A method and system for detecting fire damper construction performance

Abstract

The application discloses a method and system for detecting the structure performance of a flame arrester disc, and the method comprises the following steps: acquiring channel structure information representing the structural features of each channel in the flame arrester disc to be detected; analyzing the channel structure information to construct a channel structure model of the flame arrester disc to be detected; measuring the key feature size data of each channel based on the channel structure model; using a preset multi-grade partition interval threshold range, statistically analyzing the feature size distribution of each channel according to the key feature size data, determining the processing quality of the flame arrester disc to be detected based on the statistical result, and obtaining a corresponding detection result. The method can overcome the defects of the structure performance of the flame arrester disc in the processing state detected by manual detection, and effectively detect and evaluate the structure performance of the flame arrester disc in different use conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fireproof safety, in particular to a method and system for detecting the structure performance of a fire damper. BACKGROUND

[0002] As the last line of defense to prevent the spread of fire, fire damper is widely used in petrochemical plants and other industrial equipment with flammable materials (for example, flammable gas) in recent years. When a fire occurs, a fire damper with reliable performance can effectively prevent the spread of fire, thereby avoiding greater losses. As a key component of the fire damper, the fire damper disc plays an important role in preventing fire and explosion. In the prior art, most fire damper discs have a large number of micro-channels similar to triangles formed by winding flat belts and corrugated belts, and the fireproof function of the fire damper is realized based on the micro-channels on the fire damper disc.

[0003] Currently, the structure performance of the fire damper disc in the processing state is mainly detected by manual detection method. In the manual detection, the quality of the fire damper disc is judged by visual observation and experience. Due to the lack of scientific quantitative data information, the fireproof performance of the fire damper disc cannot be effectively evaluated by manual observation method, and an objective and effective detection result cannot be obtained. In addition, there is a method of measuring the relevant data of the corrugated belt of the fire damper disc by using a caliper at intervals to obtain quantitative data information. This method is not only cumbersome to implement, but also easy to ignore the subtle changes of the relevant data of the corrugated belt of the fire damper disc in the measurement process. Using this method also cannot obtain an effective detection result.

[0004] Therefore, in order to better detect the structure performance of the fire damper disc in different use conditions, the present application needs to provide a scheme for effectively detecting the structure performance of the fire damper disc. SUMMARY

[0005] In order to solve the above problems, the present application provides a method for detecting the structure performance of a fire damper disc, comprising: acquiring channel structure information representing the structural characteristics of each channel in the fire damper disc to be detected; analyzing the channel structure information to construct a channel structure model of the fire damper disc to be detected; measuring the key feature size data of each channel based on the channel structure model; using a pre-set multi-grade partition interval threshold range, statistically analyzing the feature size distribution of each channel according to the key feature size data, and determining the processing quality of the fire damper disc to be detected based on the statistical result, thereby obtaining a corresponding detection result.

[0006] Preferably, one of the following methods is selected to acquire the channel structure information: multi-point random sampling technology; whole linear scanning technology; whole image shooting technology; and partial image shooting splicing technology.

[0007] Preferably, the channel structure model is constructed according to the pixel point spacing in the channel structure information, in combination with the geometric characteristics of the channel structure, to reproduce the channel structure.

[0008] Preferably, the measured size data of each channel in the channel structure information is corrected by using the standard size information to obtain a correction result representing the corresponding relationship between the measured size data and the measurement value, so as to correct the measurement value in the channel structure model by using the correction result.

[0009] Preferably, in the process of generating the multi-grade partition interval threshold range, the following steps are included: calculating the upper limit value of the feature size or the design threshold value corresponding to the channel structure based on the fire resistance grade corresponding to the to-be-detected fire resistance plate, and determining a plurality of interval threshold ranges for grading and counting the key feature size data of each channel according to the upper limit value of the feature size or the design threshold value.

[0010] Preferably, in the process of determining the processing quality of the to-be-detected fire resistance plate, the following steps are included: judging the effectiveness of the to-be-detected fire resistance plate according to the key feature size data of each channel of the to-be-detected fire resistance plate and the upper limit value of the feature size or the design threshold value corresponding to the channel structure; in the case that the to-be-detected fire resistance plate is effective, the key feature size distribution of the current fire resistance plate is first counted according to the key feature size data of each channel and the plurality of interval threshold ranges, and the structural performance of the current fire resistance plate is analyzed according to the counting result.

[0011] Preferably, the key feature size data of each channel of the to-be-detected fire resistance plate is compared with the upper limit value of the feature size or the design threshold value respectively, and if the key feature size data of each channel is less than the upper limit value of the feature size or the design threshold value, the current fire resistance plate is determined to be effective; otherwise, the current fire resistance plate is ineffective.

[0012] Preferably, the interval threshold range belonging to the first type of processing error range is determined, and the processing error of the to-be-detected fire resistance plate is calculated according to the feature size distribution statistical result of each channel; the structural uniformity state of the to-be-detected fire resistance plate is determined by using the preset acceptable error threshold value according to the current processing error.

[0013] In another aspect, the present application also provides a system for detecting the structural performance of a flame arrestor, comprising the following modules: a data measurement module for obtaining channel structure information representing the structural features of each channel in a flame arrestor to be detected; a model construction module for analyzing the channel structure information and constructing a channel structure model of the flame arrestor to be detected; a data measurement and calculation module for measuring the key feature size data of each channel based on the channel structure model; and a data statistical analysis module for statistically analyzing the feature size distribution of each channel according to the key feature size data using a preset multi-grade partition interval threshold range, determining the processing quality of the flame arrestor to be detected based on the statistical result, and thus obtaining a corresponding detection result.

[0014] Preferably, the system further comprises a data calibration module for correcting the measured size data of each channel in the channel structure information using standard size information to obtain a correction result representing the corresponding relationship between the measured size data and the measurement value, so as to correct the measurement value in the channel structure model using the correction result.

[0015] Compared with the prior art, one or more embodiments of the above solution can have the following advantages or beneficial effects:

[0016] The present application proposes a method for detecting the structural performance of a flame arrestor, which automatically obtains the channel structure information of a flame arrestor in different use conditions based on machine vision technology, determines the distribution of the key feature size of each channel of the flame arrestor by constructing a corresponding equivalent mathematical model, and detects the structural performance of the flame arrestor accordingly. This method has an important guiding role for product control during the processing of the flame arrestor, performance evaluation before the installation of the flame arrestor, and performance evaluation of the used flame arrestor, etc.

[0017] Other features and advantages of the present application will be described in the following description, and some will become apparent from the description, or will be learned from the practice of the present application. The objects and other advantages of the present application will be realized and achieved by the structure particularly pointed out in the specification, claims, and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, which together with the application, serve to explain the present application, and do not constitute a limitation of the present application. In the drawings:

[0019] Figure 1 is a step diagram of the first example of the method for detecting the structural performance of a flame arrestor according to the embodiments of the present application.

[0020] Figure 2is a step diagram of a second example of a method for detecting the structural performance of a flame arrestor according to an embodiment of the present application.

[0021] Figure 3 is a module block diagram of a first example of a system for detecting the structural performance of a flame arrestor according to an embodiment of the present application.

