Intelligent management and control method and system for belt conveyor

Abstract

The application provides a belt conveyor intelligent management and control method and system, which comprises the following steps: calculating the influence correlation coefficient of particle suspension time and airflow field distribution according to a target atomization range, dynamically adjusting water mist density control parameters, and obtaining water mist concentration matching, spraying time interval and mist droplet size combination in different regions; synchronously updating the number of nozzle openings, spraying pressure, atomization angle and spraying duration of the spraying system through the target water mist density control parameters and the target atomization range, and adjusting the spraying coverage rate in real time according to the change of dust scattering trajectory; monitoring the dust capture efficiency and water mist coverage integrity of the loading point after the spraying coverage rate is adjusted, and analyzing the dust settling speed and atomization coverage uniformity of the loading point region under different spraying parameters.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method and system for intelligent control of belt conveyors. Background Technology

[0002] Belt conveyors, as core equipment for material transportation in modern industry, are widely used in mining, ports, and power industries. Their efficiency and reliability are crucial to production efficiency. However, dust generated during material loading not only threatens worker health but also potentially causes environmental pollution and equipment wear. Intelligent management methods are urgently needed to reduce the dual impact of dust pollution on production and the environment. Existing dust control technologies, such as fixed sprays or simple mechanical dust suppression devices, often have limited effectiveness due to a lack of dynamic adaptability. Especially when facing different material characteristics or changes in loading conditions, they struggle to respond accurately, leading to low dust suppression efficiency or water waste. Furthermore, these methods typically ignore the complex gas-solid interactions during material loading, failing to fundamentally address the dynamic changes in dust dispersion trajectories. During material loading, dust generation is closely related to the particle size distribution of the material. Different particle sizes, due to collisions and airflow during loading, generate different throwing angles and trajectories. Fine particles are more easily carried by airflow, prolonging suspension time and increasing the range and difficulty of dust diffusion. This phenomenon directly leads to the first core challenge in the design of intelligent spray systems: how to dynamically adjust the atomization range and water mist density based on changes in material particle size distribution to accurately cover areas where dust is raised. This makes it difficult to guarantee the coverage integrity of the spray system, significantly impacting dust suppression efficiency. If the spray system cannot adapt to these changes in real time, it will not only lead to a decrease in dust collection efficiency but may also affect the environmental control effect at the loading point due to uneven water mist distribution. Therefore, optimizing the atomization range and water mist density of the intelligent spray system under dynamic conditions of varying material loading height and particle size distribution to ensure a synergistic improvement in dust collection efficiency and water mist coverage integrity has become a key issue. Summary of the Invention

[0003] This invention provides an intelligent control method for belt conveyors, mainly including:

[0004] Real-time acquisition of particle size distribution data and loading height parameters during material loading process; extraction of particle size distribution range, particle density, moisture content and suspension performance under different material characteristics and height conditions; obtaining real-time feature dataset of material particle size distribution and height changes.

[0005] Statistical analysis was performed on the real-time feature dataset of material particle size distribution and height variation to obtain the correlation between dust scattering trajectory and airflow field distribution. A pre-established airflow dynamics model was used to predict the trajectory and determine the dynamic range of dust scattering trajectory.

[0006] If the dynamic change range of the dust scattering trajectory exceeds the preset coverage threshold, the atomization range parameter is adjusted by the dynamic change range of the dust scattering trajectory, and the spray angle and coverage area of ​​the spray device are optimized by combining the airflow field distribution data to determine the target atomization range.

[0007] Based on the target atomization range, the correlation coefficient between particle suspension time and airflow field distribution is calculated, and the water mist density control parameters are dynamically adjusted to obtain the water mist concentration ratio, spray time interval and droplet size combination in different areas.

[0008] If the particle suspension time exceeds the preset suspension time range, the water mist spray intensity is adjusted by combining the water mist concentration ratio, spray time interval and droplet size combination, and the spray particle size is adjusted by combining real-time airflow field distribution data to determine the target water mist density control parameters.

[0009] By controlling the target water mist density and the target atomization range, the number of nozzles opened, the spray pressure, the atomization angle and the spray duration of the spray system are updated synchronously, and the spray coverage is adjusted in real time according to the changes in the dust scattering trajectory.

[0010] The dust collection efficiency and water mist coverage integrity at the loading point were monitored after the adjusted spray coverage was implemented. The dust settling velocity and atomization coverage uniformity in the loading point area were analyzed under different spray parameters.

[0011] This invention provides an intelligent control system for belt conveyors, mainly comprising:

[0012] The real-time data acquisition module is used to collect particle size distribution data and loading height parameters during the material loading process in real time, and extract the particle size distribution range, particle density, moisture content and suspension performance under different material characteristics and height conditions to obtain a real-time feature dataset of material particle size distribution and height changes.

[0013] The statistical analysis module is used to perform statistical analysis on the real-time characteristic dataset of material particle size distribution and height variation, obtain the correlation between dust scattering trajectory and airflow field distribution, and use a pre-established airflow dynamics model to predict the trajectory and determine the dynamic range of dust scattering trajectory.

[0014] The trajectory prediction module is used to adjust the atomization range parameters based on the dynamic change range of the dust scattering trajectory if the dynamic change range of the dust scattering trajectory exceeds the preset coverage threshold. It also optimizes the spray angle and coverage area of ​​the spray device by combining airflow field distribution data to determine the target atomization range.

[0015] The atomization range adjustment module is used to calculate the correlation coefficient between particle suspension time and airflow field distribution based on the target atomization range, dynamically adjust the water mist density control parameters, and obtain the water mist concentration ratio, spray time interval and droplet size combination for different areas.

[0016] The water mist density adjustment module is used to adjust the water mist spray intensity by combining the water mist concentration ratio, spray time interval and droplet size combination if the particle suspension time exceeds the preset suspension time range, and adjust the spray particle size by combining real-time airflow field distribution data to determine the target water mist density control parameters.

