Intelligent adjustment method for construction dust removal equipment

Abstract

The present application relates to the technical field of construction dust removal, and specifically to a method for intelligently adjusting construction dust removal equipment, the method comprising: separately analyzing the correlation between wind speed data, dust concentration, and rotation speed data, as well as the local variation trends of wind speed data and dust concentration, to construct a comprehensive influencing factor; constructing a dust response index by analyzing the variation trends of the comprehensive influencing factor and dust concentration, as well as the difference in variation trends between the comprehensive influencing factor and dust concentration; and determining an adaptive proportional coefficient of the dust removal fan at the current moment based on the dust response index, and adjusting the rotation speed of the dust removal fan. The present application aims to improve the dust removal efficiency of the dust removal fan and reduce the risk of secondary dust.

Description

Technical Field

[0001] The present application relates to the technical field of construction dust removal, and in particular to an intelligent adjustment method for construction dust removal equipment. Background Art

[0002] With the increasing need to protect the environment and human health during industrialization, coupled with the strengthening of environmental protection policies and increased public awareness, demand for dust removal equipment continues to grow, and the market scale continues to expand. Dust removal technology has shifted from solely focusing on pollution reduction to a focus on both pollution reduction and efficiency, with energy conservation and intelligent control becoming core competitive advantages. Intelligent adjustment of dust removal equipment enables precise control and intelligent operation, improving equipment operating efficiency and maintenance convenience, reducing downtime, and increasing dust removal efficiency. Therefore, intelligent adjustment not only enhances the functionality of dust removal equipment but also serves as a key technology for achieving sustainable development. Industrial centrifugal dust removal fans, due to their high efficiency, low energy consumption, ease of maintenance, and strong adaptability, effectively remove dust particles from the air in construction scenarios, protecting worker health and reducing environmental pollution, making them the most commonly used dust removal equipment.

[0003] Industrial centrifugal dust removal fans generate centrifugal force through the high-speed rotating impeller to separate the dust in the dust-laden gas. After the gas enters the fan, it is accelerated by the impeller. The dust is thrown to the outer wall due to its heavy mass and settles in the dust collector by gravity. The clean air is discharged through the exhaust port to remove the dust. However, if the wind speed of the industrial centrifugal dust removal fan is not properly controlled, it may cause secondary dust, resulting in deterioration of air quality. Among them, the excessive rate of change of air volume is the main reason for the secondary dust generated by industrial centrifugal dust removal fans when removing dust at construction sites. When the rate of change of air volume is too large, the airflow inside the dust collector changes too quickly, disturbing the dust that has been deposited or captured, causing it to be re-suspended in the air and causing secondary dust. The existing technology cannot ensure the stability of the rate of change of the wind speed at the air outlet when controlling the wind speed at the air outlet, which leads to a decrease in the dust removal efficiency of the dust removal fan and also causes secondary dust. Summary of the Invention

[0004] In order to solve the above technical problems, the present application provides an intelligent adjustment method for construction dust removal equipment to solve the existing problems.

[0005] The intelligent adjustment method of construction dust removal equipment in this application adopts the following technical solution:

[0006] An embodiment of the present application provides an intelligent adjustment method for construction dust removal equipment, the method comprising the following steps:

[0007] Obtain the speed data of the dust removal fan at the current moment and all the collection moments before the preset time, the wind speed data at the dust removal fan outlet, and the dust concentration at the dust outlet;

[0008] Analyze the correlation between wind speed data and dust concentration and speed data at all collection moments to determine the wind-speed correlation and dust-speed correlation of the dust removal fan at the current moment; analyze the differences in the changing trends of wind speed data and dust concentration as the speed data changes at each collection moment and all collection moments in its neighborhood to determine the differences in the first and second trends at each collection moment, and combine the wind-speed correlation and dust-speed correlation to determine the comprehensive influencing factor of the dust removal fan at each collection moment;

[0009] Analyze the changing trends of the comprehensive influencing factors and dust concentrations at all collection moments respectively to determine all first segmentation moments and all second segmentation moments; determine the first and second trend degrees at the current moment based on the changing trends of the comprehensive influencing factors between all adjacent first segmentation moments and the changing trends of the dust concentrations between all adjacent second segmentation moments; determine the difference degree at the current moment by analyzing the differences in the changing trends of the comprehensive influencing factors and dust concentrations at all collection moments, and determine the dust response index of the dust removal fan at the current moment by combining the difference between the first trend degree and the second trend degree, as well as the extreme value distribution of the comprehensive influencing factors and dust concentrations at all collection moments;

[0010] Based on the dust response index, an adaptive proportional coefficient of the dust removal fan at the current moment is determined, and the rotation speed of the dust removal fan is adjusted.