[0022] Figure 4 is a module block diagram of a second example of a system for detecting the structural performance of a flame arrestor according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] The embodiments of the present application will be described in detail hereinafter with reference to the drawings and embodiments, by which the technical means applied by the present application to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, each embodiment in the present application and each feature in each embodiment can be combined with each other, and the technical solutions formed thereby are within the protection scope of the present application.

[0024] In addition, the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0025] The flame arrester is the last line of defense against the spread of fire in an accident, and in recent years, it has been widely used in industrial equipment such as petrochemical device pipelines and other equipment containing flammable materials (for example, flammable gas). When a fire condition occurs, a flame arrester with reliable performance can effectively block the fire, thereby avoiding greater losses. The flame arrestor disc, as a key component of the flame arrester, plays an important role in preventing fire and explosion, and detonation flame. In the prior art, most flame arrestor discs have countless micro-channels similar to triangles formed by winding flat belts and corrugated belts, and the flame arrester's fire blocking function is based on these micro-channels on the flame arrestor disc.

[0026] Currently, the structural performance of the flame arrestor disc in the processing state is mainly detected by manual detection method. Among them, manual detection mainly relies on visual observation and experience to judge the quality of the flame arrestor disc. Due to the lack of scientific quantitative data information, the flame arrestor disc's fire blocking performance is evaluated by manual observation method, and an objective and effective detection result cannot be obtained. In addition, there is also a method of measuring the relevant data of the flame arrestor disc corrugated belt every certain period of time to obtain quantitative data information, which is not only cumbersome to implement, but also easy to ignore the subtle changes of the flame arrestor disc corrugated belt related data in the measurement process. Using this method also cannot obtain effective detection results.

[0027] Therefore, in order to solve the above problems, the embodiment of the present application proposes a method and system for detecting the structural performance of a flame arrestor, which automatically acquires the channel structure information of the flame arrestor in different use conditions by using machine vision technology, determines the distribution of the key characteristic dimensions of each channel of the flame arrestor by constructing a corresponding equivalent mathematical model, and thus effectively detects and evaluates the structural performance of the flame arrestor in different use conditions.

[0028] Example 1

[0029] Figure 1 is a step diagram of the first example of the method for detecting the structural performance of a flame arrestor according to the embodiment of the present application. The following will describe each step of the method with reference to Figure 1 .

[0030] As shown in Figure 1 , in step S110, the channel structure information representing the structural characteristics of each channel in the flame arrestor to be detected is acquired. In the embodiment of the present application, the image processing technology is used to acquire the channel structure information representing the structural characteristics of each channel in the flame arrestor to be detected.

[0031] Further, one of the multi-point random sampling technology, the overall linear scanning technology, the overall image shooting technology and the partial image shooting splicing technology in the image processing technology is selected to acquire the channel structure information of the flame arrestor to be detected. Specifically, based on one of the above four types of image processing technologies, the corresponding image acquisition device (for example, an electronic microscope camera, a high-definition camera, a laser linear camera) is used to acquire image data containing the channel structure characteristic information of the flame arrestor to be detected, which needs to accurately reflect the distribution of the channel structure of the flame arrestor to be detected, that is, the channel structure of the flame arrestor to be detected can be reproduced by using the image data. Therefore, in the image data of the flame arrestor to be detected, at least the pixel point spacing data and the geometric characteristic data of the channel structure should be included. In the embodiment of the present application, based on the machine vision technology, one of the above four types of image processing technologies is used to automatically acquire the channel structure information representing the structural characteristics of each channel in the flame arrestor to be detected, which can effectively avoid the errors caused by manual measurement and the errors caused in the subsequent data processing process, and realize efficient and accurate acquisition of the channel structure information of the flame arrestor to be detected.

[0032] For the convenience of illustration, in the embodiments of the present application, the fire damper to be detected is classified into two types, i.e. newly processed fire damper and in-service fire damper, according to the use condition. The fire damper which has not been used after being processed is classified as the newly processed fire damper, and the fire damper which has been used is classified as the in-service fire damper. In actual application, for the fire damper to be detected with a relatively dirty surface, the fire damper should be cleaned before the structure information of each channel is acquired, so as to ensure that the channel structure information representing the structure characteristics of each channel in the current fire damper to be detected can be accurately acquired.

[0033] In an embodiment of the present application, preferably, the channel structure information representing the structure characteristics of each channel in the current fire damper to be detected is acquired by means of random sampling. The number of channels of the fire damper is different for different design parameters (for example, diameter, design value of channel characteristic size and fire damper grade). Therefore, the total number of channels of the current fire damper to be detected should be estimated before random sampling, and the number of measurement points to be selected is determined according to the estimated total number of channels of the current fire damper to be detected. The method for estimating the total number of channels of the fire damper is shown in Table 1.

[0034] Table 1: Estimation table of total number of channels of fire damper

[0035]

[0036] Wherein, H represents the design threshold value of channel characteristic size of the fire damper, and D represents the diameter of the fire damper.

[0037] According to Table 1, the total number of channels of the current fire damper to be detected is calculated in combination with the design threshold value of channel characteristic size of the current fire damper to be detected. In the embodiments of the present application, the positions of all channels of the current fire damper to be detected are determined, and the fire damper is evenly divided into multiple parts according to the distribution of channel structure. Then, one measurement point is randomly selected in each part to obtain a preferred first channel sample set. The structure characteristics of each measurement point in the first channel sample set are used to represent the structure characteristics of each channel in the current fire damper to be detected, and the corresponding channel structure information is acquired. In addition, the fire damper can also be evenly divided into multiple parts according to the distribution of channel structure according to the positions of all channels of the current fire damper to be detected. Then, one measurement point is randomly selected on each of the two sides having channel structure in each part to obtain a preferred second channel sample set. The structure characteristics of each measurement point in the second channel sample set are used to represent the structure characteristics of each channel in the current fire damper to be detected, and the corresponding channel structure information is acquired. In order to ensure that the randomly selected samples are representative, the number of samples in the preferred channel sample set should be not less than one ten-thousandth of the total number of channels of the fire damper to be detected, and not more than one thousandth of the total number of channels. It should be noted that the preferred manner of the channel sample set and the determination of the number of corresponding subsets are not limited in the present application, and can be designed according to actual needs by those skilled in the art.

[0038] Further, in step S120, the channel structure information is analyzed to construct a channel structure model of the fire damper to be detected. Specifically, step S120 analyzes the channel structure information of the fire damper to be detected in step S110, and constructs a machine-recognizable channel structure model (e.g., a geometric model) of the current fire damper to be detected according to the analysis result.

[0039] Specifically, according to the pixel point spacing in the channel structure information, combined with the geometric characteristics of the channel structure, the channel structure model is constructed to reproduce the channel structure. First, the pixel point spacing data of each channel is extracted from the channel structure information of the fire damper to be detected in step S110, and the characteristic size measurement value of each channel of the current fire damper to be detected is determined according to the pixel point spacing data. Then, according to the known polygon closest to the channel structure of the current fire damper to be detected, the geometric shape of the channel structure of the current fire damper to be detected is determined. Next, based on the characteristic size measurement value of each channel of the current fire damper to be detected, combined with the geometric shape of the channel structure of the current fire damper to be detected, the channel structure model of the current fire damper to be detected is constructed, thereby constructing the channel structure of the current fire damper to be detected, and the measurement value of the structural characteristic size of each channel is marked in the current channel structure model.