[0017] The spray parameter update module is used to synchronously update the number of nozzles opened, spray pressure, atomization angle and spray duration of the spray system by controlling the target water mist density and the target atomization range, and to adjust the spray coverage in real time according to the changes in the dust scattering trajectory.

[0018] The performance monitoring module is used to monitor the dust collection efficiency and water mist coverage integrity at the loading point after the adjusted spray coverage is implemented, and to analyze the dust settling velocity and atomization coverage uniformity in the loading point area under different spray parameters.

[0019] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0020] This invention discloses an intelligent control method for belt conveyors. This method collects real-time data on material particle size distribution and loading height, analyzes the correlation between dust scattering trajectory and airflow distribution, and dynamically predicts the dust diffusion range. Based on the prediction results, this invention adaptively adjusts spray parameters such as atomization range and water mist density to achieve precise dust suppression. Specifically, this invention adjusts the spray coverage area according to the dust scattering trajectory, controls the water mist density through a combination of water mist concentration ratio and droplet size, and optimizes the spray angle and intensity in conjunction with the airflow distribution. This invention can also monitor dust collection efficiency and water mist coverage integrity in real time, continuously optimizing spray parameters. This method can significantly improve dust suppression during material loading, reduce dust pollution, and improve the working environment. Attached Figure Description

[0021] Figure 1 This is a flowchart of the intelligent control method for belt conveyors according to the present invention.

[0022] Figure 2 This is a schematic diagram of the structure of an intelligent control system for a belt conveyor according to the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] like Figure 1-2 The intelligent control method and system for belt conveyors in this embodiment may specifically include:

[0025] S101. Collect particle size distribution data and loading height parameters during the material loading process in real time, extract the particle size distribution range, particle density, moisture content and suspension performance under different material characteristics and height conditions, and obtain a real-time feature dataset of material particle size distribution and height changes.

[0026] Continuous scanning and measurement of the material during loading process are performed to obtain particle size distribution data at different loading heights. A density detection device simultaneously detects particle density changes at each height layer, resulting in a first data matrix relating particle size distribution to density. Based on the density distribution information recorded in the first data matrix, a moisture content detection device measures the moisture content distribution of the material within each particle size range. A suspension performance testing device records the suspension performance parameters of the particles under different moisture content conditions, resulting in a second data matrix relating moisture content to suspension performance. If the moisture content and suspension performance data in the second data matrix show abnormal fluctuations, a high-frequency acquisition mode is activated to record the material distribution state during loading height changes. Data filtering is used to denoise the collected multi-dimensional feature data, resulting in a third data matrix of material characteristic changes. Based on the material characteristic change information in the third data matrix, a sliding window is used to extract the particle size distribution range fluctuation characteristics within different time periods. Data fusion processing integrates the real-time monitoring results from each detection device to determine a complete real-time feature dataset of material particle size distribution and height changes.

[0027] Specifically, the particle size detection device continuously scans the material using the principle of laser scattering. When the laser beam irradiates the particle surface, it generates scattered light at different angles, and the scattering angle is inversely proportional to the particle size.

[0028] In one possible implementation, particles detected at a loading height of 0.5 meters are mainly concentrated in the 50-100 micrometer range, while at a height of 1.5 meters, the particle distribution expands to the 20-150 micrometer range. The density detection device simultaneously records the changes in bulk density of each layer of material; the bottom layer typically has a density of 1.8 g / cm³, while the top layer's density decreases to 1.2 g / cm³. This layered detection method accurately reflects the vertical distribution characteristics of the material, providing fundamental data support for subsequent analysis. Based on the acquired first data matrix, the moisture content detection device uses the dielectric constant measurement principle to determine the moisture content of materials with different particle sizes.

[0029] It should be noted that changes in moisture content directly affect the cohesive force and flowability between particles. When the moisture content increases from 3% to 8%, the van der Waals forces between fine particles significantly increase. The suspension performance testing device evaluates the suspension characteristics by measuring the settling velocity of particles in a fluid. Particles with higher moisture content have an increased effective diameter due to the adsorption of water molecules on their surface, resulting in a lower settling velocity.

[0030] For example, 50-micron particles with a moisture content of 5% settle at a velocity of 0.8 mm / s, while the settling velocity drops to 0.5 mm / s when the moisture content reaches 10%. When the second data matrix shows abnormal fluctuations in moisture content and suspension performance data, a high-frequency acquisition mode is activated to capture transient changes.

[0031] Specifically, under normal circumstances, the moisture content variation should be controlled within ±2%. If fluctuations exceeding this threshold are detected, it indicates a significant change in the material's state. Data filtering employs a moving average algorithm to eliminate measurement noise, with the filtering window length set to 10 consecutive sampling points. This preserves the true trend while effectively suppressing random interference. The resulting third data matrix clearly demonstrates the temporal evolution of the material's properties.

[0032] In one embodiment, the sliding window technique extracts feature parameters at preset time intervals, with a window length of 30 seconds and a sliding step size of 10 seconds, ensuring the continuity and completeness of feature extraction. Data fusion processing comprehensively processes multi-source information from different detection devices, integrating data from each sensor using a weighted average method. The weighting coefficients are determined based on the sensor accuracy and reliability.

[0033] For example, particle size detection data has a weight of 0.4, density detection data has a weight of 0.3, and moisture content detection data has a weight of 0.3. This fusion strategy can fully utilize the advantages of various detection methods, improve the overall measurement accuracy and data reliability, and form a complete dataset containing particle size distribution range, spatial location information, and temporal variation characteristics, providing a scientific basis for the precise control and optimization of the material loading process.

[0034] S102. Statistical analysis is performed on the real-time characteristic dataset of material particle size distribution and height variation to obtain the correlation between dust scattering trajectory and airflow field distribution. A pre-established airflow dynamics model is used to predict the trajectory and determine the dynamic range of dust scattering trajectory.

