Thermal insulation material quality detection method and device, electronic equipment and storage medium

Abstract

The invention provides a thermal insulation material quality detection method and device, electronic equipment and a storage medium, and belongs to the technical field of material detection, and the method comprises the following steps: constructing a three-dimensional structure image based on X-ray scanning data of a thermal insulation material; and determining an initial pore set based on the voxel set meeting a first condition, wherein the first condition is that image gray value distribution data in the three-dimensional structure image is smaller than or equal to a first image gray threshold. And dividing the initial pore set based on a clustering algorithm to obtain a plurality of pore regions, each pore region including a plurality of pore elements with the pore spacing less than or equal to a first spacing, and the region spacing of different pore regions being greater than the first spacing. Calculating porosity and pore distribution data of the thermal insulation material based on the plurality of pore areas, and determining a quality analysis result of the thermal insulation material based on the porosity and the pore distribution data. The accuracy of quality detection of the thermal insulation material can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of material detection, and more specifically, relates to a method and device for detecting the quality of thermal insulation materials, electronic equipment, and storage media. Background Art

[0002] In many fields, including construction and industry, the quality of insulation materials is crucial for energy conservation, environmental control, and maintaining the performance of equipment and buildings. As global attention to energy conservation and emission reduction continues to increase, the demand for high-efficiency insulation materials continues to grow.

[0003] However, existing insulation material quality testing methods have numerous limitations. Traditional testing techniques only examine surface features such as the material's appearance and dimensions, making it difficult to delve into the material's internal microstructure. This makes them less adaptable to complex insulation materials. For example, rigid foam insulation materials have complex and diverse internal pore elements. Traditional density testing methods may not accurately reflect the material's internal microstructure, resulting in low accuracy in insulation material quality testing and analysis. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a method and device for detecting the quality of thermal insulation materials, electronic equipment, and storage media to improve the accuracy of thermal insulation material quality detection.

[0005] A first aspect of the embodiments of the present disclosure provides a method for detecting the quality of thermal insulation materials, comprising: Construct a three-dimensional structural image based on X-ray scanning data of thermal insulation materials; determining an initial pore set based on a voxel set that satisfies a first condition, wherein the first condition is that image grayscale value distribution data in the three-dimensional structural image is less than or equal to a first image grayscale threshold; dividing the initial pore set based on a clustering algorithm to obtain a plurality of pore regions, each pore region containing a plurality of pore elements having a pore spacing less than or equal to a first spacing, and a region spacing between different pore regions being greater than the first spacing; Porosity and pore distribution data of the thermal insulation material are calculated based on the plurality of pore regions, and a quality analysis result of the thermal insulation material is determined based on the porosity and pore distribution data.

[0006] A second aspect of the embodiments of the present disclosure provides a thermal insulation material quality detection device, comprising: a three-dimensional construction module, configured to determine an initial pore set based on a voxel set that satisfies a first condition, wherein the first condition is that image grayscale value distribution data in the three-dimensional structural image is less than or equal to a first image grayscale threshold; divide the initial pore set based on a clustering algorithm to obtain a plurality of pore regions, each pore region containing a plurality of pore elements having a pore spacing less than or equal to a first spacing, and a region spacing between different pore regions being greater than the first spacing; A quality analysis module is used to calculate the porosity and pore distribution data of the thermal insulation material based on the multiple pore areas, and determine the quality analysis result of the thermal insulation material based on the porosity and pore distribution data.

[0007] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned thermal insulation material quality detection method when executing the computer program.

[0008] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned thermal insulation material quality detection method are implemented.

[0009] The beneficial effects of the insulation material quality detection method and device, electronic device, and storage medium provided by the embodiments of the present disclosure are: by constructing a three-dimensional structural image through X-ray scanning, it is possible to deeply explore the microstructure inside the insulation material, accurately reflect the complex conditions of the pores inside the material, and improve the accuracy of quality detection. At the same time, this method can also realize non-destructive testing.