[0040] In this way, step S120 not only obtains the structural characteristic size measurement value of each channel of the current fire damper to be detected, but also constructs the channel structure model. Among them, the channel structure characteristic size measurement value of the current fire damper to be detected is obtained according to the pixel point spacing data of each channel, by calculating the actual distance of the fire damper channel corresponding to the pixel point spacing in the channel structure image of the fire damper to be detected in step S110, and then calculating the structural size data of each channel of the fire damper based on the collected image, which is recorded as the structural characteristic size measurement value of the corresponding channel.

[0041] Further, in step S130, key feature size data of each channel is measured based on the channel structure model. In the embodiment of the present application, the key feature size data of each channel of the current fire damper to be detected is measured based on the channel structure model of the current fire damper to be detected in step S120. The channel structure model of the current fire damper to be detected in step S120 is constructed according to the known polygon closest to the channel structure of the current fire damper to be detected, so that each channel structure in the channel structure model can be analyzed according to the geometric properties of the corresponding known polygon. Meanwhile, in the process of constructing the channel structure model of the current fire damper to be detected, a large amount of channel structure feature size data is often used to construct the channel structure model so that the constructed channel structure model can accurately reflect the channel structure information of the current fire damper to be detected. Therefore, when the channel structure model is used to evaluate the structural performance of the current fire damper to be detected, the number of channel structure feature size data involved in the detection should be appropriately simplified. Therefore, in the embodiment of the present application, the current fire damper to be detected is analyzed by using the key feature size data, thereby reducing the complexity of the detection process. Specifically, the key feature size data is determined according to the geometric properties of the corresponding known polygon of the channel structure of the current fire damper to be detected. The key feature size data is used as the main representative index of the size feature of the corresponding channel.

[0042] The measurement method of the key feature size data of each channel of the fire damper to be detected is described below. When the channel structure of the fire damper to be detected is approximately triangular, a triangular channel structure model is constructed, and the height of the current triangular channel is determined as the key feature size of the current channel. When the channel structure of the fire damper to be detected is approximately trapezoidal, a trapezoidal channel structure model is constructed, and the height of the current trapezoidal channel is determined as the key feature size of the current channel. When the channel structure of the fire damper to be detected is approximately rhombic, a rhombic channel structure model is constructed, and the short diagonal of the current triangular channel is determined as the key feature size of the current channel.

[0043] Further, in step S140, the key feature size data of each channel in step S130 is statistically analyzed according to the preset multi-grade partition interval threshold range, and the processing quality of the current fire damper to be detected is determined according to the statistical results of the feature size distribution of each channel, thereby obtaining the corresponding detection result. Specifically, according to the upper limit value or the design threshold value of the feature size corresponding to the channel structure of the current fire damper to be detected, a multi-grade partition interval threshold range is divided. In each grade interval threshold range, the distribution of the key feature size data in step S130 in each interval is counted and analyzed. Finally, the processing quality of the current fire damper to be detected is determined by using the current statistical analysis result, and the corresponding detection result is obtained.

[0044] The channel of the flame arrestor needs to meet certain characteristic size requirements to achieve the flame arrest function. In addition, due to the severity of the fire conditions, in actual application, different characteristic size upper limit values or design threshold values of the channel are generally designed for the flame arrestor to make the flame arrestor have different flame arrest capabilities. The flame arrest grade is used to calibrate the corresponding flame arrest capability of the flame arrestor. It should be noted that the ISO16852 international flame arrestor test standard gives the characteristic size upper limit value of the channel of the flame arrestor corresponding to different flame arrest grades, and the corresponding representative gas combustible, as shown in Table 2.

[0045] Table 2 ISO16852 international flame arrestor test standard

[0046]

[0047]

[0048] In the step of utilizing the preset multi-grade partition interval threshold range, the characteristic size upper limit value or design threshold value of the channel of the current flame arrestor is calculated based on the flame arrest grade corresponding to the flame arrestor to be detected, and a plurality of interval threshold ranges for grading and counting the key characteristic size data of each channel are determined according to the characteristic size upper limit value or design threshold value. According to Table 2, the characteristic size upper limit value or design threshold value of the channel of the current flame arrestor to be detected is determined in combination with the flame arrest grade of the current flame arrestor to be detected. The characteristic size upper limit value or design threshold value is taken as the structure performance evaluation target value of the current flame arrestor to be detected, and a plurality of interval threshold ranges are divided according to the structure performance evaluation target value, and the distribution of the key characteristic size data in each interval in step S130 is counted.

[0049] Specifically, the structure performance evaluation target value is set as H, and multiple interval thresholds with increasingly larger target value demarcated threshold range are selected in sequence. And several continuous interval thresholds are selected from the multiple interval thresholds as the error acceptable interval for judging the processing quality. Preferably, the multiple-level division is performed according to the interval threshold ranges of (0, 0.8H), (0.8H, 0.9H), (0.9H, 1.0H), (1.0H, 1.1H), (1.1H, 1.2H), (1.2H, +∞), etc. Among them, the two interval threshold ranges of (0.9H, 1.0H) and (1.0H, 1.1H) are taken as the error acceptable interval of the embodiments of the present application, and the rest are taken as the error unacceptable interval. It should be noted that the upper limit threshold of the feature size corresponding to the channel of the to-be-detected flame arrester is determined according to the flame arrester rating MESG value in the ISO16852 international flame arrester test standard. Next, the effectiveness of the current to-be-detected flame arrester is judged based on the structure performance evaluation target value of the current to-be-detected flame arrester. When the current to-be-detected flame arrester is determined to be in the effective state, the structure performance of the current to-be-detected flame arrester is analyzed according to the corresponding key feature size distribution statistical result. It should be noted that according to the different required statistical result accuracy, the multiple-level division interval threshold range can also be divided according to the interval threshold ranges of (0, 0.95H), (0.95H, 1.05H), (1.05H, +∞), etc. The specific division method is not limited in the present application, and the person skilled in the art can design according to the actual needs.

[0050] Further, in the process of determining the processing quality of the to-be-detected flame arrester, the effectiveness of the to-be-detected flame arrester is judged according to the key feature size data of each channel of the to-be-detected flame arrester, according to the upper limit value or design threshold of the feature size corresponding to the channel structure. Among them, in the case that the to-be-detected flame arrester is effective, the distribution of the key feature size data of the current flame arrester in the multiple-level division interval threshold range is first counted according to the key feature size data of each channel and the multiple-level division interval threshold range, and the structure performance of the current flame arrester is analyzed according to the statistical result. In the embodiments of the present application, the size of the channel structure of the to-be-detected flame arrester needs to meet two determination criteria. One is whether the item, that is, the effectiveness of the to-be-detected flame arrester is judged according to the key feature size data of each channel of the to-be-detected flame arrester, according to the upper limit value or design threshold of the feature size corresponding to the channel structure. Further, in the case that the to-be-detected flame arrester is effective, the judgment item is executed, that is, the key feature size distribution of the current flame arrester is first counted according to the key feature size data of each channel and the multiple-level division interval threshold range, and the structure performance of the current flame arrester is analyzed according to the statistical result.