[0011] Preferably, the method for determining the wind-rotation correlation and the dust-rotation speed correlation of the dust removal fan at the current moment is:

[0012] The correlation between the rotation speed data and the wind speed data at all collection moments before the current moment, as well as the correlation between the rotation speed data and the dust concentration data, are calculated respectively, and recorded as the wind-rotation correlation degree and dust-rotation correlation degree of the dust removal fan at the current moment.

[0013] Preferably, the method for determining the difference between the first and second trends at each collection moment is:

[0014] Before the current moment, the speed data of all acquisition moments and all acquisition moments in its neighborhood are used as independent variables, and the wind speed data and dust concentration are used as dependent variables for fitting, respectively, to obtain the speed-wind speed fitting line and the speed-dust concentration fitting line at each acquisition moment;

[0015] The difference in the slope of the speed-wind speed fitting line between each collection moment and the previous collection moment, as well as the difference in the slope of the speed-dust concentration line, were calculated respectively and recorded as the first trend difference and the second trend difference at each collection moment.

[0016] Preferably, the expression of the comprehensive influencing factor of the dust removal fan at each collection time is: Where a and c represent the wind-rotation correlation and dust-rotation correlation of the dust removal fan at the current moment, respectively; b i d i They represent the first trend difference and the second trend difference at the acquisition time i before the current time respectively.

[0017] Preferably, the method for determining all the first segmentation moments and all the second segmentation moments is:

[0018] The comprehensive influencing factors and dust concentrations of all sampling moments before the current moment are fitted respectively to obtain the comprehensive influencing factor fitting curve and the dust concentration fitting curve;

[0019] The extreme values ​​on the comprehensive influencing factor curve and the dust concentration fitting curve are extracted respectively, and the collection time corresponding to all extreme values ​​on the comprehensive influencing factor curve is recorded as the first segmentation time, and the collection time corresponding to all extreme values ​​on the dust concentration curve is recorded as the second segmentation time.

[0020] Preferably, the method for determining the first and second trend degrees at the current moment is:

[0021] The comprehensive influencing factors of all sampling moments between each group of adjacent first segmentation moments and the dust concentrations of all sampling moments between any group of adjacent second segmentation moments are respectively used as inputs of the trend detection algorithm, and the trend statistics between each group of adjacent first segmentation moments and the trend statistics between any group of adjacent second segmentation moments are output;

[0022] The mean of the trend statistics between all adjacent first segmentation moments is taken as the first trend degree at the current moment;

[0023] The mean of the trend statistics between all adjacent second segmentation moments is taken as the second trend degree at the current moment.

[0024] Preferably, the method for determining the difference at the current moment is:

[0025] The trend value H of the impact factor between the kth group of adjacent first segmentation moments k The expression is: Where h k represents the trend statistic between the kth group of adjacent first segmentation moments;

[0026] The dust concentration trend value P between the mth group of adjacent first segmentation moments m The expression is: Where p m represents the trend statistic between the mth group of adjacent second split moments;

[0027] The difference between the trend values ​​of all influencing factors and the trend values ​​of all dust concentrations is taken as the difference degree at the current moment.

[0028] Preferably, the expression of the dust response index of the dust removal fan at the current moment is: Where C represents the dust response index of the dust removal fan at the current moment; q represents the difference between the first trend degree and the second trend degree at the current moment; g represents the difference degree at the current moment; M and N represent the total number of extreme values ​​on the comprehensive influencing factor fitting curve and the total number of extreme values ​​on the dust concentration fitting curve at the current moment, respectively; exp() represents an exponential function with a natural constant as the base; ε represents a preset constant greater than 0.

[0029] Preferably, the expression of the adaptive proportional coefficient of the dust removal fan at the current moment is: Kp ′ =exp(-k×C)×Kp; where Kp ′ represents the adaptive proportional coefficient of the dust removal fan at the current moment; C represents the dust response index of the dust removal fan at the current moment; k represents the preset value; Kp represents the preset initial proportional coefficient; exp() represents the exponential function with a natural constant as the base.