[0010] Using a clustering algorithm to segment pores into multiple pore regions allows for targeted analysis of pore distribution data, providing more detailed data for evaluating insulation material performance. Separately calculating porosity and pore distribution data allows for more precise analysis and assessment of insulation material quality, improving the comprehensiveness of quality analysis and the accuracy of analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0012] Figure 1 A schematic flow chart of a method for detecting the quality of thermal insulation materials provided in one embodiment of the present disclosure; Figure 2A structural block diagram of a thermal insulation material quality detection device provided in one embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present disclosure with unnecessary detail.

[0014] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0015] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for detecting the quality of thermal insulation materials provided in one embodiment of the present disclosure. The method may include S101 to S103.

[0016] S101: Constructing a three-dimensional structural image based on X-ray scanning data of the thermal insulation material.

[0017] In this embodiment, the thermal insulation material may include sliced ​​rigid foam insulation material or other thermal insulation materials. X-ray scanning data refers to data generated by converting signals received by a detector after X-rays penetrate the thermal insulation material. A three-dimensional structural image refers to a stereoscopic image that intuitively displays the internal structure of the thermal insulation material.

[0018] For example, when conducting quality inspections on rigid foam insulation boards, sample slices from different batches of insulation boards are placed in an X-ray scanning device, which emits X-rays from multiple angles to penetrate the board. Because the solid portion and internal pores of the rigid foam board absorb and scatter X-rays to different degrees, the detector can capture the differential signals and convert them into two-dimensional projection images. Based on the two-dimensional projection images at different angles, a three-dimensional structural image of the internal structure of the insulation board can be constructed. By analyzing the three-dimensional structural image, the distribution of pores within the insulation board can be obtained, and the quality of the insulation material can be comprehensively analyzed.

[0019] S102: Determine an initial pore set based on a voxel set that meets a first condition, where the first condition is that image grayscale value distribution data in the three-dimensional structural image is less than or equal to a first image grayscale threshold; divide the initial pore set based on a clustering algorithm to obtain multiple pore regions, each pore region containing multiple pore elements with pore spacing less than or equal to a first spacing, and the regional spacing between different pore regions is greater than the first spacing.

[0020] In this embodiment, determining the initial pore set based on the voxel set that meets the first condition includes: A plurality of non-adjacent initial voxels are randomly selected from a voxel set of which the image grayscale value distribution data of the three-dimensional structure image is less than or equal to the first image grayscale threshold.

[0021] Starting from the initial voxel, the adjacent voxels are searched based on the breadth-first algorithm until no adjacent voxels are found. All the adjacent voxels found are regarded as a pore element.

[0022] After searching all the initial voxels, the remaining voxels in the voxel set that do not constitute pore elements are used as a new voxel set. The above steps are repeated for the new voxel set until all voxels constitute pore elements.

[0023] All pore elements containing voxel numbers greater than or equal to the first voxel number are taken as the initial pore set.

[0024] In this embodiment, image grayscale value distribution data refers to the grayscale value data for each voxel in the 3D structural image. Grayscale values ​​are related to the material's X-ray absorption and range from 0 (black, representing high X-ray absorption, identified as a porous area) to 255 (white, representing low X-ray absorption, identified as a solid material portion). The first image grayscale threshold is the grayscale limit used to distinguish between pores and solid materials. This first image grayscale threshold can be determined based on the insulation material's characteristics and X-ray scanning parameters. For example, for certain insulation materials, extensive experiments have determined that voxels with grayscale values ​​of 120 or below correspond to pore elements.

[0025] The initial voxel refers to a voxel randomly selected from a set of voxels that meet the grayscale threshold condition and used as the starting point for the pore search. The clustering algorithm can group the pore elements in the initial pore set based on characteristics such as the distance between pores. The first spacing can be set according to the microstructural characteristics of the insulation material and the actual application requirements. For example, for a certain type of foam insulation material, the first spacing is set to 10μm to ensure that the pore area with concentrated pore distribution is demarcated. The number of the first voxels serves as the voxel number standard for judging whether a pore element is included in the initial pore set. It can be determined based on the pore characteristics of the material and the analysis accuracy requirements. For example, it is set to 50 voxels. Pore elements greater than or equal to this number are considered valid pores.

