipment includes anemometers and temperature and humidity sensors, deployed in open areas around the material yard, collecting data once per second. Dust monitoring equipment includes optical particle size analyzers and dust concentration sensors, deployed in the work area, slope area, and yard boundary, respectively, capable of real-time detection of dust concentrations of different particle sizes, collecting data once every 5 seconds. Visual acquisition equipment uses high-definition network cameras, erected at the highest point in the material yard, covering the entire work area, used to identify the type, location, and operation of working equipment. 3D scanning equipment uses single-line LiDAR, mounted on the tops of pillars on both sides of the material yard, with a scanning range covering the entire material pile, achieving centimeter-level accuracy.

[0023] The industrial edge controller uses an industrial-grade processor based on the ARM architecture, and its local computing power meets the real-time computing requirements, making it suitable for the complex industrial environment of the material yard.

[0024] When the system is first deployed or calibrated quarterly, an initial calibration should be performed. The specific steps are as follows: Establish a unified global coordinate system; with the southwest corner of the material yard as the origin, establish a global coordinate system with the east as the horizontal axis, the north as the vertical axis, and the vertical upward as the vertical axis, measure the installation coordinates of all equipment and enter them into the system to complete the spatial position calibration.

[0025] Calibrate the parameters of the spraying device; for each spraying device, measure the horizontal range, vertical range, atomization cone angle and actual atomized particle size at three pressure levels of 0.8 MPa, 1.0 MPa and 1.2 MPa respectively, fit the corresponding curves of pressure and range, and pressure and particle size, and store them in the spraying device parameter library.

[0026] Enter material characteristics; enter the dust generation characteristics of the currently stored materials, including dust generation wind speed threshold, material angle of repose, reference particle size distribution, and reference moisture content, and establish a material characteristic database.

[0027] Calibrate the dust generation during operations; obtain the dust generation of typical operations such as loader unloading, grab bucket dropping, and vehicle driving under different wind speeds through on-site measurements, and establish a benchmark correspondence between operation actions and dust generation.

[0028] Initial topography scan; activate lidar to complete the first full-area scan, generate the initial 3D model of the material pile, complete the first calculation of the spray coverage dead zone, and generate the initial blind zone map.

[0029] like Figure 1 As shown, when the system is running in real time, the following control flow is executed in a 10-second cycle; Step S1: Multi-source data acquisition and dynamic calculation of dust generation 1.1 The sensing layer module collects real-time data from the entire site: environmental parameters include wind speed, wind direction, ambient temperature, and relative humidity; dust data includes PM2.5 at each monitoring point. 2.5 Concentration, PM 10 Concentration, total suspended particulate matter concentration, and dust particle size distribution; operational data includes the type, location, movement trajectory, and operational actions of the equipment used on site.

[0030] 1.2 Based on the location and operation of the operating equipment, identify the spatial location of each dust-generating point and classify them into three categories according to the form of dust generation: dust generation concentrated at the unloading point is a point source, dust generation along the road where vehicles travel is a line source, and large-area dust generation caused by wind erosion on the surface of the material pile is a surface source.

[0031] 1.3 Calculate the instantaneous dust generation at each dust-generating point by combining the type of work action, the dust-generating characteristics of the corresponding material, and the real-time wind speed. The specific calculation process is as follows: First, retrieve the baseline dust generation amount corresponding to the current operation under the baseline wind speed condition from the operation source strength baseline library; then calculate the ratio of the real-time wind speed to the baseline wind speed, and take the wind speed correction coefficient by raising the corresponding material wind erosion index to the power of the ratio; finally, multiply the baseline dust generation amount by the wind speed correction coefficient to obtain the instantaneous dust source strength of the dust generation point under the current working condition.

[0032] The baseline wind speed is uniformly set at 3 meters per second. The wind erosion index for coal is set at 1.8, and for sand and gravel at 2.2. The wind erosion index for each type of material can be calibrated through on-site measurements. The higher the wind speed, the higher the wind speed correction coefficient, and the greater the corresponding instantaneous dust generation.

[0033] Calculation Example: The baseline dust generation of a loader unloading operation at a reference wind speed is 0.02 grams per second. The real-time wind speed at the site is 5 meters per second, corresponding to a wind erosion index of 1.8 for the coal material. First, calculate the ratio of 5 to 3, which is approximately 1.67. Then, raise 1.67 to the power of 1.8 to obtain a wind speed correction factor of approximately 2.5. Finally, multiply 0.02 by 2.5 to obtain the current instantaneous dust source intensity of 0.05 grams per second.

