Installation process of smoke exhaust duct based on multidimensional data monitoring

Abstract

The present application relates to the technical field of pipeline installation, and particularly relates to a smoke exhaust air pipe installation process based on multidimensional data monitoring, which hoists the smoke exhaust air pipe according to drawings at a predetermined site, determines whether the smoke exhaust air pipe has abnormal risks based on comparison between gray scale fluctuation parameters of each monitoring area, determines vibration fluctuation curves of each stress observation point of the smoke exhaust air pipe in the hoisting process, performs synchronous vibration analysis to determine an abnormal tendency category of the smoke exhaust air pipe, determines to increase a lifting point at a characteristic area of the air pipe based on the abnormal tendency category, or marks a defect risk area based on the gray scale fluctuation parameters and issues a warning. The present application realizes rapid identification of whether there is an abnormal risk according to a surface image of the smoke exhaust air pipe, adaptively adjusts parameters of the smoke exhaust air pipe installation according to synchronous vibration analysis of each stress observation point, and improves process precision and installation efficiency of the smoke exhaust air pipe installation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline installation, in particular to a smoke exhaust air pipe installation process based on multidimensional data monitoring. BACKGROUND

[0002] The smoke exhaust system is a key facility to ensure the fire safety of buildings, and its installation quality is directly related to whether smoke can be effectively removed and fresh air can be transported to gain valuable time for personnel evacuation and fire rescue. As a core component of the system, the installation accuracy, sealing performance and structural integrity of the smoke exhaust air pipe are crucial. At present, the installation of the smoke exhaust air pipe, especially the whole prefabricated air pipe arising with the development of the building industry, mainly relies on the traditional hoisting process. However, whether the smoke exhaust air pipe is in a stable and safe state during the entire hoisting process mainly depends on the naked eye observation and personal experience of the ground command personnel and the hoisting operators. It is difficult to discover in real time and accurately whether the smoke exhaust air pipe is deformed slightly during the hoisting process, and it is also difficult to quickly and accurately judge the cause of the slight deformation on site. It is difficult to distinguish whether the deformation is caused by the quality defects of the smoke exhaust air pipe product, such as uneven structural strength, or by the improper setting of the hoisting parameters, such as the unreasonable position of the hoisting point. The adjustment is largely blind, which affects the precision and efficiency of the smoke exhaust air pipe installation process. Therefore, improving the process precision and installation efficiency of the smoke exhaust air pipe installation is a technical problem to be solved.

[0003] For example, Chinese patent application publication No. CN115164396A discloses a laboratory air pipe installation structure and its installation method, which includes two groups of supports and a cross beam arranged between the two groups of supports. The support includes an anchoring part and a hoisting pipe. The anchoring part is used to connect with the floor. One end of the hoisting pipe is fixedly connected with the anchoring part. The other end of the hoisting pipe is detachably connected with a guide pipe. The cross beam is slidably sleeved with the hoisting pipe and the guide pipe. The anchoring part is connected with a lifting assembly for lifting the cross beam. The hoisting pipe is provided with a supporting member for supporting the cross beam. The air pipe is installed by the inverted hoisting method, which reduces the time and intensity of high-altitude work and improves the installation efficiency of the air pipe.

[0004] The existing technology also has the following problems:

[0005] The existing technology does not consider that the smoke exhaust air pipe may be deformed during hoisting due to the influence of the hoisting point and the self-weight. The existing technology cannot quickly identify whether there is an abnormal risk according to the surface image of the smoke exhaust air pipe. The existing technology does not consider the structural particularity of the smoke exhaust air pipe, which is prone to stress propagation during installation. The existing technology does not adaptively adjust the parameters of the smoke exhaust air pipe installation according to the synchronous vibration analysis of each stress observation point, which affects the process precision and installation efficiency of the smoke exhaust air pipe installation. SUMMARY

[0006] To this end, the present application provides a kind of installation process of smoke exhaust duct based on multidimensional data monitoring, to overcome the prior art cannot according to the surface image of smoke exhaust duct quickly identify whether there is abnormal risk, cannot according to the synchronous vibration analysis of each stress observation point adaptively adjust the parameter of smoke exhaust duct installation, affect the process precision and installation efficiency of smoke exhaust duct installation problem.

[0007] To achieve the above object, the present application provides a kind of installation process of smoke exhaust duct based on multidimensional data monitoring, comprising:

[0008] the smoke exhaust duct is hoisted according to drawing in predetermined site;

[0009] the surface of the smoke exhaust duct is divided into several monitoring areas, the surface image of each monitoring area under the same illumination angle is obtained in preset monitoring period, and the gray scale parameter of each monitoring area in hoisting process is obtained based on the surface image to determine the gray scale fluctuation parameter;

[0010] determine whether the smoke exhaust duct has abnormal risk based on the comparison between the gray scale fluctuation parameters of each monitoring area;

[0011] in response to the existence of abnormal risk of the smoke exhaust duct, the vibration parameter of each stress observation point of the smoke exhaust duct in hoisting process is determined in preset monitoring period, and the vibration fluctuation curve corresponding to each stress observation point is respectively constructed;

[0012] synchronous vibration analysis is carried out based on the vibration fluctuation curve of each stress observation point to determine the abnormal tendency category of the smoke exhaust duct;

[0013] determine the overload characteristic parameter based on the vibration fluctuation curve of adjacent stress observation point based on the abnormal tendency category to mark the characteristic area of wind pipe, increase lifting point in the characteristic area of wind pipe, or mark the defect risk area based on the gray scale fluctuation parameter and issue early warning.

