Dust removal method and device based on intelligent analysis

Abstract

The invention relates to the technical field of intelligent dust removal, in particular to a dust removal method and device based on intelligent analysis, and the method comprises the steps: controlling a fan to operate based on an initial rotating speed, obtaining a smoke dust coverage area and a concentration gray scale of smoke dust, and determining an area change rate and a concentration change rate of the smoke dust; determining a basic set value of the rotating speed of the fan according to the trend state of smoke and visual dust load; in response to the trend state of the smoke, dust characteristic parameters are extracted to determine the extra air volume demand, and the total air volume demand is determined by combining the basic set value of the fan rotating speed to obtain the deviation prediction amount of the initial rotating speed; and reflecting dust inertia based on an inertia coefficient, determining an arc-shaped baffle movement parameter to adjust a hood opening area corresponding to smoke dust and an initial rotating speed or adopting a mixed adjustment strategy to adjust the initial rotating speed and determining an inertia weight to adjust the movement parameter. Whether the current wind power parameter can meet the dust characteristics or not is determined according to the smoke dust trend state and the dust characteristic parameter of the dust production point, and the dust removal strategy is adaptively pre-adjusted.

Description

Technical Field

[0001] This invention relates to the field of intelligent dust removal technology, and in particular to a dust removal method and apparatus based on intelligent analysis. Background Technology

[0002] Dust removal technology is widely used in various fields such as industrial production, cleanrooms, warehousing and logistics, and modern buildings. It is a key link in ensuring product quality, stable equipment operation, and occupational health of personnel. Traditional dust removal methods mainly rely on timed control, constant air volume control, or simple feedback control based on a single threshold trigger.

[0003] Currently common dust removal systems typically employ timed start / stop modes, constant airflow / constant power modes, or simple threshold trigger modes. With the development of IoT technology, some "intelligent" dust removal concepts have emerged, such as remote control start / stop or simple device linkage. However, most of these methods still remain at the level of single-point, localized automation, lacking macroscopic perception and in-depth analysis of the generation and diffusion patterns of dust throughout the entire space, and thus failing to achieve forward-looking prediction and precise control.

[0004] Chinese Patent Publication No. CN113083771A discloses a dust removal device and method based on intelligent analysis, including a worktable, a clamping unit located on the worktable, a lifting unit fixedly installed on the worktable, and a cleaning unit located on the lifting unit. The clamping unit can clamp an object, and the clamping unit and the lifting unit reciprocate in a direction perpendicular to the worktable. The lifting unit drives the cleaning unit to move until the object is inside the cleaning unit, whereby the cleaning unit cleans the object. The cleaning unit allows the workpiece to be fixed in position, and by moving the dust removal device, the workpiece can be cleaned. Furthermore, the dust removal device can clean not only the inner or outer circumferential walls of the workpiece but also its axial direction. However, the aforementioned dust removal device and method based on intelligent analysis have the following problems: It is impossible to determine whether the current wind parameters can meet the dust characteristics and adapt the dust removal strategy based on the dust trend and dust characteristic parameters at the dust generation point. Summary of the Invention

[0005] To address this, the present invention provides a dust removal method and apparatus based on intelligent analysis, which overcomes the problem in the prior art that it is impossible to determine whether the current wind parameters can meet the dust characteristics and adaptively pre-adjust the dust removal strategy based on the dust trend state and dust characteristic parameters of the dust generation point.

[0006] To achieve the above objectives, the present invention provides a dust removal method based on intelligent analysis, comprising: The dust removal device's fan is continuously operated based on the initial rotation speed control, and the actual image of the dust generation point is obtained to determine the dust coverage area and concentration gray level. The area change rate and concentration change rate of the smoke and dust are determined based on the smoke and dust coverage area, concentration grayness, and initial detection cycle, so as to determine the trend state of the smoke and dust and the visual dust load, and the basic set value of the fan speed is determined according to the visual dust load. Dust characteristic parameters, including diffusion coefficient and inertia coefficient, are extracted in response to the trend state of the smoke and dust to determine additional air volume requirements. The total air volume requirement is determined by combining the additional air volume requirement and the basic set value of the fan speed, so as to obtain the predicted deviation from the initial speed. Based on the deviation prediction, determine whether the total air volume requirement matches the initial speed of the fan, whether the current wind force parameters can meet the dust characteristics, and determine the adjustment strategy based on the dust characteristic parameters. Based on the inertia coefficient reflecting the dust inertia, the movement parameters of the arc-shaped baffle are determined to adjust the corresponding hood area of ​​the dust and the initial speed of the fan, or a hybrid adjustment strategy is adopted to adjust the initial speed and determine the inertia weight to adjust the movement parameters of the arc-shaped baffle. In response to the adjustment strategy of the corresponding workstation, it is determined whether the movement parameters conflict, and the dynamic priority scores of several dust-generating points are determined based on the dust parameters in order to adjust the movement parameters of the arc baffle and compensate the dust removal air volume according to the efficiency loss coefficient. The dust removal effect is determined based on the changing trends of smoke and dust coverage area and concentration ash, and the weights of air volume regulation and hood area regulation in the hybrid regulation strategy are adjusted by adjusting the inertia weight.