[0026] In this embodiment, a clustering algorithm can group pore elements based on their clustering, thereby dividing the insulation material into multiple regions. The clustering algorithm uses a first spacing as a metric to determine the spatial distance relationship between pore elements. When the distance between pore elements (i.e., the pore spacing) is less than or equal to the first spacing, they have a high degree of clustering and are classified into the same pore region. The region spacing can be the minimum distance between two pore regions, that is, the distance between the two pore elements with the smallest distance between them.

[0027] For example, all pore elements in the initial pore set are traversed, and the pore spacing between any two pore elements is calculated. For example, the Euclidean distance between the centroids of two pore elements can be calculated as the pore spacing. Based on the selected density-based spatial clustering of applications with noise (DBSCAN) algorithm, an unprocessed pore element is randomly selected from the initial pore set as the starting point. Based on the first spacing, all adjacent pore elements with a pore spacing less than or equal to the first spacing around the starting point are searched and classified as the same potential pore region. Using these newly added pore elements as new starting points, the region is continuously expanded until no new pore elements that meet the distance conditions are found, completing the preliminary division of a pore region.

[0028] Repeat the above steps for the remaining unprocessed pore elements in the initial pore set until all pore elements have been assigned to their corresponding pore regions. During the partitioning process, the spacing between different pore regions is checked in real time to ensure that the regional spacing between different pore regions is greater than the initial spacing. If the conditions are not met, such as when the distance between two regions is too close, the partitioning results need to be adjusted and the assignment of pore elements at the boundary needs to be re-evaluated to ensure the independence and accuracy of each pore region.

[0029] Exemplary step 1: Collect slice samples of insulation materials of varying types and qualities and perform X-ray scanning to obtain a 3D structural image. Analyze the grayscale distribution of known pores and solid material regions in the 3D structural image, and determine a first image grayscale threshold based on material characteristics such as composition and density.

[0030] Step 2: Traverse all voxels in the three-dimensional structural image whose grayscale values ​​are less than or equal to the grayscale threshold of the first image, randomly select multiple spatially non-adjacent voxels as initial voxels, and record their three-dimensional coordinates.

[0031] Step 3: Starting from an initial voxel, search its neighboring voxels layer by layer using a breadth-first algorithm. Add the neighboring voxels that meet the grayscale threshold to the current set of pore elements. Continue searching until no new neighboring voxels meet the criteria, at which point a pore element is determined. Repeat this process for all initial voxels.

[0032] Step 4: After completing a round of search, check whether there are any remaining voxels in the voxel set that do not constitute pore elements. If so, these remaining voxels are combined into a new voxel set and step 3 is repeated until all voxels constitute pore elements.

[0033] Step 5: Calculate the number of voxels contained in each pore element, and select the pore elements whose voxel number is greater than or equal to the first voxel number. These pore elements together constitute the initial pore set.

[0034] In this embodiment, a breadth-first algorithm is used to search adjacent voxels from the initial voxel to construct pore elements. This layer-by-layer expansion search method can efficiently traverse and connect adjacent pore voxels, avoid unnecessary repeated searches and calculations, and improve computational efficiency. Randomly selecting non-adjacent initial voxels can avoid analysis deviations caused by a fixed starting point, making the results more random and comprehensive. The first number of voxels is set as a standard because it takes into account that pore elements that are too small may be caused by noise or imaging errors and have little effect on the actual performance of the material. By screening out these small elements and retaining pores with practical significance, effective information can be extracted from massive voxel data for material quality assessment. By dividing the initial pore set and classifying the pore areas through a clustering algorithm, it is helpful to more specifically process pore elements in different areas in subsequent analysis, avoid global redundant calculations, and achieve more accurate quality analysis and assessment.

[0035] S103: Calculating porosity and pore distribution data of the thermal insulation material based on the plurality of pore regions, and determining a quality analysis result of the thermal insulation material based on the porosity and pore distribution data.