[0030] Preferably, the adjusting the speed of the dust removal fan includes:

[0031] The deviation between the current speed data of the dust removal fan and the preset target speed is used as the input of the PID controller, and the speed control signal is output to adjust the speed of the dust removal fan.

[0032] This application has at least the following beneficial effects:

[0033] The present application constructs a comprehensive influencing factor by separately analyzing the correlation between wind speed data, dust concentration and speed data, as well as the local change trends of wind speed data and dust concentration, which can comprehensively reflect the overall influence of dust removal fan speed on wind speed and dust concentration changes. By considering the wind-rotation correlation and dust-speed correlation, it can more accurately evaluate the influence of fan speed on dust removal effect, provide a basis for optimizing speed adjustment, and thus reduce the possibility of secondary dust. Further, by analyzing the respective change trends of the comprehensive influencing factor and dust concentration, as well as the difference in the change trends of the comprehensive influencing factor and dust concentration, a dust response index is constructed to quantify the influence of dust removal fan speed on dust concentration control effect. By comparing the actual impact of the comprehensive influencing factor and the dust concentration change trend on speed adjustment and dust suppression effect, the accuracy of speed control is improved, ensuring smooth wind speed changes and avoiding the re-oxygenation of secondary dust caused by excessive wind speed changes. The present application improves the accuracy of dust removal fan speed adjustment by comprehensively analyzing the correlation between wind speed, dust concentration and speed, improves the dust removal efficiency of the dust removal fan and reduces the risk of secondary dust. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0035] Figure 1 A flowchart of the steps of an intelligent adjustment method for construction dust removal equipment provided in one embodiment of the present application;

[0036] Figure 2 A schematic diagram of the dust response index extraction process provided for one embodiment of the present application. DETAILED DESCRIPTION

[0037] To further illustrate the technical means and effects employed by this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effects of an intelligent adjustment method for construction dust removal equipment proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0038] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0039] The specific scheme of the intelligent adjustment method for construction dust removal equipment provided by this application is described in detail below with reference to the accompanying drawings.

[0040] An embodiment of the present application provides an intelligent adjustment method for construction dust removal equipment. Specifically, the following intelligent adjustment method for construction dust removal equipment is provided. Figure 1 , the method comprises the following steps:

[0041] Step S1: Obtain the speed data of the dust removal fan at the current moment and all the collection moments before the preset time, the wind speed data at the air outlet of the dust removal fan and the dust concentration at the dust outlet.

[0042] When adjusting the gear of the industrial centrifugal dust removal fan, the fan speed directly affects the wind speed at the air outlet. Increasing the fan speed will lead to an increase in wind speed; decreasing the speed will reduce the wind speed. Not only that, the wind speed also has a direct impact on the dust removal efficiency. Excessively high wind speed may cause dust to be re-suspended, resulting in secondary dust.

[0043] To this end, a speed sensor is installed at the shaft end of the dust removal fan to obtain the speed data of the dust removal fan at the current moment and all collection moments before the preset time period; a wind speed sensor is installed at the air outlet of the dust collector to obtain the wind speed data at the air outlet of the dust removal fan at the current moment and all collection moments before the preset time period; a dust concentration sensor is installed at the dust exhaust port of the dust collector to obtain the dust concentration at the dust exhaust port of the dust collector at the current moment and all collection moments before the preset time period, wherein the sampling frequency of all data is set to f.

[0044] It should be noted that the values ​​of the preset duration and data sampling frequency f are both manually set. In this embodiment, the preset duration is 5 minutes and the sampling frequency f is 20 Hz. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0045] Furthermore, in order to eliminate the influence of data dimension, all the collected rotational speed data, wind speed data and dust concentration are normalized. There are many commonly used normalization methods. In this embodiment, the z-score normalization method is used to normalize the collected wind speed data, dust concentration data and implementation rotational speed data. The implementer can also use the maximum and minimum value normalization method to normalize the data. Regarding the selection of normalization method, this embodiment does not impose any special restrictions.

[0046] It should be noted that the z-score normalization method is a well-known technology, and the specific process of normalizing the data will not be described in detail.

