Intelligent management and control method and system for belt conveyor

Abstract

The invention provides an intelligent management and control method and system for a belt conveyor, and the method comprises the steps: calculating an influence correlation coefficient between particle suspension time and airflow field distribution according to a target atomization range, dynamically adjusting a water mist density control parameter, and obtaining a water mist concentration ratio, a spraying time interval and a fog drop size combination of different regions; according to the target water mist density control parameters and the target atomization range, the nozzle opening number, the spraying pressure, the atomization angle and the spraying duration time of the spraying system are synchronously updated, and the spraying coverage rate is adjusted in real time according to the change of the dust scattering track; and monitoring the dust trapping efficiency and the water mist coverage integrity of the loading point of the adjusted spray coverage rate, and analyzing the dust settling velocity and the atomization coverage uniformity of the loading point area under different spray parameters.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, in particular to a belt conveyor intelligent management and control method and system. BACKGROUND

[0002] As the core equipment for material transportation in modern industry, belt conveyors are widely used in mining, port and power fields, and their high efficiency and reliability are crucial to production efficiency. However, the dust generated during the material loading process not only threatens the health of workers, but also may cause environmental pollution and equipment wear and tear, and intelligent management and control methods are urgently needed to reduce the dual impact of dust pollution on production and the environment. Existing dust control technologies, such as fixed spraying or simple mechanical dust suppression devices, often have limited effectiveness due to lack of dynamic adaptability, especially when facing different material characteristics or changes in loading conditions, making it difficult to respond accurately, resulting in low dust suppression efficiency or water resource waste. In addition, these methods often ignore the complex gas-solid interaction in the material loading process, failing to fundamentally solve the problem of dynamic changes in dust lifting trajectory. During the material loading process, the generation of dust is closely related to the particle size distribution of the material. Different particle sizes of the material produce different throwing angles and trajectories due to collision and air flow during loading, and fine particles are more easily carried by the air flow, prolonging the suspension time and increasing the range and difficulty of dust diffusion. This phenomenon directly leads to the first core challenge in the design of an intelligent spraying system: how to dynamically adjust the atomization range and water mist density according to the changes in the particle size distribution of the material, to accurately cover the dust lifting area. This makes it difficult to ensure the completeness of the spraying system coverage, and the dust suppression efficiency is significantly affected. If the spraying system cannot adapt to these changes in real time, not only will the dust capture efficiency decrease, but also the environmental control effect at the loading point may be affected due to uneven water mist distribution. Therefore, how to optimize the atomization range and water mist density of the intelligent spraying system under the dynamic conditions of material loading height changes and particle size distribution differences to ensure the coordinated improvement of dust capture efficiency and water mist coverage completeness has become a key problem. SUMMARY

[0003] The present application provides a belt conveyor intelligent management and control method, mainly including:

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

[0005] Statistical analysis of the real-time characteristic data set of material particle size distribution and height change, obtaining the correlation between dust throwing trajectory and air flow field distribution, using a pre-established air flow dynamics model for trajectory prediction, and determining the dynamic change range of dust throwing trajectory;

[0006] If the dynamic change range of the dust throwing trajectory exceeds the preset coverage threshold, the atomization range parameter is adjusted through the dynamic change range of the dust throwing trajectory, the spray device is optimized in terms of the spray angle and the coverage area in combination with the airflow field distribution data, and the target atomization range is determined;

[0007] According to the target atomization range, the influence correlation coefficient of the particle suspension time and the airflow field distribution is calculated, the water mist density control parameter is dynamically adjusted, and the water mist concentration ratio, the spray time interval and the mist droplet size combination in different regions are obtained;

[0008] If the particle suspension time exceeds the preset suspension time range, the water mist spray intensity is adjusted through the water mist concentration ratio, the spray time interval and the mist droplet size combination, the spray particle size is adjusted in combination with the real-time airflow field distribution data, and the target water mist density control parameter is determined;

[0009] Through the target water mist density control parameter and the target atomization range, the number of nozzle openings, the spray pressure, the atomization angle and the spray duration of the spray system are synchronously updated, and the spray coverage rate is adjusted in real time according to the change of the dust throwing trajectory;

[0010] The dust capture efficiency of the loading point and the water mist coverage integrity of the spray coverage rate after adjustment are monitored, and the dust settling speed and the atomization coverage uniformity of the loading point region under different spray parameters are analyzed.

[0011] The application provides a belt conveyor intelligent management and control system, which mainly comprises:

[0012] A real-time data acquisition module is configured to acquire particle size distribution data and loading height parameters in a material loading process in real time, extract particle size distribution ranges, particle densities, moisture contents and suspension performances under different material characteristics and height conditions, and obtain real-time characteristic data sets of material particle size distribution and height change.