[0036] In this embodiment, each pore region includes a plurality of densely distributed pore elements. Porosity refers to the proportion of pores in the thermal insulation material and is an important indicator for measuring thermal insulation performance. Pore distribution data may include pore size distribution, shape distribution, and connectivity data. Pore size distribution data may include the number and average size of pores of different sizes in different pore regions; shape distribution data may include the circularity and rectangularity of the pores; connectivity data may include the number of connected pores, the connected porosity, etc. The quality analysis result refers to a comprehensive evaluation of the quality of the thermal insulation material, which may include the thermal insulation performance grade, material stability assessment, etc.

[0037] For example, the quality of thermal insulation materials is closely related to their porosity and pore distribution. Porosity that is too high or too low will affect the performance of the thermal insulation material. Thermal insulation materials with uniform pore distribution and moderate pore size usually have good thermal insulation performance, mechanical properties, etc. The calculated porosity and pore distribution data are compared with the ideal performance indicators of the thermal insulation material. If the porosity is within the ideal range, the pore distribution is uniform, and the pore size meets the requirements, it is considered that the thermal insulation material is of good quality and has good thermal insulation performance and mechanical properties. If the porosity deviates from the ideal value, or the pore distribution is uneven, and there are pores that are too large or too small, it will lead to problems such as decreased thermal insulation performance and insufficient mechanical strength of the thermal insulation material, thereby judging that the quality of the thermal insulation material is defective and it is necessary to further improve the production process or adjust the material formula.

[0038] As can be seen from the above, this embodiment uses a breadth-first algorithm to expand the search layer by layer to construct pore elements from adjacent voxels, avoiding unnecessary repeated searches and calculations, significantly improving the computational efficiency of processing 3D structural images. Randomly selecting non-adjacent initial voxels ensures comprehensiveness and randomness in the analysis, reducing analytical bias.

[0039] This example uses the first voxel count as a criterion to filter out unnecessary small pore elements, accurately extracting effective information for material quality assessment from massive data. This example uses a clustering algorithm to partition the initial pore set and categorize pore regions. This allows subsequent analysis to more specifically process pore elements in different regions, avoiding redundant global calculations and improving analysis accuracy and efficiency.

[0040] This embodiment calculates the porosity, pore distribution and other data of the thermal insulation material based on the porous area. With the help of multi-dimensional indicators such as porosity, pore size and shape distribution, and connectivity, the thermal insulation performance level and stability of the thermal insulation material are accurately evaluated, providing a strong basis for material application and optimization.

[0041] In one embodiment of the present disclosure, determining a quality analysis result of a thermal insulation material based on porosity and pore distribution data includes: Determine the insulation level of the insulation material based on its porosity.

[0042] Determine the quality analysis results of the insulation material based on pore distribution data and insulation grade.

[0043] In this embodiment, the porosity includes regional porosity and global porosity.

[0044] Determine the insulation level of the insulation material based on porosity, including: The insulation mass fraction of the insulation material is calculated based on the region number, regional porosity, and global porosity of multiple porous regions.

[0045] If the thermal insulation quality score is less than the first score threshold, the thermal insulation level of the thermal insulation material is determined to be the first level.

[0046] If the thermal insulation quality score is greater than or equal to the first score threshold, the thermal insulation level of the thermal insulation material is determined to be the second level.

[0047] In this embodiment, the thermal insulation mass fraction of the thermal insulation material is calculated based on the number of regions, regional porosity, and global porosity of the plurality of pore regions, including: The thermal insulation quality score of the insulation material is calculated based on the region number, regional porosity, global porosity and quality evaluation function of multiple porous regions.

[0048] The quality assessment function is:

[0049] Where S represents the thermal insulation mass fraction of the thermal insulation material, n represents the number of pore areas, represents the regional porosity of the i-th pore region, Indicates the global porosity of the insulation material; represents the weighted influencing factor of regional porosity, represents the weighted influence factor of global porosity, Represents the standard deviation of regional porosity.

[0050] In this embodiment, It represents the average value of the regional porosity of all pore regions, reflecting the overall level of porosity in each pore region. It can reflect the influence of the dispersion degree of regional porosity on the thermal insulation quality fraction, and the standard deviation The larger the value, the greater the difference in regional porosity. Dividing by n accounts for the effect of the number of porous regions on the degree of dispersion. A larger number of porous regions indicates a more dispersed pore structure, and this dispersion has a relatively smaller impact on the overall score. Subtracting this term from the regional porosity average means that more dispersed regional porosity contributes less to the thermal insulation quality score. represents the contribution of regional porosity-related factors to the thermal insulation mass fraction, Represents the contribution of global porosity to the thermal insulation mass fraction.