[0014] Further, the process of determining a plurality of gray scale fluctuation parameters based on a plurality of gray scale parameters of each monitoring area in hoisting process comprises,

[0015] the average value of gray scale value of monitoring area is obtained in preset monitoring period, and the average value of gray scale value is determined as gray scale parameter;

[0016] the absolute value of difference of gray scale parameter of adjacent monitoring time is calculated, and the absolute value of difference is determined as fluctuation factor;

[0017] the average value of fluctuation factor is determined as the gray scale fluctuation parameter of the monitoring area.

[0018] Furthermore, the process of determining whether the smoke exhaust duct poses any abnormal risk includes,

[0019] If the comparison between the grayscale fluctuation parameters of each monitoring area meets the abnormal risk conditions, then the smoke exhaust duct is determined to have an abnormal risk.

[0020] The abnormal risk condition is that the variance of the grayscale fluctuation parameter in each monitoring area exceeds a preset variance threshold.

[0021] Furthermore, the process of constructing the vibration wave curves corresponding to each force observation point includes,

[0022] Vibration parameters of several stress observation points are acquired at several acquisition times within a preset monitoring period;

[0023] Establish a rectangular coordinate system with time as the horizontal axis and the vibration parameters as the vertical axis, plot the curve of the vibration parameters changing with time, and determine the curve as the vibration fluctuation curve.

[0024] Furthermore, the process of performing synchronous vibration analysis to determine the vibration tendency coefficient includes,

[0025] Obtain the vibration fluctuation curves corresponding to each stress observation point of the smoke exhaust duct;

[0026] Calculate the average overlap between the vibration fluctuation curves, and determine the average overlap as the vibration tendency coefficient of the smoke exhaust duct.

[0027] Furthermore, the process of determining the abnormal tendency category of the smoke exhaust duct includes,

[0028] If the vibration tendency coefficient of the smoke exhaust duct exceeds the preset vibration tendency coefficient threshold, the abnormal tendency category of the smoke exhaust duct is determined to be the duct abnormal tendency category.

[0029] If the vibration tendency coefficient of the smoke exhaust duct does not exceed the preset vibration tendency coefficient threshold, then the abnormal tendency category of the smoke exhaust duct is determined to be the hoisting abnormal tendency category.

[0030] Furthermore, if the abnormal tendency category is the duct abnormal tendency category, then the defect risk area is marked and an early warning is issued based on the grayscale fluctuation parameter;

[0031] If the abnormal tendency category is the hoisting abnormal tendency category, then the overload characterization parameters are determined based on the vibration fluctuation curves of adjacent stress observation points to mark the duct characteristic area, and a hoisting point is added at the center of the duct characteristic area.

[0032] Furthermore, the process of marking defect risk areas includes,

[0033] If the grayscale fluctuation parameter of the monitored area exceeds the preset grayscale fluctuation parameter threshold, the monitored area will be marked as a defect risk area and an early warning will be issued.

[0034] Furthermore, the process of determining overload characterization parameters based on the vibration fluctuation curves of adjacent stress observation points includes,

[0035] Obtain the vibration wave curves of adjacent stress observation points, determine the average amplitude of each vibration wave curve, and use the average amplitude as the load characterization parameter;

[0036] The absolute value of the difference between the load characterization parameters of adjacent stress observation points is determined as the overload characterization parameter of the adjacent stress observation points.

[0037] Furthermore, the process of marking characteristic areas of the duct includes,

[0038] If the overload characterization parameters and the load characterization parameters of adjacent stress observation points meet the duct characteristic conditions, then the area between the adjacent stress observation points is marked as the duct characteristic area, and a suspension point is added at the center of the duct characteristic area.

[0039] The duct characteristic condition is that the overload characterization parameter does not exceed the preset overload characterization parameter threshold, and the load characterization parameters of the adjacent stress observation points all exceed the preset load characterization parameter threshold.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention hoists the smoke exhaust duct according to the drawings at a predetermined location, acquires surface images of each monitoring area under the same illumination angle within a preset monitoring period, acquires several grayscale parameters of each monitoring area during the hoisting process based on the surface images to determine grayscale fluctuation parameters, determines whether there is any abnormal risk in the smoke exhaust duct based on the comparison between the grayscale fluctuation parameters of each monitoring area, and in response to the existence of abnormal risk in the smoke exhaust duct, determines several vibration parameters of each stress observation point of the smoke exhaust duct during the hoisting process within the preset monitoring period, and constructs corresponding parameters for each stress observation point. The vibration fluctuation curves of the smoke exhaust duct are used to perform synchronous vibration analysis based on the vibration fluctuation curves of each stress observation point to determine the abnormal tendency category of the smoke exhaust duct. Based on the abnormal tendency category, overload characterization parameters are determined based on the vibration fluctuation curves of adjacent stress observation points to mark the characteristic areas of the duct. Suspension points are added to the characteristic areas of the duct, or defect risk areas are marked based on grayscale fluctuation parameters and warnings are issued. Thus, it is possible to quickly identify the existence of abnormal risks based on the surface image of the smoke exhaust duct, and adaptively adjust the installation parameters of the smoke exhaust duct based on the synchronous vibration analysis of each stress observation point, thereby improving the process accuracy and installation efficiency of the smoke exhaust duct.

