Intelligent adjusting method for building construction dust removal equipment

Abstract

The invention relates to the technical field of building construction dust removal, in particular to an intelligent adjusting method for building construction dust removal equipment, and the method comprises the steps: respectively analyzing the correlation between wind speed data and rotating speed data and the correlation between dust concentration and rotating speed data, and the local change trend of the wind speed data and the dust concentration, and constructing a comprehensive influence factor; by analyzing the respective change trends of the comprehensive influence factor and the dust concentration and the difference of the comprehensive influence factor and the dust concentration in the change trends, constructing a flying dust response index; and on the basis of the dust raising response index, determining a self-adaptive proportionality coefficient of the dust removal fan at the current moment, and adjusting the rotating speed of the dust removal fan. The dust removal efficiency of the dust removal fan is improved, and the risk of reentrainment of dust is reduced.

Description

Technical Field

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

[0002] With the need to protect the environment and human health in the process of industrialization, as well as the strengthening of environmental protection policies and the improvement of public environmental awareness, the demand for dust removal equipment industry continues to grow, the market scale continues to expand, and dust removal technology has shifted from single pollution reduction to both pollution reduction and efficiency. Energy saving and intelligent control have become core competitiveness. The intelligent adjustment of dust removal equipment can achieve precise control and intelligent operation, improve the operating efficiency and maintenance convenience of equipment, reduce downtime, and improve dust removal efficiency. Therefore, intelligent adjustment not only improves the functionality of dust removal equipment, but is also one of the key technologies to achieve sustainable development. Industrial centrifugal dust removal fans have become the most commonly used dust removal equipment because of their high efficiency, low energy consumption, easy maintenance and strong adaptability. They can effectively remove dust particles in the air in construction scenes, protect workers' health and reduce environmental pollution.

[0003] Industrial centrifugal dust removal fans generate centrifugal force through the high-speed rotating impeller to separate the dust from 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. 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 and lead to 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 fast, disturbing the dust that has been deposited or captured, causing it to re-suspend in the air and causing secondary dust. When controlling the wind speed of the air outlet, the existing technology cannot ensure the stability of the rate of change of the wind speed at the air outlet, resulting in a decrease in the dust removal efficiency of the dust removal fan and causing 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 air outlet of the dust removal fan, 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 respectively, so as to determine the wind-speed correlation and dust-speed correlation of the dust removal fan at the current moment; analyze the difference in the changing trend of wind speed data and dust concentration with speed data at each collection moment and all collection moments in its neighborhood respectively, so as to determine the difference between the first and second trends at each collection moment, and determine the comprehensive influencing factor of the dust removal fan at each collection moment by combining the wind-speed correlation and dust-speed correlation;

[0009] Analyze the changing trends of the comprehensive influencing factors and dust concentrations at all collection moments respectively, and determine all the first segmentation moments and all the 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, and the extreme value distribution of the comprehensive influencing factors and dust concentrations at all collection moments;

[0010] Based on the dust response index, the 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 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 speed correlation degree of the dust removal fan at the current moment respectively.

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

[0014] Before the current moment, the speed data of all the acquisition moments and all the acquisition moments in its neighborhood are taken as independent variables, and the wind speed data and dust concentration are taken 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 rotational speed-wind speed fitting line between each collection moment and the previous collection moment, as well as the difference in the slope of the rotational speed-dust concentration line, are calculated respectively, and recorded as the first trend difference and the second trend difference at each collection moment, respectively.

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

[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 the collection 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 the collection moments between each group of adjacent first segmentation moments and the dust concentrations of all the collection moments between any group of adjacent second segmentation moments are respectively used as the input of the trend test 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 average 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: In the formula, h k Represents the trend statistics 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: In the formula, p m Represents the trend statistics between the mth group of adjacent second segmentation 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: Wherein, 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.

[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 a preset value; Kp represents a preset initial proportional coefficient; exp() represents an 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 respectively analyzing the correlation between wind speed data and dust concentration and rotation speed data, as well as the local change trend of wind speed data and dust concentration, which can comprehensively reflect the overall influence of the dust removal fan speed on the wind speed and dust concentration changes. By considering the wind-rotation correlation and dust-rotation correlation, it can more accurately evaluate the influence of the fan speed on the dust removal effect, provide a basis for the optimization adjustment of the speed, and thus reduce the possibility of secondary dust. Further, by analyzing the change trends of the comprehensive influencing factors and dust concentration, as well as the difference in the change trends of the comprehensive influencing factors and dust concentration, a dust response index is constructed to quantify the influence of the dust removal fan speed on the dust concentration control effect. By comparing the actual influence of the comprehensive influencing factors and the dust concentration change trend on the speed adjustment and dust suppression effect, the accuracy of the speed control is improved, the wind speed change is ensured to be smooth, and the secondary dust re-oxygenation caused by the rapid wind speed change is avoided. The present application improves the accuracy of the dust removal fan speed adjustment by comprehensively analyzing the correlation between wind speed and dust concentration and rotation 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 in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0035] Figure 1 A flowchart of a method for intelligently adjusting dust removal equipment for construction provided in one embodiment of the present application;

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

[0037] In order to further explain the technical means and effects adopted by this application to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of an intelligent adjustment method for construction dust removal equipment proposed in this application, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[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 the present 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. An increase in the fan speed will lead to an increase in the wind speed; a decrease in the speed will reduce the wind speed. Not only that, the wind speed also has a direct impact on the dust removal efficiency. Excessive wind speed may cause the 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 within the preset time period before it; 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 within the preset time period before it; 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 within the preset time period before it, wherein the sampling frequency of all data is set to f.