[0051] Next, the specific judgment method of the effectiveness of the fire damper to be detected is described in detail. In this process, the key feature size data of each channel of the current fire damper to be detected is compared with the corresponding structural performance evaluation target value of the fire damper. If the key feature size data of each channel is less than the structural performance evaluation target value, it is determined that the current fire damper to be detected is effective; otherwise, the current fire damper to be detected is ineffective.

[0052] Further, in the case of determining that the current fire damper to be detected is effective, the structural performance thereof is further analyzed according to the distribution statistical result of the key feature size. Specifically, the number of data corresponding to each interval threshold range when the key feature size data of each channel of the current fire damper to be detected falls within the interval threshold range is counted, so as to obtain the distribution statistical result of the key feature size data of each channel of the current fire damper to be detected, and then the structural performance information of the current fire damper to be detected is analyzed according to the statistical result.

[0053] Further, the determination method of the processing quality of the fire damper to be detected (i.e., analyzing the structural performance of the current fire damper) is described in detail. The interval threshold range belonging to the first type of processing error range is determined, and the processing error of the current fire damper to be detected is calculated according to the feature size distribution statistical result of each channel of the current fire damper, and then the structural uniformity state of the current fire damper to be detected is determined according to the current processing error and the preset acceptable error threshold. Specifically, the number of key feature size data in the (0, 0.8H), (0.8H, 0.9H), (0.9H, 1.0H), (1.0H, 1.1H), (1.1H, 1.2H), (1.2H, +∞) interval threshold range of step S130 is counted, and the proportion of the total number of data points in each interval threshold range is calculated. Then, the proportion data of the interval belonging to the first type of processing error range is selected, and the processing error of the current fire damper to be detected is calculated according to the proportion data (i.e., the sum of the proportion data of the interval belonging to the first type of processing error range is determined as the processing error of the current fire damper to be detected). Then, the structural uniformity state of the current fire damper to be detected is determined according to the processing error and the preset acceptable error threshold. When the processing error belongs to the preset acceptable error threshold range, it is determined that the structural uniformity of the current fire damper to be detected is good and the quality is reliable. Accordingly, the error analysis result representing the processing quality of the current fire damper to be detected is output.

[0054] In the embodiment of the present application, the first type of processing error range is used to represent the reasonable size processing range corresponding to the quality qualified flame arrester to be detected. After the processing error of each channel of the flame arrester is calculated based on the actual processing size, the current processing error needs to be compared with the acceptable error threshold. The acceptable error threshold is used to represent the threshold level of the acceptable degree of the processing error of the current flame arrester to be detected. If the current processing error is below the threshold level, it indicates that the actual size of each channel of the current flame arrester can support the normal use of the flame arrester; if the current processing error exceeds the threshold level, it indicates that the actual size of each channel of the current flame arrester cannot support the normal use of the flame arrester. For example, the processing error of the current flame arrester to be detected is acceptable within the error interval of -10% to +10%, and the key feature size data within the interval ranges of (0.9H, 1.0H) and (1.0H, 1.1H) are reasonable processing data, and the rest of the key feature size data are unreasonable processing data. The proportion of the unreasonable processing data in the total data points represents the unreasonable processing error of the current flame arrester to be detected. When the unreasonable processing error is less than 10%, it is determined that the structure uniformity of the current flame arrester to be detected is good and the quality is reliable. It should be noted that the determination of the first type of processing error range is related to the actual detection accuracy required, and those skilled in the art can determine it according to actual needs.

[0055] Finally, the key feature size data obtained in the detection process of the structure performance of the flame arrester to be detected is used to calculate the processing error corresponding to the current flame arrester to be detected, so as to evaluate the processing quality of the current flame arrester to be detected, and output the processing quality analysis result representing whether the current processing error belongs to the normal acceptable processing level.

[0056] Example 2

[0057] Based on the above embodiment one, in order to ensure the accuracy of the evaluation result of the flame arrester to be detected, the method for detecting the structure performance of the flame arrester provided by the embodiment of the present application further comprises: correcting the measured size data of each channel in the channel structure information by using the standard size information to obtain a correction result representing the corresponding relationship between the measured size data and the measurement value, so as to correct the measurement value (such as the measurement value of the key feature size data) in the current channel structure model by using the correction result. Figure 2 is the step diagram of the second example of the method for detecting the structure performance of the flame arrester according to the embodiment of the present application. The following refers to Figure 2 The second example of the method for detecting the structure performance of the flame arrester according to the embodiment of the present application is described in detail.

[0058] As Figure 2As shown, step S210 obtains channel structure information representing the structural characteristics of each channel in the to-be-detected flame arrestor. Then, step S220 analyzes the channel structure information to construct a channel structure model of the to-be-detected flame arrestor. Then, step S240 measures the key characteristic size data of each channel based on the channel structure model constructed in step S220. Finally, step S250 uses the preset multi-grade partition interval threshold range to statistically analyze the characteristic size distribution of each channel according to the measured value of the key characteristic size data in step S240, and determines the processing quality of the to-be-detected flame arrestor based on the statistical result, thereby obtaining the corresponding detection result.

[0059] It should be noted that in this embodiment, step S210 is similar to the method described in step S110 above, step S230 is similar to the method described in step S120 above, and step S240 is similar to the method described in step S130 above, so the present embodiment will not repeat the description of steps S210, S230 and S240.

[0060] In step S230, the measured size of each channel is corrected based on the channel structure information obtained in step S220. The measured size data of each channel in the channel structure information is corrected using the standard size information to obtain a correction result representing the corresponding relationship between the measured size data and the measured value, so as to measure the key characteristic size data using the correction result. The correction scale in the standard size information is determined using the unit standard size data (unit distance, unit circle). Specifically, based on the measured value of the structural characteristic size of each channel of the current to-be-detected flame arrestor in step S220, the measured size data of each channel in the channel structure information is corrected using the unit standard size data. The measured size of the current to-be-detected flame arrestor channel is compensated and calibrated according to the error value between the unit standard size data and the unit size data corresponding to the measured value in the channel structure information, thereby obtaining the correction result representing the corresponding relationship between the measured size data and the measured value, and compensating and calibrating the key characteristic size based on the correction result. It should be noted that in the present embodiment, in addition to using a single standard scale to correct the measured size data, a plurality of standard scales can also be used to correct the measured size data to improve the accuracy of the correction result and reduce the measurement error. The correction method of the measured size is not specifically limited in the present application, and those skilled in the related art can design it according to the required accuracy of the measurement result.

[0061] Example 3

[0062] Based on the method for detecting the structural performance of the flame arrestor described in the above embodiment one, the present embodiment also provides a system for detecting the structural performance of the flame arrestor (hereinafter referred to as "detection system"). Figure 3is a module block diagram of a first example of a system for detecting the structural performance of a flame arrestor according to an embodiment of the present application.