[0047] Step S2: Analyze the correlation between the wind speed data and the dust concentration and the speed data at all collection moments respectively to determine the wind-speed correlation and the dust-speed correlation of the dust removal fan at the current moment; analyze the difference in the changing trend of the wind speed data and the dust concentration as the speed data changes at each collection moment and all collection moments in its neighborhood respectively to determine the difference between the first and second trends at each collection moment, and combine the wind-speed correlation and the dust-speed correlation to determine the comprehensive influencing factor of the dust removal fan at each collection moment.

[0048] The speed of the dust removal fan directly affects the wind speed at the dust removal fan outlet. Under ideal operating conditions, there is a linear relationship between the speed and wind speed data. That is, when the fan speed increases, the wind speed at the air outlet will also increase proportionally. At the same time, over time, when the fan speed increases to a certain threshold, further increases in speed will not only affect the wind speed, but also affect the dust concentration at the dust removal fan outlet.

[0049] Therefore, in order to better understand the impact of fan speed on the changing trends of wind speed and dust concentration, the correlation between wind speed data, dust concentration and speed data, as well as the local changing trends of wind speed data and dust concentration, is analyzed to determine the comprehensive influencing factors of the dust removal fan at the current moment. Specifically:

[0050] (1) Calculate the correlation between the rotation speed data and wind speed data at all acquisition moments before the current moment, as well as the correlation between the rotation speed data and dust concentration data, and record them as the wind-rotation correlation degree and dust-rotation correlation degree of the dust removal fan at the current moment, respectively.

[0051] It should be noted that there are many methods for measuring the similarity between data groups. In this embodiment, the absolute value of the Pearson correlation coefficient between the rotational speed data and the wind speed data at all collection moments before the current moment, as well as the absolute value of the Pearson correlation coefficient between the rotational speed data and the dust concentration data are calculated respectively to measure the similarity between the rotational speed data and the wind speed data, as well as the correlation between the rotational speed data and the dust concentration data. The implementer may also use other methods for measuring the correlation between data groups, such as the Spearman correlation coefficient and the Kendall rank correlation coefficient. This embodiment does not impose any special restrictions on the selection of methods for measuring the correlation between data groups.

[0052] The calculation steps of the Pearson correlation coefficient are well-known techniques, and the specific calculation process will not be described in detail.

[0053] (2) Further, before the current moment, the rotation speed data of each acquisition moment and all acquisition moments in its neighborhood are used as independent variables, and the wind speed data is used as the dependent variable for fitting, and the rotation speed-wind speed fitting line of each acquisition moment is obtained;

[0054] Before the current moment, the speed data of all acquisition moments and all acquisition moments in its neighborhood are used as independent variables, and the wind speed data and dust concentration are used as dependent variables for fitting, respectively, to obtain the speed-wind speed fitting line and the speed-dust concentration fitting line at each acquisition moment;

[0055] It should be noted that there are many commonly used linear fitting methods. In this embodiment, the linear least squares method is used to fit the rotational speed data and wind speed data, as well as to fit the rotational speed data and dust concentration. The implementer may also use other linear fitting methods. This embodiment does not impose any special restrictions on the selection of linear fitting methods.

[0056] Among them, the linear least squares method is a well-known technology in the field of linear fitting, and the specific process of using it to fit the data will not be described in detail.

[0057] Furthermore, the difference in the slope of the speed-wind speed fitting line between each collection moment and the previous collection moment, as well as the difference in the slope of the speed-dust concentration line, are calculated and recorded as the first trend difference and the second trend difference at each collection moment, respectively.

[0058] It should be noted that there are many methods for measuring the differences between data. In this embodiment, the absolute value of the difference in the slope of the speed-wind speed fitting line between each collection moment and the previous collection moment, as well as the absolute value of the difference in the slope of the speed-dust concentration line, are calculated respectively to measure the difference in the slope of the speed-wind speed fitting line and the difference in the slope of the speed-dust concentration line before adjacent collection moments. The implementer may also adopt other methods such as ratios based on specific circumstances to measure the differences between data. This embodiment does not impose any special restrictions on the selection of methods for measuring the differences between data.

[0059] (3) Based on the differences between the first and second trends at each collection time obtained above, and combined with the wind-rotation correlation and dust-rotation speed correlation, the comprehensive influencing factor of the dust removal fan at each collection time is determined, specifically:

[0060] The comprehensive impact factor A of the dust removal fan at the time i before the current time i The expression is: Where a and c represent the wind-rotation correlation and dust-rotation correlation of the dust removal fan at the current moment, respectively; b i d i They represent the first trend difference and the second trend difference at the acquisition time i before the current time respectively.