[0013] A statistical analysis module is configured to statistically analyze the real-time characteristic data sets of material particle size distribution and height change, obtain the correlation between a dust throwing trajectory and an airflow field distribution, perform trajectory prediction by using a pre-established airflow dynamics model, and determine a dynamic change range of the dust throwing trajectory.

[0014] A trajectory prediction module is configured to adjust an atomization range parameter through the dynamic change range of the dust throwing trajectory if the dynamic change range of the dust throwing trajectory exceeds a preset coverage threshold, optimize a spray angle and a coverage area of a spray device in combination with airflow field distribution data, and determine a target atomization range.

[0015] An atomization range adjustment module is configured to calculate an influence correlation coefficient of a particle suspension time and an airflow field distribution according to the target atomization range, dynamically adjust a water mist density control parameter, and obtain a water mist concentration ratio, a spray time interval and a mist droplet size combination in different regions.

[0016] a water mist density adjustment module, configured to adjust the water mist spraying intensity by combining the water mist concentration ratio, spraying time interval and mist droplet size, and to determine the target water mist density control parameter by adjusting the spraying particle size in combination with the real-time airflow field distribution data, if the particle suspension time exceeds the preset suspension time range;

[0017] a spraying parameter updating module, configured to update the nozzle opening number, spraying pressure, atomization angle and spraying duration of the spraying system in synchronization with the target water mist density control parameter and the target atomization range, and to adjust the spraying coverage rate in real time according to the change of the dust scattering trajectory;

[0018] a performance monitoring module, configured to monitor the dust capture efficiency and water mist coverage integrity of the loading point after the spraying coverage rate is adjusted, and to analyze the dust settling speed and atomization coverage uniformity of the loading point area under different spraying parameters.

[0019] The technical scheme provided by the embodiment of the present application can include the following beneficial effects:

[0020] The present application discloses a belt conveyor intelligent control method. The method collects material particle size distribution and loading height data in real time, analyzes the correlation between dust scattering trajectory and airflow field distribution, and dynamically predicts the dust diffusion range. According to the prediction result, the present application adaptively adjusts the spraying parameters such as atomization range and water mist density to achieve precise dust suppression. Specifically, the present application adjusts the spraying coverage area according to the dust scattering trajectory, controls the water mist density by combining water mist concentration ratio and mist droplet size, and optimizes the spraying angle and intensity in combination with the airflow field distribution. The present application can also monitor the dust capture efficiency and water mist coverage integrity in real time, and continuously optimize the spraying parameters. The method can significantly improve the dust suppression effect during the material loading process, reduce dust pollution, and improve the working environment. BRIEF DESCRIPTION OF DRAWINGS

[0021] Fig. 1 The flowchart of the belt conveyor intelligent control method of the present application.

[0022] Fig. 2 The structural diagram of a belt conveyor intelligent control system of the present application. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be described in detail below in combination with the drawings and specific embodiments.

[0024] As Figs. 1-2 , the belt conveyor intelligent control method and system of the present embodiment can specifically include:

[0025] S101, 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, and obtaining real-time characteristic data set of material particle size distribution and height change.

[0026] The material during the loading process is continuously scanned and measured to obtain particle size distribution data at different loading height positions. The density detection device synchronously detects the particle density change of each height layer to obtain a first data matrix related to the particle size distribution and density. According to the density distribution information recorded in the first data matrix, the moisture content detection device is used to determine the moisture content distribution of each particle size interval material. The suspension performance test device records the suspension performance parameters of the particles under different moisture content conditions to obtain a second data matrix related to the moisture content and suspension performance. If the moisture content and suspension performance related data in the second data matrix shows abnormal fluctuation, a high-frequency acquisition mode is started to record the material distribution state during the change of loading height. The data filtering processing is used to denoise the collected multi-dimensional characteristic data to obtain a third data matrix of material characteristic change. According to the material characteristic change information in the third data matrix, the sliding window is used to extract the particle size distribution range fluctuation characteristics in different time periods. The real-time monitoring results of each detection device are integrated through data fusion processing to determine the complete real-time characteristic data set of material particle size distribution and height change.