[0035] Statistical analysis tools are used to perform frequency statistics and distribution calculations on real-time feature datasets of material particle size distribution and height variation, obtaining the frequency distribution of dust particle scattering at different height layers. Correlation calculation methods are used to analyze the correlation strength between dust scattering trajectories and the surrounding airflow field distribution, obtaining first correlation data between dust scattering and the airflow field. Based on the trajectory and airflow field correlation information in the first correlation data, a pre-established airflow dynamics model is used to numerically calculate the motion trajectory of dust particles. A trajectory calculation processor, combined with particle mass and airflow velocity parameters, calculates the dust's motion path in three-dimensional space, obtaining second trajectory data predicting the dust scattering trajectory. If the predicted trajectory range in the second trajectory data exceeds a preset boundary threshold, the trajectory change boundary is re-evaluated. Discrete trajectory points in the second trajectory data are processed to obtain third boundary data showing continuous dust scattering trajectory boundary changes. Based on the trajectory boundary change information recorded in the third boundary data, the maximum diffusion boundary of dust scattering is calculated. By dynamically integrating trajectory boundary data from different time points, the dynamic change range of the dust scattering trajectory throughout the entire loading process is determined.

[0036] Specifically, the statistical analysis tool quantifies the granularity and height data in the real-time feature dataset based on histogram statistics and frequency distribution calculation principles.

[0037] In one possible implementation, when the loading height reaches 2 meters, the dust particle throwing frequency in the 30-50 micrometer range is 120 times per second, while the throwing frequency of particles in the 100-150 micrometer range drops to 80 times per second. Correlation calculations quantify the correlation strength between dust trajectories and the airflow field using the Pearson correlation coefficient method. When the airflow velocity is 3 meters per second, the correlation coefficient between the dust throwing trajectory and the wind direction reaches 0.85, indicating a strong correlation. This correlation analysis can reveal the intrinsic connection between dust movement and environmental airflow, providing a reliable data foundation for subsequent trajectory prediction. Based on the correlation established in the first correlation data, the airflow dynamics model uses Stokes' law from fluid mechanics to calculate the motion state of particles in the airflow.

[0038] Specifically, the model considers parameters such as particle diameter, density, and airflow viscosity coefficient, and determines the spatial trajectory of the particles by solving a system of equations of motion. The trajectory calculation processor divides the three-dimensional space into grid cells, each cell measuring 0.1 m × 0.1 m × 0.1 m, and calculates the position coordinates and velocity vectors of dust particles within the grid.

[0039] For example, a dust particle with a mass of 0.05 milligrams moves at a vertical speed of 1.2 meters per second and is offset horizontally by an airflow at a speed of 0.8 meters per second. This precise trajectory calculation method can accurately predict the movement path of dust particles in three-dimensional space.

[0040] In one embodiment, when the second trajectory data shows that the predicted trajectory exceeds the set safety boundary, the dynamic range calculation process is triggered to perform boundary reassessment.

[0041] It should be noted that the preset boundary threshold is determined based on the safety requirements of the working environment, typically set to a range of 5 meters horizontally and 3 meters vertically from the loading point. The trajectory boundary calculation process uses spline interpolation to connect discrete trajectory prediction points into a continuous curve, with the interpolation node spacing set to 0.2 meters to ensure the smoothness and continuity of the trajectory boundary. When the dust trajectory extends to a horizontal distance of 6.5 meters at a certain moment, the boundary calculation process automatically adjusts the boundary range, expanding the new boundary to an 8-meter range. This dynamic adjustment mechanism can adapt to changes in the dust diffusion range in real time.

[0042] Specifically, the boundary expansion calculation process uses an envelope algorithm to determine the maximum possible diffusion range of dust. This algorithm traverses all trajectory prediction points, finding the farthest point in each direction to form the outer boundary surrounding the entire diffusion area. The dynamic range integration process employs time series analysis to arrange and merge boundary data from different time points in chronological order.

[0043] For example, in the first 10 minutes of the loading process, the dust dispersion range is mainly concentrated within a 3-meter radius, while in the middle of the loading process, the dispersion range may expand to a 5-meter radius. By integrating this time-series data, the determined dynamic variation range can fully cover all areas that dust may reach throughout the entire loading process, providing accurate spatial boundary references for environmental protection and safety.

[0044] S103. If the dynamic change range of the dust scattering trajectory exceeds the preset coverage threshold, the atomization range parameter is adjusted by the dynamic change range of the dust scattering trajectory, and the spray angle and coverage area of ​​the spray device are optimized by combining the airflow field distribution data to determine the target atomization range.

[0045] If the dynamic range of the dust scattering trajectory exceeds a preset coverage threshold, the difference between the dynamic range and the coverage threshold is calculated. Based on this difference, the atomization range parameters of the spray device are adjusted to obtain the adjusted first atomization parameter data. Based on the atomization range adjustment information in the first atomization parameter data, airflow field analysis is used to analyze the wind speed and direction information in the airflow field distribution data. Combined with the airflow field distribution information, the optimal spray angle of the spray device is calculated to obtain the optimized second spray angle data. Based on the angle optimization information recorded in the second spray angle data, the coverage area of ​​the spray device is recalculated. The calculated coverage area is matched with the dust scattering trajectory range to obtain the regionally adapted third coverage area data. Based on the regional matching results in the third coverage area data, the atomization range parameters and coverage area information are integrated to determine the operating parameters of the spray device, resulting in a target atomization range that meets the dust scattering trajectory coverage requirements.

[0046] Specifically, the range comparison processing is based on the principle of numerical comparison, and triggers the adjustment mechanism by establishing the difference between the dust diffusion range and the preset coverage threshold.