[0063] As shown in Figure 3 The detection system according to the embodiment of the present application includes a data measurement module 31, a model construction module 32, a data measurement calculation module 33, a data statistical analysis module 34, a data receiving module and a data output module. Specifically, the data measurement module 31 is configured to obtain channel structure information representing the structural features of each channel in the flame arrestor to be detected, according to the method described in step S110 above; the model construction module 32 is configured to analyze the channel structure information obtained by the data measurement module 31 and construct a channel structure model of the flame arrestor to be detected, according to the method described in step S120 above; the data measurement calculation module 33 is configured to measure the key feature size data of each channel based on the channel structure model constructed by the model construction module 32, according to the method described in step S130 above; the data statistical analysis module 34 is configured to statistically analyze the feature size distribution of each channel based on the key feature size data measured by the data measurement calculation module 33, using a plurality of preset multi-level partition interval threshold ranges, determine the processing quality of the flame arrestor to be detected based on the statistical results, and thus obtain the corresponding detection result, according to the method described in step S140 above; the data receiving module is configured to receive the channel structure information representing the structural features of each channel in the flame arrestor to be detected obtained by the data measurement module 31; and the data output module is configured to output the analysis results of the data statistical analysis module 34 on the statistical analysis of the feature size distribution of each channel and the corresponding detection result.

[0064] Example 4

[0065] Based on the method for detecting the structural performance of a flame arrestor described in Embodiment Two above, the present embodiment further provides a system for detecting the structural performance of a flame arrestor (hereinafter referred to as “detection system”). Figure 4 is a module block diagram of a second example of a system for detecting the structural performance of a flame arrestor according to an embodiment of the present application.

[0066] As shown in Figure 4As shown, the detection system in the embodiment of the present application comprises a data measurement module 41, a model construction module 42, a data measurement calculation module 43, a data statistical analysis module 44, a data calibration module 45, a data receiving module and a data output module. Specifically, the data measurement module 41 is configured to obtain channel structure information representing the structural features of each channel in the to-be-detected flame arrester according to the method described in step S210; the model construction module 42 is configured to analyze the channel structure information obtained by the data measurement module 41 and construct a channel structure model of the to-be-detected flame arrester according to the method described in step S220; the data measurement calculation module 43 is configured to measure the key feature size data of each channel based on the channel structure model according to the method described in step S240; the data calibration module 45 is configured to correct the measured size data of each channel in the channel structure information by using the standard size information to obtain a correction result representing the corresponding relationship between the measured size data and the measurement value, correct the measurement value in the channel structure model by using the correction result, and then measure the key feature size data based on the correction result according to the method described in step S230; the data statistical analysis module 44 is configured to statistically analyze the feature size distribution of each channel by using the preset multi-grade partition interval threshold range according to the key feature size data measured by the data measurement calculation module 43, determine the processing quality of the to-be-detected flame arrester based on the statistical result, and thus obtain the corresponding detection result according to the method described in step S250; the data receiving module is configured to receive the channel structure information representing the structural features of each channel in the to-be-detected flame arrester obtained by the data measurement module 41; and the data output module is configured to output the analysis result of the data statistical analysis module 34 in the statistical analysis of the feature size distribution of each channel and output the corresponding detection result.

[0067] In actual application, the flame arrester can be classified into a corrugated plate type flame arrester, a parallel plate type flame arrester and other structural specification flame arrester (for example, a micro-circular channel flame arrester) according to the structural form of the channel; and can be classified into a newly processed flame arrester which is completed after processing and has not been used and an in-service flame arrester which has been used according to the use condition. The present application will be further described in combination with the specific embodiments of the present application for various types of to-be-detected flame arrester.

[0068] Example 5

[0069] In a specific embodiment of the present application, the to-be-detected flame arrester is a newly processed corrugated plate flame arrester, the flame arrester rating MESG value is IIA, the feature size design value of the channel is 0.8 mm, and the diameter size design value is 300 mm.

[0070] Since the current to-be-detected flame arrestor is a corrugated plate flame arrestor, its winding processing technology causes the channel structure of the current to-be-detected flame arrestor to be approximately triangular (the structure of each channel of the current to-be-detected flame arrestor is that the three corners are connected by curves, and the bottom edge is an approximately straight curve), and the triangular channel structure model of the current to-be-detected flame arrestor is constructed by using the measured values of the characteristic dimensions of the channels of the current to-be-detected flame arrestor obtained after correction.

[0071] According to the design value 0.8 mm of the characteristic dimension of the channel of the current to-be-detected flame arrestor, the channel quantity estimation method of the flame arrestor described in Embodiment One of the present application is used to obtain that the total number of channels of the current to-be-detected flame arrestor is about 84375, and then the number of selected measurement points is 9-84. Next, 42 channel samples representing the structural characteristics of each channel of the current to-be-detected flame arrestor are selected from the 9-84 measurement points, and the corresponding pixel point spacing data is extracted, and the measured values of the characteristic dimensions of the 42 channel samples are determined according to the pixel point spacing data. Since the channel structure of the corrugated plate flame arrestor is approximately triangular, the channel structure of the current to-be-detected flame arrestor is determined to be triangular. The channel structure model of the current to-be-detected flame arrestor is constructed by using the measured values of the characteristic dimensions of the 42 selected channel samples in combination with the triangular channel structure of the current to-be-detected flame arrestor, and the measured values of the structural characteristic dimensions of each channel are marked in the current channel structure model.

[0072] Next, the measured dimension data of the 42 channels is corrected by using the 1 mm standard dimension data. In this embodiment, the measured dimension data is 0.95 mm and 0.96 mm, so the compensation calibration result of the measured value of the key characteristic dimension data of the current to-be-detected flame arrestor is recorded as measured dimension data+0.045 mm.

[0073] The channel structure model obtained after size correction is taken as the research object, the height of the triangle is taken as the key characteristic dimension data of the current to-be-detected flame arrestor, and is respectively recorded as H1, H2, H3……H42. According to the method described in Embodiment One, the upper limit value of the characteristic dimension of the channel of the current to-be-detected flame arrestor is 0.94 mm, and the upper limit threshold value of the characteristic dimension is compared with the 42 corrected key characteristic dimension data respectively, and it is obtained that the 42 corrected key characteristic dimension data are all less than 0.94 mm, and the current to-be-detected flame arrestor is determined to be effective. Then, the design value 0.8 mm of the channel structure is taken as the structural performance evaluation target value H of the current to-be-detected flame arrestor, and the statistical results of the distribution of the 42 corrected key characteristic dimension data are obtained, as shown in Table 3.