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

[0028] In one possible implementation, the detected particles at a loading height of 0.5 meters are mainly concentrated in the range of 50-100 microns, while the particle distribution at a height of 1.5 meters is expanded to the range of 20-150 microns. The density detection device synchronously records the bulk density change of each layer of material. The bottom layer density usually reaches 1.8 grams per cubic centimeter, and the top layer density decreases to 1.2 grams per cubic centimeter. This layered detection method can accurately reflect the distribution characteristics of the material in the vertical direction, providing basic data support for subsequent analysis. Based on the obtained first data matrix, the moisture content detection device determines the moisture content of different particle size materials using the dielectric constant measurement principle.

[0029] It should be noted that the change of moisture content directly affects the adhesion between particles and the flowability. When the moisture content increases from 3% to 8%, the van der Waals force between fine particles significantly increases. The suspension performance test device evaluates the suspension characteristics of the particles by measuring the settling velocity of the particles in the fluid. The particles with higher moisture content increase the effective diameter due to the adsorption of water molecules on the surface, resulting in a decrease in settling velocity.

[0030] For example, the settling velocity of 50-micron particles with 5% moisture content is 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 that the moisture content and suspension performance data fluctuate abnormally, the high-frequency acquisition mode is activated to capture the transient change process.

[0031] Specifically, the moisture content should normally be controlled within ±2%, and if fluctuations exceeding this threshold are detected, it indicates that the material state has changed significantly. Data filtering uses a moving average algorithm to eliminate measurement noise, and the filter window length is set to 10 consecutive sampling points, which can both preserve the true trend and effectively suppress random interference. The third data matrix obtained through this processing method can clearly show the time evolution of material characteristics.

[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 of 10 seconds, ensuring the continuity and integrity of feature extraction. Data fusion processing integrates multi-source information from different detection devices, and uses a weighted average method to integrate sensor data, with weight coefficients determined according to sensor accuracy and reliability.

[0033] For example, the particle size detection data weight is 0.4, the density detection data weight is 0.3, and the moisture content detection data weight is 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 data set containing particle size distribution range, spatial location information, and time variation characteristics, providing scientific basis for precise control and optimization of the material loading process.

[0034] S102, statistical analysis of real-time feature data sets of material particle size distribution and height variation, correlation between dust throwing trajectory and airflow field distribution, trajectory prediction using pre-established airflow dynamics model, determination of dynamic variation range of dust throwing trajectory.

[0035] The statistical analysis tool is used to perform frequency statistics and distribution calculation on the real-time characteristic data set of the material particle size distribution and height variation, to obtain the dust particle scattering frequency distribution in different height layers, to analyze the correlation strength between the dust scattering trajectory and the surrounding airflow field distribution by a correlation calculation method, and to obtain first correlation data of the dust scattering and the airflow field correlation. According to the trajectory and airflow field correlation information in the first correlation data, a pre-established airflow dynamics model is used to perform numerical calculation on the motion trajectory of the dust particle, and a trajectory calculation processor is used to calculate the motion path of the dust in the three-dimensional space by combining the particle mass and the airflow velocity parameter, to obtain second trajectory data of the dust scattering trajectory prediction. If the prediction trajectory range in the second trajectory data exceeds a preset boundary threshold, the trajectory boundary is re-evaluated, the discrete trajectory points in the second trajectory data are processed, and third boundary data of the continuous dust scattering trajectory boundary variation are obtained. According to the trajectory boundary variation information recorded in the third boundary data, the maximum diffusion boundary of the dust scattering is calculated, and the trajectory boundary data at different time nodes are integrated by a dynamic range to determine the dynamic variation range of the dust scattering trajectory in the entire loading process.

[0036] Specifically, the statistical analysis tool quantitatively processes the particle size and height data in the real-time characteristic data set based on the histogram statistics and frequency distribution calculation principle.

[0037] In a possible implementation, when the loading height reaches 2 meters, the dust particle scattering frequency of the particle size in the range of 30-50 microns is 120 times per second, and the particle scattering frequency of the particle size in the range of 100-150 microns is reduced to 80 times per second. The correlation calculation process quantifies the correlation strength between the dust trajectory and the airflow field by the Pearson correlation coefficient method. When the airflow velocity is 3 meters per second, the correlation coefficient of the dust scattering trajectory and the wind direction reaches 0.85, indicating strong correlation. This correlation analysis can reveal the internal relationship between the dust motion and the environmental airflow, and provide a reliable data basis for subsequent trajectory prediction. Based on the correlation relationship established in the first correlation data, the airflow dynamics model calculates the motion state of the particle in the airflow by using the Stokes law in fluid mechanics.