[0041] In particular, this invention determines whether there are abnormal risks in smoke exhaust ducts by comparing the grayscale fluctuation parameters of each monitoring area. It is understood that acquiring surface images of each monitoring area eliminates the need to install sensors on the duct surface, does not affect the hoisting operation, and avoids the risk of sensor damage. It can cover the entire surface of the smoke exhaust duct. Compared with point sensors, such as strain gauges, it can perform full-field monitoring and can capture tiny or initial deformations that are difficult for the human eye to observe. It can issue an early warning before the deformation develops into a serious accident. Traditional processes mostly identify anomalies after hoisting and static inspection. If local anomalies, such as local overload, are not detected during hoisting, disassembly and rework are required after installation. By identifying abnormal risks early, the invention avoids delayed warnings, reduces rework costs, transforms subjective experience into objective data-driven decision-making, improves reliability, and eliminates the uncertainty and fatigue factors of human judgment. Thus, it realizes the rapid identification of abnormal risks based on the surface images of smoke exhaust ducts, improving the process accuracy and installation efficiency of smoke exhaust duct installation.

[0042] In particular, this invention determines several vibration parameters at each stress observation point of the smoke exhaust duct during the hoisting process, and constructs vibration fluctuation curves corresponding to each stress observation point. It can be understood that the stress observation point, i.e., the hoisting point, is the stress contact point between the hoisting device and the smoke exhaust duct, and its vibration parameters are strongly correlated with the overall stress state of the smoke exhaust duct. Due to the special structure of the smoke exhaust duct, which is usually connected in multiple sections, stress propagation is easily generated during installation. When the smoke exhaust duct is subjected to external forces, it will vibrate. The vibration parameters measured at the stress observation points can characterize the overall stress state of the smoke exhaust duct and the state of the hoisting system. Constructing vibration fluctuation curves corresponding to each stress observation point transforms discrete data into a dynamic process and physical phenomena into analyzable and quantifiable data. Thus, it realizes the acquisition of the state of each stress observation point and improves the process accuracy and installation efficiency of the smoke exhaust duct.

[0043] In particular, this invention uses synchronous vibration analysis based on the vibration fluctuation curves of each stress observation point to determine the abnormal tendency category of the smoke exhaust duct. It can be understood that determining the abnormal tendency category of the smoke exhaust duct through synchronous vibration analysis solves a classic on-site problem: whether the defect lies in the duct itself or in the parameter settings during installation. This transforms the process, which previously required experienced technicians to judge based on experience, into a data-driven, objective, and automated classification process. This provides direction for further identification and analysis, avoids wasted time and secondary damage caused by misjudgments, improves the reliability and intelligence of the process, reduces reliance on human experience, ensures the stability and consistency of decision-making quality, shortens troubleshooting time, improves construction efficiency, and reduces labor costs. Ultimately, this achieves the classification of abnormal tendency categories for smoke exhaust ducts, improving the process accuracy and installation efficiency of smoke exhaust duct installation.

[0044] In particular, this invention marks defect risk areas and issues warnings based on grayscale fluctuation parameters under the category of abnormal tendencies in ductwork. This can be understood as changing the traditional inspection method that requires workers to rely on experience to tap and explore large areas. Instead, it guides maintenance personnel to the most likely problem locations for inspection, improving efficiency. During the hoisting process, warnings are issued before defects could lead to complete duct failure or safety accidents, avoiding the high costs of disassembly and replacement, as well as potential rework and safety risks, resulting from installing potentially hazardous components into the system. Defect judgment is transformed from a subjective process relying on personal experience to a data-driven objective decision-making process, reducing reliance on specific skilled workers and improving the consistency and reliability of judgment standards. At the same time, based on image analysis, non-contact measurement does not affect the hoisting operation itself. Furthermore, it enables the selection of subsequent identification and analysis methods based on the synchronous vibration analysis of each stress observation point, improving the process accuracy and installation efficiency of smoke exhaust duct installation.

[0045] In particular, under the category of abnormal lifting tendency, this invention determines overload characterization parameters based on the vibration fluctuation curves of adjacent stress observation points to mark the characteristic areas of the duct. Lifting points are added at the midpoint of these characteristic areas. This allows for precise positioning and avoids blind additions. In traditional methods, workers may try adding lifting points at several locations based on experience, a process that is inefficient and may yield poor results. By directly and precisely locating the theoretically most effective reinforcement point through data, the efficiency and quality of adjustments are improved, and the load is scientifically distributed, preventing permanent deformation of the smoke exhaust duct due to overload. If the overload balancing area is overlooked, the middle section of the smoke exhaust duct may experience permanent deformation due to excessive mid-span bending moment, affecting sealing and structural safety. Blindly adding lifting points would also compromise safety. Excessive constraints can lead to localized stress concentration in the smoke exhaust duct, such as bulging of steel plates between lifting points. This reduces resource waste, lowers construction costs, and improves lifting safety. While short-term overload of lifting points may only manifest as slight vibration, long-term operation can cause fatigue deformation of the lifting point supports, and even lead to duct collapse. At the same time, by increasing the number of lifting points to optimize the lifting scheme, the problem of insufficient rigidity caused by excessive span is solved from a mechanical perspective. This not only eliminates the current abnormal vibration but also ensures that the duct maintains its ideal shape during lifting, avoiding permanent deformation. This guarantees the straightness and flatness of the final installation. Furthermore, it enables adaptive adjustment of the smoke exhaust duct installation parameters based on synchronous vibration analysis at each stress observation point, improving the process accuracy and installation efficiency of the smoke exhaust duct. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating the installation process of smoke exhaust ducts based on multi-dimensional data monitoring, as described in an embodiment of the present invention.

[0047] Figure 2This is a flowchart illustrating the logic of determining whether there is an abnormal risk in the smoke exhaust duct according to an embodiment of the present invention.