[0047] In one possible implementation, when the horizontal diffusion range of the dust dispersion trajectory reaches 8 meters while the preset coverage threshold is only 6 meters, the calculated difference is 2 meters, indicating insufficient coverage. The atomization parameter adjustment process automatically adjusts key parameters of the spray device, such as pressure, flow rate, and droplet size, based on this difference. When the difference is 2 meters, the spray pressure increases from 0.3 MPa to 0.45 MPa, and the atomization flow rate increases from 50 liters per minute to 75 liters per minute. This adaptive adjustment mechanism based on the difference ensures a dynamic match between the atomization coverage range and the dust diffusion range. Based on the adjustment scheme determined from the first atomization parameter data, the airflow field analysis process uses vector decomposition to quantitatively analyze the wind speed and direction data.

[0048] Specifically, when the ambient wind speed is 4 m / s and the wind direction is 45 degrees northeast, the airflow field analysis decomposes the wind speed vector into a horizontal component of 3.2 m / s and a vertical component of 1.8 m / s. The angle optimization process combines this airflow field distribution information to calculate the compensation angle of the spray device. When the prevailing wind direction is 45 degrees northeast, the spray angle of the spray device needs to be deflected 15 degrees southwest to counteract the wind's influence.

[0049] For example, the original spray angle was 0 degrees vertically upward, but after angle optimization, it was adjusted to deflect 15 degrees to the southwest. This ensures that the atomized droplets can still accurately cover the target area under the action of wind.

[0050] In one embodiment, the coverage area calculation process uses a geometric projection method to determine the adjusted actual coverage area.

[0051] It should be noted that the coverage area of ​​the spray device is elliptical, and the lengths of its major and minor axes are closely related to the spray angle and pressure parameters. When the spray angle is adjusted to 15 degrees and the spray pressure is 0.45 MPa, the major axis of the coverage area reaches 12 meters and the minor axis is 8 meters. The area adaptation process performs geometric matching analysis between this elliptical coverage area and the irregular diffusion area of ​​the dust scattering trajectory, evaluating the coverage effect by calculating the overlap area ratio. When the overlap area accounts for more than 85% of the dust diffusion area, the coverage effect is considered good; if the overlap area is less than 80%, further parameter adjustments are needed.

[0052] Specifically, the parameter integration process comprehensively processes atomization range parameters and coverage area information to form a complete configuration scheme for the spray device's operating parameters. This process considers the interrelationships between pressure, angle, and flow rate; when pressure increases, the spray angle needs to be adjusted accordingly to maintain optimal atomization. The target range setting process determines the final operating state based on the integrated parameter configuration, including the spray device's start-up timing, duration, and stop conditions.

[0053] For example, the spraying device is activated when the dust concentration exceeds 50 milligrams per cubic meter, and stops spraying when the concentration drops below 20 milligrams per cubic meter. Through this precise parameter setting and control mechanism, the final determined target atomization range can achieve comprehensive and effective coverage of the dust scattering trajectory, significantly improving dust suppression and environmental protection levels.

[0054] S104. Based on the target atomization range, calculate the correlation coefficient between particle suspension time and airflow field distribution, dynamically adjust the water mist density control parameters, and obtain the water mist concentration ratio, spray time interval and droplet size combination for different regions.

[0055] Based on the spatial distribution information within the target atomization range, the suspension time of particles of different sizes in each region is measured, and the correlation coefficient between particle suspension time and airflow field distribution is calculated, yielding the first correlation coefficient data for particle suspension and airflow. Based on the correlation strength information in the first correlation coefficient data, the water mist density control parameter is dynamically adjusted, and the water mist concentration ratio for each region is calculated in conjunction with the particle suspension characteristics of different regions, obtaining the second regionalized concentration ratio data. Based on the regional concentration distribution information in the second concentration ratio data, the injection time interval parameter for each region is calculated, and the droplet size parameter is adjusted according to the concentration ratio requirements, yielding the third parameter combination data of time interval and droplet size. Based on the time interval and droplet size information in the third parameter combination data, the water mist concentration ratio, injection time interval, and droplet size parameters are comprehensively integrated, and corresponding parameter combination schemes are assigned to different regions, resulting in the water mist concentration ratio, injection time interval, and droplet size combination for different regions.

[0056] Specifically, the suspension time calculation is based on the theory of gravity settling, and the residence time of particles of different sizes is determined by analyzing their suspension characteristics in the air.

[0057] In one possible implementation, the theoretical settling velocity of 10-micrometer dust particles in still air is 0.3 mm / s, while in windy environments, their suspension time can be extended to 2-3 times. The correlation coefficient calculation employs correlation analysis from statistics to quantify the numerical relationship between particle suspension time and airflow intensity. When the airflow velocity is 2 m / s, the correlation coefficient between particle suspension time and airflow intensity reaches 0.92, indicating a strong positive correlation. This high correlation demonstrates that the airflow field has a decisive influence on particle suspension behavior, providing a scientific basis for subsequent water mist density adjustment. Based on the quantitative relationship established in the first correlation coefficient data, the density control adjustment process achieves precise control of water mist density by changing the operating parameters of the spray device.

[0058] Specifically, water mist density control involves the coordinated adjustment of three key elements: nozzle pressure, flow distribution, and atomization degree. Concentration ratio calculations are performed based on the differences in particle suspension characteristics across different regions, employing a regional ratio strategy to determine the water mist concentration requirements for each area.

[0059] For example, in areas where particles have a longer suspension time, the water mist concentration needs to be increased to 800 mg / m³, while in areas where the suspension time is shorter, the concentration can be reduced to 400 mg / m³. This differentiated formulation method enables efficient utilization of water mist resources and avoids waste caused by over-spraying.

[0060] In one embodiment, the time interval calculation process employs a periodic control strategy to determine the optimal injection frequency based on the particle suspension period.

[0061] It should be noted that the spray interval is inversely proportional to the particle suspension time. When the particle suspension time is 30 seconds, the spray interval should be set to 10 seconds to ensure continuous and effective coverage. Droplet size adjustment is achieved by precisely controlling the nozzle orifice diameter and pressure parameters. When a higher concentration ratio is required, the droplet diameter is increased from 50 micrometers to 80 micrometers. Larger droplets have stronger inertia and longer suspension time. This coordinated matching of size and time interval ensures uniform spatial and temporal distribution of the water mist, improving dust capture efficiency.