[0051] In this embodiment, the heat preservation level includes a first level and a second level. The heat preservation performance of the first level is higher than that of the second level.

[0052] Determine insulation material quality analysis results based on pore distribution data and insulation grade, including: If the thermal insulation level is the first level, a quality analysis result of the thermal insulation material is determined based on the first weight coefficient and the pore distribution data.

[0053] If the thermal insulation level is the second level, the quality analysis result of the thermal insulation material is determined based on the second weight coefficient and the pore distribution data.

[0054] The first weight coefficient and the second weight coefficient both represent the weight of the influence of the pore distribution data on the mass analysis result. The first weight coefficient is greater than the second weight coefficient.

[0055] In this embodiment, the thermal insulation level refers to the classification of the thermal insulation performance of the thermal insulation material. The first level represents relatively low thermal insulation performance, and the second level represents relatively high thermal insulation performance.

[0056] The thermal insulation quality score is a value calculated by combining the number of porous regions, regional porosity, and global porosity across multiple regions. It is used to evaluate thermal insulation performance. The first score threshold is the critical score used to classify thermal insulation levels and can be determined based on experimentation and practical application experience.

[0057] The first weight coefficient and the second weight coefficient respectively reflect the coefficients of the degree of influence of the pore distribution data on the quality analysis results. If the insulation level corresponding to the first weight coefficient is low, the pore distribution has a greater impact on the quality at the low insulation level; if the insulation level corresponding to the second weight coefficient is high, the pore distribution has a relatively small impact on the quality at the high insulation level.

[0058] For example, analyzing pore distribution data includes counting the number of pores of different sizes, measuring the average spacing between pores, and evaluating pore shape. For example, the number of pores in different diameter ranges can be recorded, and the average distance between adjacent pore centers can be measured.

[0059] If the insulation level is level 1, the pore distribution has a significant impact on the quality due to the large first weight coefficient. The pore distribution data is combined with the first weight coefficient and weighted summation is performed to highlight the significant impact of the pore distribution on the quality analysis results, resulting in the quality analysis results at this time.

[0060] If the insulation level is the second level, the calculation is based on the second weight coefficient and the pore distribution data. Because the second weight coefficient is small, the influence of pore distribution on the quality analysis results is relatively small. The quality analysis results are obtained based on the pore distribution data, considering the good insulation performance itself.

[0061] This embodiment achieves an accurate assessment of thermal insulation performance by comprehensively considering porosity and pore distribution data. This embodiment not only distinguishes between regional porosity and global porosity, but also introduces the thermal insulation mass fraction to quantify the thermal insulation level, making the assessment more scientific and objective. At the same time, according to different thermal insulation levels, the influence weight of pore distribution data on the quality analysis results is flexibly adjusted, which not only highlights the importance of pore distribution at low thermal insulation levels, but also considers the influence of other factors at high thermal insulation levels. This embodiment improves the accuracy and reliability of quality analysis and provides strong support for the research and development, production and application of thermal insulation materials.

[0062] In one embodiment of the present disclosure, the porosity includes regional porosity and global porosity.

[0063] Calculates the porosity of insulation materials based on multiple pore regions, including: The regional porosity is calculated based on the volume of each pore region and the volume of all pores in that pore region.

[0064] The global porosity is calculated based on the volume of the pores in all pore regions and the volume of the insulation material.

[0065] In this embodiment, the pore distribution data includes pore size distribution data.

[0066] Calculates pore distribution data for insulation materials based on multiple pore regions, including: The pores in each pore region are randomly sampled to obtain a first number of regional pore sets.

[0067] The maximum pore volume, minimum pore volume and average pore volume of the pore set in each region are calculated.

[0068] The pore size distribution data were obtained based on the maximum pore volume, minimum pore volume and average pore volume of the pore collection in all regions.