[0048] Figure 3 This is a flowchart illustrating the logic for determining the abnormal tendency category of smoke exhaust ducts in an embodiment of the present invention.

[0049] Figure 4 This is a flowchart illustrating the logic for marking defect risk areas in an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0051] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0052] It should be noted that in the description of this invention, the terms "upper," "lower," "inner," "outer," etc., which indicate the direction or positional relationship, are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0053] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0054] Please see Figure 1 The diagram shows the steps of the smoke exhaust duct installation process based on multi-dimensional data monitoring according to an embodiment of the present invention. The smoke exhaust duct installation process based on multi-dimensional data monitoring according to the present invention includes:

[0055] Step S100: Hoist the smoke exhaust duct at the designated location according to the drawings;

[0056] Step S200: Divide the surface of the smoke exhaust duct into several monitoring areas, acquire surface images of each monitoring area under the same illumination angle within a preset monitoring period, and acquire several grayscale parameters of each monitoring area during the hoisting process based on the surface images to determine the grayscale fluctuation parameters.

[0057] Specifically, the monitoring area can be evenly divided. The area of ​​the monitoring area is the product of the surface area of ​​the smoke exhaust duct and the area division factor. The area division factor can be set by those skilled in the art according to the accuracy requirements of the smoke exhaust duct installation process. The higher the accuracy requirement, the smaller the area division factor should be. The value range can be [0.05, 0.1], and preferably, it can be 0.08.

[0058] Specifically, the preset monitoring cycle can be set by those skilled in the art based on the accuracy requirements of the smoke exhaust duct installation process. The higher the accuracy requirement, the longer the preset monitoring cycle is set. The value range can be [3, 8], with the interval unit being min. Preferably, it can be 5 min.

[0059] Specifically, the illumination angle can be set by those skilled in the art based on the actual structural characteristics and installation environment characteristics of the smoke exhaust duct. The illumination angle can be in the range of [45, 90], with the unit being °. Preferably, it can be 60°.

[0060] Specifically, the embodiments of the present invention do not impose specific limitations on the method of obtaining surface images and grayscale parameters. Preferably, surface images can be obtained by an industrial camera, and the surface images acquired by the industrial camera can be digitally processed by machine vision technology to extract quantized grayscale information from the image pixel level to obtain grayscale parameters. Of course, other methods can also be used, which will not be elaborated here.

[0061] Step S300: Determine whether there is any abnormal risk in the smoke exhaust duct based on the comparison between the grayscale fluctuation parameters of each monitoring area;

[0062] Step S400: In response to the abnormal risk of the smoke exhaust duct, determine several vibration parameters of each stress observation point of the smoke exhaust duct during the hoisting process within a preset monitoring period, and construct vibration fluctuation curves corresponding to each stress observation point respectively.

[0063] Specifically, the embodiments of the present invention do not impose specific limitations on the method of obtaining vibration parameters at each force observation point. The vibration parameter is the amplitude value. Preferably, mechanical vibration can be obtained by a piezoelectric accelerometer, the analog signal is converted into digital data, and features are extracted from the original signal to obtain the vibration parameter. Of course, other methods can also be used, which will not be elaborated here.

[0064] Specifically, the stress observation point is the lifting point where the hoisting device contacts the surface of the smoke exhaust duct.

[0065] Step S500: Perform synchronous vibration analysis based on the vibration fluctuation curves of each stress observation point to determine the abnormal tendency category of the smoke exhaust duct.

[0066] Step S600: Based on the abnormal tendency category, determine the overload characterization parameters based on the vibration fluctuation curves of adjacent stress observation points to mark the duct feature area, add suspension points in the duct feature area, or mark the defect risk area based on the grayscale fluctuation parameters and issue an early warning.

[0067] Specifically, the process of determining several grayscale fluctuation parameters based on several grayscale parameters of each monitoring area during the hoisting process includes,

[0068] The average grayscale value of the monitoring area is obtained at several monitoring times within a preset monitoring period, and the average grayscale value is determined as the grayscale parameter.

[0069] Calculate the absolute value of the difference in grayscale parameters between adjacent monitoring times, and determine the absolute value of the difference as the fluctuation factor;

[0070] The mean value of the fluctuation factor is determined as the grayscale fluctuation parameter of the monitoring area.

[0071] Specifically, the interval between adjacent monitoring times can be set by those skilled in the art based on the accuracy requirements of the smoke exhaust duct installation process. The higher the accuracy requirement, the shorter the interval should be. The value range can be [20, 40], with the unit of interval being seconds. Preferably, it can be 30 seconds.

[0072] Please see Figure 2 As shown, this is a flowchart illustrating the logic of determining whether there is an abnormal risk in the smoke exhaust duct according to an embodiment of the present invention. The process of determining whether there is an abnormal risk in the smoke exhaust duct includes:

[0073] If the comparison between the grayscale fluctuation parameters of each monitoring area meets the abnormal risk conditions, then the smoke exhaust duct is determined to have an abnormal risk.

[0074] If the comparison between the grayscale fluctuation parameters of each monitoring area does not meet the abnormal risk conditions, then it is determined that the smoke exhaust duct does not have any abnormal risk.

[0075] The abnormal risk condition is that the variance of the grayscale fluctuation parameter in each monitoring area exceeds a preset variance threshold.

[0076] Specifically, the preset variance threshold is the product of the variance threshold reference value and the variance threshold coefficient. The variance threshold reference value is the mean variance of the grayscale fluctuation parameter of the smoke exhaust duct under the same working conditions in historical data. The variance threshold coefficient can be set by those skilled in the art according to the accuracy requirements of the smoke exhaust duct installation process. The higher the accuracy requirement, the smaller the variance threshold coefficient should be. The value range can be [1.1, 1.25], preferably 1.15.