[0061] Furthermore, according to the comprehensive influencing factors of the dust removal fan at the current moment, it can be understood that the wind-rotation correlation and dust-rotation correlation of the dust removal fan reflect the synchronization of the changing trend of the rotation speed data and the changing trend of the wind speed data, as well as the synchronization of the changing trend of the rotation speed data and the changing trend of the dust concentration, respectively. The greater the wind-rotation correlation and dust-rotation correlation, the greater the influence of the rotation speed on the wind speed and dust concentration; and the first trend difference reflects the degree of linear difference between the changing trends of the rotation speed and wind speed between adjacent collection moments in actual situations, and the second trend difference reflects the degree of change of dust concentration with the change of the rotation speed; when the wind-rotation correlation is greater and the first trend difference is greater, The smaller it is, the smaller the difference in the changing trend between the wind speed data and the rotation speed data, the greater the linear correlation, the greater the influence of the rotation speed on the wind speed, and the changes in the rotation speed and wind speed will indirectly affect the changes in the dust concentration, so the greater the indirect influence on the dust concentration; at the same time, the greater the dust-rotation speed correlation, the greater the second trend difference, indicating that the speed has a greater influence on the dust concentration. Therefore, when the dust-rotation speed correlation is greater, the smaller the first trend difference, the greater the dust-rotation speed correlation, the greater the second trend difference, then the greater the comprehensive impact factor, indicating that the control effect of the fan speed on the dust concentration is more significant, that is, the increase or decrease in the speed has a more obvious effect on reducing the dust concentration;

[0062] On the contrary, the smaller the dust-speed correlation, the smaller the second trend difference, which means that the speed has a smaller impact on the dust concentration. Therefore, when the dust-speed correlation is smaller, the first trend difference is larger, the dust-speed correlation is larger, the second trend difference is smaller, and the comprehensive impact factor is smaller, which means that the speed has a smaller impact on the change of dust concentration.

[0063] Step S3: Analyze the comprehensive influencing factors and the changing trends of dust concentration at all collection moments respectively, and determine all first segmentation moments and all second segmentation moments; determine the first and second trend degrees at the current moment based on the changing trends of the comprehensive influencing factors between all adjacent first segmentation moments and the changing trends of the dust concentration between all adjacent second segmentation moments; determine the difference degree at the current moment by analyzing the differences in the changing trends of the comprehensive influencing factors and dust concentration at all collection moments, and determine the dust response index of the dust removal fan at the current moment in combination with the difference between the first trend degree and the second trend degree, as well as the extreme distribution of the comprehensive influencing factors at all collection moments.

[0064] In the dust removal system of an industrial centrifugal dust removal fan, dust emission occurs if the wind speed at the outlet exceeds a certain critical value. However, dust emission is only triggered when the wind speed reaches the critical value. As a result, the dust concentration at the dust outlet becomes uneven, manifesting as intermittent dust concentration and varying degrees of fluctuation.

[0065] Therefore, in order to analyze whether there is a causal relationship between the comprehensive influencing factor and the dust concentration, and thus more accurately determine the degree of influence of the dust removal fan speed on the dust concentration, the exhaust dust concentration can be controlled by adjusting the dust removal fan speed. By analyzing the respective changing trends of the comprehensive influencing factor and the dust concentration, as well as the difference in the changing trends of the comprehensive influencing factor and the dust concentration, the dust response index of the dust removal fan at the current moment is determined, specifically:

[0066] (1) Fitting the comprehensive influencing factors and dust concentrations at all sampling moments before the current moment respectively to obtain a comprehensive influencing factor fitting curve and a dust concentration fitting curve;

[0067] Furthermore, the extreme point extraction algorithm is used to extract the extreme values ​​on the comprehensive influencing factor curve and the dust concentration fitting curve respectively, and the collection time corresponding to all extreme values ​​on the comprehensive influencing factor curve is recorded as the first segmentation time, and the collection time corresponding to all extreme values ​​on the dust concentration curve is recorded as the second segmentation time.

[0068] Among them, the extreme point extraction algorithm is a well-known technology, and the specific principle and process of extracting the extreme value on the curve will not be repeated here.