[0062] Specifically, parameter integration processing systematically combines control parameters from three dimensions—concentration ratio, time interval, and droplet size—to form a diversified spray control scheme. This integration process considers the interrelationships between parameters; when droplet size increases, the spray interval needs to be extended accordingly to avoid excessive accumulation. Regional allocation processing assigns a specific parameter combination scheme to each region based on its characteristics within the target atomization range.

[0063] For example, in the core area with high dust concentration, a high-concentration solution of 900 mg / m³, a short time interval of 8 seconds, and a large droplet size of 90 micrometers is used, while in the peripheral area, a medium-concentration solution of 600 mg / m³, a medium time interval of 12 seconds, and a medium droplet size of 65 micrometers is used. Through this refined regional allocation and parameter combination, the final combination of water mist concentration ratio, spray time interval, and droplet size can achieve precise matching of dust characteristics in different areas, significantly improving the overall dust suppression effect and system operating efficiency.

[0064] S105. If the particle suspension time exceeds the preset suspension time range, the water mist spray intensity is adjusted by combining the water mist concentration ratio, spray time interval and droplet size, and the spray particle size is adjusted by combining real-time airflow field distribution data to determine the target water mist density control parameters.

[0065] If the particle suspension time exceeds a preset suspension duration range, the particle suspension time is compared and analyzed against the preset range. The water mist spray intensity is adjusted based on the water mist concentration ratio, spray time interval, and droplet size combination parameters to obtain the first spray intensity parameter after intensity adjustment. Based on the intensity adjustment data in the first spray intensity parameter, real-time airflow distribution data is obtained and processed. Combined with the airflow distribution data, the spray particle size is dynamically adjusted to obtain the second particle size parameter after particle size adjustment. Based on the particle adjustment data recorded in the second particle size parameter, the adjusted spray intensity and particle size are comprehensively calculated. Through density strategy optimization processing combined with particle size changes, the water mist density control strategy is optimized to obtain the third density control parameter after optimization. Based on the control optimization data in the third density control parameter, the various parameters of the water mist density control are finally set. The results of processing and integrating the adjustment of spray intensity and particle size are determined, and the target water mist density control parameter that meets the particle suspension duration requirement is determined.

[0066] Specifically, the suspension time comparison processing is based on the time threshold comparison principle. It triggers the adjustment mechanism by establishing a numerical comparison relationship between the actual suspension time of the particles and a preset range.

[0067] In one possible implementation, when the actual suspension time of dust particles reaches 45 seconds while the preset suspension time range is 30 seconds, the comparison result shows an excess of 15 seconds, indicating that the intensity adjustment program needs to be activated immediately. The spray intensity adjustment process coordinates and adjusts the parameters based on this time difference, considering a combination of three dimensions: water mist concentration ratio, spray interval, and droplet size. When the suspension time exceeds the preset range, the spray intensity needs to be increased from 0.8 liters per square meter per second to 1.2 liters per square meter per second, while the spray interval is shortened from 12 seconds to 8 seconds. This intensity adjustment mechanism based on time difference can quickly respond to changes in the particle suspension state. Based on the adjustment scheme determined in the first spray intensity parameter, the airflow field data acquisition and processing uses a multi-point monitoring method to collect environmental airflow information in real time.

[0068] Specifically, this process collects airflow data in three-dimensional space using wind speed sensors and anemometers distributed throughout the work area, including key parameters such as horizontal wind speed, vertical airflow, and turbulence intensity. The particle size adjustment process combines this real-time airflow distribution data with a dynamic matching strategy to adjust the diameter of the spray particles.

[0069] For example, when the horizontal wind speed is 3.5 meters per second and the vertical airflow speed is 0.8 meters per second, the diameter of the spray particles needs to be adjusted from the original 60 micrometers to 80 micrometers. The larger particles have stronger wind resistance and a more stable trajectory. This airflow data-driven particle size adjustment method can ensure that the spray particles maintain the expected motion characteristics in complex airflow environments.

[0070] In one embodiment, the density control calculation process employs a multivariate optimization method to comprehensively analyze the adjusted spray intensity and particle size.

[0071] It should be noted that water mist density control involves the coordinated calculation of three core elements: spatial density distribution, temporal density variation, and particle density gradient. Density strategy optimization is based on the impact of particle size changes, re-evaluating and adjusting the original density control strategy. When the spray particle diameter increases from 60 micrometers to 80 micrometers, the number of particles per unit volume decreases by approximately 30%, requiring compensation for density loss by increasing the spray frequency or spray pressure.

[0072] For example, the original density control strategy required maintaining 800 droplets per cubic meter of space. After adjusting the particle size, the spray frequency needed to be increased from 15 times per minute to 20 times per minute to maintain the same spatial density.

[0073] Specifically, the parameter setting process systematically integrates the aforementioned adjustment results to form a complete water mist density control parameter configuration scheme. This process considers the interrelationships between spray intensity, particle size, and density distribution, and determines the optimal parameter combination through iterative optimization. The target density determination process calculates the final target water mist density control parameters based on the integrated parameter configuration, including the spatial density target value, time control nodes, and dynamic adjustment thresholds.

[0074] For example, the final target parameters include a spatial density of 1200 mg / m³, a spray intensity of 1.2 liters / m² / s, a particle diameter of 80 micrometers, and a spray interval of 8 seconds. Through the precise setting and dynamic adjustment of these multi-dimensional parameters, the final target water mist density control parameters can effectively address the problem of excessive particle suspension time, achieve efficient capture and settling control of dust particles, and significantly improve the overall dust suppression effect.

[0075] S106. By controlling the target water mist density and the target atomization range, the number of nozzles opened, the spray pressure, the atomization angle and the spray duration of the spray system are updated synchronously, and the spray coverage is adjusted in real time according to the changes in the dust scattering trajectory.