[0007] Furthermore, the changes in the smoke and dust coverage area and concentration ash value per unit time, calculated according to the initial detection cycle, are recorded as the area change rate and concentration change rate. When the area change rate is greater than the first change threshold and the concentration change rate is greater than the second change threshold, the smoke and dust are judged to be in the first trend state. When the area change rate is greater than the third change threshold but less than the first change threshold, and the concentration change rate is less than the fourth change threshold, the smoke and dust are judged to be in the second trend state. When the area change rate is greater than the third change threshold and the concentration change rate is greater than the fourth change threshold, the smoke and dust are judged to be in the third trend state.

[0008] Furthermore, the visual dust load is determined based on the smoke and dust coverage area and concentration grayness to determine the basic set value of the fan speed; When the dust is in the third trend state, the dust characteristic parameters are extracted, including the diffusion coefficient and the inertia coefficient. The additional air volume requirement is calculated based on the dust characteristic parameters. The total air volume requirement is determined based on the additional air volume requirement and the basic set value of the fan speed. The deviation prediction between the total air volume requirement and the actual air volume at the current initial speed of the fan is calculated.

[0009] Furthermore, when the ratio of the predicted deviation to the actual air volume is less than or equal to the deviation threshold, it is determined that the total air volume demand is basically matched with the initial speed of the fan, and the current wind force parameters meet the dust characteristics. When the ratio of the predicted deviation to the actual actual air volume is greater than the deviation threshold, it is determined that the total air volume demand does not match the initial speed of the fan, and the current wind force parameters cannot meet the dust characteristics. An adjustment strategy is then determined based on the inertia coefficient in the dust characteristic parameters.

[0010] Furthermore, the process of determining the adjustment strategy based on the inertia coefficient includes: When the inertia coefficient is greater than or equal to the first preset value, it is determined that the dust inertia exceeds the normal range, and the arc-shaped baffle is controlled to move at the initial moving speed to reduce the area of ​​the corresponding smoke hood opening; When the inertia coefficient is less than or equal to the second preset value, it is determined that the dust inertia is below the normal range, and the initial speed of the fan is increased according to the total air volume requirement and the inertia coefficient. When the inertia coefficient is greater than the second preset value but less than the first preset value, a hybrid adjustment strategy is adopted.

[0011] Furthermore, the process of employing a hybrid regulation strategy includes: Determine the speed compensation value and inertia weight, increase the initial speed of the fan according to the speed compensation value, and adjust the initial moving speed of the arc baffle according to the inertia weight; The area of ​​the smoke hood corresponding to the moving arc-shaped baffle is adjusted according to the inertia weight. When the inertia coefficient is greater than the second preset value and less than the first preset value, the initial speed of the fan is increased according to the speed compensation value, and the arc-shaped baffle is controlled to move to the target suction port width according to the adjusted initial moving speed.

[0012] Furthermore, the process of determining whether the movement parameters conflict includes: When the total air volume requirement of the corresponding workstation does not match the initial speed of the fan, the air force parameters cannot meet the dust characteristics, and the determined adjustment strategy requires adjusting the area of ​​the corresponding dust hood, it is determined that the adjustment strategy of the corresponding workstation is in conflict.

[0013] Furthermore, priority scores are determined based on visual dust load and risk trend values, with the risk trend values ​​being related to area change rate and concentration change rate; When the priority ratio of the priority score of the current workstation to the priority score of the corresponding workstation is greater than or equal to the critical ratio, it is determined that the urgency of the current workstation exceeds that of the corresponding workstation, and dust removal is carried out according to the determined adjustment strategy. When the priority ratio between the priority score of the current workstation and the priority score of the corresponding workstation is less than the critical ratio, it is determined that the urgency of the current workstation and the corresponding workstation is equivalent. The target suction port width is increased according to the ratio of the priority scores, and the dust removal air volume is compensated according to the efficiency loss coefficient.

[0014] Furthermore, the rate of decrease in smoke and dust coverage area and concentration ash was measured according to the initial detection cycle; When the descent rate is greater than the calibrated rate, it is determined that the descent rate is better than expected; When the descent rate is less than or equal to the calibrated rate, it is determined that the descent rate is lower than expected, and the weights of airflow adjustment and hood area adjustment are changed by adjusting the inertia weight. The inertia weight is increased or decreased based on the ratio of the descent rate to the calibration rate. If the descent rate increases when the inertia weight is increased or decreased, the current optimization strategy is solidified.