[0044] It should be noted that the values ​​of the preset time length and the data sampling frequency f are manually set. In this embodiment, the preset time length 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 the normalization method, this embodiment does not make 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 wind speed data at all collection moments and the correlation between the dust concentration and the rotation speed data to determine the wind-rotation correlation and dust-rotation 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 at each collection moment and all collection moments in its neighborhood when the rotation speed data changes to determine the difference between the first and second trends at each collection moment, and combine the wind-rotation correlation and the dust-rotation 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 working conditions, there is a linear relationship between the speed data and the wind speed data, that is, when the fan speed increases, the wind speed at the outlet will also increase in a corresponding proportion. At the same time, as time goes by, when the fan speed increases to a certain threshold, the further increase 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 trend of wind speed and dust concentration, the comprehensive influencing factors of the dust removal fan at the current moment are determined by analyzing the correlation between wind speed data, dust concentration and speed data, as well as the local changing trend of wind speed data and dust concentration. Specifically:

[0050] (1) Calculate 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, and record them as the wind-rotation correlation and dust-rotation correlation 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 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 at each collection moment and all collection moments in its neighborhood are used as independent variables, and the wind speed data are used as dependent variables for fitting, so as to obtain the rotation speed-wind speed fitting line at each collection moment;

[0054] Before the current moment, the speed data of all the acquisition moments and all the acquisition moments in its neighborhood are taken as independent variables, and the wind speed data and dust concentration are taken 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 the wind speed data, as well as to fit the rotational speed data and the dust concentration. The implementer may also use other linear fitting methods. This embodiment does not impose any special restrictions on the selection of the linear fitting method.

[0056] Among them, the linear least square 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 repeated here.

[0057] Furthermore, the difference in the slope of the rotational speed-wind speed fitting line between each collection moment and the previous collection moment, as well as the difference in the slope of the rotational 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 difference 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 before adjacent collection moments and the difference in the slope of the speed-dust concentration line. The implementer may also adopt other methods such as ratios that can measure the difference between data according to specific circumstances. This embodiment does not impose any special restrictions on the selection of methods for measuring the difference 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 influencing factor A of the dust removal fan at the collection time i before the current time i The expression is: In the formula, a and c represent the wind-rotation correlation and dust-rotation speed correlation of the dust removal fan at the current moment respectively; b i d i They respectively represent the first trend difference and the second trend difference at the acquisition time i before the current time.

[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 speed correlation of the dust removal fan reflect the synchronization of the changing trend of the rotation speed data with the changing trend of the wind speed data, and the synchronization of the changing trend of the rotation speed data with the changing trend of the dust concentration, respectively. The greater the wind-rotation correlation and dust-rotation speed 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 the wind speed between adjacent collection moments in actual situations, and the second trend difference reflects the degree of change of the dust concentration with the change of the rotation speed; when the wind-rotation correlation is larger, and the first trend difference is The smaller it is, the smaller the difference in the changing trend between the wind speed data and the rotation speed data is, the greater the linear correlation is, 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 indirect influence on the dust concentration is greater; at the same time, the greater the dust-rotation speed correlation is, the greater the difference in the second trend is, indicating that the rotation speed has a greater influence on the dust concentration. Therefore, when the dust-rotation speed correlation is greater, the first trend difference is smaller, the dust-rotation speed correlation is greater, the second trend difference is greater, and the comprehensive influence factor is greater, 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 is, the smaller the second trend difference is, 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 greater, the dust-speed correlation is greater, 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 the first segmentation moments and all the 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 concentrations 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 and the extreme distribution of the comprehensive influencing factors at all collection moments.

[0064] In the dust exhaust system of industrial centrifugal dust removal fans, if the wind speed at the air outlet exceeds a certain critical value, dust will be generated. However, dust will only be triggered when the wind speed reaches the critical value. Therefore, the dust concentration at the dust outlet is uneven, which is manifested as intermittent dust concentration and changes of varying sizes.

[0065] Therefore, in order to analyze whether there is a causal relationship between the comprehensive influencing factor and the dust concentration, so as to more accurately determine the 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 change trends of the comprehensive influencing factor and the dust concentration, as well as the difference in the change 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, an 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 process of extracting the extreme value on the curve will not be repeated.