[0038] Specifically, the model considers parameters such as particle diameter, density, and airflow viscosity coefficient, and determines the spatial trajectory of the particle by solving the motion equation set. The trajectory calculation processor divides the three-dimensional space into grid units, and each unit has a size of 0.1 meter x 0.1 meter x 0.1 meter. The position coordinates and velocity vectors of the dust particles in the grid are calculated.

[0039] For example, the motion velocity of a dust particle with a mass of 0.05 milligrams in the vertical direction is 1.2 meters per second, and the offset velocity in the horizontal direction affected by the airflow is 0.8 meters per second. This accurate trajectory calculation method can accurately predict the motion path of the dust in the three-dimensional space.

[0040] In an 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 reevaluation.

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

[0042] Specifically, the boundary expansion calculation process determines the maximum possible diffusion range of dust scattering through an envelope algorithm. This algorithm traverses all trajectory prediction points to find the farthest distance point in each direction, forming an outer boundary that surrounds the entire diffusion area. The dynamic range integration process uses time series analysis to arrange and integrate boundary data at different time nodes in chronological order.

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

[0044] S103, if the dynamic change range of the dust scattering trajectory exceeds the preset coverage threshold, adjust the atomization range parameter through the dynamic change range of the dust scattering trajectory, optimize the spray angle and coverage area of the spray device in combination with the airflow field distribution data to determine the target atomization range.

[0045] If the dynamic change range of the dust throwing trajectory exceeds the preset coverage threshold, a difference value is calculated between the dynamic change range and the coverage threshold, the atomization range parameter of the spraying device is adjusted according to the range difference value, and first atomization parameter data after adjustment is obtained. According to the atomization range adjustment information in the first atomization parameter data, the airflow field distribution data is analyzed by using an airflow field analysis method to analyze the wind speed and wind direction information, and the best spraying angle of the spraying device is calculated according to the airflow field distribution information, to obtain second spraying angle data after optimization. According to the angle optimization information recorded in the second spraying angle data, the coverage area of the spraying device is recalculated, the calculated coverage area is matched and analyzed with the dust throwing trajectory range, and third coverage area data after area adaptation is obtained. According to the area matching result in the third coverage area data, the atomization range parameter and the coverage area information are integrated to determine the working parameter of the spraying device, and the target atomization range meeting the dust throwing trajectory coverage requirement is obtained.

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

[0047] In one possible implementation, when the horizontal diffusion range of the dust throwing trajectory reaches 8 meters and the preset coverage threshold is only 6 meters, the difference calculation result is 2 meters, indicating that the existing coverage range is insufficient. The atomization parameter adjustment processing automatically adjusts the key parameters such as pressure, flow rate and droplet size of the spraying device according to this difference value information. When the difference value is 2 meters, the spraying pressure is increased from the original 0.3 MPa to 0.45 MPa, and the atomization flow rate is increased from 50 liters per minute to 75 liters per minute. This self-adaptive adjustment mechanism based on the difference value can ensure that the atomization coverage range and the dust diffusion range remain dynamically matched. Based on the adjustment scheme determined in the first atomization parameter data, the airflow field analysis processing uses a vector decomposition method to quantitatively analyze the wind speed and wind direction data.

[0048] Specifically, when the environmental wind speed is 4 meters per second and the wind direction angle is northeast 45 degrees, the airflow field analysis processing decomposes the wind speed vector into a horizontal component of 3.2 meters per second and a vertical component of 1.8 meters per second. The angle optimization processing calculates the compensation angle of the spraying device in combination with these airflow field distribution information. When the dominant wind direction is northeast 45 degrees, the spraying angle of the spraying device needs to be deflected 15 degrees to the southwest to offset the wind force.

[0049] For example, the original spraying angle is vertically upward 0 degrees, and after angle optimization, it is adjusted to deflect 15 degrees to the southwest, which can ensure that the atomized droplets can still accurately cover the target area under the action of wind force.

[0050] In one embodiment, the coverage area calculation processing determines the actual coverage range after adjustment by using a geometric projection method.

[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 process determines the residence time of particles of different sizes in the air based on the theory of gravitational sedimentation by analyzing the suspension characteristics of particles of different sizes in the air.

[0057] In one possible implementation, the theoretical settling velocity of a dust particle with a particle size of 10 microns in still air is 0.3 millimeters per second, and in a windy environment, the suspension time can be extended to 2-3 times the original. The correlation coefficient calculation process uses the correlation analysis method in statistics to quantify the numerical relationship between the particle suspension time and the airflow field intensity. When the airflow velocity is 2 meters per second, the correlation coefficient between the particle suspension time and the airflow intensity reaches 0.92, indicating a strong positive correlation. This high correlation indicates that the airflow field has a decisive influence on the 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 realizes accurate control of the water mist density by changing the working parameters of the spray device.