[0015] This invention provides a dust removal device based on intelligent analysis, comprising: A gas collection hood is installed at the top of several dust-generating points and has a conical structure. The top and bottom of the gas collection hood are respectively provided with an air outlet and several air inlets. An arc-shaped baffle, located on the top of the gas collection hood, is a movable component used to adjust the corresponding hood opening area of ​​several dust-generating points by moving it.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention uses visual analysis to perceive dust load and characteristics in real time, and dynamically adjusts airflow and hood area, fundamentally avoiding the energy waste of traditional dust removal systems that operate continuously at full power, achieving precise on-demand dust removal and energy saving; it introduces intelligent analysis based on diffusion coefficient and inertia coefficient, enabling the system to automatically identify and differentiate dust of different properties (such as dryness, wetness, and particle size), overcoming the problem of decreased dust removal efficiency caused by changes in processing materials or processes, and improving adaptability to complex working conditions; through multi-trend state judgment, mixed weight adjustment, and multi-dust source conflict arbitration mechanism, the system has the ability of dynamic decision-making, self-correction, and global optimization, ensuring the stability and reliability of dust removal effect in variable and complex production environments, enhancing the system's decision-making intelligence and robustness, and comprehensively improving dust removal efficiency and system automation level.

[0017] Furthermore, this invention determines the trend state of smoke and dust based on the changing trends of the smoke and dust coverage area and concentration. In the first trend state, the smoke and dust not only expand in area but also increase in density. In the second trend state, the smoke and dust area increases while the concentration decreases, indicating that the dust source has stopped operating. The third trend state includes situations where smoke and dust are clearly present but it is impossible to accurately determine whether the smoke and dust are in the first or second trend state. By performing characteristic analysis on the dust in the smoke and dust, this invention replaces the traditional single threshold judgment, significantly improving the foresight and accuracy of the response. It overcomes the problem of poor dust removal adaptability caused by changes in processing materials and processes, and improves energy efficiency and adaptability.

[0018] Furthermore, the characteristics of the dust generated during processing vary depending on the properties of the processed materials. Easily diffused dust requires a larger airflow to contain it, while dust with high inertia requires higher kinetic energy (wind speed) to capture it. Therefore, further analysis is needed to determine whether the basic setpoint of the fan speed can meet the dust characteristics. This invention analyzes the properties of the dust when it is in the third trend state, as different properties require different levels of dust collection. By introducing precise calculations of the diffusion coefficient and inertia coefficient, a deep perception and intelligent response to the physical characteristics of the dust are achieved, quantifying the dust's diffuseability and motion inertia, thereby calculating the precise additional airflow requirement. This solves the problem of over- or under-duration dust removal caused by the inability to identify dust characteristics in traditional methods. By comparing the total airflow requirement with the current airflow in real time, the system can intelligently determine whether the current configuration matches and automatically select the optimal adjustment strategy based on the inertia coefficient. This significantly improves the adaptability and dust removal accuracy for different processed materials producing dust with different characteristics, overcoming the problem of unstable dust removal effects caused by varying working conditions in the production line. It achieves the optimal balance between dust removal efficiency and energy consumption optimization in complex production environments.

[0019] Furthermore, this invention achieves precise and adaptive treatment of dust with different inertia by setting dual thresholds and a hybrid adjustment strategy for the inertia coefficient. When the dust inertia is abnormal, a strategy of enhanced positioning or increased airflow is adopted; when the inertia is centered, the airflow and hood area are smoothly adjusted by inertia weight, achieving seamless switching between high-velocity narrow suction and large-volume wide suction strategies. This overcomes the limitations of a single strategy, ensuring that both particles with high inertia that are difficult to capture and easily diffused light dust can be efficiently removed. At the same time, the baffle moving speed and target width are incorporated into a unified calculation model, which greatly improves the system's coordination and response efficiency, thereby maintaining the best balance between dust removal effect, energy consumption and equipment wear under complex and variable dust-generating conditions.

[0020] Furthermore, during implementation, there may be conflicts in movement commands due to the setup of dust removal devices and workstations. When movement commands conflict, this invention accurately identifies the dominant dust source and balanced working conditions by dynamically calculating the priority scores of each dust-generating point, and intelligently decides to adopt a key dust removal or balanced compensation strategy. It also uses the efficiency loss coefficient to accurately quantify the performance degradation caused by positioning compromise, and automatically calculates the air volume compensation value accordingly, ensuring that the overall dust removal efficiency can still be maintained by increasing the total air volume when the baffle cannot be accurately positioned.

[0021] Furthermore, this invention establishes a dynamic optimization mechanism based on the descent rate, realizing negative feedback adjustment of the dust removal strategy. By monitoring the difference between the dust removal effect and the calibration value in real time, it automatically adjusts the inertial weight in reverse and intelligently adjusts the ratio of air volume to hood area. When the descent rate increases after adjustment, the optimization strategy is solidified, forming effective experience accumulation. It can adapt to different operating conditions and gradually approach the optimal dust removal parameter configuration, effectively solving the pain point that traditional fixed parameter systems cannot adapt to changes in dust generation characteristics, and significantly improving the long-term operating efficiency and intelligence level of the device.