[0069] (2) The comprehensive influencing factors of all the collection moments between each group of adjacent first segmentation moments and the dust concentrations of all the collection 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] Further, 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 average 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 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: In the formula, h k Represents the trend statistics 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: In the formula, p m Represents the trend statistics between the mth group of adjacent second segmentation 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: Wherein, 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, which is used to prevent the denominator from being 0, wherein the value of ε is artificially set. In this embodiment, the value of ε is 0.01, and the implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0082] Further, 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 will not cause the change of the dust concentration data, and the smaller the dust response index; the greater the difference at the current moment, it indicates that the control effect of the speed change on the dust concentration is not significant, and the smaller the dust response index; |MN| represents the inconsistency of the trend change of the comprehensive influencing factor and the dust concentration, the inconsistency of the trend change of the two sequences, that is, the inconsistency of the change of the two sequences at the time point. The larger the value of |MN|, the more inconsistent the change of the comprehensive influencing factor and the dust concentration, 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 schematic diagram of the dust response index extraction process 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 rotation speed of the dust removal fan.

[0086] The PID algorithm is used to control the speed of the dust removal fan, and the outlet wind speed is maintained at an ideal set value by adjusting the fan speed to ensure dust removal efficiency and avoid 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 time that dust stays in the dust removal fan is shortened 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 response speed of the controller to deviations. The larger the Kp, the faster the controller reacts to deviations.

[0088] In this regard, 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 a preset value; Kp represents a preset initial proportional coefficient; exp() represents an exponential function with a natural constant as the base.

[0090] It should be noted that the value of the preset value k is artificially set to prevent the deviation response from being too slow and causing the adjustment time to be too long due to the value of Kp′ being too small. In this embodiment, the value of the preset value k is 0.2, and the implementer can also set it according to 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 set manually. The values ​​of Ki and Kd have been set in the above content, and 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 rotation speed data of the dust removal fan and the preset target rotation speed is used as the input of the PID controller, and a rotation speed control signal is output to adjust the rotation speed of the dust removal fan.

[0094] It should be noted that the value of the preset target speed in this embodiment is artificially set. 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] Among them, the working principle of the PID controller is a well-known technology, and its specific operating principle will not be repeated here.

[0096] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some 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 referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0098] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement 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 protection scope 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 air outlet of the dust removal fan, 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 respectively, so as to determine the wind-speed correlation and dust-speed correlation of the dust removal fan at the current moment; analyze the difference in the changing trend of wind speed data and dust concentration with speed data at each collection moment and all collection moments in its neighborhood respectively, so as to determine the difference between the first and second trends at each collection moment, and determine the comprehensive influencing factor of the dust removal fan at each collection moment by combining the wind-speed correlation and dust-speed correlation; Analyze the changing trends of the comprehensive influencing factors and dust concentrations at all collection moments respectively, and determine all the first segmentation moments and all the 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, and the extreme value distribution of the comprehensive influencing factors and dust concentrations at all collection moments; Based on the dust response index, the 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 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 speed correlation degree of the dust removal fan at the current moment respectively.

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 time is: Before the current moment, the speed data of all the acquisition moments and all the acquisition moments in its neighborhood are taken as independent variables, and the wind speed data and dust concentration are taken 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 rotational speed-wind speed fitting line between each collection moment and the previous collection moment, as well as the difference in the slope of the rotational speed-dust concentration line, are calculated respectively, and recorded as the first trend difference and the second trend difference at each collection moment, respectively.

4. The intelligent adjustment method for construction dust removal equipment according to claim 1, characterized in that: The comprehensive influencing factor A of the dust removal fan at each collection time i The expression is: In the formula, a and c represent the wind-rotation correlation and dust-rotation speed correlation of the dust removal fan at the current moment respectively; b i d i They respectively represent the first trend difference and the second trend difference at the acquisition time i before the current time.

5. The intelligent adjustment method for construction dust removal equipment according to claim 1, characterized in that: The method for determining all the first segmentation moments and all the second segmentation moments is: The comprehensive influencing factors and dust concentrations of all the collection 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.

6. 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 the collection moments between each group of adjacent first segmentation moments and the dust concentrations of all the collection moments between any group of adjacent second segmentation moments are respectively used as the input of the trend test 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 average of the trend statistics between all adjacent second segmentation moments is taken as the second trend degree at the current moment.

7. The intelligent adjustment method for construction dust removal equipment according to claim 6, 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: In the formula, h k Represents the trend statistics 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: In the formula, p m Represents the trend statistics between the mth group of adjacent second segmentation 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.

8. The intelligent adjustment method for construction dust removal equipment according to claim 5, characterized in that: The expression of the dust response index of the dust removal fan at the current moment is: In the formula, 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.

9. 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 a preset value; Kp represents a preset initial proportional coefficient; exp() represents an exponential function with a natural constant as the base.

10. 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 comprises: 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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