[0069] In this embodiment, pore size distribution data can reflect the distribution of pores of different sizes in the insulation material. A regional pore set refers to a set of pores randomly sampled from each pore region. The first quantity is the total number of pore regions, and each pore region corresponds to a randomly sampled regional pore set. The maximum pore volume can reflect the upper limit of the pore size in the set. The minimum pore volume can reflect the lower limit of the pore size in the set. The average pore volume can reflect the average level of pore size in the set.

[0070] For example, in the production process of new polystyrene board insulation materials, the porosity and pore distribution need to be strictly controlled. By calculating the regional porosity, we can understand the porosity of different parts of the polystyrene board. If the porosity of a certain area is too high, it may lead to insufficient strength in that part; calculating the global porosity can grasp the overall proportion of pores in the entire board and ensure that the thermal insulation performance meets the standards. For pore size distribution data, by analyzing the maximum, minimum and average pore volumes, we can determine whether the pore size is uniform. If the maximum pore volume is too large and the number is large, heat can easily be conducted through these large pores, reducing the thermal insulation effect; polystyrene boards with moderate average pore volume and uniform distribution usually have better thermal insulation and mechanical properties. Through this analysis of porosity and pore distribution data, the quality of thermal insulation materials can be evaluated more accurately. Enterprises can optimize production processes based on these data, improve product quality, and produce thermal insulation materials that better meet the energy-saving needs of buildings.

[0071] This example constructs a regional pore set by randomly sampling each pore region and then calculating its maximum, minimum, and average pore volumes. This effectively captures the pore size distribution within each region. Aggregating the relevant volume data for all regional pore sets provides a comprehensive picture of the pore size distribution within the insulation material, providing a basis for in-depth analysis of material properties.

[0072] In one embodiment of the present disclosure, the pore distribution data includes pore shape distribution data.

[0073] Calculates pore distribution data for insulation materials based on multiple pore regions, including: The pores in each pore region are randomly sampled to obtain a first number of regional pore sets.

[0074] The average circularity and average rectangularity of the pore set in each region were calculated.

[0075] The pore shape distribution data were obtained based on the average circularity and average rectangularity of the pore set in all regions.

[0076] In this embodiment, the pore shape distribution data is used to describe the characteristics and distribution of the pore shape in the thermal insulation material. The average circularity can be used to measure the average degree to which the pore shape in the regional pore set is close to a circle. For each pore in the set, based on its perimeter C and area A, the circularity calculation formula (circularity = ) to calculate the circularity and then find the average value to get the average circularity.

[0077] The average rectangularity refers to the average degree to which the pore shapes in a regional pore set approach a rectangle. It can be obtained by calculating the ratio of the pore area to the area of ​​the smallest rectangle that can contain the pore, and averaging all the pores in the set.

[0078] For example, this method can be used to evaluate product quality in the production of ceramic-based thermal insulation materials. For example, during the production process, if the average circularity is high, it means that the pores in the material are mostly close to circular. This shape can make the material disperse heat more evenly when subjected to thermal shock, thereby improving thermal stability. If the average rectangularity is high, it means that the pores have more rectangular shapes, which will affect the gas circulation inside the material and thus affect the thermal insulation performance. Manufacturers can adjust the process parameters accordingly, such as changing the sintering temperature and time, to control the pore shape distribution, and produce ceramic-based thermal insulation materials with excellent thermal insulation performance and stable mechanical properties to meet the thermal insulation needs in high-temperature environments such as industrial kilns.

[0079] This example quantifies the degree to which the pore shapes within a single region resemble circles or rectangles by calculating the average circularity and average rectangularity of regional pore collections. Aggregating this data across all regional pore collections provides a holistic view of the distribution of pore shapes within the insulation material. This provides a basis for analyzing material properties, given that different pore shapes have varying effects on material properties—for example, circular pores may help evenly distribute stress in some cases, while rectangular pores may affect the direction of heat conduction within the material.

[0080] Corresponding to the thermal insulation material quality detection method of the above embodiment, Figure 2 This is a structural block diagram of a thermal insulation material quality detection device provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The thermal insulation material quality detection device 20 includes: a three-dimensional construction module 21, a pore area division module 22 and a quality analysis module 23.