[0022] Furthermore, the gas collection hood is equipped with an arc-shaped baffle inside, which divides the large hood opening into smaller openings, pointing them above the workpiece (pollution source). This greatly reduces the hood opening area, and correspondingly reduces the exhaust volume. A smaller suction volume is used to effectively control the escape of polluting gases, resulting in better efficiency. Attached Figure Description

[0023] Figure 1 This is a schematic flowchart of the dust removal method based on intelligent analysis in an embodiment of the present invention; Figure 2 This is a schematic diagram of the dust removal device in an embodiment of the present invention; Figure 3 This is a schematic diagram of the process for determining the adjustment strategy based on the inertia coefficient in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the process for determining whether the dust removal effect meets expectations in an embodiment of the present invention; In the diagram: 1-Gas collection hood, 2-Arc-shaped baffle, 3-Workpiece, 4-Inlet, 5-Outlet. Detailed Implementation

[0024] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0025] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0026] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0027] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0028] Please see Figures 1-4 As shown, Figure 1 This is a schematic flowchart of the dust removal method based on intelligent analysis in an embodiment of the present invention; Figure 2 This is a schematic diagram of the dust removal device in an embodiment of the present invention; Figure 3 This is a schematic diagram of the process for determining the adjustment strategy based on the inertia coefficient in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the process of determining whether the dust removal effect meets expectations in an embodiment of the present invention.

[0029] This invention provides a dust removal method based on intelligent analysis, comprising: Step S1: Based on the initial rotation speed, control the fan of the dust removal device to run continuously, and obtain the actual image of the dust generation point to determine the dust coverage area and concentration gray level; Step S2: Determine the area change rate and concentration change rate of the smoke and dust based on the smoke and dust coverage area, concentration grayness, and initial detection cycle, so as to determine the trend state of the smoke and dust and the visual dust load, and determine the basic set value of the fan speed according to the visual dust load. Step S3: Extract dust characteristic parameters in response to the trend state of the smoke and dust to determine the additional air volume requirement, the dust characteristic parameters including diffusion coefficient and inertia coefficient; Step S4: Combine the additional air volume requirement with the basic set value of the fan speed to determine the total air volume requirement, so as to obtain the predicted deviation from the initial speed. Step S5: Based on the deviation prediction, determine whether the total air volume demand matches the initial speed of the fan, whether the current wind force parameters can meet the dust characteristics, and determine the adjustment strategy according to the dust characteristic parameters. Step S6: Based on the inertia coefficient reflecting the dust inertia, determine the arc-shaped baffle movement parameters to adjust the corresponding hood opening area of ​​the dust and the initial speed of the fan, or adopt a hybrid adjustment strategy to adjust the initial speed and determine the inertia weight to adjust the arc-shaped baffle movement parameters. Step S7: In response to the adjustment strategy of the corresponding workstation, determine whether the movement parameters conflict, determine the dynamic priority score of several dust-generating points based on the dust parameters, adjust the movement parameters of the arc baffle and compensate the dust removal air volume according to the efficiency loss coefficient. Step S8: Determine whether the dust removal effect meets expectations based on the changing trends of the smoke and dust coverage area and concentration grayness, and adjust the weights of air volume adjustment and hood area adjustment in the hybrid adjustment strategy by adjusting the inertia weight.

[0030] Specifically, this invention uses visual analysis to perceive dust load and characteristics in real time, dynamically adjusting airflow and hood area. This fundamentally avoids the energy waste of traditional dust removal systems that operate continuously at full power, achieving precise on-demand dust removal and energy saving. It introduces intelligent analysis based on diffusion and inertia coefficients, enabling the system to automatically identify and differentiate dust of different properties (such as dryness / wetness, particle size), overcoming the problem of decreased dust removal efficiency due to changes in processing materials or procedures, and improving adaptability to complex working conditions. Through multi-trend state judgment, mixed weight adjustment, and multi-dust source conflict arbitration mechanisms, the system possesses dynamic decision-making, self-correction, and global optimization capabilities, ensuring the stability and reliability of dust removal effects in variable and complex production environments, enhancing the system's decision-making intelligence and robustness, and comprehensively improving dust removal efficiency and system automation level.

[0031] In this embodiment, the application scenario of the present invention is a processing production line, which uses a non-enclosed external air collection hood to control the air volume to absorb dust pollutants for dust removal, and the fan of the dust removal device runs continuously at the initial speed.

[0032] By installing industrial cameras, real-time images of dust-generating points above the workpiece and at the bottom of the air intake are captured to obtain the dispersion pattern and approximate range of the dust.

[0033] The smoke and dust coverage area and concentration grayscale are obtained. The smoke and dust coverage area is the total area of ​​the region in the actual image where the pixel value is identified as smoke and dust. The concentration grayscale is the average grayscale value of the pixels in the above area. The higher the grayscale value or the brighter and whiter it is, the higher the dust concentration or the greater the particle density.

[0034] The changes in the smoke and dust coverage area and concentration grayness per unit time are calculated according to the initial detection period and recorded as the area change rate and concentration change rate. The area change rate is the change in smoke and dust coverage area within the initial detection period divided by the initial detection period, and the concentration change rate is the change in concentration grayness within the initial detection period divided by the initial detection period.