[0077] Specifically, this invention determines whether there are abnormal risks in smoke exhaust ducts by comparing the grayscale fluctuation parameters of each monitoring area. It is understood that acquiring surface images of each monitoring area eliminates the need to install sensors on the duct surface, does not affect hoisting operations, and avoids the risk of sensor damage. It can cover the entire surface of the smoke exhaust duct. Compared to point sensors, such as strain gauges, it allows for full-field monitoring, capturing minute or initial deformations that are difficult for the human eye to observe, and issuing warnings before the deformation develops into a serious accident. Traditional processes often rely on static inspections after hoisting. If local anomalies, such as local overload, are not detected during hoisting, disassembly and rework are required after installation. By identifying abnormal risks early, delayed warnings are avoided, rework costs are reduced, subjective experience is transformed into objective data-driven decision-making, improving reliability and eliminating the uncertainty and fatigue factors of human judgment. Therefore, it achieves rapid identification of abnormal risks based on surface images of smoke exhaust ducts, improving the process accuracy and installation efficiency of smoke exhaust duct installation.

[0078] Specifically, it can be understood that when a smoke exhaust duct is hoisted, if a smooth, rigid movement occurs, such as an ideal vertical lift, the movement of all points on its surface in the image is synchronous and consistent. The grayscale value of each monitoring area will show an overall change, and the relative grayscale relationship between areas remains unchanged. If the smoke exhaust duct undergoes non-rigid deformation, different monitoring areas will experience relative displacement. This relative displacement will cause differences in the lighting conditions of each area in the image, thus manifesting as asynchronous changes in the grayscale values ​​of each area. The grayscale parameter is the average grayscale value of all pixels within each monitoring area, expressed as a single, stable... A fixed value is used to characterize the overall brightness of the monitoring area at a certain moment, thereby eliminating the interference of local noise in the image. The grayscale fluctuation parameter, i.e. the average fluctuation factor, is the average of all fluctuation factors of a monitoring area over a period of time. It can characterize the instability of the movement of the area throughout the entire monitoring cycle. Under normal hoisting conditions, the smoke exhaust duct moves as a whole, and the grayscale fluctuation parameters of all areas should be relatively consistent. The dispersion between these parameter values ​​is small. Thus, it is possible to quickly identify the presence of abnormal risks based on the surface image of the smoke exhaust duct, thereby improving the process accuracy and installation efficiency of the smoke exhaust duct.

[0079] Specifically, the process of constructing the vibration wave curves corresponding to each force observation point includes,

[0080] Vibration parameters of several stress observation points are acquired at several acquisition times within a preset monitoring period;

[0081] Establish a rectangular coordinate system with time as the horizontal axis and the vibration parameters as the vertical axis, plot the curve of the vibration parameters changing with time, and determine the curve as the vibration fluctuation curve.

[0082] Specifically, the interval between adjacent data collection times can be set by those skilled in the art based on the accuracy requirements of the smoke exhaust duct installation process. The higher the accuracy requirement, the shorter the interval should be. The value range can be [0.5, 2], with the unit being seconds. Preferably, it can be 1 second.

[0083] Specifically, the embodiments of the present invention do not limit the method of constructing each vibration wave curve. For example, the vibration wave curve can be fitted by MATLAB correlation fitting software. Of course, other forms can also be used, which will not be elaborated here.

[0084] Specifically, this invention determines several vibration parameters at each stress observation point of the smoke exhaust duct during the hoisting process, and constructs vibration fluctuation curves corresponding to each stress observation point. It can be understood that the stress observation point, i.e., the hoisting point, is the stress contact point between the hoisting device and the smoke exhaust duct, and its vibration parameters are strongly correlated with the overall stress state of the smoke exhaust duct. When the smoke exhaust duct is subjected to external forces, it will vibrate. The vibration parameters measured at the stress observation point can characterize the stress state of the smoke exhaust duct and the state of the hoisting system. Constructing vibration fluctuation curves corresponding to each stress observation point transforms discrete data into a dynamic process and physical phenomena into analyzable and quantifiable data. This achieves the acquisition of the state at each stress observation point, improving the process accuracy and installation efficiency of the smoke exhaust duct installation.

[0085] Specifically, the process of performing synchronous vibration analysis to determine the vibration tendency coefficient includes,

[0086] Obtain the vibration fluctuation curves corresponding to each stress observation point of the smoke exhaust duct;

[0087] Calculate the average overlap between the vibration fluctuation curves, and determine the average overlap as the vibration tendency coefficient of the smoke exhaust duct.

[0088] Specifically, the embodiments of the present invention do not limit the method of determining the degree of overlap. For example, the cosine similarity method can be used to determine it. After vectorizing the vibration wave curve, the cosine similarity is calculated, and the obtained cosine similarity is determined as the degree of overlap. Of course, other methods can also be used, which will not be elaborated here.

[0089] Please see Figure 3 As shown, it is a flowchart illustrating the logic of determining the abnormal tendency category of the smoke exhaust duct according to an embodiment of the present invention. The process of determining the abnormal tendency category of the smoke exhaust duct includes,

[0090] If the vibration tendency coefficient of the smoke exhaust duct exceeds the preset vibration tendency coefficient threshold, the abnormal tendency category of the smoke exhaust duct is determined to be the duct abnormal tendency category.