[0034] Step S2, Spatiotemporal prediction of dust dispersion with terrain correction 2.1 The latest three-dimensional topographic data of the stockpile is used to perform terrain correction on the simplified Gaussian plume diffusion model, and the dust concentration at each point downwind is calculated.

[0035] The specific calculation logic is as follows: taking the dust-generating point as the origin, the dust concentration is calculated point by point along the wind direction. The calculation is based on the amount of dust generated per unit time, divided by the product of the wind speed and the diffusion parameters in both the horizontal and vertical directions to obtain the basic concentration value; then, based on the horizontal distance between the calculation point and the downwind main axis, the concentration attenuation caused by horizontal diffusion is calculated using an exponential relationship; finally, the upward natural diffusion component of dust and the downward diffusion component after dust hits the ground are calculated separately, and the two components are superimposed to obtain the total dust concentration at that calculation point.

[0036] The horizontal diffusion parameter and the vertical diffusion parameter reflect the intensity of dust diffusion in the horizontal and vertical directions, respectively. The values ​​of both parameters gradually increase with the downstream distance of the wind direction.

[0037] The basic concentration calculation formula for the Gaussian plume diffusion model is as follows:

[0038] in, For calculation points Dust concentration at a location, dimension mg / m³ 3 ; The instantaneous dust generation (source strength) at the dust-generating point is expressed in mg / s. It is obtained by dynamic calculation in step S1.3, which combines the type of operation, the dust generation characteristics of the material, and the real-time wind speed. The benchmark dust generation of coal is calibrated by on-site measurement, and the wind erosion index is taken as 1.8 for coal and 2.2 for sand and gravel. The real-time average wind speed at a height of 10m is expressed in m / s. The wind speeds collected in real time by the anemometer are converted to the height of 10m according to the wind speed profile exponential law. In the entire text, u refers to the real-time average wind speed at this height of 10m. The distance from the calculation point to the downwind point of the dust-generating point is measured in meters; it is obtained by measuring along the main wind axis with the dust-generating point as the origin of the coordinate system. The lateral distance from the point to the downwind axis is measured in meters and obtained by projection onto the global coordinate system. The vertical height of the point above the ground is measured in meters (m); it is obtained by combining the three-dimensional topography data of the material pile with the global coordinate system. The effective source height is in meters; that is, the height of the material pile surface at the location of the dust generation point, which is extracted from the three-dimensional topographic scanning data. , These are the diffusion parameters in the horizontal and vertical directions, respectively, both in meters; both vary with downwind distance. The diffusion rate increases with increasing volume, as determined by the Pasquill-Gifford diffusion curve and Briggs' empirical formula.

[0039]

[0040] In the formula, , , , The empirical coefficients related to atmospheric stability levels are dimensionless; atmospheric stability levels are obtained from tables based on real-time surface wind speed and solar radiation levels using the Turner method; each stability level corresponds to... , , , The coefficient values ​​are determined by consulting the Briggs formula table. This coefficient table is stored in the system as the factory default parameters and can be used in the online calibration in step S6 to perform the least squares method based on the deviation between the actual monitored concentration and the predicted concentration. , Perform iterative corrections.

[0041] 2.2 Terrain correction is achieved by adjusting the initial dust dispersion height and dispersion parameters, specifically including three types of correction: The first type is the correction for the height of the material pile. If the dust generation point is located on the surface of the material pile, the actual height of the corresponding position of the material pile is included in the initial dust diffusion height to correct the starting height of dust diffusion, so as to avoid treating dust generated at high places as ground dust and causing the near-ground concentration to be calculated too low.

[0042] The second category is leeward slope vortex correction. When the calculation location is on the leeward side of the stockpile, the vertical diffusion parameter value is appropriately increased according to the actual height of the stockpile and the magnitude of the incoming wind speed to simulate the diffusion enhancement effect caused by vortices on the leeward side of the stockpile and improve the concentration prediction accuracy in the leeward area. When the calculation location is on the leeward side of the material pile (i.e., the line connecting the calculation point and the dust generation point passes through the material pile body), increase the vertical diffusion parameter according to the following formula:

[0043] In the formula, The corrected vertical diffusion parameter is in meters (m). The leeward eddy correction coefficient is dimensionless and is set to 0.35 by default at the factory. In step S6, the least squares method is used for iterative calibration based on the deviation between the actual leeward concentration monitoring value and the predicted value. The height of the stockpile, in meters, is extracted from three-dimensional topographic data. The horizontal width of the material pile from the windward side to the leeward side, in meters, is extracted from the three-dimensional topographic data. For reference wind speed, take 3 m / s; The real-time average wind speed at a height of 10m, in units of m / s, is consistent with the definition of u in step 2.1; this correction simulates the diffusion enhancement effect caused by eddies at the leeward side of the material pile.