[0074] Table 3 Statistical Table of Key Characteristic Dimension Data Distribution of Embodiment Five To-be-detected Flame Arrestor

[0075] Interval Number (pcs) Proportion (%) (0,0.64) 1 2.4 (0.64,0.72) 3 7.1 (0.72,0.8) 26 61.9 (0.8,0.88) 12 28.6 (0.88,0.94) 0 0

[0076] In the embodiment of the present application, the two intervals of (0.72, 0.8) and (0.8, 0.88) are reasonable machining error intervals, and the three intervals of (0, 0.64), (0.64, 0.72) and (0.88, 0.94) are unreasonable machining error intervals. It is calculated that the key feature size data in the threshold range of the three unreasonable machining error intervals accounts for 9.5% of the total data amount (i.e. 2.4% + 7.1% + 0%), which belongs to the acceptable error range (-10% ~ +10%), so it is determined that the current flame arrester structure uniformity to be detected is good and the quality is reliable. Further, the processing quality analysis result of the flame arrester to be detected is output, that is, for the new processing corrugated plate flame arrester with the flame arrester grade MESG value of IIA, the feature size design value of the channel is 0.8mm, and the diameter size design value is 300mm, the machining error is 9.5%, which belongs to the acceptable error range (-10% ~ +10%).

[0077] Example 6

[0078] In one specific embodiment of the present application, the current flame arrester to be detected is a new processing corrugated plate flame arrester, the flame arrester grade MESG value is IIA, the feature size design value of the channel is 0.8mm, and the diameter size design value is 300mm.

[0079] In the embodiment of the present application, the whole linear scanning measurement technology is used to obtain the channel structure information representing the structure characteristics of each channel of the current flame arrester to be detected. It should be noted that, since there is a difference between solid and hollow in the channel structure characteristics of the flame arrester, the solid part and hollow part information of the distinguishable channel structure is obtained according to laser scanning, and then the solid edge and hollow area of the channel of the current flame arrester to be detected are determined, so as to realize the acquisition of the channel structure information. In addition, in order to ensure that all channel information of the current flame arrester to be detected can be scanned in the process of acquiring the channel structure, the channel structure information is preferably acquired by fixing the current flame arrester to be detected and moving the laser scanner in a plane. The number of channels of the current flame arrester to be detected obtained by this method is 81206.

[0080] The laser scanner completes a full scan of the passage structure of the current fire damper to be detected according to a predetermined driving track and constructs a projection map of the corresponding fire damper. According to the pixel position, a passage model curve of the fire damper is constructed based on the measured value of the corrected characteristic dimension of the passage of the current fire damper to be detected, and the characteristic dimension value of the passage is defined as the maximum distance between the two curves. Preferably, when the passage model of the fire damper to be detected is a cubic spline curve, the upper curve f(x), the lower curve g(x) and the distance h(x) = f(x)-g(x) between the two curves are defined respectively. Based on the cubic spline curve, the maximum h(x) value in the interval (a, b) is obtained, which is taken as the key characteristic dimension data of the passage of the current fire damper to be detected. Wherein, the interval (a, b) is the value range of the characteristic dimension independent variable of the passage of the current fire damper to be detected.

[0081] Next, the measured dimension data correction process of the passage in the passage structure information of the embodiment of the present application is described in detail. Preferably, the measured dimension data of the passage of the current fire damper to be detected is corrected by using a 1mm standard circle. Specifically, five 1mm standard circles belonging to the current fire damper to be detected are determined by taking the measured value as the standard, and the measurement error is obtained by calculating the difference between the five 1mm standard circles obtained according to the measured value and the actual 1mm standard circle, and the average measurement error is further calculated as-0.37μm. Then, the measured value of the key characteristic dimension data of the current fire damper to be detected is compensated and calibrated, which is recorded as measured dimension data+0.0037mm.

[0082] According to the method of embodiment one, the upper limit value of the characteristic dimension of the passage of the current fire damper to be detected is 0.94mm, which is compared with the key characteristic dimension data after compensation and calibration. In the embodiment of the present application, all the key characteristic dimension data are less than 0.94mm, at this time it is determined that the current fire damper to be detected is effective. The design value of the characteristic dimension of the passage 0.8mm is taken as the structure performance evaluation target value H of the current fire damper to be detected, and the distribution statistical result of the key characteristic dimension data of the current fire damper to be detected is obtained, as shown in Table 4.

[0083] Table 4 Statistical table of distribution of key characteristic dimension data of fire damper to be detected in example six

[0084] Interval Number (pcs) Proportion (%) (0,0.64) 856 1.05 (0.64,0.72) 6095 7.51 (0.72,0.8) 52136 64.2 (0.8,0.88) 22087 27.2 (0.88,0.94) 32 0.04

[0085] Among them, the two intervals of (0.72, 0.8) and (0.8, 0.88) are reasonable processing error intervals, and the three intervals of (0, 0.64), (0.64, 0.72) and (0.88, 0.94) are unreasonable processing error intervals. Through calculation, it is obtained that the key feature size data in the threshold range of the three unreasonable processing error intervals accounts for 8.6% of the total data amount (i.e. 1.05%+7.51%+0.04%), which belongs to the acceptable error range (-10%~+10%), at this time it is determined that the uniformity of the current to-be-detected flame arrester structure is good and the quality is reliable. Further, the processing quality analysis result of the to-be-detected flame arrester is output, that is, for the new processing corrugated plate flame arrester with the flame arrester grade MESG value of IIA, the feature size design value of the channel is 0.8mm, and the diameter size design value is 300mm, the processing error is 8.6%, which belongs to the acceptable error range (-10%~+10%).

[0086] Example 7

[0087] In a specific embodiment of the present application, the current to-be-detected flame arrester is a new processing corrugated plate flame arrester, the flame arrester grade MESG value of which is IIA, the feature size design value of the channel is 0.8mm, and the diameter size design value is 300mm.

[0088] In the embodiment of the present application, the partial image shooting splicing measurement technology or the whole image shooting measurement technology is adopted to obtain the channel structure information representing the structural features of each channel in the current to-be-detected flame arrester. It should be noted that when the diameter of the to-be-detected flame arrester is small, the whole image shooting measurement technology is adopted, that is, the to-be-detected flame arrester is placed on the horizontal plane, and a high-definition camera is fixed above the to-be-detected flame arrester to focus and shoot the to-be-detected flame arrester to obtain the corresponding channel structure information; when the diameter of the to-be-detected flame arrester is large, due to the visual deviation of the camera shooting, the partial image shooting splicing measurement technology is adopted, that is, the surface of the whole to-be-detected flame arrester is divided into multiple parts, and each part is shot, then multiple local photos are spliced to obtain the channel structure information of the whole to-be-detected flame arrester. Since the photo splicing technology in the prior art is mature, the related photo splicing technology and method will not be described here. In addition, the partial image shooting splicing measurement technology of randomly shooting the photos of the surface of the whole to-be-detected flame arrester to obtain the surface information of the channel of the flame arrester can also be adopted. In the embodiment, the acquisition mode of the original spliced photo of the to-be-detected flame arrester and the number of photos are not specifically limited, and a person skilled in the art can select and design according to actual needs.

[0089] Preferably, 16 sets of local photos of the current to-be-detected flame arrester are taken by using a fixed high-definition camera focal length, and the 16 sets of local photos are spliced to obtain the overall channel structure information of the current to-be-detected flame arrester. In the embodiment of the present application, the number of channels of the current to-be-detected flame arrester is 82415. Next, according to the overall channel structure information of the current to-be-detected flame arrester, the measured characteristic size data correction method and the model construction method of the channel of the flame arrester as described in Embodiment Five are used to construct a corresponding triangular channel structure model by using the corrected characteristic size measurement value of the channel of the current to-be-detected flame arrester. Then, taking the channel structure model as the research object, the height of each triangle is taken as the key characteristic size data of the to-be-detected flame arrester.