[0081] The three-dimensional construction module 21 is used to construct a three-dimensional structural image based on the X-ray scanning data of the thermal insulation material.

[0082] The pore region division module 22 is used to determine an initial pore set based on a voxel set that meets a first condition, where the first condition is that the image grayscale value distribution data in the three-dimensional structural image is less than or equal to a first image grayscale threshold; the initial pore set is divided based on a clustering algorithm to obtain multiple pore regions, each of which contains multiple pore elements with a pore spacing less than or equal to a first spacing, and the regional spacing between different pore regions is greater than the first spacing.

[0083] The quality analysis module 23 is used to calculate the porosity and pore distribution data of the thermal insulation material based on the multiple pore regions, and determine the quality analysis result of the thermal insulation material based on the porosity and pore distribution data.

[0084] In one embodiment of the present disclosure, the quality analysis module 23 is specifically configured to determine the thermal insulation level of the thermal insulation material based on the porosity.

[0085] Determine the quality analysis results of the insulation material based on pore distribution data and insulation grade.

[0086] In one embodiment of the present disclosure, the porosity includes regional porosity and global porosity. The quality analysis module 23 is further configured to calculate the thermal insulation quality score of the thermal insulation material based on the number of regions, regional porosity, and global porosity of the plurality of porous regions.

[0087] If the thermal insulation quality score is less than the first score threshold, the thermal insulation level of the thermal insulation material is determined to be the first level.

[0088] If the thermal insulation quality score is greater than or equal to the first score threshold, the thermal insulation level of the thermal insulation material is determined to be the second level.

[0089] In one embodiment of the present disclosure, the thermal insulation level includes a first level and a second level. The thermal insulation performance of the first level is higher than that of the second level. The quality analysis module 23 is further configured to determine a quality analysis result of the thermal insulation material based on the first weight coefficient and the pore distribution data if the thermal insulation level is the first level.

[0090] If the thermal insulation level is the second level, the quality analysis result of the thermal insulation material is determined based on the second weight coefficient and the pore distribution data.

[0091] The first weight coefficient and the second weight coefficient both represent the weight of the influence of the pore distribution data on the mass analysis result. The first weight coefficient is greater than the second weight coefficient.

[0092] In one embodiment of the present disclosure, the porosity includes regional porosity and global porosity. The mass analysis module 23 is further configured to calculate the regional porosity based on the volume of each pore region and the volume of all pores in the pore region.

[0093] The global porosity is calculated based on the volume of the pores in all pore regions and the volume of the insulation material.

[0094] In one embodiment of the present disclosure, the pore distribution data includes pore size distribution data. The mass analysis module 23 is further configured to randomly sample the pores in each pore region to obtain a first number of regional pore sets.

[0095] The maximum pore volume, minimum pore volume and average pore volume of the pore set in each region are calculated.

[0096] The pore size distribution data were obtained based on the maximum pore volume, minimum pore volume and average pore volume of the pore collection in all regions.

[0097] In one embodiment of the present disclosure, the pore distribution data includes pore shape distribution data. The quality analysis module 23 is further configured to randomly sample the pores in each pore region to obtain a first number of regional pore sets.

[0098] The average circularity and average rectangularity of the pore set in each region were calculated.

[0099] The pore shape distribution data were obtained based on the average circularity and average rectangularity of the pore set in all regions.

[0100] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0101] It should be understood that in the embodiments of the present disclosure, the processor 301 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0102] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0103] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0104] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the insulation material quality detection method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device 300 described in the embodiments of the present disclosure, which will not be repeated here.

[0105] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0106] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0107] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0108] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0110] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present disclosure.