[0035] When the area change rate is greater than the first change threshold and the concentration change rate is greater than the second change threshold, the smoke and dust are judged to be in the first trend state. When the area change rate is greater than the third change threshold but less than the first change threshold, and the concentration change rate is less than the fourth change threshold, the smoke and dust are judged to be in the second trend state. When the area change rate is greater than the third change threshold and the concentration change rate is greater than the fourth change threshold, the smoke and dust are judged to be in the third trend state. The visual dust load is calculated in real time. The visual dust load is the product of the smoke and dust coverage area and the concentration of gray. The basic set value of the fan speed is determined based on the visual dust load. During implementation, the basic setting value of the fan speed is determined by the visual dust load based on the pre-set correspondence table between the visual dust load and the basic setting value of the fan speed.

[0036] Wherein, the first change threshold is greater than the third change threshold, the second change threshold is greater than the fourth change threshold, the value range of the first change threshold is 100~300 pixels² / second, the value range of the third change threshold is 20~60 pixels² / second, the value range of the second change threshold is 5~15 gray levels / second, and the value range of the fourth change threshold is 0~2 gray levels / second.

[0037] Specifically, this invention determines the trend state of smoke and dust based on the changing trends of smoke and dust coverage area and concentration. In the first trend state, the smoke and dust not only expand in area but also increase in density. In the second trend state, the smoke and dust area increases while the concentration decreases, indicating that the dust source has stopped operating. The third trend state includes situations where smoke and dust are clearly present but it is impossible to accurately determine whether the smoke and dust is in the first or second trend state. By performing characteristic analysis on the dust in the smoke and dust, this invention replaces the traditional single threshold judgment, significantly improving the foresight and accuracy of the response. It overcomes the problem of poor dust removal adaptability caused by changes in processing materials and processes, and improves energy efficiency and adaptability.

[0038] When the dust is in the third trend state, the dust characteristic parameters are extracted, including the diffusion coefficient and the inertia coefficient. The diffusion coefficient is calculated by analyzing the diffusion rate of dust edge pixels between consecutive frames of the actual image of the dust, and represents the degree to which the dust is easily diffused; The inertia coefficient is calculated by analyzing the degree to which the motion of the main region of the dust cloud lags behind the background airflow, and represents the particle inertia of the dust. In practice, the main region of the smoke cloud is locked by the target tracking algorithm, and the centroid coordinates of the main region in the actual image of the smoke cloud are obtained. The actual motion vector of the smoke cloud is obtained by subtracting the centroid coordinates of the main region in the actual image of the smoke cloud in the previous frame from the centroid coordinates. The coordinates of the center of the gas collection hood in the image and the typical starting coordinates of the dust source are calibrated. The background airflow direction vector is the normalized result of subtracting the typical starting coordinates of the dust source from the coordinates of the center of the gas collection hood in the image. The expected downstream velocity component is obtained by projecting the actual motion vector of the dust cloud onto the background airflow direction vector. The expected downstream velocity component is the dot product of the actual motion vector and the background airflow direction vector, i.e., expected downstream velocity component = actual motion vector · background airflow direction vector. The lateral deviation velocity is obtained by calculating the component of the actual motion vector in the direction perpendicular to the background airflow direction vector. The lateral deviation velocity = |actual motion vector - (expected downstream velocity component × background airflow direction vector)|. The inertia coefficient is equal to the normalized (lateral deviation velocity / (|actual motion vector| + constant)), and the constant is 0.01 in practice.

[0039] Understandably, the higher the proportion of lateral deviation velocity to total velocity, the more significant the effect of inertial resistance on the smoke and dust following the airflow, and the larger the inertial coefficient of the smoke and dust.

[0040] The additional air volume requirement is calculated based on the dust characteristic parameters. The additional air volume requirement = first weighting coefficient × diffusion coefficient + second weighting coefficient × inertia coefficient. In practice, the first weighting coefficient ranges from 0.5 to 2.0, and the second weighting coefficient ranges from 0.2 to 1.5.