[0044] The third category is slope slip diffusion correction. This addresses secondary dust generated by materials rolling down slopes, reducing the amount of dust generated per unit time that was originally concentrated at the point source location. The dust source extends along the slope as a continuous line. Specifically, starting from the dust-generating point, a length of [length missing] is drawn along the direction of the slope's roll-off. The line segment is used as the line source, and the line source intensity is... The dimensionless value is mg / (s·m); The dimension is m, and the length of the slope roll-off path is extracted from the 3D topographic data. For each micro-element on the line source, according to... The concentration is calculated using the formulas, and then integrated along the source length to obtain the corrected spatial concentration distribution, thereby simulating the diffusion characteristics of continuous dust generation during the process of particles rolling down the slope.

[0045] 2.3 Combining the instantaneous dust generation and real-time wind field parameters obtained in step 1, the modified diffusion model is quickly calculated in the edge controller. With a time interval of 10 seconds, the spatiotemporal distribution of dust diffusion in the next 60 seconds is output, including the dust concentration in each area at each time point, the time when the dust arrives in the area, and the transmission distance of the dust from the dust generation point to the area.

[0046] Step S3: Calculation of optimal droplet size for each zone 3.1 Based on the three-dimensional topographic data of the stockpile, the entire stockyard is divided into four characteristic areas: the top working area, the slope area, the ground driving area, and the boundary buffer zone. The dust particle characteristics and dust transmission distances are different in different areas.

[0047] 3.2 For each region, the comprehensive collection efficiency is calculated based on the physical mechanism of dust collection, by combining real-time dust particle size distribution data with the dust transport distance of that region.

[0048] The physical mechanism of dust collection includes three mechanisms: inertial collision, interception, and Brownian diffusion. The collection efficiency of a single spherical droplet for a single dust particle is calculated as follows (the formula system is derived from the classic model of wet scrubbing dust removal): Inertial collision capture efficiency :

[0049]

[0050] In the formula, It is dimensionless; The Stokes number is dimensionless. The Cunningham slip correction factor is dimensionless and applies to PM. 10 For particles with a diameter of 1.0 or larger, use 1.0; for submicron particles, use... Calculation, where The mean free path of gas molecules is taken as 6.5 × 10⁻⁶ in air. 8 m; The particle size is measured in meters and is obtained in real time by an optical particle size analyzer. The density of dust particles, in kg / m³ 3 Coal dust is taken as 1.3×10 3 kg / m 3 Entered from the material properties database; The relative velocity between dust particles and the airflow field, in m / s, is estimated by the difference between the real-time wind speed and the axial velocity of the droplets. Let be the dynamic viscosity of the gas, in units of Pa·s. For air at room temperature and pressure, take 1.79 × 10⁻⁶. 5 Pa s; Let be the droplet diameter, dimensionless (m), which is used as the variable in this solution.

[0051] Interception and capture efficiency:

[0052]

[0053] In the formula For efficient interception and capture, dimensionless; The interception parameters are dimensionless. The particle size is in meters (m). Let be the droplet diameter, used as a solution variable.

[0054] Brownian diffusion trapping efficiency:

[0055]

[0056]

[0057] In the formula The Brownian diffusion trapping efficiency is dimensionless. These are Peckley numbers, dimensionless; Let be the Brownian diffusion coefficient of the dust, with dimensions m. 2 / s; Let be the Boltzmann constant, taken as 1.381 × 10⁻⁶. 23 J / K; This represents the gas phase temperature, measured in degrees Celsius, and is collected in real time by a temperature and humidity sensor; the definitions of other symbols are the same as above.

[0058] Comprehensive capture efficiency model: Assuming the three capture mechanisms mentioned above are independent of each other, the overall capture efficiency of a single droplet for a single dust particle is... for:

[0059] In the formula For comprehensive collection efficiency, dimensionless, is the droplet diameter. The function.