[0058] Specifically, water mist density control involves coordinated adjustment of three key elements: nozzle pressure, flow distribution, and atomization degree. The concentration ratio calculation process determines the water mist concentration requirements of each region according to the differences in particle suspension characteristics in different regions using a regional matching strategy.

[0059] For example, in regions where the particle suspension time is longer, the water mist concentration needs to be increased to 800 milligrams per cubic meter, while in regions where the suspension time is shorter, the concentration can be reduced to 400 milligrams per cubic meter. This differentiated matching method can achieve efficient use of water mist resources and avoid waste caused by excessive spraying.

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

[0061] It should be noted that the injection time interval is inversely proportional to the particle suspension time, and when the particle suspension time is 30 seconds, the injection interval should be set to 10 seconds to ensure continuous and effective coverage. The droplet size adjustment process precisely adjusts the droplet diameter by controlling the nozzle aperture and pressure parameters. When the concentration ratio needs to be increased, the droplet diameter increases from the original 50 microns to 80 microns, and the increased droplet has stronger inertia and longer suspension time. This coordinated matching of size and time interval can ensure the uniform distribution of water mist in space and time, improving the dust capture efficiency.

[0062] Specifically, the parameter integration process systematically combines the control parameters of concentration ratio, time interval, and droplet size in three dimensions to form diversified spray control schemes. The integration process considers the mutual influence relationship between parameters. When the droplet size increases, the spray interval needs to be correspondingly extended to avoid excessive accumulation. The regional allocation process allocates a dedicated parameter combination scheme for each region according to the characteristics of different regions within the target atomization range.

[0063] For example, in the core area with high dust concentration, a combination scheme of high concentration ratio of 900 mg per cubic meter, short time interval of 8 seconds, and large droplet size of 90 microns is adopted, while in the edge area, a combination scheme of medium concentration ratio of 600 mg per cubic meter, medium time interval of 12 seconds, and medium droplet size of 65 microns is adopted. Through this fine regional allocation and parameter combination, the final water mist concentration ratio, spray time interval, and droplet size combination can achieve precise matching of dust characteristics in different regions, significantly improving the overall dust suppression effect and system operation efficiency.

[0064] S105, if the particle suspension time exceeds the preset suspension time range, adjust the water mist spray intensity through the water mist concentration ratio, spray time interval, and droplet size combination, adjust the spray particle size based on the real-time airflow field distribution data, and determine the target water mist density control parameter.

[0065] If the particle suspension time exceeds the preset suspension time range, compare and analyze the particle suspension time and the preset range, adjust the water mist spray intensity based on the water mist concentration ratio, spray time interval, and droplet size combination parameters, and obtain the first spray intensity parameter after intensity adjustment. According to the intensity adjustment data in the first spray intensity parameter, obtain the real-time airflow field distribution data, dynamically adjust the spray particle size based on the airflow field distribution data, and obtain the second particle size parameter after particle size adjustment. According to the particle adjustment data recorded in the second particle size parameter, comprehensively calculate the adjusted spray intensity and particle size, optimize the water mist density control strategy through the density strategy optimization process combined with the particle size change, and obtain the third density control parameter after optimization. According to the control optimization data in the third density control parameter, finally set each parameter of the water mist density control, determine the adjustment result of the spray intensity and particle size, and determine the target water mist density control parameter that meets the particle suspension time requirement.

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

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

[0068] Specifically, the process collects airflow data in three-dimensional space, including horizontal wind speed, vertical airflow, and turbulence intensity, through wind speed sensors and wind direction instruments distributed in the work area. The particle size adjustment process uses a dynamic matching strategy to adjust the diameter of the spray particles based on these real-time airflow field distribution data.

[0069] For example, when the horizontal wind speed is monitored to be 3.5 meters per second and the vertical airflow speed is 0.8 meters per second, the spray particle diameter needs to be adjusted from the original 60 microns to 80 microns. The increased particle has stronger wind resistance and more stable motion 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 uses 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 change, and particle density gradient. The density strategy optimization process reevaluates and adjusts the original density control strategy based on the impact of particle size changes. When the spray particle diameter increases from 60 microns to 80 microns, the number of particles per unit volume decreases by about 30%. At this time, the density loss needs to be compensated by increasing the spray frequency or increasing the spray pressure.

[0072] For example, the original density control strategy requires maintaining 800 mist droplets per cubic meter of space. After the particle size adjustment, the spray frequency needs 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 mutual constraint relationship between injection 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, including spatial density target value, time control node, and dynamic adjustment threshold, based on the integrated parameter configuration.