[0041] The total air volume requirement is determined based on the additional air volume requirement and the basic set value of the fan speed, wherein the total air volume requirement is the sum of the additional air volume requirement and the basic set value of the fan speed. Calculate the predicted deviation between the total air volume demand and the actual air volume at the current initial speed of the fan. Predicted deviation = total air volume demand - actual air volume. When the ratio of the predicted deviation to the actual air volume is less than or equal to the deviation threshold, it is determined that the total air volume demand is basically matched with the initial speed of the fan, and the current wind force parameters meet the dust characteristics. When the ratio of the predicted deviation to the actual actual air volume is greater than the deviation threshold, it is determined that the total air volume demand does not match the initial speed of the fan, and the current wind force parameters cannot meet the dust characteristics. An adjustment strategy is then determined based on the inertia coefficient in the dust characteristic parameters. Specifically, the characteristics of the dust generated during processing vary depending on the properties of the processed materials. Easily diffused dust requires a larger airflow to contain it, while dust with high inertia requires higher kinetic energy (wind speed) to capture it. Therefore, further analysis is needed to determine whether the basic setpoint of the fan speed can meet the dust characteristics. This invention analyzes the properties of dust when it is in the third trend state, as different properties require different levels of dust collection. By introducing precise calculations of the diffusion coefficient and inertia coefficient, a deep perception and intelligent response to the physical characteristics of dust are achieved, quantifying the dust's diffuseability and motion inertia to calculate the precise additional airflow requirement. This solves the problem of over- or under-duration dust collection caused by the inability to identify dust characteristics in traditional methods. By comparing the total airflow requirement with the current airflow in real time, the system can intelligently determine whether the current configuration matches and automatically select the optimal adjustment strategy based on the inertia coefficient. This significantly improves the adaptability and dust collection accuracy for different processed materials and dust with different characteristics, overcoming the problem of unstable dust collection effects caused by varying operating conditions in the production line. It achieves the optimal balance between dust collection efficiency and energy consumption optimization in complex production environments.

[0042] When the inertia coefficient is greater than or equal to the first preset value, it is determined that the dust inertia exceeds the normal range and is difficult to be captured by the airflow to change direction. The arc-shaped baffle is controlled to move at the initial moving speed to reduce the area of ​​the smoke and dust hood opening. Specifically, based on the ratio of the first preset value to the inertia coefficient and the ratio of the actual air volume to the total air volume requirement, the arc-shaped baffle is moved at the initial moving speed to reduce the area of ​​the smoke and dust hood opening.

[0043] When the inertia coefficient is less than or equal to the second preset value, it is determined that the dust inertia is below the normal range, and the initial speed of the fan is increased according to the ratio of the total air volume demand to the actual air volume and the ratio of the second preset value to the inertia coefficient. When the inertia coefficient is greater than the second preset value and less than the first preset value, a hybrid adjustment strategy is adopted to determine the speed compensation value, wherein the speed compensation value = inertia weight × air volume compensation gain coefficient × inertia coefficient; In the formula, the inertia weight = (inertia coefficient - second preset value) / (first preset value - second preset value), and the value range of the air volume compensation gain is 150-300 RPM.

[0044] In practice, the initial speed of the fan is increased according to the speed compensation value, and the initial moving speed of the arc baffle is adjusted according to the inertia weight. The adjusted initial moving speed = maximum moving speed × (1 - inertia weight). The area of ​​the smoke hood corresponding to the arc-shaped baffle after its movement is adjusted according to the inertia weight. In practice, the target suction port width is positively correlated with the area of ​​the smoke hood corresponding to the smoke. Target suction port width = maximum target width - (maximum target width - minimum target width) × (1 - inertia weight). Specifically, when the inertia coefficient is greater than the second preset value but less than the first preset value, the initial speed of the fan is increased according to the speed compensation value, and the arc-shaped baffle is controlled to move to the target suction port width according to the adjusted initial moving speed.

[0045] In practice, the first preset value is 0.7, the second preset value is 0.3, the maximum target width is 0.6m, and the minimum target width is 0.15m.

[0046] Specifically, this invention achieves precise and adaptive treatment of dust with different inertia by setting dual thresholds and a hybrid adjustment strategy for the inertia coefficient. When the dust inertia is abnormal, a strategy of enhanced positioning or increased airflow is adopted; when the inertia is centered, the airflow and hood area are smoothly adjusted by inertia weight, achieving seamless switching between high-velocity narrow suction and high-volume wide suction strategies. This overcomes the limitations of a single strategy, ensuring that both particles with high inertia that are difficult to capture and easily diffused lightweight dust can be efficiently removed. At the same time, the baffle moving speed and target width are incorporated into a unified calculation model, which greatly improves the system's coordination and response efficiency, thereby maintaining the optimal balance between dust removal effect, energy consumption and equipment wear under complex and variable dust-generating conditions.

[0047] When the total air volume requirement of the corresponding workstation does not match the initial speed of the fan, the wind force parameters cannot meet the dust characteristics, and the determined adjustment strategy requires adjusting the area of ​​the corresponding dust hood, it is determined that the adjustment strategy of the corresponding workstation is in conflict. In practice, the corresponding workstation is the processing workstation opposite the current workstation.

[0048] Specifically, the dynamic priority score of the dust-generating point is calculated based on the dust parameters corresponding to the workstation. The priority score is calculated as: visual dust load + risk trend value, where the risk trend value is calculated as: area change rate × concentration change rate. When the priority ratio of the priority score of the current workstation to the priority score of the corresponding workstation is greater than or equal to the critical ratio, it is determined that the urgency of the current workstation exceeds that of the corresponding workstation, and dust removal is carried out according to the determined adjustment strategy. When the priority ratio between the priority score of the current workstation and the priority score of the corresponding workstation is less than the critical ratio, it is determined that the urgency of the current workstation and the corresponding workstation is equal. The target suction port width is increased according to the ratio of the priority scores, and the dust removal air volume is compensated according to the efficiency loss coefficient. Specifically, the efficiency loss coefficient = 1 - (the priority score of the current workstation / the sum of the priority scores). Based on the efficiency loss coefficient, the fan speed is increased to the target speed, where the target speed = the adjusted initial speed × (1 + air volume compensation gain × efficiency loss coefficient).