[0060] Method for finding the optimal droplet size: Within the system-defined atomization particle size range Within (20μm to 120μm in this example), traverse at a predetermined step size (1μm in this example). Substitute the values ​​into the above formula to calculate the corresponding overall capture efficiency. The curve of overall collection efficiency versus droplet size was obtained. This curve exhibits a single-peak trend of first increasing and then decreasing within the commonly used atomization droplet size range. The droplet size corresponding to the peak efficiency was taken as the theoretically optimal droplet size for this region. :

[0061] in, The value of the independent variable that maximizes the latter term is taken as follows: within the atomization particle size range set by the system, the droplet diameters are discretely traversed from small to large at a predetermined step size (1 μm in this example), and the comprehensive collection efficiency described in step 3.2 is calculated one by one. The droplet diameter that maximizes the comprehensive collection efficiency is taken as the theoretical optimal droplet particle size for this region. The lower limit and upper limit of the atomization particle size range are the minimum and maximum atomization particle sizes that the atomizing device can achieve under the current spray pressure (20 μm and 120 μm in this example, respectively). When there are multiple droplet diameters with a comprehensive collection efficiency difference of less than a set threshold (0.5% in this example), the smaller droplet diameter is taken as the theoretical optimal droplet particle size. When the comprehensive collection efficiency changes monotonically within the atomization particle size range without a peak, the droplet diameter corresponding to the larger comprehensive collection efficiency at the endpoint of the interval is taken as the theoretical optimal droplet particle size.

[0062] At the same time, adjustments are made based on the dust transmission distance: when the transmission distance... Greater than the preset threshold (Taking 30m as an example) when, Based on the relational formula Increasing the droplet size enhances the droplets' ability to withstand wind. The distance correction factor is 0.15 by default and can be calibrated in step S6 according to the actual dust suppression efficiency deviation; after distance correction... This is the final target droplet size for that region.

[0063] 3.3 After obtaining the optimal droplet size for each region, the pressure-droplet size correspondence curve in the spray device parameter library is called and converted into the target spray pressure for the corresponding region spray device.

[0064] The inertial collision mechanism relies on the inertia of dust particles to be captured after colliding with the droplets. The finer the droplets and the coarser the dust, the higher the collision efficiency. The interception mechanism relies on dust particles being adhered to the surface of the droplets when they brush past them. The finer the droplets, the higher the interception efficiency. The Brownian diffusion mechanism relies on the random Brownian motion of fine dust particles colliding with the droplets. The finer the droplets, the higher the diffusion efficiency.

[0065] It should be noted that although the collection efficiency of all three types increases with droplet fineness, when the droplets are too fine, the wind's blowing effect on the droplets is significantly enhanced. Droplets are dispersed by the wind before reaching the target area, resulting in a substantial reduction in the actual number of effective droplets reaching the target area, and thus a decrease in overall effective collection efficiency. Therefore, after comprehensive calculations within the commonly used atomization particle size range, the overall effective collection efficiency exhibits a single-peak trend of first increasing and then decreasing. The droplet size corresponding to the highest point of the efficiency curve is the theoretically optimal droplet size for that region.

[0066] Step S4: Fog Field Cooperative Scheduling and Dead Zone Compensation 4.1 Spatiotemporal Matching of Fog Field: Based on the spatiotemporal distribution of dust diffusion, the corresponding spray devices in the coverage area are matched, and the opening time, closing time and spray angle of each spray device are calculated to make the spatial coverage of the fog field coincide with the diffusion range of dust, and to make the effective action time of the fog field coincide with the arrival time of dust, so as to achieve feedforward control of the fog field being deployed before dust arrives.

[0067] At the same time, based on the optimal droplet size and real-time wind speed in the current area, the lateral drift distance of the droplets within the effective range is calculated, and the horizontal and pitch angles of the spray device are calibrated in reverse to offset the fog field offset caused by the wind field and ensure that the fog field accurately covers the target dust area.

[0068] The specific calculation process for the lateral offset of the droplets is as follows: First, based on the current droplet size, the density of the droplets and air, and the initial axial velocity and attenuation law of the wind-driven airflow, estimate the average axial flight velocity of the droplets within the range; then, based on the horizontal straight-line distance from the spray device to the target area, calculate the total time required for the droplets to travel from the nozzle to the target area; finally, multiply the total flight time by the lateral wind speed perpendicular to the spray direction to obtain the lateral offset distance of the droplets during the entire flight process. Based on this offset distance, adjust the horizontal spray angle of the spray device in the opposite direction so that the actual landing point of the fog field completely coincides with the target dust area; The calculation of droplet lateral offset is based on a particle kinematics model. Taking a single droplet as the research object, a local coordinate system is established in the injection direction: the initial position of the droplet is set as the nozzle coordinate system. The initial velocity is The ambient wind speed is The droplets are subject to air resistance during flight, and their equation of motion is described by Newton's second law:

[0069] In the formula The mass of a single droplet is expressed in kg. Calculated, where The density of water is taken as 1000 kg / m³. 3 , The drag coefficient is dimensionless and is calculated for spherical droplets according to... calculate, Let be the relative Reynolds number, where For air density, take 1.225 kg / m³. 3 , For the gas dynamic viscosity, take 1.79 × 10⁻⁶. 5 Pa·s; The windward surface area of ​​the fog droplets. Dimensions are m 2 , Let be the instantaneous velocity vector of the droplets, with dimensions in m / s; Let be the environmental wind field velocity vector, with dimensions m / s (represented by a vector symbol with an arrow, to distinguish it from the aforementioned scalar u); the above equation is numerically integrated using the fourth-order Runge-Kutta method, with the droplet velocity solved step by step at a predetermined time step (0.01s in this example). With displacement The spray continues until the droplets reach the plane of the target area (i.e., the projection of the displacement in the spray direction is equal to the horizontal straight-line distance from the nozzle to the target area). Record total flight time .

[0070] Lateral offset distance of droplets throughout their flight The magnitude of the displacement vector component perpendicular to the injection direction:

[0071] In the formula, for The projection vector of the droplet displacement onto the plane perpendicular to the jet direction at any given moment, with dimensions m.

[0072] Based on this lateral offset distance, adjust the horizontal spray angle of the spray device in the opposite direction. Let the azimuth angle of the target area be... The corrected horizontal jet angle for:

[0073] Similarly, the pitch angle correction uses the same principle, adjusting according to the vertical offset component to ensure that the actual landing point of the fog field completely coincides with the target dust area.

[0074] 4.2 Coverage Blind Spot Identification and Priority Ranking: The current blind spot map is accessed, and combined with the dust diffusion path, the dust is sorted from shortest to longest arrival time in the blind spot, with shorter arrival times indicating higher priority. Blind spots not affected by dust are temporarily excluded from the coverage plan to avoid unnecessary water wastage.

[0075] 4.3 Dead-point coverage scheduling: For high-priority dead-points, the pitch angle, horizontal angle, and spray pressure of existing surrounding spray devices are adjusted first to expand the effective coverage range of the spray devices and achieve coverage of the dead-points. If the existing spray devices still cannot effectively cover the area after adjustment, the backup spray devices in the corresponding area are activated for precise supplementary spraying.

[0076] 4.4 Based on the above calculation results, the final operating parameters of each spray device are generated, including switch status, on-time, horizontal angle, pitch angle and spray pressure.

[0077] Step S5, Spray Execution and Status Feedback The edge controller sends the final operating parameters to each executing device. The variable frequency water pump adjusts the output pressure to the target value, the servo mechanism adjusts the spray device to the target angle, and the solenoid valve controls the on / off of the spray device according to the set timing sequence. All devices work together to complete the dust suppression spraying operation. During the execution process, the operating status of the equipment and the pipeline pressure are monitored in real time, and fine adjustments are made immediately when deviations occur.

[0078] Step S6: Effect Feedback and Online Model Self-calibration 6.1 After the spraying has been carried out for a period of time, the dust concentration and particle size distribution data of each monitoring point are collected. The predicted concentration and the actual concentration, the predicted particle size change and the actual particle size change are compared, and the actual dust suppression efficiency of this spraying is calculated.

[0079] 6.2 Based on the deviation between the actual results and the predicted results, the least squares method is used to perform online iterative calibration of the parameters of the three types of models. The core logic of the calibration is to collect multiple sets of deviation data between predicted values ​​and actual monitoring values, and to gradually adjust the coefficients of the corresponding models with the goal of minimizing the sum of the squares of all deviations, so that the predicted results of the models gradually approach the actual monitoring results on site.

[0080] The three types of calibration are as follows: The terrain correction coefficient of the first type of calibration dust diffusion model is adjusted according to the overall deviation of the concentration prediction, and the magnitude of the leeward eddy correction is adjusted to improve the overall accuracy of the diffusion prediction. The material matching coefficient of the second type of calibration dust collection mechanism model is adjusted according to the deviation of particle size collection efficiency, and the correction coefficient of collection efficiency calculation is adjusted to optimize the actual effect of particle size matching. The third type of calibration spray device's effective range parameters are used to correct the actual effective range of the spray device based on the concentration data at the edge of the dead zone, thereby improving the accuracy of dead zone identification and coverage calculation.