[0074] For example, the final determined target parameters include a spatial density of 1200 milligrams per cubic meter, an injection intensity of 1.2 liters per square meter per second, a particle diameter of 80 microns, and an injection interval of 8 seconds. Through the precise setting and dynamic adjustment of these multi-dimensional parameters, the final determined target water mist density control parameters can effectively address the problem of particle suspension time exceeding the standard, achieve efficient capture and settling control of dust particles, and significantly improve the overall dust suppression effect.

[0075] S106, based on the target water mist density control parameters and the target atomization range, the number of open nozzles, the injection 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 change in the dust throwing trajectory.

[0076] Through the comprehensive information of the target water mist density control parameters and the target atomization range, the working parameters of the spray device are uniformly updated, the number of open nozzles is determined according to the density control parameters and the atomization range requirements, and the first spray control parameters of the nozzle opening configuration are obtained. According to the nozzle opening configuration data in the first spray control parameters, the injection pressure of each open nozzle is set, and the atomization angle of each nozzle is adjusted according to the atomization range coverage requirement, and the second injection control parameters of the pressure and angle configuration are obtained. According to the pressure and angle configuration data recorded in the second injection control parameters, the spray duration of each nozzle is calculated, the change information of the dust throwing trajectory is obtained in real time through the trajectory change monitoring process, and the third dynamic control parameters associated with the trajectory change are obtained. According to the trajectory change data in the third dynamic control parameters, the current spray coverage effect is evaluated in real time, the spray coverage is adjusted in real time according to the change in the dust throwing trajectory, and the dynamic adjustment control of the spray coverage is realized.

[0077] Specifically, the parameter synchronous update process is based on the centralized control principle, and the unified regulation and control of the spray device is realized by establishing the mapping relationship between the target water mist density control parameters and the target atomization range.

[0078] In one possible implementation, when the target water mist density requirement is 1200 mg per cubic meter and the target misting range covers a radius of 8 meters, 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 determines the optimal nozzle opening configuration scheme using a region division strategy.

[0079] For example, in a circular coverage area with a radius of 8 meters, 12 nozzles need to be opened to ensure uniform density distribution, with 4 nozzles in the core area, 6 nozzles in the middle area, and 2 nozzles in the peripheral area. This nozzle configuration based on dual constraints of density and range can achieve precise spatial coverage control. Based on the nozzle opening scheme determined in the first spray control parameter, the pressure setting process uses a hierarchical pressure control strategy to configure the corresponding working pressure for nozzles at different positions.

[0080] Specifically, the 4 nozzles in the core area require higher working pressure to produce finer droplets and larger spray volume, with a pressure setting of 0.6 MPa, while the 2 nozzles in the peripheral area are set to 0.4 MPa to expand the coverage range. The angle adjustment process sets different atomization angle parameters for each nozzle in combination with the geometric requirements of the misting range. The core area nozzles use a 60-degree conical atomization angle to ensure dense coverage, the middle area nozzles use a 90-degree angle to achieve transition coverage, and the peripheral nozzles use a 120-degree wide-angle atomization to achieve boundary coverage. This differential pressure and angle configuration can form a gradient atomization distribution pattern, improving overall coverage efficiency.

[0081] In one embodiment, the time control process calculates the optimal working timing of each nozzle using a pulse spraying strategy.

[0082] It should be noted that the control of spray duration directly affects the accumulation effect of water mist in space and resource utilization efficiency. The continuous spraying time of the core area nozzles is set to 15 seconds, with an interval of 5 seconds, forming a working proportion of 75%, while the peripheral nozzles use a mode of 10 seconds continuously and 10 seconds intermittently, with a working proportion of 50%. The trajectory change monitoring process tracks the spatial position change of the dust throwing trajectory in real time through a distributed sensor network, and when it is monitored that the dust trajectory deviates 2 meters to the northeast, the nozzles in the corresponding area need to be adjusted accordingly. This time control mechanism based on trajectory feedback can achieve dynamic response and precise positioning.

[0083] Specifically, the coverage rate evaluation process uses a grid evaluation method to quantitatively analyze the current spraying effect. This process divides the target atomization range into 1 meter x 1 meter grid cells, and detects whether the water mist density in each cell meets the preset requirements. The coverage rate dynamic adjustment process adjusts the spray parameters in real time based on the trajectory change data and coverage rate evaluation results to maintain the best coverage effect.