[0049] In practice, the critical ratio ranges from 1.5 to 2, and the air volume compensation gain is 0.8.

[0050] Specifically, during implementation, there may be conflicts in movement commands due to the setup of dust removal devices and workstations. When movement commands conflict, this invention accurately identifies the dominant dust source and balanced working conditions by dynamically calculating the priority scores of each dust-generating point, and intelligently decides to adopt a strategy of focused dust removal or balanced compensation. Furthermore, it uses the efficiency loss coefficient to accurately quantify the performance degradation caused by positioning compromises, and automatically calculates the air volume compensation value accordingly, ensuring that the overall dust removal efficiency can still be maintained by increasing the total air volume when the baffle cannot be accurately positioned.

[0051] The rate of decrease in smoke and dust coverage area and concentration ash level is detected according to the initial detection cycle. When the rate of decrease is greater than the calibrated rate, it is determined that the rate of decrease is better than expected. When the descent rate is less than or equal to the calibrated rate, it is determined that the descent rate is lower than expected, and the weights of airflow adjustment and hood area adjustment are changed by adjusting the inertia weight. The inertial weight is dynamically optimized based on the ratio of the descent rate to the calibration rate. Specifically, the inertia weight is increased or decreased according to the ratio of the descent rate to the calibration rate. If the descent rate increases when the inertia weight is increased or decreased, the current optimization strategy is fixed. In practice, the calibration rate is a calibration value set according to the detected smoke and dust coverage area and concentration gray level, which is used to control the dust removal effect of the dust removal device within the expected time.

[0052] Specifically, this invention establishes a dynamic optimization mechanism based on the descent rate to achieve negative feedback adjustment of the dust removal strategy. By monitoring the difference between the dust removal effect and the calibration value in real time, it automatically adjusts the inertial weight in reverse and intelligently adjusts the ratio of air volume to hood area. When the descent rate increases after adjustment, the optimization strategy is solidified, forming effective experience accumulation. It can adapt to different operating conditions and gradually approach the optimal dust removal parameter configuration, effectively solving the pain point that traditional fixed parameter systems cannot adapt to changes in dust generation characteristics, and significantly improving the long-term operating efficiency and intelligence level of the device.

[0053] This invention provides a dust removal device based on intelligent analysis, comprising: A gas collection hood 1 is set on top of several dust-generating points and has a conical structure. The top and bottom of the gas collection hood are respectively provided with an air outlet 5 and several air inlets 4. The arc-shaped baffle 2, which is located on the top of the gas collection hood, is a movable component used to adjust the corresponding hood opening area of ​​several dust-generating points by moving it.

[0054] Specifically, the gas collection hood is equipped with an arc-shaped baffle inside, which divides the large hood opening into smaller openings, pointing them above the workpiece 3 (the pollution source). This greatly reduces the hood opening area, and correspondingly reduces the exhaust volume. By using a smaller suction volume, the escape of polluting gas is effectively controlled, resulting in better efficiency.

[0055] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A dust removal method based on intelligent analysis, characterized in that, include: Based on the initial speed control of the fan operation, the smoke and dust coverage area and concentration grayness are obtained to determine the area change rate and concentration change rate of the smoke and dust, so as to determine the trend state of the smoke and dust and the visual dust load to determine the basic set value of the fan speed. Dust characteristic parameters are extracted in response to the trend of dust to determine additional air volume demand. The total air volume demand is determined by combining the basic set value of the fan speed to obtain the predicted deviation from the initial speed. Based on the deviation prediction, determine whether the current wind parameters can meet the dust characteristics, and determine the adjustment strategy based on the dust characteristic parameters; Based on the inertia coefficient reflecting the inertia of dust, the movement parameters of the arc-shaped baffle are determined to adjust the corresponding hood area of ​​the dust and the initial speed of the fan, or a hybrid adjustment strategy is adopted to adjust the initial speed and determine the inertia weight to adjust the movement parameters of the arc-shaped baffle. In response to the adjustment strategy of the corresponding workstation, determine whether the movement parameters conflict, determine the dynamic priority score of several dust-generating points based on the dust parameters, adjust the movement parameters of the arc baffle and compensate the dust removal air volume according to the efficiency loss coefficient. The dust removal effect is determined based on the changing trends of smoke and dust coverage area and concentration ash, and the weights of air volume regulation and hood area regulation in the hybrid regulation strategy are adjusted by adjusting the inertia weight.