[0081] 6.3 After the parameter calibration is completed, return to step 1 and enter the next control cycle.

[0082] After the daily work is completed, start the 3D topography scanning and blind spot calibration process. The specific steps are as follows: The lidar is controlled to perform a full-area scan to acquire three-dimensional point cloud data of the entire material pile. After noise reduction and stitching, the latest three-dimensional shape model of the material pile is reconstructed.

[0083] Based on the latest topographic model, and combined with the rated parameters of each spray device and the wind field benchmark correction model, the effective coverage space range of each spray device is calculated point by point to generate a spray coverage space model.

[0084] The geometric calculation method for the effective coverage area of ​​a single spray device is as follows: The effective coverage area of ​​each spray device is represented by a frustum-shaped spatial region with the nozzle as its vertex and its axis along the spray direction. This region is determined by the following parameters: Nozzle installation coordinates The dimension m is obtained from global coordinate system calibration; Effective range The dimension is m; it is obtained from the field calibration test of the spray device under the corresponding working pressure. The calibration method is as follows: the horizontal range is measured at three pressure levels: 0.8MPa, 1.0MPa, and 1.2MPa. ,Pick , The effective range coefficient is set to 0.77 based on relevant research on wind-assisted sprayers. 1.0, the factory default value of this system is 0.85, and it can be adjusted in step S6 based on the concentration data at the edge of the dead corner. Make corrections; Atomizing cone angle half angle The dimension is angle; it is determined by the nozzle's factory parameters combined with the pressure calibration curve, and the higher the pressure, the smaller the cone angle. Horizontal spray angle With pitch angle The dimension is angle; output by the collaborative scheduling module; Wind field baseline correction: Under crosswind conditions, the effective range is extended along the downwind direction and shortened along the upwind direction. The corrected effective range is... for:

[0085] In the formula This is the wind field correction factor, dimensionless, with a factory default value of 0.1, and is calibrated in step S6; Let be the projected component of the ambient wind speed in the direction of the jet, in m / s. Let be the initial axial velocity of the droplet, in m / s.

[0086] Therefore, the geometric model of the effective coverage area of ​​a single spray device is as follows: (based on nozzle coordinates) For the cone apex, the unit vector in the injection direction With axis as the axis, the semi-cone angle is... axial length is A frustum-shaped space. Any point within this space. satisfy: vector exist Projected length satisfy ; and and The included angle satisfy ; The surface of the material pile is divided into several 1m×1m square grid cells. For the center point of each grid cell, it is determined whether it is included in the effective coverage area of ​​at least one spray device (i.e., satisfying the above two conditions). Grid cells not covered by any spray device are marked as coverage blind spots. All marked grid cells together form the updated full-field blind spot map.

[0087] Spatial overlay analysis is performed between the spray coverage spatial model and the stockpile surface model to identify coverage blind spots. The specific identification process is as follows: the stockpile surface is divided into several 1-meter by 1-meter square grid cells; for each grid cell, it is determined whether it is covered by the effective coverage area of ​​at least one spray device; grid cells not covered by any spray device are marked as coverage blind spots; all marked grid cells together form an updated full-field blind spot map.

[0088] Based on the latest topographic model, the boundaries of the material yard zones are updated, and the basic parameters for terrain correction of the dust diffusion model are also updated to provide the latest benchmark data for real-time control the following day.

[0089] The complete numerical example is as follows: The measured wind speed at an open-air coal yard was 5 meters per second, and the wind direction was due west. A loader was unloading material at the western end of the pile top, which was identified as point source dust generation.

[0090] First, calculate the instantaneous dust generation: The baseline dust generation for this operation is 0.02 grams per second, the baseline wind speed is 3 meters per second, the wind erosion index is 1.8, and the calculated wind speed correction factor is approximately 2.5. The final instantaneous dust generation is 0.05 grams per second, and the corresponding height of the material pile is 8 meters.

[0091] Subsequently, a topographically corrected diffusion prediction was performed: the 8-meter height of the material pile was included in the effective source height, and the diffusion model was corrected. It was calculated that the dust plume would reach the eastern end of the pile top in 30 seconds and the eastern boundary in 60 seconds, with an expected peak concentration of 0.8 mg / m³ at the boundary.

[0092] Next, the optimal droplet size is calculated: for the top-of-the-line work area, real-time dust is measured in PM2.5. 10 Based on particle-based data and combined with transmission distance traversal calculation of comprehensive collection efficiency, the optimal droplet size corresponding to the peak efficiency is found to be 40 micrometers, corresponding to a spray pressure of 1.0 MPa.