[0076] By combining the target water mist density control parameters and the target atomization range, the operating parameters of the spray device are updated uniformly. The number of nozzles to be opened is determined based on the density control parameters and atomization range requirements, resulting in the first spray control parameter for nozzle opening configuration. Based on the nozzle opening configuration data in the first spray control parameter, the spray pressure of each opened nozzle is set, and the atomization angle of each nozzle is adjusted according to the atomization range coverage requirements, resulting in the second spray control parameter for pressure and angle configuration. Based on the pressure and angle configuration data recorded in the second spray control parameter, the spray duration of each nozzle is calculated. The change information of the dust scattering trajectory is obtained in real time through trajectory change monitoring and processing, resulting in the third dynamic control parameter that correlates time control with trajectory change. Based on the trajectory change data in the third dynamic control parameter, the current spray coverage effect is evaluated in real time, and the spray coverage rate is adjusted in real time according to the changes in the dust scattering trajectory, achieving dynamic adjustment and control of the spray coverage rate.

[0077] Specifically, the parameter synchronization update processing is based on the principle of centralized control, and achieves unified control of the spray device by establishing a mapping relationship between the target water mist density control parameter and the target atomization range.

[0078] In one possible implementation, when the target water mist density requirement is 1200 mg / m³ and the target atomization range coverage radius is 8 m, the parameter synchronization update process automatically calculates the required total spray volume and spatial distribution requirements. Based on these comprehensive parameter information, the nozzle opening control process uses a region division strategy to determine the optimal nozzle opening configuration.

[0079] For example, within a circular coverage area with an 8-meter radius, 12 nozzles need to be activated to ensure uniform density distribution: 4 nozzles in the core area, 6 nozzles in the middle area, and 2 nozzles in the outer area. This nozzle configuration based on both density and range constraints enables precise spatial coverage control. Based on the nozzle activation scheme determined in the first spray control parameters, the pressure setting process employs a graded pressure control strategy to configure corresponding working pressures for nozzles at different locations.

[0080] Specifically, the four nozzles in the core area require higher operating pressure to produce finer droplets and a larger jet volume, with the pressure set at 0.6 MPa. The two nozzles in the outer area are set at 0.4 MPa to expand the coverage area. Angle adjustment is implemented based on the geometric requirements of the atomization range, setting different atomization angle parameters for each nozzle. The core area nozzles use a 60-degree conical atomization angle to ensure dense coverage, the middle area nozzles use a 90-degree angle for transitional coverage, and the outer area nozzles use a 120-degree wide-angle atomization to achieve boundary coverage. This differentiated pressure and angle configuration creates a gradient atomization distribution pattern, improving overall coverage efficiency.

[0081] In one embodiment, the timing control process uses a pulse jet strategy to calculate the optimal operating timing of each nozzle.

[0082] It should be noted that controlling the spray duration directly affects the accumulation effect of water mist in space and resource utilization efficiency. The core area nozzles are set to spray continuously for 15 seconds with a 5-second interval, achieving a 75% operational rate, while the peripheral nozzles use a 10-second continuous spray followed by a 10-second interval, achieving a 50% operational rate. Trajectory change monitoring and processing uses a distributed sensor network to track the spatial positional changes of the dust dispersion trajectory in real time. When the dust trajectory is detected to have shifted 2 meters northeast, the nozzles in the corresponding area need to adjust their operating status accordingly. This trajectory feedback-based time control mechanism enables dynamic response and precise positioning.

[0083] Specifically, the coverage assessment process employs a gridded evaluation method to quantitatively analyze the current spraying effect. This process divides the target atomization area into 1-meter × 1-meter grid cells, and checks whether the water mist density within each cell meets the preset requirements. The dynamic coverage adjustment process adjusts the spraying parameters in real time based on trajectory change data and coverage assessment results to maintain optimal coverage.

[0084] For example, if a dust trajectory deviates, causing the coverage of a certain area to drop from 85% to 70%, the nozzle pressure for that area needs to be increased from 0.4 MPa to 0.5 MPa, while the spray duration is extended from 10 seconds to 12 seconds. Through this real-time monitoring and dynamic adjustment mechanism, the spray coverage can be consistently maintained at an effective level above 80%, ensuring comprehensive tracking and efficient interception of dust scattering trajectories, and improving the accuracy and reliability of dust control.

[0085] S107. Monitor the dust collection efficiency and water mist coverage integrity at the loading point after the adjusted spray coverage rate, and analyze the dust settling velocity and atomization coverage uniformity in the loading point area under different spray parameters.

[0086] The monitoring of the adjusted spray coverage control effect involves real-time detection of the spray coverage status in the loading point area, determination of the dust collection efficiency at the loading point, and obtaining first monitoring effect data relating coverage and collection efficiency. Based on the collection efficiency information in the first monitoring effect data, a comprehensive assessment of the water mist coverage integrity is conducted, analyzing the impact of different spray parameter combinations on coverage integrity, and obtaining second integrity assessment data relating parameters and integrity. Based on the parameter influence information recorded in the second integrity assessment data, the dust settling velocity in the loading point area is quantitatively detected, and the variation law of settling velocity under different spray parameter conditions is analyzed, obtaining third velocity analysis data relating parameters and settling velocity. Based on the settling velocity variation information in the third velocity analysis data, the atomization coverage uniformity is measured and evaluated, analyzing the differences in uniformity performance under different spray parameters, and obtaining a comprehensive analysis result of dust settling velocity and atomization coverage uniformity in the loading point area.

[0087] Specifically, the coverage monitoring process is based on optical detection principles, using a laser light curtain sensor array deployed in the loading point area to monitor the spray coverage status in real time.