[0084] For example, when the dust trajectory deviates and causes the coverage rate of a certain area to decrease from 85% to 70%, the corresponding nozzle pressure of the area needs to be increased from 0.4 MPa to 0.5 MPa, and the spraying duration needs to be extended from 10 seconds to 12 seconds. Through this real-time monitoring and dynamic adjustment mechanism, the spraying coverage rate can be maintained at an effective level of more than 80% at all times, ensuring comprehensive tracking and efficient interception of the dust scattering trajectory, and improving the accuracy and reliability of dust control.

[0085] S107, monitor the dust capture efficiency of the loading point and the water mist coverage integrity after the spraying coverage rate is adjusted, and analyze the dust settling speed and atomization coverage uniformity of the loading point area under different spraying parameters.

[0086] The monitoring effect of the spraying coverage rate control effect is monitored, the spraying coverage state of the loading point area is detected in real time, the dust capture efficiency value of the loading point is determined, and first monitoring effect data related to the coverage rate and the capture efficiency are obtained. According to the capture efficiency information in the first monitoring effect data, the water mist coverage integrity is comprehensively evaluated, the influence degree of different spraying parameter combinations on the coverage integrity is analyzed, and second integrity evaluation data related to the parameters and the integrity are obtained. According to the parameter influence information recorded in the second integrity evaluation data, the dust settling speed of the loading point area is quantitatively detected, the variation law of the settling speed under different spraying parameter conditions is analyzed, and third speed analysis data related to the parameters and the settling speed are obtained. According to the settling speed variation information in the third speed analysis data, the atomization coverage uniformity is measured and evaluated, the uniformity performance difference under different spraying parameters is analyzed, and the comprehensive analysis result of the dust settling speed and the atomization coverage uniformity of the loading point area is obtained.

[0087] Specifically, the coverage rate monitoring process is based on the optical detection principle, and a laser curtain sensor array is arranged in the loading point area to monitor the spraying coverage state in real time.

[0088] In one possible implementation, the laser curtain covers the entire loading area in a grid manner, and when the water mist particles pass through the light beam, a scattering signal is generated, and the sensor detects the change in signal strength to determine the coverage density of the area. The capture efficiency measurement process uses the mass balance method to quantitatively evaluate the dust capture effect, and a dust concentration monitoring device is arranged at the loading point to calculate the capture efficiency by comparing the change in dust concentration before and after spraying.

[0089] For example, when the dust concentration before spraying is 120 mg per cubic meter and the dust concentration after spraying is reduced to 25 mg per cubic meter, the capture efficiency reaches 79.2%. This double monitoring mechanism can establish a quantitative relationship between coverage and capture 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 a spatial scanning technique to comprehensively evaluate the integrity of the water mist coverage.

[0090] Specifically, the process performs three-dimensional scanning in the loading point area through a mobile mist droplet detection device to identify the distribution of blank areas and overlapping areas. The parameter correlation analysis process uses multivariate regression analysis to study the influence of different spray parameter combinations on coverage integrity. When the nozzle pressure is increased from 0.4 MPa to 0.6 MPa, the coverage integrity increases from 85% to 92%, and when the atomization angle is adjusted from 60 degrees to 90 degrees, the coverage integrity further increases to 95%. This parameter sensitivity analysis can reveal 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 to accurately measure the motion state of dust particles.

[0092] It should be noted that the dust settling velocity directly reflects the immediate effect of spray dust suppression and is a key indicator for evaluating spray performance. The measurement device irradiates the suspended dust with a laser beam, and calculates the settling velocity of the particles based on the frequency shift of the reflected light. The parameter comparison analysis process establishes a settling velocity database under different spray parameter conditions. When the water mist density is 800 mg per cubic meter, the average dust settling velocity is 2.3 mm per second, and when the density increases to 1200 mg, the settling velocity increases to 3.1 mm per second. This quantitative parameter effect correlation analysis can determine the optimal combination of spray parameter configurations.

[0093] Specifically, the uniformity detection process uses statistical methods to quantitatively evaluate the spatial distribution uniformity of atomized coverage. The process divides the loading point area into multiple detection grids, and calculates the uniformity coefficient by measuring the density distribution of mist droplets in 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 atomized coverage uniformity coefficient reaches 0.88, the corresponding dust settling velocity is 2.8 mm per second and the capture 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 fine-tuning of spray parameters, achieving continuous optimization of dust control effect at loading points and comprehensive improvement of system performance.