[0090] In the embodiment, the measured size data of the channel is 1.023 mm, and the compensation calibration of the measurement value of the key characteristic size data of the current to-be-detected flame arrester is recorded as measured size data-0.023 mm.

[0091] According to the method of Embodiment One, the upper limit value of the characteristic size of the channel of the current to-be-detected flame arrester is 0.94 mm, which is compared with the corrected key characteristic size data respectively to obtain an analysis result that all the key characteristic size data are less than 0.94 mm, and at this time it is determined that the current to-be-detected flame arrester is effective. Taking the design value 0.8 mm of the characteristic size of the channel as the structure performance evaluation target value H of the current to-be-detected flame arrester, the distribution statistical result of the key characteristic size data of the current to-be-detected flame arrester is obtained, as shown in Table 5.

[0092] Table 5 Statistical Table of Distribution of Key Characteristic Size Data of Flame Arrester to be Detected in Embodiment Seven

[0093] Interval Number (pcs) Proportion (%) (0,0.64) 923 1.14 (0.64,0.72) 6524 8.03 (0.72,0.8) 52346 64.46 (0.8,0.88) 22566 27.79 (0.88,0.94) 56 0.07

[0094] Among them, the two intervals (0.72, 0.8) and (0.8, 0.88) are reasonable processing error intervals, and the three intervals (0, 0.64), (0.64, 0.72) and (0.88, 0.94) are unreasonable processing error intervals. Through calculation, it is found that the key characteristic size data within the threshold range of the three unreasonable processing error intervals accounts for 9.24% of the total data amount (i.e. 1.14%+8.03%+0.07%), which belongs to the acceptable error range (-10%~+10%), and at this time it is determined that the current to-be-detected flame arrester has good structure uniformity and reliable quality. Further, the processing quality analysis result of the to-be-detected flame arrester is output, that is, for the newly processed corrugated plate flame arrester with flame arrester grade MESG value of IIA, channel characteristic size design value of 0.8 mm and diameter size design value of 300 mm, the processing error is 9.24%, which belongs to the acceptable error range (-10%~+10%).

[0095] Example 8

[0096] In one specific embodiment of the present application, the current fire damper to be detected is a serviceable corrugated fire damper, the fire damper has a fire resistance rating MESG value of IIB3, the channel characteristic size design value is unknown, and the diameter size design value is 150 mm.

[0097] The method according to Embodiment Six is used to obtain the channel structure information of the current fire damper to be detected, and it is found that the number of channels of the current fire damper to be detected is 42178. Then, the method according to Embodiment Six is used to construct the channel structure model curve of the current fire damper to be detected, and the corresponding key characteristic size data is obtained. Finally, the method according to Embodiment Five is used to correct the measured characteristic size data in the channel structure information of the current fire damper to be detected. In this embodiment, the measured characteristic size data is 0.995 mm, and the compensation calibration of the measured value of the current fire damper to be detected is recorded as measured size data + 0.005 mm.

[0098] The method according to Embodiment One is used to obtain the upper limit value of the characteristic size of the channel of the current fire damper to be detected, which is 0.65 mm. The upper limit value of the characteristic size is compared with the key characteristic size data, and it is found that all the key characteristic size data in this embodiment is less than 0.65 mm, and the current fire damper to be detected is determined to be effective. Since the channel characteristic size design value of the current fire damper to be detected is unknown, four interval thresholds of (0, 0.7H0), (0.7H0, 0.8H0), (0.8H0, 0.9H0) and (0.9H0, H0) are used to statistically analyze the distribution of the key characteristic size data in this embodiment, as shown in Table 6.

[0099] Table 6 Statistical Table of Key Characteristic Size Data Distribution of the Fire Damper to be Detected in Embodiment Eight

[0100] Interval Number (pcs) Proportion (%) (0，0.7H0) 1236 2.93 (0.7H0, 0.8H0) 27658 65.57 (0.8H0, 0.9H0) 12496 29.63 (0.9H0, H0) 788 1.87

[0101] Wherein, in two intervals of (0.7H0, 0.8H0) and (0.8H0, 0.9H0), the key feature size data is most concentrated, accounting for 95.2% of all key feature size data (i.e. 65.57% + 29.63%), and the machining error of the current to-be-detected flame arrester is 4.8% (i.e. 2.93% + 1.87%), which belongs to the acceptable error range (-10%~+10%). At the same time, it can be inferred that the channel feature size design value of the current to-be-detected flame arrester is in the range of 0.8H0±0.1H0. At this time, it is determined that the current to-be-detected flame arrester structure is better and the quality is reliable. Further, the machining quality analysis result of the to-be-detected flame arrester is output, that is, for the in-service corrugated plate flame arrester with flame arrester grade MESG value of IIB3, channel feature size design value unknown, and diameter size design value of 150mm, the machining error is 4.8%, which belongs to the acceptable error range (-10%~+10%).

[0102] Example 9

[0103] In one specific embodiment of the present application, the current to-be-detected flame arrester is a newly machined parallel plate flame arrester, which is a flame arrester with flame arrester grade MESG value of IIA, parallel plate interval feature size design value of 1.2mm, and machining precision of ±5%.

[0104] According to the method described in Embodiment Six, the channel structure information of the current to-be-detected flame arrester is obtained, and the number of channels of the current to-be-detected flame arrester is 135. Then, according to the method described in Embodiment Six, the channel structure model curve of the current to-be-detected flame arrester is constructed, and the corresponding key feature size data is obtained. Finally, according to the method described in Embodiment Five, the measured feature size data in the channel structure information of the current to-be-detected flame arrester is corrected. In the present application embodiment, the measured feature size data is 1.0042mm, and the compensation calibration of the measured value of the key feature size data of the current to-be-detected flame arrester is recorded as measured feature size data-0.0042mm.

[0105] According to the method described in Embodiment One, the upper limit value of the feature size of the channel of the current to-be-detected flame arrester is 0.65mm, and the comparison between the feature size design value as the structure performance evaluation target value of the current to-be-detected flame arrester and the above-mentioned feature size upper limit value can be known. In the present application embodiment, the feature size design value of the current to-be-detected flame arrester is greater than the corresponding feature size upper limit value, and it cannot be evaluated according to the feature size design value, so in the present application embodiment, the machining precision control of the current to-be-detected flame arrester is mainly evaluated. Taking the design value 1.2mm of the current to-be-detected flame arrester as the intermediate value, the key feature size data is divided into (0, 0.95H), (0.95H, 1.05H) and (1.05H, +∞) three intervals according to the precision data for statistical analysis, as shown in Table 7.