[0111] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0112] The above are only specific embodiments of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or replacements within the technical scope disclosed in this disclosure, and such modifications or replacements should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A method for detecting the quality of thermal insulation materials, characterized in that: include: Construct a three-dimensional structural image based on X-ray scanning data of thermal insulation materials; determining an initial pore set based on a voxel set that satisfies a first condition, wherein the first condition is that image grayscale value distribution data in the three-dimensional structure image is less than or equal to a first image grayscale threshold; Dividing the initial pore set based on a clustering algorithm to obtain a plurality of pore regions, each pore region containing a plurality of pore elements with pore spacing less than or equal to a first spacing, and regional spacing between different pore regions being greater than the first spacing; Porosity and pore distribution data of the thermal insulation material are calculated based on the plurality of pore regions, and a quality analysis result of the thermal insulation material is determined based on the porosity and pore distribution data.

2. The thermal insulation material quality detection method according to claim 1, characterized in that: The determining of the quality analysis result of the thermal insulation material based on the porosity and pore distribution data includes: determining an insulation level of the insulation material based on the porosity; A quality analysis result of the thermal insulation material is determined based on the pore distribution data and the thermal insulation grade.

3. The thermal insulation material quality detection method according to claim 2, characterized in that: The porosity includes regional porosity and global porosity; Determining the thermal insulation level of the thermal insulation material based on the porosity includes: Calculating a thermal insulation mass fraction of the thermal insulation material based on the number of regions of the plurality of pore regions, the regional porosity, and the global porosity; If the thermal insulation quality score is less than a first score threshold, determining that the thermal insulation level of the thermal insulation material is the first level; If the thermal insulation quality score is greater than or equal to the first score threshold, the thermal insulation level of the thermal insulation material is determined to be the second level.

4. The thermal insulation material quality detection method according to claim 2, characterized in that: The thermal insulation level includes a first level and a second level; the thermal insulation performance of the first level is higher than that of the second level; The determining of the quality analysis result of the thermal insulation material based on the pore distribution data and the thermal insulation level includes: If the thermal insulation level is the first level, determining a quality analysis result of the thermal insulation material based on a first weight coefficient and the pore distribution data; If the thermal insulation level is the second level, determining a quality analysis result of the thermal insulation material based on a second weight coefficient and the pore distribution data; The first weight coefficient and the second weight coefficient both represent the weight of influence of pore distribution data on mass analysis results; the first weight coefficient is greater than the second weight coefficient.

5. The thermal insulation material quality detection method according to claim 1, characterized in that: The porosity includes regional porosity and global porosity; Calculating the porosity of the thermal insulation material based on the plurality of pore regions includes: Calculating regional porosity based on the volume of each pore region and the volume of all pores in that pore region; The global porosity is calculated based on the volume of the pores in all pore regions and the volume of the insulation material.

6. The thermal insulation material quality detection method according to claim 5, characterized in that: The pore distribution data includes pore size distribution data; Calculating pore distribution data of the thermal insulation material based on the plurality of pore regions includes: Randomly sampling pores in each pore region to obtain a first number of regional pore sets; Calculate the maximum pore volume, minimum pore volume and average pore volume of the pore set in each region; The pore size distribution data were obtained based on the maximum pore volume, minimum pore volume and average pore volume of the pore collection in all regions.

7. The thermal insulation material quality detection method according to claim 5, characterized in that: The pore distribution data includes pore shape distribution data; Calculating pore distribution data of the thermal insulation material based on the plurality of pore regions includes: Randomly sampling pores in each pore region to obtain a first number of regional pore sets; Calculate the average circularity and average rectangularity of the pore set in each region; The pore shape distribution data were obtained based on the average circularity and average rectangularity of the pore set in all regions.

8. A thermal insulation material quality detection device, characterized in that: include: A three-dimensional construction module is used to construct a three-dimensional structural image based on X-ray scanning data of the thermal insulation material; a pore region division module, configured to determine an initial pore set based on a voxel set that satisfies a first condition, wherein the first condition is that image grayscale value distribution data in the three-dimensional structure image is less than or equal to a first image grayscale threshold; Dividing the initial pore set based on a clustering algorithm to obtain a plurality of pore regions, each pore region containing a plurality of pore elements with pore spacing less than or equal to a first spacing, and regional spacing between different pore regions being greater than the first spacing; A quality analysis module is used to calculate the porosity and pore distribution data of the thermal insulation material based on the multiple pore areas, and determine the quality analysis result of the thermal insulation material based on the porosity and pore distribution data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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