[0088] In one possible implementation, a laser light curtain covers the entire loading area in a grid pattern. When water mist particles pass through the beam, they generate a scattering signal. Sensors determine the coverage density of the area by detecting changes in signal intensity. The dust collection efficiency is measured using a mass balance method to quantitatively evaluate the dust collection effect. This is achieved by installing a dust concentration monitoring device at the loading point and comparing the changes in dust concentration before and after spraying to calculate the collection efficiency.

[0089] For example, when the dust concentration was 120 mg / m³ before spraying and decreased to 25 mg / m³ after spraying, the collection efficiency reached 79.2%. This dual monitoring mechanism can establish a quantitative relationship between coverage and collection efficiency, providing accurate basic data for subsequent parameter optimization. Based on the correlation established in the first monitoring effect data, the integrity detection process uses spatial scanning technology to comprehensively assess the integrity of the water mist coverage.

[0090] Specifically, this process uses a mobile droplet detection device to perform a three-dimensional scan of the loading point area to identify the distribution of covered blank and overlapping areas. Parameter correlation analysis employs multiple regression analysis to study the impact of different spray parameter combinations on coverage integrity. When the nozzle pressure increases from 0.4 MPa to 0.6 MPa, coverage integrity improves from 85% to 92%, while adjusting the atomization angle from 60 degrees to 90 degrees further improves coverage integrity to 95%. This parameter sensitivity analysis reveals the contribution of each spray parameter to the coverage effect, guiding the direction of parameter optimization.

[0091] In one embodiment, the settling velocity measurement process uses laser Doppler velocimetry technology to accurately measure the motion state of dust particles.

[0092] It should be noted that dust settling velocity directly reflects the immediate effect of spray dust suppression and is a key indicator for evaluating spray performance. The measuring device illuminates suspended dust with a laser beam and calculates the particle settling velocity based on the frequency shift of the reflected light. Parameter comparison and analysis establishes a database of settling velocities under different spray parameters. When the water mist density is 800 mg / m³, the average dust settling velocity is 2.3 mm / s, while when the density increases to 1200 mg / m³, the settling velocity increases to 3.1 mm / s. This quantitative parameter-effect correlation analysis can determine the optimal combination of spray parameters.

[0093] Specifically, the uniformity detection process employs statistical methods to quantitatively evaluate the spatial distribution uniformity of the atomized coverage. This process divides the loading point area into multiple detection grids, calculating the uniformity coefficient by measuring the droplet density distribution within each grid. The comprehensive analysis process correlates the settling velocity data with the uniformity detection results to establish a multi-dimensional performance evaluation system.

[0094] For example, when the atomization coverage uniformity coefficient reaches 0.88, the corresponding dust settling velocity is 2.8 mm / s, and the collection efficiency is 82%. Through this multi-index comprehensive analysis, the final analysis results can fully reflect the differences in dust suppression effects under different spray parameter configurations, providing a scientific basis for the fine adjustment of spray parameters, and achieving continuous optimization of dust control effect at the loading point and comprehensive improvement of system performance.

[0095] This invention provides an intelligent control system for belt conveyors, mainly comprising:

[0096] The real-time data acquisition module is used to collect particle size distribution data and loading height parameters during the material loading process in real time, and extract the particle size distribution range, particle density, moisture content and suspension performance under different material characteristics and height conditions to obtain a real-time feature dataset of material particle size distribution and height changes.

[0097] The statistical analysis module is used to perform statistical analysis on the real-time characteristic dataset of material particle size distribution and height variation, obtain the correlation between dust scattering trajectory and airflow field distribution, and use a pre-established airflow dynamics model to predict the trajectory and determine the dynamic range of dust scattering trajectory.

[0098] The trajectory prediction module is used to adjust the atomization range parameters based on the dynamic change range of the dust scattering trajectory if the dynamic change range of the dust scattering trajectory exceeds the preset coverage threshold. It also optimizes the spray angle and coverage area of ​​the spray device by combining airflow field distribution data to determine the target atomization range.

[0099] The atomization range adjustment module is used to calculate the correlation coefficient between particle suspension time and airflow field distribution based on the target atomization range, dynamically adjust the water mist density control parameters, and obtain the water mist concentration ratio, spray time interval and droplet size combination for different areas.

[0100] The water mist density adjustment module is used to adjust the water mist spray intensity by combining the water mist concentration ratio, spray time interval and droplet size combination if the particle suspension time exceeds the preset suspension time range, and adjust the spray particle size by combining real-time airflow field distribution data to determine the target water mist density control parameters.

[0101] The spray parameter update module is used to synchronously update the number of nozzles opened, spray pressure, atomization angle and spray duration of the spray system by controlling the target water mist density and the target atomization range, and to adjust the spray coverage in real time according to the changes in the dust scattering trajectory.

[0102] The performance monitoring module is used to monitor the dust collection efficiency and water mist coverage integrity at the loading point after the adjusted spray coverage is implemented, and to analyze the dust settling velocity and atomization coverage uniformity in the loading point area under different spray parameters.

[0103] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present invention will not describe the various possible combinations separately. Furthermore, various different embodiments of the present invention can also be arbitrarily combined, as long as they do not violate the spirit of the present invention, and should also be regarded as the content disclosed by the present invention.