[0106] Table 7 Example nine to be detected fire damper key feature size data distribution situation statistics table

[0107] Interval Number (pcs) Proportion (%) (0,1.14) 7 5.19％ (1.14,1.26) 128 94.81％ (1.26,﹢∞) 0 0

[0108] Wherein, (0.95H, 1.05H) is an acceptable interval, (0, 0.95H) and (1.05H, +∞) are unacceptable intervals. In the acceptable interval (1.14, 1.26), the key feature size data accounts for 94.81% of the total data, and there is no key feature size data exceeding 1.05H in the embodiment of the application. At this time, it is determined that the uniformity of the current fire damper to be detected is good, and the quality is reliable. Further, the processing quality analysis result of the fire damper to be detected is output, that is, for the new processing parallel plate fire damper with a fire resistance rating MESG value of IIA, a channel feature size design value of 1.2mm, and a processing precision of ±5%, the processing error is 5.19%, which belongs to the error acceptable range (-10%~+10%).

[0109] The application provides a method and system for detecting the structure performance of a fire damper, which are based on machine vision technology, automatically acquire the channel structure information of the fire damper under different use conditions and construct the equivalent mathematical model of the corresponding fire damper structure, solve the problem that the structure performance of the fire damper cannot be accurately and effectively detected and evaluated, and provide a basis for effectively evaluating the fire resistance performance and flow performance of the fire damper. Meanwhile, the structure of the fire damper is detected and evaluated in a non-destructive manner, thereby providing a basis for evaluating the fire resistance performance and service life of the fire damper in service.

[0110] The above description is only a specific implementation case of the application, and the protection scope of the application is not limited thereto. Any modification or replacement of the application within the technical specifications of the application by those skilled in the art shall be within the protection scope of the application.

[0111] Of course, the application can have other various embodiments, and those skilled in the art can make various corresponding changes and modifications according to the application without departing from the spirit and essence of the application, and these corresponding changes and modifications shall be within the protection scope of the claims of the application.

[0112] Those skilled in the art should understand that the modules or steps of the present application described above can be realized by general computing devices, which can be centralized on a single computing device or distributed on a network composed of multiple computing devices, and optionally, they can be realized by program codes executable by computing devices, so that they can be stored in storage devices and executed by computing devices, or they can be respectively manufactured into individual integrated circuit modules, or multiple modules or steps among them can be manufactured into a single integrated circuit module to realize. Thus, the present application is not limited to any specific combination of hardware and software.

[0113] Although the embodiments of the present application are disclosed as above, the above description is only for the purpose of facilitating understanding of the present application, and is not intended to limit the present application. Any modification and change in the form and details of the embodiments of the present application can be made by those skilled in the art without departing from the spirit and scope of the present application, and the patent protection scope of the present application shall be subject to the scope defined by the appended claims.

Claims

1. A method for testing the structural performance of a flame arrester disc, comprising: Obtain channel structure information that characterizes the structural features of each channel in the flame arrestor panel to be tested; The channel structure information is analyzed, and the pixel spacing data of each channel is extracted to determine the characteristic size measurement value of each channel of the current flame arrestor plate to be tested. Then, based on the known polygon that is closest to the channel structure of the current flame arrestor plate to be tested, the geometric shape of the channel structure of the current flame arrestor plate to be tested is determined, thereby constructing the channel structure model of the flame arrestor plate to be tested. Based on the channel structure model, the key feature dimensions of each channel are measured according to the geometric properties of the known polygon corresponding to the channel structure of the current flame arrestor plate to be tested. By utilizing a preset multi-level division interval threshold range, statistical analysis is performed on the characteristic size distribution of each channel based on the key feature size data. The processing quality of the flame arrestor plate to be tested is determined based on the statistical results, thereby obtaining the corresponding test results. The interval threshold range includes calculating the upper limit value or design threshold value of the characteristic size corresponding to the channel structure based on the flame arrestor plate to be tested, and determining multiple interval threshold ranges for classifying and statistically analyzing the key feature size data of each channel based on the upper limit value or design threshold value of the characteristic size.

2. The method according to claim 1, characterized in that, Choose any one of the following methods to obtain the channel structure information: Multi-point random sampling technique; Overall linear scanning technology; Overall image capture technology; Partial image stitching technology.

3. The method according to claim 1 or 2, characterized in that, include: Based on the pixel spacing in the channel structure information and combined with the geometric features of the channel structure, the channel structure model is constructed to reproduce the channel structure.

4. The method according to claim 3, characterized in that, The method further includes: The measured dimensions of each channel in the channel structure information are corrected using standard dimension information to obtain a correction result that characterizes the correspondence between the measured dimensions and the measured values. The correction result is then used to correct the measured values ​​in the channel structure model.

5. The method according to claim 4, characterized in that, The process of determining the processing quality of the flame arrestor plate to be tested includes: Based on the key feature dimension data of each channel of the fire arrester plate to be tested, the effectiveness of the fire arrester plate to be tested is judged according to the upper limit value or design threshold of the feature dimension corresponding to the channel structure. If the fire arrestor panel to be tested is effective, the distribution of key feature dimensions of the current fire arrestor panel is first statistically analyzed based on the key feature dimension data of each channel and the threshold range of the multiple intervals, and the structural performance of the current fire arrestor panel is analyzed based on the statistical results.

6. The method according to claim 5, characterized in that, include: The key feature dimension data of each channel of the fire arrester to be tested are compared with the upper limit value of the feature dimension or the design threshold. If the key feature dimension data of each channel is less than the upper limit value of the feature dimension or the design threshold, the current fire arrester is determined to be valid; otherwise, the current fire arrester is invalid.

7. The method according to claim 5 or 6, characterized in that, include: Determine the threshold range of the first type of processing error range, and calculate the processing error of the flame arrestor plate to be tested based on the statistical results of the characteristic size distribution of each channel; Based on the current processing error, the structural uniformity of the flame arrestor plate to be tested is determined using a preset acceptable error threshold.

8. A system for testing the structural performance of a flame arrester disc, the system comprising the following modules: The data measurement module is used to acquire channel structure information that characterizes the structural features of each channel in the flame arrestor panel under test; The model building module is used to analyze the channel structure information, extract the pixel spacing data of each channel to determine the characteristic size measurement value of each channel of the current fire arrestor plate to be detected, and then determine the geometric shape of the channel structure of the current fire arrestor plate to be detected based on the known polygon that is closest to the channel structure of the current fire arrestor plate to be detected, thereby constructing the channel structure model of the fire arrestor plate to be detected. The data measurement and calculation module is used to measure the key feature dimensions of each channel based on the channel structure model and according to the geometric properties of the known polygon corresponding to the channel structure of the current flame arrestor plate to be tested. The data statistical analysis module is used to perform statistical analysis on the characteristic size distribution of each channel based on the key characteristic size data, using a preset multi-level division interval threshold range. Based on the statistical results, it determines the processing quality of the flame arrestor plate to be tested, thereby obtaining the corresponding test results. The interval threshold range includes calculating the upper limit value or design threshold value of the characteristic size corresponding to the channel structure based on the fire resistance rating corresponding to the fire-resistant plate to be detected, and determining multiple interval threshold ranges for classifying and statistically analyzing the key characteristic size data of each channel according to the upper limit value or design threshold value of the characteristic size.

9. The system according to claim 8, characterized in that, The system also includes: The data calibration module is used to correct the measured size data of each channel in the channel structure information using standard size information, and to obtain a correction result that characterizes the correspondence between the measured size data and the measured value, so as to use the correction result to correct the measured value in the channel structure model.

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