[0069] (2) The comprehensive influencing factors of all the sampling moments between each group of adjacent first segmentation moments and the dust concentrations of all the sampling moments between any group of adjacent second segmentation moments are respectively used as the input of the trend detection algorithm, and the trend statistics between each group of adjacent first segmentation moments and the trend statistics between any group of adjacent second segmentation moments are output;

[0070] Furthermore, the mean of the trend statistics between all adjacent first segmentation moments is taken as the first trend degree at the current moment;

[0071] The mean of the trend statistics between all adjacent second segmentation moments is taken as the second trend degree at the current moment.

[0072] It should be noted that there are many commonly used trend test algorithms. In this embodiment, the Mann-Kendall trend test algorithm is used to perform trend statistics on the comprehensive influencing factors between adjacent first segmentation moments, and to perform trend statistics on the dust concentration between adjacent second segmentation moments. The implementer may also use other methods such as the Kendall trend test algorithm. This embodiment does not impose any special restrictions on the selection of the trend test algorithm.

[0073] The specific process of obtaining trend statistics using the Mann-Kendall trend test algorithm is a well-known technology, and its specific principle and process will not be repeated here.

[0074] (3) Based on the trend statistics between the adjacent first segmentation moments and the trend statistics between the adjacent second segmentation moments, respectively obtain the impact factor trend value and the dust concentration trend value, and based on the difference between the impact factor trend value and the dust concentration trend value, determine the difference degree at the current moment, specifically:

[0075] The trend value H of the impact factor between the kth group of adjacent first segmentation moments k The expression is: Where h k represents the trend statistic between the kth group of adjacent first segmentation moments;

[0076] The dust concentration trend value P between the mth group of adjacent first segmentation moments m The expression is: Where p m represents the trend statistic between the mth group of adjacent second split moments;

[0077] The difference between the trend values ​​of all influencing factors and the trend values ​​of all dust concentrations is taken as the difference degree at the current moment.

[0078] It should be noted that there are many methods for measuring the differences between data groups. In this embodiment, the Euclidean distance between all influencing factor trend values ​​and all dust concentration trend values ​​is calculated to measure the overall difference between the influencing factor trend values ​​and the dust concentration trend values. The implementer may also use other methods such as Manhattan distance to measure the differences between data groups. This embodiment does not impose any special restrictions on the selection of methods for measuring the differences between data groups.

[0079] The steps for calculating the Euclidean distance are well-known techniques, and the specific calculation process will not be described in detail in this embodiment.

[0080] (4) Further, based on the difference degree at the current moment, combined with the difference between the first trend degree and the second trend degree, and the extreme distribution of the comprehensive influencing factors at all acquisition moments, the dust response index of the dust removal fan at the current moment is determined, specifically:

[0081] The expression of the dust response index C of the dust removal fan at the current moment is: In the formula, q represents the difference between the first trend degree and the second trend degree at the current moment; g represents the difference degree at the current moment; M and N represent the total number of extreme values ​​on the comprehensive influencing factor fitting curve and the total number of extreme values ​​on the dust concentration fitting curve at the current moment, respectively; exp() represents an exponential function with a natural constant as the base; ε represents a constant preset greater than 0, which is used to prevent the denominator from being 0, where the value of ε is artificially set. In this embodiment, the value of ε is 0.01. The implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0082] Furthermore, according to the dust response index of the dust removal fan at the current moment, it can be understood that the greater the difference between the first trend degree and the second trend degree, the smaller the correlation between the change trend of the comprehensive influencing factor and the dust concentration, that is, the change of the comprehensive influencing factor does not cause the change of the dust concentration data, and the smaller the dust response index; the greater the difference at the current moment, the less significant the control effect of the speed change on the dust concentration, and the smaller the dust response index; |MN| represents the inconsistency of the trend change of the comprehensive influencing factor and the dust concentration, that is, the inconsistency of the trend change of the two sequences, that is, the degree of inconsistency of the changes of the two sequences at the time point. The larger the value of |MN|, the more inconsistent the changes of the comprehensive influencing factor and the dust concentration, and the smaller the dust response index, indicating that the control effect of the dust removal fan speed on the dust concentration is less significant;

[0083] On the contrary, the smaller the difference between the first trend degree and the second trend degree, the smaller the difference at the current moment, the smaller the value of |MN|, and the smaller the dust response index, which means that the control effect of the dust removal fan speed on the dust concentration is more significant, that is, the increase in speed has a more obvious effect on the dust level.