2. The dust removal method based on intelligent analysis according to claim 1, characterized in that, The changes in the smoke and dust coverage area and concentration ash value per unit time are calculated according to the initial detection cycle and recorded as the area change rate and concentration change rate. When the area change rate is greater than the first change threshold and the concentration change rate is greater than the second change threshold, the smoke and dust are judged to be in the first trend state. When the area change rate is greater than the third change threshold but less than the first change threshold, and the concentration change rate is less than the fourth change threshold, the smoke and dust are judged to be in the second trend state. When the area change rate is greater than the third change threshold and the concentration change rate is greater than the fourth change threshold, the smoke and dust are judged to be in the third trend state.

3. The dust removal method based on intelligent analysis according to claim 2, characterized in that, The visual dust load is determined based on the smoke and dust coverage area and concentration grayness to determine the basic set value of the fan speed. When the dust is in the third trend state, the dust characteristic parameters are extracted, including the diffusion coefficient and the inertia coefficient. The additional air volume requirement is calculated based on the dust characteristic parameters. The total air volume requirement is determined based on the additional air volume requirement and the basic set value of the fan speed. The deviation prediction between the total air volume requirement and the actual air volume at the current initial speed of the fan is calculated.

4. The dust removal method based on intelligent analysis according to claim 3, characterized in that, When the ratio of the predicted deviation to the actual air volume is less than or equal to the deviation threshold, it is determined that the total air volume demand is basically matched with the initial speed of the fan, and the current wind force parameters meet the dust characteristics. When the ratio of the predicted deviation to the actual actual air volume is greater than the deviation threshold, it is determined that the total air volume demand does not match the initial speed of the fan, and the current wind force parameters cannot meet the dust characteristics. An adjustment strategy is then determined based on the inertia coefficient in the dust characteristic parameters.

5. The dust removal method based on intelligent analysis according to claim 4, characterized in that, The process of determining the adjustment strategy based on the inertia coefficient includes: When the inertia coefficient is greater than or equal to the first preset value, it is determined that the dust inertia exceeds the normal range, and the arc-shaped baffle is controlled to move at the initial moving speed to reduce the area of ​​the corresponding smoke hood opening; When the inertia coefficient is less than or equal to the second preset value, it is determined that the dust inertia is below the normal range, and the initial speed of the fan is increased according to the total air volume requirement and the inertia coefficient. When the inertia coefficient is greater than the second preset value but less than the first preset value, a hybrid adjustment strategy is adopted.

6. The dust removal method based on intelligent analysis according to claim 5, characterized in that, The process of employing a hybrid regulation strategy includes: Determine the speed compensation value and inertia weight, increase the initial speed of the fan according to the speed compensation value, and adjust the initial moving speed of the arc baffle according to the inertia weight; The area of ​​the smoke hood corresponding to the moving arc-shaped baffle is adjusted according to the inertia weight. When the inertia coefficient is greater than the second preset value and less than the first preset value, the initial speed of the fan is increased according to the speed compensation value, and the arc-shaped baffle is controlled to move to the target suction port width according to the adjusted initial moving speed.

7. The dust removal method based on intelligent analysis according to claim 6, characterized in that, The process of determining whether movement parameters conflict includes: When the total air volume requirement of the corresponding workstation does not match the initial speed of the fan, the air force parameters cannot meet the dust characteristics, and the determined adjustment strategy requires adjusting the area of ​​the corresponding dust hood, it is determined that the adjustment strategy of the corresponding workstation is in conflict.

8. The dust removal method based on intelligent analysis according to claim 7, characterized in that, Priority scores are determined based on visual dust load and risk trend values, with risk trend values ​​being related to area change rate and concentration change rate. When the priority ratio of the priority score of the current workstation to the priority score of the corresponding workstation is greater than or equal to the critical ratio, it is determined that the urgency of the current workstation exceeds that of the corresponding workstation, and dust removal is carried out according to the determined adjustment strategy. When the priority ratio between the priority score of the current workstation and the priority score of the corresponding workstation is less than the critical ratio, it is determined that the urgency of the current workstation and the corresponding workstation is equivalent. The target suction port width is increased according to the ratio of the priority scores, and the dust removal air volume is compensated according to the efficiency loss coefficient.

9. The dust removal method based on intelligent analysis according to claim 8, characterized in that, The rate of decrease in smoke and dust coverage area and concentration ash level was measured according to the initial detection cycle. When the descent rate is greater than the calibrated rate, it is determined that the descent rate is better than expected; When the descent rate is less than or equal to the calibrated rate, it is determined that the descent rate is lower than expected, and the weights of airflow adjustment and hood area adjustment are changed by adjusting the inertia weight. The inertia weight is increased or decreased based on the ratio of the descent rate to the calibration rate. If the descent rate increases when the inertia weight is increased or decreased, the current optimization strategy is solidified.

10. A dust removal device employing the intelligent analysis-based dust removal method as described in claims 1-9, characterized in that, include: A gas collection hood is installed at the top of several dust-generating points and has a conical structure. The top and bottom of the gas collection hood are respectively provided with an air outlet and several air inlets. An arc-shaped baffle, located on the top of the gas collection hood, is a movable component used to adjust the corresponding hood opening area of ​​several dust-generating points by moving it.

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