[0091] If the vibration tendency coefficient of the smoke exhaust duct does not exceed the preset vibration tendency coefficient threshold, then the abnormal tendency category of the smoke exhaust duct is determined to be the hoisting abnormal tendency category.

[0092] Specifically, the preset vibration tendency coefficient threshold is the product of the vibration tendency coefficient reference value and the vibration tendency factor. The vibration tendency coefficient reference value is the average vibration tendency coefficient of the abnormal tendency category of the duct under the same working conditions in historical data. The vibration tendency factor can be set by those skilled in the art according to the accuracy requirements of the smoke exhaust duct installation process. The higher the accuracy requirement, the smaller the vibration tendency factor is set. The value range can be [1.1, 1.25], preferably 1.15.

[0093] Specifically, this invention uses synchronous vibration analysis based on the vibration fluctuation curves at various stress observation points to determine the abnormal tendency category of smoke exhaust ducts. This synchronous vibration analysis solves a classic on-site problem: whether the issue lies with the duct itself or with flawed installation parameters. It transforms the previous process, which required experienced technicians to rely on their expertise, into a data-driven, objective, and automated classification process. This provides direction for further identification and analysis, avoids wasted time and secondary damage caused by misjudgments, improves process reliability and intelligence, reduces reliance on human experience, ensures the stability and consistency of decision-making quality, shortens troubleshooting time, improves construction efficiency, and reduces labor costs. Ultimately, it achieves the classification of abnormal tendencies in smoke exhaust ducts, improving the process accuracy and installation efficiency of smoke exhaust duct installation.

[0094] Specifically, it can be understood that the abnormal lifting tendency category refers to the subtle deformation of the smoke exhaust duct caused by uneven external excitation. When the abnormality originates from the lifting parameters, it is equivalent to an abnormal external excitation force applied to the smoke exhaust duct, resulting in different vibration responses at various stress observation points of the smoke exhaust duct. For example, a certain lifting point bears most of the load. The abnormal duct tendency category also refers to the change in the structural characteristics of the smoke exhaust duct. When the abnormality originates from the defects in the smoke exhaust duct itself, such as uneven structural stiffness, the structural dynamic characteristics of the smoke exhaust duct as a whole, such as stiffness, change, resulting in a more uniform vibration. Furthermore, the vibrations caused by the inherent defects are transmitted to the entire structure. Therefore, the vibration curves measured at all stress observation points will show a similar shape, that is, the overlap between the curves is high. By averaging the overlap between each pair of stress observation points, the vibration tendency coefficient is obtained, which can characterize the vibration synchronization of the entire smoke exhaust duct. The larger the vibration tendency coefficient, the higher the vibration synchronization of the smoke exhaust duct; the smaller the vibration tendency coefficient, the lower the vibration synchronization of the smoke exhaust duct. Thus, the abnormal tendency categories of the smoke exhaust duct are classified, improving the process accuracy and installation efficiency of the smoke exhaust duct.

[0095] Specifically, if the abnormal tendency category is the duct abnormal tendency category, then the defect risk area is marked and an early warning is issued based on the grayscale fluctuation parameter;

[0096] If the abnormal tendency category is the hoisting abnormal tendency category, then the overload characterization parameters are determined based on the vibration fluctuation curves of adjacent stress observation points to mark the duct characteristic area, and a hoisting point is added at the center of the duct characteristic area.

[0097] Specifically, the additional suspension point can be set at the midpoint of the line connecting the two ends of the stress observation point in the characteristic area of ​​the duct.

[0098] Please see Figure 4 The diagram shown is a logical flowchart for marking defect risk areas according to an embodiment of the present invention. The process of marking defect risk areas includes:

[0099] If the grayscale fluctuation parameter of the monitored area exceeds the preset grayscale fluctuation parameter threshold, the monitored area will be marked as a defect risk area and an early warning will be issued.

[0100] If the grayscale fluctuation parameter of the monitored area does not exceed the preset grayscale fluctuation parameter threshold, the monitored area will not be marked.

[0101] Specifically, the preset grayscale fluctuation parameter threshold is the product of the grayscale fluctuation parameter reference value and the grayscale fluctuation factor. The grayscale fluctuation parameter reference value is the average grayscale fluctuation parameter with defect risk under the same working conditions in historical data. The grayscale fluctuation factor can be set by those skilled in the art according to the accuracy requirements of the smoke exhaust duct installation process. The higher the accuracy requirement, the smaller the grayscale fluctuation factor is set. The value range can be [1.05, 1.15], preferably 1.1.

[0102] Specifically, in this embodiment of the invention, under the category of abnormal tendencies in ductwork, defect risk areas are marked and warnings are issued based on grayscale fluctuation parameters. This changes the traditional method of inspection, which requires workers to rely on experience to extensively tap and explore, guiding maintenance personnel to the most likely locations of problems, thus improving efficiency. During hoisting, warnings are issued before defects could lead to complete duct failure or safety accidents, avoiding the high costs of disassembly and replacement, as well as potential rework and safety risks associated with installing potentially hazardous components into the system. Defect judgment is transformed from a subjective process relying on personal experience to a data-driven objective decision-making process, reducing reliance on specific skilled workers and improving the consistency and reliability of judgment standards. Furthermore, based on image analysis, non-contact measurement does not affect the hoisting operation itself. This allows for the adaptive selection of subsequent identification and analysis methods based on synchronous vibration analysis at each stress observation point, improving the process accuracy and installation efficiency of smoke exhaust ducts.