[0084] Preferably, the dust response index extraction process diagram provided in this embodiment is as follows Figure 2 shown.

[0085] Step S4: Based on the dust response index of the dust removal fan at the current moment, determine the adaptive proportional coefficient of the dust removal fan at the current moment, and adjust the speed of the dust removal fan.

[0086] A PID algorithm is used to control the speed of the dust removal fan. By adjusting the fan speed, the outlet air speed is maintained at an ideal set value to ensure dust removal efficiency and prevent secondary dust. Specifically, in this embodiment, the initial values ​​of the proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd in the PID control algorithm are set to 30, 15, and 5, respectively.

[0087] When the wind speed increases too much, the dust stays in the dust removal fan for a shorter time and the chance of being charged decreases. That is, if the wind speed of the dust removal fan increases too quickly, the dust will not be effectively settled and will be raised again, forming secondary dust. The proportional coefficient Kp in the PID controller method determines the controller's response speed to deviations. The larger Kp is, the faster the controller reacts to deviations.

[0088] To this end, the initial proportional coefficient in the PID controller is adaptively adjusted based on the dust response index, specifically:

[0089] The expression of the adaptive proportional coefficient of the dust removal fan at the current moment is: Kp ′ =exp(-k×C)×Kp; where Kp ′represents the adaptive proportional coefficient of the dust removal fan at the current moment; C represents the dust response index of the dust removal fan at the current moment; k represents the preset value; Kp represents the preset initial proportional coefficient; exp() represents the exponential function with a natural constant as the base.

[0090] It should be noted that the value of the preset value k is set artificially to prevent the deviation response from being too slow due to the value of Kp′ being too small, resulting in an excessively long adjustment time. In this embodiment, the value of the preset value k is 0.2, and the implementer can also set it by himself based on the specific situation. This embodiment does not impose any special restrictions.

[0091] In addition, it should be understood that the value of the preset initial proportional coefficient Kp is 30 set in the above content. The values ​​of Kp, Ki and Kd are all manually set. The values ​​of Ki and Kd have been set in the above content. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions on the values ​​of the proportional coefficient Kp, the integral coefficient Ki and the differential coefficient Kd.

[0092] It can be seen from this that the larger the dust response index is, the more significant the control effect of the fan speed on the dust concentration is, that is, the more obvious the effect of the change in speed on the change in dust concentration is. At this time, the proportional coefficient should be reduced to prevent the secondary dust caused by the faster speed change rate causing the wind force change rate to increase.

[0093] Furthermore, the deviation between the current speed data of the dust removal fan and the preset target speed is used as the input of the PID controller, and a speed control signal is output to adjust the speed of the dust removal fan.

[0094] It should be noted that the value of the preset target speed in this embodiment is set manually. The value of the target speed in this embodiment is 1800r / min. Since the setting of the target speed will vary depending on the device and the gear, the implementer can set it according to the specific situation. This embodiment does not impose any special restrictions.

[0095] The working principle of the PID controller is a well-known technology, and its specific operating principle will not be described in detail.

[0096] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0097] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0098] The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them. Modifications to the technical solutions described in the aforementioned embodiments, or equivalent replacements of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. An intelligent adjustment method for construction dust removal equipment, characterized in that: The method comprises the following steps: Obtain the speed data of the dust removal fan at the current moment and all the collection moments before the preset time, the wind speed data at the dust removal fan outlet, and the dust concentration at the dust outlet; Analyze the correlation between wind speed data and dust concentration and speed data at all collection moments to determine the wind-speed correlation and dust-speed correlation of the dust removal fan at the current moment; analyze the difference in the trend of wind speed data and dust concentration with speed data at each collection moment and all collection moments in its neighborhood to determine the difference between the first and second trends at each collection moment, and calculate the comprehensive impact factor A of the dust removal fan at each collection moment. i , A i The expression is: Where a and c represent the wind-rotation correlation and dust-rotation correlation of the dust removal fan at the current moment, respectively; b i d i They represent the first trend difference and the second trend difference at the acquisition time i before the current time, respectively; The comprehensive influencing factors and dust concentrations of all sampling moments before the current moment are fitted respectively to obtain the comprehensive influencing factor fitting curve and the dust concentration fitting curve; The extreme values ​​on the comprehensive influencing factor curve and the dust concentration fitting curve are extracted respectively, and the collection time corresponding to all extreme values ​​on the comprehensive influencing factor curve is recorded as the first segmentation time, and the collection time corresponding to all extreme values ​​on the dust concentration curve is recorded as the second segmentation time; based on the change trend of the comprehensive influencing factor between all adjacent first segmentation times and the change trend of the dust concentration between all adjacent second segmentation times, the first and second trend degrees at the current moment are determined; by analyzing the difference in the change trend of the comprehensive influencing factor and dust concentration at all collection times, the difference degree at the current moment is determined, and the dust response index of the dust removal fan at the current moment is calculated. The expression is: Where C represents the dust response index of the dust removal fan at the current moment; q represents the difference between the first trend degree and the second trend degree at the current moment; g represents the difference degree at the current moment; M and N represent the total number of extreme values ​​on the comprehensive influencing factor fitting curve and the total number of extreme values ​​on the dust concentration fitting curve at the current moment, respectively; exp() represents an exponential function with a natural constant as the base; ε represents a constant preset to be greater than 0; Based on the dust response index, an adaptive proportional coefficient of the dust removal fan at the current moment is determined, and the rotation speed of the dust removal fan is adjusted.