[0093] Next, calculate the droplet wind-induced drift: the target area is about 40 meters away from the spray device. Based on the wind-driven airflow speed, the average flight speed of the droplets is estimated to be about 10 meters per second, and the total flight time is about 4 seconds. Based on the lateral wind speed of 5 meters per second, the lateral drift distance of the droplets is about 20 meters. The horizontal angle of the spray device is reversed and deflected eastward by about 27 degrees to offset this drift.

[0094] Finally, blind spot coverage is performed: the recessed area in the middle of the pile top is identified as a coverage blind spot, and dust will reach this blind spot in 20 seconds, so it is determined to be the highest priority; the pitch angle of the surrounding spray devices is adjusted to rise by 5 degrees to expand the coverage range and achieve coverage of the recessed blind spot.

[0095] After the final spraying was performed, the measured peak concentration at the field boundary was 0.25 mg / m³, and the dust suppression efficiency reached 68.75%, which is about 30 percentage points higher than that of traditional fixed parameter spraying, and the water consumption was reduced by about 25%.

[0096] In summary, the intelligent spray control method, system, and device for dust suppression in material yards constructs an active and precise control system that deeply couples dust diffusion prediction, droplet size adaptation, and morphological blind spot compensation. It achieves spatiotemporal feedforward interception of dust through a terrain-corrected diffusion model, replacing the traditional reactive response mode; it replaces fixed-parameter or coarse-level switching atomization control with dynamic particle size matching driven by the capture mechanism; and it replaces static coverage schemes with fixed-angle coverage through morphological perception-based blind spot compensation. By simultaneously eliminating the inherent defects of traditional schemes in terms of time, particle size, and space, it fundamentally improves the accuracy and operational reliability of dust suppression control.

[0097] It also enables the reuse of multi-dimensional data and system-level joint collaboration. The three-dimensional morphology data of the material pile simultaneously supports diffusion model correction, regional characteristic division and coverage blind zone identification. The optimal particle size parameter simultaneously serves to improve the collection efficiency and calibrate the wind-induced offset. The dust prediction results are used for fog field timing scheduling and blind zone priority judgment. Under the premise of ensuring that the emission at the site boundary meets the standards, this control system can simultaneously reduce water consumption, reduce excessive material wetting, and maintain stable long-term operation, taking into account multiple engineering requirements such as environmental compliance, production process quality and operation and maintenance costs.

[0098] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. An intelligent spray control method for dust suppression in material yards, characterized in that: Includes the following steps: S1: Real-time collection of environmental parameters, dust monitoring data and operation status data of the entire material yard, identification of the location and type of dust generation points, and dynamic calculation of the instantaneous dust generation at each dust generation point; S2: Obtain the three-dimensional topographic data of the current material pile, perform terrain correction on the dust diffusion model based on the three-dimensional topographic data, and calculate the spatial distribution and temporal pattern of dust diffusion within a set time period in the future by combining the instantaneous dust generation and real-time wind field parameters, and obtain the arrival time, expected concentration and dust transmission distance of dust at each location. S3: Based on the three-dimensional topography data of the material pile, the material yard is divided into multiple feature regions. Combining the real-time dust particle size distribution data and the dust transmission distance of the corresponding region, the optimal droplet size for each region is calculated according to the physical mechanism of dust capture and converted into the corresponding spray operation parameters. S4: Match the corresponding spraying device to the spatiotemporal distribution of dust diffusion, and adjust the operating parameters and start-up sequence of each spraying device by correcting the droplet offset caused by the wind field in combination with the optimal droplet size; at the same time, identify blind areas that cannot be covered by spraying based on the three-dimensional topography data of the material pile, prioritize the blind areas in combination with the dust diffusion path, and perform supplementary spraying scheduling for high-priority blind areas. S5: Issue operating instructions to each spraying device to complete the dust suppression spraying operation; S6: Collect dust monitoring data after spraying, compare the actual dust suppression effect with the predicted results, and automatically calibrate the parameters of the relevant models online.

2. The intelligent spray control method for dust suppression in material yards according to claim 1, characterized in that: In step S1, the types of dust-generating points include concentrated point sources formed by unloading points, continuous line sources formed by vehicle travel routes, and large-area surface sources formed by wind erosion on the surface of material piles; the instantaneous dust generation is dynamically calculated by combining the type of operation, the dust generation characteristics of the material, and the real-time wind speed.