[0103] Specifically, it can be understood that local structural defects can cause local abnormal deformation. Under fixed lighting and viewing angle, the grayscale changes of the surface image of the smoke exhaust duct directly reflect its microscopic angle and distance changes. An area with internal defects such as uneven material, weak bonding, or broken internal reinforcing ribs will have lower structural stiffness than normal areas. When subjected to hoisting force, this weak point will produce deformations different from the surrounding normal areas, such as slight bulging, depression, or twisting. This local unstable deformation will cause abnormal changes in the light reflection characteristics of the area, manifested as drastic fluctuations in grayscale values. The larger the grayscale fluctuation parameter of the monitored area, the more significant the instability shown by this area under stress, and the greater the possibility that it is a defect area. Thus, the subsequent identification and analysis method can be adaptively selected based on the synchronous vibration analysis of each stress observation point, thereby improving the process accuracy and installation efficiency of the smoke exhaust duct.

[0104] Specifically, the process of determining overload characterization parameters based on vibration wave curves at adjacent stress observation points includes,

[0105] Obtain the vibration wave curves of adjacent stress observation points, determine the average amplitude of each vibration wave curve, and use the average amplitude as the load characterization parameter;

[0106] The absolute value of the difference between the load characterization parameters of adjacent stress observation points is determined as the overload characterization parameter of the adjacent stress observation points.

[0107] Specifically, the process of marking characteristic areas of air ducts includes,

[0108] If the overload characterization parameters and the load characterization parameters of adjacent stress observation points meet the duct characteristic conditions, then the area between the adjacent stress observation points is marked as the duct characteristic area, and a suspension point is added at the center of the duct characteristic area.

[0109] If the overload characterization parameters and the load characterization parameters of adjacent stress observation points do not meet the duct characteristic conditions, then the area between the adjacent stress observation points will not be marked.

[0110] The duct characteristic condition is that the overload characterization parameter does not exceed the preset overload characterization parameter threshold, and the load characterization parameters of the adjacent stress observation points all exceed the preset load characterization parameter threshold.

[0111] Specifically, the preset overload characterization parameter threshold is the product of the overload characterization parameter reference value and the overload factor, and the preset load characterization parameter threshold is the product of the load characterization parameter reference value and the load factor. The overload characterization parameter reference value is the average overload characterization parameter under the same working condition in historical data, and the load characterization parameter reference value is the average load characterization parameter under the same working condition in historical data. The overload factor and load factor can be set by those skilled in the art according to the accuracy requirements of the smoke exhaust duct installation process. The higher the accuracy requirement, the larger the overload factor is set, and the value range can be [1.15, 1.3]. The smaller the load factor is set, the value range can be [1.1, 1.25]. Preferably, the overload factor can be 1.2 and the load factor can be 1.15.

[0112] Specifically, in this embodiment of the invention, under the category of abnormal lifting tendency, overload characterization parameters are determined based on the vibration fluctuation curves of adjacent stress observation points to mark the characteristic area of ​​the duct. A lifting point is added at the midpoint of this characteristic area. This means that adding a lifting point at the midpoint of the characteristic area achieves precise positioning and avoids blind addition. In traditional methods, workers may try to add lifting points at several locations based on experience, which is inefficient and may not be effective. By directly and accurately locating the theoretically most effective reinforcement point through data, adjustment efficiency and quality are improved, load is scientifically distributed, and permanent deformation of the smoke exhaust duct due to overload is avoided. If the overload balancing area is missed, the middle section of the smoke exhaust duct may suffer permanent deformation due to excessive mid-span bending moment, affecting sealing and structural safety. Blindly adding lifting points... Excessive constraint at lifting points can lead to localized stress concentration in the smoke exhaust duct, such as bulging of the steel plates between lifting points. This reduces resource waste, lowers construction costs, and improves lifting safety. While overloading of lifting points may only manifest as slight vibration in the short term, long-term operation can cause fatigue deformation of the lifting point supports, and even lead to duct collapse. At the same time, by increasing the number of lifting points to optimize the lifting scheme, the problem of insufficient rigidity caused by excessive span is solved from a mechanical perspective. This not only eliminates the current abnormal vibration but also ensures that the duct maintains its ideal shape during lifting, avoiding permanent deformation. This guarantees the straightness and flatness of the final installation. Furthermore, it enables adaptive adjustment of the installation parameters of the smoke exhaust duct based on synchronous vibration analysis at each stress observation point, improving the process accuracy and installation efficiency of the smoke exhaust duct.

[0113] Specifically, it can be understood that the load characterization parameter, i.e., the average amplitude of the vibration fluctuation curve, is directly related to the magnitude of the dynamic load borne by the stress observation point. A larger load characterization parameter means a greater force at that point and more severe vibration. The overload characterization parameter, i.e., the absolute value of the difference between the load characterization parameters of two adjacent suspension points, can characterize the force balance between stress observation points. When two adjacent stress observation points simultaneously exhibit excessively high load characterization parameters (i.e., both have large loads) and their overload characterization parameters are very small, the span of the smoke exhaust duct between these two stress observation points... The high-span area is the main weak point in the current hoisting scheme. The smoke exhaust duct may experience the greatest downward deflection in this area due to its own weight and bending moment. It is necessary to add support to this area, which can divide the original large span into two smaller spans, thereby improving rigidity and stability. Adding a support point at the midpoint of the line connecting the two ends of the stress observation points in the characteristic area of ​​the duct is the most effective way to reduce deflection and optimize the internal force distribution. In addition, the installation parameters of the smoke exhaust duct can be adaptively adjusted according to the synchronous vibration analysis of each stress observation point, thereby improving the process accuracy and installation efficiency of the smoke exhaust duct.