Claims

1. A method for intelligent control of belt conveyors, characterized in that, The method includes: The process involves collecting particle size distribution data and loading height parameters during material loading to generate a real-time feature dataset containing particle size distribution range, particle density, moisture content, and suspension performance. This includes: continuously scanning the material with a particle size detection device to obtain particle size distribution data at different loading heights; measuring the particle density at each height layer with a density detection device to generate a first data matrix relating particle size distribution and density; measuring the moisture content of each particle size range using a moisture content detection device based on the density distribution data in the first data matrix; recording the suspension performance parameters at different moisture contents to generate a second data matrix relating moisture content and suspension performance; recording the material distribution state with varying loading height based on the moisture content and suspension performance data in the second data matrix; filtering the collected data to generate a third data matrix relating material characteristic changes; extracting particle size distribution range fluctuation characteristics based on the material characteristic data in the third data matrix; fusing data from various detection devices to generate the real-time feature dataset; and performing statistical analysis on the real-time feature dataset to determine the correlation data between dust scattering trajectory and airflow field distribution, based on airflow dynamics. The system uses a learning model to predict the dynamic range of dust scattering trajectories. Based on a comparison of this dynamic range with a preset coverage threshold, it adjusts the atomization range parameters of the spray device, optimizes the spray angle and coverage area using the airflow field distribution data, and generates a target atomization range. Based on this target atomization range, it calculates the correlation coefficient between particle suspension time and airflow field distribution, adjusts the water mist density control parameters, and generates a combination of water mist concentration ratio, spray time interval, and droplet size. Based on a comparison of the particle suspension time with a preset suspension duration range, it adjusts the spray intensity of the water mist concentration ratio, spray time interval, and droplet size combination, optimizes the spray particle size using the airflow field distribution data, and generates target water mist density control parameters. Using the target water mist density control parameters and the target atomization range, it updates the number of nozzles opened, spray pressure, atomization angle, and spray duration of the spray device, and adjusts the spray coverage rate based on the dynamic range of the dust scattering trajectory. It monitors the dust collection efficiency and water mist coverage integrity of the spray coverage rate, generating analytical data on dust settling velocity and atomization coverage uniformity.

2. The intelligent control method for belt conveyors according to claim 1, characterized in that, The statistical analysis of the real-time feature dataset to determine the correlation data between dust scattering trajectory and airflow field distribution includes: Frequency statistics and distribution calculations are performed on the real-time feature dataset to generate dust particle scattering frequency distribution data at different height levels; based on the scattering frequency distribution data, the correlation strength between dust scattering trajectory and airflow field distribution is calculated to generate the correlation data.

3. The intelligent control method for belt conveyors according to claim 1, characterized in that, The step of adjusting the atomization range parameter of the spray device based on the comparison result between the dynamic change range and the preset coverage threshold includes: Calculate the difference between the dynamic change range and the preset coverage threshold; based on the difference, adjust the atomization range parameter through atomization parameter adjustment processing to generate adjusted atomization parameter data.

4. The intelligent control method for belt conveyors according to claim 1, characterized in that, The process of calculating the correlation coefficient between particle suspension time and airflow field distribution based on the target atomization range, adjusting water mist density control parameters, and generating combinations of water mist concentration ratio, injection time interval, and droplet size includes: The suspension time data of particles of different sizes within the target atomization range are measured, and correlation coefficient data between the particle suspension time and the airflow field distribution is generated through correlation coefficient calculation. Based on the correlation coefficient data, the water mist density control parameter is adjusted to generate water mist concentration ratio data for each region. Based on the water mist concentration ratio data, the injection time interval parameter is generated, and the droplet size parameter is generated through droplet size adjustment. The water mist concentration ratio data, the injection time interval parameter, and the droplet size parameter are fused to generate the combination of water mist concentration ratio, injection time interval, and droplet size.

5. The intelligent control method for belt conveyors according to claim 1, characterized in that, The step of adjusting the water mist spray intensity based on the comparison result of the particle suspension time and the preset suspension time range includes: Calculate the difference between the particle suspension time and the preset suspension time range; based on the difference, adjust the spray intensity parameters of the water mist concentration ratio, spray time interval, and droplet size combination.

6. The intelligent control method for belt conveyors according to claim 1, characterized in that, The step of updating the number of nozzles opened by the spray device based on the target water mist density control parameter and the target atomization range includes: The target water mist density control parameters and the target atomization range data are integrated; based on the integrated data, the number of nozzles to be opened is determined through nozzle opening control processing.

7. The intelligent control method for belt conveyors according to claim 1, characterized in that, The monitoring of dust collection efficiency and water mist coverage integrity of the spray coverage includes: The system detects the coverage status data of the spray coverage rate; based on the coverage status data, it generates dust collection efficiency data through collection efficiency measurement processing; based on the dust collection efficiency data, it generates water mist coverage integrity data through integrity detection processing.

8. An intelligent control system for a belt conveyor, characterized in that, The system for implementing the intelligent control method for belt conveyors as described in claim 1 comprises: a real-time data acquisition module, used to acquire particle size distribution data and loading height parameters during the material loading process in real time, extract particle size distribution range, particle density, moisture content, and suspension performance under different material characteristics and height conditions, and obtain a real-time feature dataset of material particle size distribution and height changes; a statistical analysis module, used to perform statistical analysis on the real-time feature dataset of material particle size distribution and height changes, obtain the correlation between dust scattering trajectory and airflow field distribution, use a pre-established airflow dynamics model for trajectory prediction, and determine the dynamic change range of dust scattering trajectory; a trajectory prediction module, used to adjust the atomization range parameters based on the dynamic change range of dust scattering trajectory if the dynamic change range of dust scattering trajectory exceeds a preset coverage threshold, optimize the spray angle and coverage area of ​​the spray device by combining airflow field distribution data, and determine the target atomization range; and an atomization range adjustment module, used to calculate the influence correlation coefficient between particle suspension time and airflow field distribution based on the target atomization range, dynamically adjust the water mist density control parameters, and obtain the water mist concentration ratio, spray time interval, and droplet size combination for different areas. The water mist density adjustment module adjusts the water mist spray intensity by combining water mist concentration ratio, spray time interval, and droplet size if the particle suspension time exceeds the preset suspension time range. It also adjusts the spray particle size based on real-time airflow distribution data to determine the target water mist density control parameters. The spray parameter update module updates the number of nozzles opened, spray pressure, atomization angle, and spray duration of the spray system synchronously based on the target water mist density control parameters and the target atomization range. It also adjusts the spray coverage in real time according to changes in the dust scattering trajectory. The performance monitoring module monitors the dust collection efficiency and water mist coverage integrity at the loading point after the spray coverage is adjusted, and analyzes the dust settling velocity and atomization coverage uniformity in the loading point area under different spray parameters.

Images