[0095] The application provides a belt conveyor intelligent management and control system, mainly comprising:

[0096] A real-time data acquisition module is configured to acquire particle size distribution data and loading height parameters in a real-time manner during material loading, extract particle size distribution ranges, particle densities, moisture contents and suspension performances under different material characteristics and height conditions, and obtain real-time characteristic data sets of material particle size distribution and height changes;

[0097] A statistical analysis module is configured to statistically analyze the real-time characteristic data sets of material particle size distribution and height changes, obtain the correlation between dust throwing trajectories and airflow field distributions, perform trajectory prediction by using a pre-established airflow dynamics model, and determine a dynamic change range of the dust throwing trajectories;

[0098] A trajectory prediction module is configured to, if the dynamic change range of the dust throwing trajectories exceeds a preset coverage threshold, adjust atomization range parameters through the dynamic change range of the dust throwing trajectories, optimize a spraying angle and a coverage area of a spraying device in combination with airflow field distribution data, and determine a target atomization range;

[0099] An atomization range adjustment module is configured to calculate an influence correlation coefficient between particle suspension time and airflow field distribution according to the target atomization range, dynamically adjust water mist density control parameters, and obtain water mist concentration ratios, spraying time intervals and mist droplet size combinations in different regions;

[0100] A water mist density adjustment module is configured to, if the particle suspension time exceeds a preset suspension time range, adjust water mist spraying intensity through the water mist concentration ratios, the spraying time intervals and the mist droplet size combinations, adjust spraying particle sizes in combination with real-time airflow field distribution data, and determine target water mist density control parameters;

[0101] A spraying parameter updating module is configured to, through the target water mist density control parameters and the target atomization range, synchronously update a nozzle opening number, a spraying pressure, an atomization angle and a spraying duration of a spraying system, and adjust a spraying coverage rate in a real-time manner according to changes in the dust throwing trajectories;

[0102] A performance monitoring module is configured to monitor dust capture efficiency and water mist coverage integrity of a loading point after the spraying coverage rate is adjusted, and analyze dust settling speeds and atomization coverage uniformity in a loading point region under different spraying parameters.

[0103] It should be further noted that the various technical features described in the above specific embodiments can be combined in any suitable manner, and in order to avoid unnecessary repetition, the various possible combinations of features are not all listed here. In addition, any combination of the various embodiments of the present application can also be made, as long as it does not deviate from the spirit of the present application, and it should also be considered as disclosed by the present application.

Claims

1. A method for intelligent control of belt conveyors, characterized in that, The method includes: Data on particle size distribution and loading height during material loading are collected to generate a real-time feature dataset containing particle size distribution range, particle density, moisture content, and suspension performance. Statistical analysis is performed on this dataset to determine the correlation between dust scattering trajectory and airflow field distribution. Based on an airflow dynamics model, the dynamic range of dust scattering trajectory is predicted. According to the comparison between the dynamic range and a preset coverage threshold, the atomization range parameters of the spray device are adjusted. The spray angle and coverage area are optimized in conjunction with the airflow field distribution data to generate a target atomization range. 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 adjusted to generate water mist. The system combines concentration ratio, injection time interval, and droplet size. Based on a comparison of the particle suspension time with a preset suspension time range, the injection intensity of the water mist concentration ratio, injection time interval, and droplet size combination is adjusted. Combined with the airflow field distribution data, the spray particle size is optimized to generate target water mist density control parameters. Using the target water mist density control parameters and the target atomization range, the number of nozzles opened, injection pressure, atomization angle, and spray duration of the spray device are updated. The spray coverage rate is adjusted based on the dynamic change range of the dust scattering trajectory. The dust collection efficiency and water mist coverage integrity of the spray coverage rate are monitored to generate 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 particle size distribution data and loading height parameters collected during the material loading process are used to generate a real-time feature dataset containing particle size distribution range, particle density, moisture content, and suspension performance, including: The particle size distribution data at different loading heights is obtained by continuously scanning the material using the particle size detection device. The particle density at each height layer is measured using the density detection device, generating a first data matrix relating particle size distribution to density. Based on the density distribution data in the first data matrix, the moisture content of each particle size range is measured using the moisture content detection device, and the suspension performance parameters at different moisture contents are recorded, generating a second data matrix relating moisture content to suspension performance. Based on the moisture content and suspension performance data in the second data matrix, the material distribution state with varying loading heights is recorded, and the collected data is filtered to generate a third data matrix relating material characteristic changes. Based on the material characteristic data in the third data matrix, the fluctuation characteristics of the particle size distribution range are extracted, and the data from each detection device are fused to generate the real-time feature dataset.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. An intelligent control system for a belt conveyor, characterized in that, The system includes: 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, 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. 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. 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. 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. 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. 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. 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.

Images