[0114] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An installation process for smoke extraction ducts based on multi-dimensional data monitoring, characterized in that, include: The smoke exhaust ducts were hoisted at the designated locations according to the drawings; The surface of the smoke exhaust duct is divided into several monitoring areas. Within a preset monitoring period, surface images of each monitoring area under the same illumination angle are obtained. Based on the surface images, several grayscale parameters of each monitoring area during the hoisting process are obtained to determine the grayscale fluctuation parameters. The process of determining several grayscale fluctuation parameters based on several grayscale parameters of each monitoring area during the hoisting process includes: obtaining the average grayscale value of the monitoring area at several monitoring times within a preset monitoring period, determining the average grayscale value as the grayscale parameter, calculating the absolute value of the difference between grayscale parameters at adjacent monitoring times, determining the absolute value of the difference as the fluctuation factor, and determining the average value of the fluctuation factor as the grayscale fluctuation parameter of the monitoring area. Based on the comparison of the grayscale fluctuation parameters in each monitoring area, it is determined whether the smoke exhaust duct has any abnormal risks. In response to the abnormal risk of the smoke exhaust duct, several vibration parameters of each stress observation point of the smoke exhaust duct during the hoisting process are determined within a preset monitoring period, and vibration fluctuation curves corresponding to each stress observation point are constructed respectively. Synchronous vibration analysis is performed based on the vibration fluctuation curves at each stress observation point to determine the abnormal tendency category of the smoke exhaust duct. Based on the abnormal tendency category, overload characterization parameters are determined based on the vibration fluctuation curves of adjacent stress observation points to mark the duct feature area. Suspension points are added to the duct feature area, or defect risk areas are marked and warnings are issued based on the grayscale fluctuation parameters.

2. The smoke exhaust duct installation process based on multi-dimensional data monitoring according to claim 1, characterized in that, The process of determining whether the smoke exhaust duct has any abnormal risks includes, If the comparison between the grayscale fluctuation parameters of each monitoring area meets the abnormal risk conditions, then the smoke exhaust duct is determined to have an abnormal risk. The abnormal risk condition is that the variance of the grayscale fluctuation parameter in each monitoring area exceeds a preset variance threshold.

3. The smoke exhaust duct installation process based on multi-dimensional data monitoring according to claim 2, characterized in that, The process of constructing the vibration wave curves corresponding to each force observation point includes, Vibration parameters of several stress observation points are acquired at several acquisition times within a preset monitoring period; Establish a rectangular coordinate system with time as the horizontal axis and the vibration parameters as the vertical axis, plot the curve of the vibration parameters changing with time, and determine the curve as the vibration fluctuation curve.

4. The smoke exhaust duct installation process based on multi-dimensional data monitoring according to claim 3, characterized in that, The process of performing synchronous vibration analysis to determine the vibration tendency coefficient includes, Obtain the vibration fluctuation curves corresponding to each stress observation point of the smoke exhaust duct; Calculate the average overlap between the vibration fluctuation curves, and determine the average overlap as the vibration tendency coefficient of the smoke exhaust duct.

5. The smoke exhaust duct installation process based on multi-dimensional data monitoring according to claim 4, characterized in that, The process of determining the abnormal tendency category of the smoke exhaust duct includes, If the vibration tendency coefficient of the smoke exhaust duct exceeds the preset vibration tendency coefficient threshold, the abnormal tendency category of the smoke exhaust duct is determined to be the duct abnormal tendency category. If the vibration tendency coefficient of the smoke exhaust duct does not exceed the preset vibration tendency coefficient threshold, then the abnormal tendency category of the smoke exhaust duct is determined to be the hoisting abnormal tendency category.

6. The smoke exhaust duct installation process based on multi-dimensional data monitoring according to claim 5, characterized in that, If the abnormal tendency category is the duct abnormal tendency category, then the defect risk area is marked and an early warning is issued based on the grayscale fluctuation parameter; If the abnormal tendency category is the hoisting abnormal tendency category, then the overload characterization parameters are determined based on the vibration fluctuation curves of adjacent stress observation points to mark the duct characteristic area, and a hoisting point is added at the center of the duct characteristic area.

7. The smoke exhaust duct installation process based on multi-dimensional data monitoring according to claim 6, characterized in that, The process of marking defect risk areas includes, If the grayscale fluctuation parameter of the monitored area exceeds the preset grayscale fluctuation parameter threshold, the monitored area will be marked as a defect risk area and an early warning will be issued.

8. The smoke exhaust duct installation process based on multi-dimensional data monitoring according to claim 7, characterized in that, The process of determining overload characterization parameters based on vibration wave curves at adjacent stress observation points includes: Obtain the vibration wave curves of adjacent stress observation points, determine the average amplitude of each vibration wave curve, and use the average amplitude as the load characterization parameter; The absolute value of the difference between the load characterization parameters of adjacent stress observation points is determined as the overload characterization parameter of the adjacent stress observation points.

9. The smoke exhaust duct installation process based on multi-dimensional data monitoring according to claim 8, characterized in that, The process of marking characteristic areas of air ducts includes, If the overload characterization parameters and the load characterization parameters of adjacent stress observation points meet the duct characteristic conditions, then the area between the adjacent stress observation points is marked as the duct characteristic area, and a suspension point is added at the center of the duct characteristic area. The duct characteristic condition is that the overload characterization parameter does not exceed the preset overload characterization parameter threshold, and the load characterization parameters of the adjacent stress observation points all exceed the preset load characterization parameter threshold.

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