3. The intelligent spray control method for dust suppression in material yards according to claim 1, characterized in that: In step S2, the terrain correction specifically includes three types of correction methods: adding a material pile height lifting term to correct the initial dust diffusion height, adding a leeward slope eddy correction term to correct the diffusion path of the leeward area of ​​the material pile, and adding a slope slip diffusion term to correct the secondary dust diffusion trajectory generated by particles rolling down the slope.

4. The intelligent spray control method for dust suppression in material yards according to claim 1, characterized in that: In step S3, the material yard is divided into four characteristic areas based on the three-dimensional morphology of the material pile: the top working area, the slope area, the ground driving area, and the boundary buffer zone. The physical mechanism of dust collection comprehensively considers three collection mechanisms: inertial collision, interception, and Brownian diffusion. The curve of the overall collection efficiency as a function of droplet size is calculated, and the droplet size corresponding to the peak efficiency is taken as the optimal droplet size.

5. The intelligent spray control method for dust suppression in material yards according to claim 1, characterized in that: In step S4, the specific method for correcting the droplet offset caused by the wind field is as follows: based on the current optimal droplet size and real-time wind field parameters, calculate the lateral offset distance of the droplets within the effective range, and calibrate the spray angle of the spray device in the reverse direction to counteract the influence of the wind field on the spatial position of the fog field.

6. The intelligent spray control method for dust suppression in material yards according to claim 1, characterized in that: In step S4, the priority ranking rule for coverage blind spots is as follows: they are ranked from shortest to longest time when dust arrives in the blind spot, with the shorter the arrival time, the higher the priority. Blind spots without dust passing through are not subject to supplementary spraying. When performing supplementary spraying scheduling, the coverage range is expanded by adjusting the angle and pressure of the existing spraying device first. If effective coverage cannot be achieved, the backup supplementary spraying device is activated.

7. The intelligent spray control method for dust suppression in material yards according to claim 1, characterized in that: In step S6, the model parameters for online automatic calibration include the terrain correction coefficient of the dust diffusion model, the material adaptation coefficient of the dust capture mechanism model, and the effective range parameters of the spray device. The calibration process is based on the difference between dust monitoring data before and after spraying, and iterative fitting correction is performed using the least squares method.

8. The intelligent spray control method for dust suppression in material yards according to claim 1, characterized in that: It also includes a periodic morphology update step: triggering a full-area 3D scan at a set time after the end of each day's work, reconstructing the latest 3D morphology data of the material pile, recalculating the effective coverage range of each spraying device, updating the coverage blind zone map, and simultaneously correcting the terrain parameters of the dust diffusion model and the regional boundaries that match the particle size of the zones.

9. An intelligent spray control system for dust suppression in material yards, comprising the intelligent spray control method for dust suppression in material yards as described in any one of claims 1-8, characterized in that: It includes a perception layer module, a 3D topography scanning module, a dust diffusion prediction module, a droplet size calculation module, a fog field collaborative scheduling module, a spray execution module, and a closed-loop correction module; The sensing layer module is connected to the dust diffusion prediction module, the droplet size calculation module and the closed-loop correction module respectively, and is used to collect and output environmental parameters, dust monitoring data and operation status data. The three-dimensional morphology scanning module is connected to the dust diffusion prediction module, the droplet size calculation module and the fog field collaborative scheduling module respectively, and is used to collect and output the three-dimensional morphology data of the material pile. The dust diffusion prediction module is signal-connected to the fog field collaborative scheduling module. It is used to receive dust generation data and three-dimensional topographic data of the material pile. After calculation by the topographically corrected diffusion model, it outputs the spatiotemporal distribution data of dust diffusion. The droplet size calculation module is signal-connected to the fog field collaborative scheduling module. It is used to receive dust particle size distribution data, dust transmission distance data and material yard zoning data, and output the optimal droplet size parameters for each region after calculation by the dust collection mechanism model. The fog field collaborative scheduling module is signal-connected to the spray execution module. It is used to receive dust diffusion spatiotemporal distribution data, optimal droplet size parameters and coverage blind zone data, and output the operating parameters and timing instructions of each spray device after comprehensive calculation. The closed-loop correction module is connected to the dust diffusion prediction module, the droplet size calculation module, and the fog field collaborative scheduling module, respectively, and is used to compare dust data before and after spraying and to calibrate the model parameters of each module online.

10. An intelligent spray control device for dust suppression in material yards, characterized in that: It is equipped with the intelligent spray control system for dust suppression in material yards as described in claim 9.

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