2. The intelligent adjustment method for construction dust removal equipment according to claim 1, characterized in that: The method for determining the wind-rotation correlation and dust-rotation speed correlation of the dust removal fan at the current moment is: The correlation between the rotation speed data and wind speed data at all collection moments before the current moment, as well as the correlation between the rotation speed data and dust concentration data, are calculated respectively, and recorded as the wind-rotation correlation degree and dust-rotation correlation degree of the dust removal fan at the current moment.

3. The intelligent adjustment method for construction dust removal equipment according to claim 1, characterized in that: The method for determining the difference between the first and second trends at each acquisition moment is: Before the current moment, the speed data of all acquisition moments and all acquisition moments in its neighborhood are used as independent variables, and the wind speed data and dust concentration are used as dependent variables for fitting, respectively, to obtain the speed-wind speed fitting line and the speed-dust concentration fitting line at each acquisition moment; The difference in the slope of the speed-wind speed fitting line between each collection moment and the previous collection moment, as well as the difference in the slope of the speed-dust concentration line, were calculated respectively and recorded as the first trend difference and the second trend difference at each collection moment.

4. The intelligent adjustment method for construction dust removal equipment according to claim 1, characterized in that: The method for determining the first and second trend degrees at the current moment is: The comprehensive influencing factors of all sampling moments between each group of adjacent first segmentation moments and the dust concentrations of all sampling moments between any group of adjacent second segmentation moments are respectively used as inputs of the trend detection algorithm, and the trend statistics between each group of adjacent first segmentation moments and the trend statistics between any group of adjacent second segmentation moments are output; The mean of the trend statistics between all adjacent first segmentation moments is taken as the first trend degree at the current moment; The mean of the trend statistics between all adjacent second segmentation moments is taken as the second trend degree at the current moment.

5. The intelligent adjustment method for construction dust removal equipment according to claim 4, characterized in that: The method for determining the difference at the current moment is: The trend value H of the impact factor between the kth group of adjacent first segmentation moments k The expression is: Where h k represents the trend statistic between the kth group of adjacent first segmentation moments; The dust concentration trend value P between the mth group of adjacent first segmentation moments m The expression is: Where p m represents the trend statistic between the mth group of adjacent second split moments; The difference between the trend values ​​of all influencing factors and the trend values ​​of all dust concentrations is taken as the difference degree at the current moment.

6. The intelligent adjustment method for construction dust removal equipment according to claim 1, characterized in that: The expression of the adaptive proportional coefficient of the dust removal fan at the current moment is: Kp ′ =exp(-k×C)×Kp; where Kp ′ represents the adaptive proportional coefficient of the dust removal fan at the current moment; C represents the dust response index of the dust removal fan at the current moment; k represents the preset value; Kp represents the preset initial proportional coefficient; exp() represents the exponential function with a natural constant as the base.

7. The intelligent adjustment method for construction dust removal equipment according to claim 1, characterized in that: The adjusting of the speed of the dust removal fan includes: The deviation between the current speed data of the dust removal fan and the preset target speed is used as the input of the PID controller, and the speed control signal is output to adjust the speed of the dust removal fan.

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