A rebound strength detection system for energy-saving and environment-friendly curtain wall material

Abstract

The present application relates to the field of material detection, and more particularly to a rebound strength detection system for energy-saving and environment-friendly curtain wall materials, which is provided with a data acquisition module, a prior analysis module, a rebound detection module, a rebound analysis module and a detection early warning module, determines a detection point, analyzes the rebound anomaly characteristic value of the detection point and the adjacent rebound anomaly characteristic value of the adjacent detection point, determines the state of the material to be detected, divides the rebound detection area, determines the corresponding area rebound anomaly characteristic value, analyzes the anomaly tendency, calculates the area anomaly characteristic value in combination with the environmental influence characteristic value and the area rebound anomaly characteristic value according to the analysis result of the strong anomaly tendency, and determines the state of the material to be detected to determine whether to issue a material replacement early warning signal. The rebound strength of the material in use is analyzed, including single-point detection and area detection based on single-point detection, so as to reduce the detection times, improve the detection efficiency and detection accuracy.

Description

Technical Field

[0001] This invention relates to the field of materials testing, and in particular to a rebound strength testing system for energy-saving and environmentally friendly curtain wall materials. Background Technology

[0002] Against the backdrop of the construction industry's transformation towards energy conservation and environmental protection, curtain walls, as a core component of building envelopes, directly impact a building's energy efficiency, safety, and service life. Energy-saving and environmentally friendly curtain wall materials, needing to balance thermal insulation, light transmission, and structural stability, face increasingly stringent requirements for their mechanical properties, particularly rebound strength. Rebound strength not only reflects a material's ability to recover after external impact or long-term loads but is also a key indicator for assessing its fatigue resistance, durability, and safety redundancy. However, traditional testing methods suffer from cumbersome operation, insufficient accuracy, and significant errors due to reliance on manual judgment. Furthermore, they are ill-suited for the comprehensive evaluation of the multi-dimensional mechanical properties of new composite curtain wall materials, failing to efficiently provide data support for material selection, engineering quality control, and curtain wall structure optimization. Therefore, there is an urgent need to develop an automated, high-precision rebound strength testing system to meet the performance testing needs of energy-saving and environmentally friendly curtain wall materials throughout the entire process of research, development, production, and application.

[0003] Chinese Patent Publication No. CN119178688A discloses a rebound strength testing device for building curtain walls. First, based on the ultrasonic waveform data deviation between each monitoring point and other monitoring points, the monitoring feature point with the smallest ultrasonic waveform data deviation from other monitoring points is selected, ensuring that the curtain wall material structure corresponding to the detection feature point best represents the overall structural characteristics of the curtain wall material. Then, monitoring points with high similarity between their corresponding ultrasonic waveform data and the ultrasonic waveform data of the detection feature point are used as effective monitoring points for comprehensive rebound height calculation. This makes the comprehensive rebound height obtained by combining the height effectiveness of each effective monitoring point with the corresponding initial rebound height more accurate, reducing the influence of random factors when testing the strength of curtain wall materials based on the comprehensive rebound height and improving the accuracy of curtain wall material quality testing.

[0004] Chinese Patent Publication No. CN117168960A discloses a strength testing device and method for building curtain walls, including a movable base and a mounting plate. A first hydraulic actuator is installed inside the movable base, and a first hydraulic rod is installed at the output end of the first hydraulic actuator. The other end of the first hydraulic rod is fixedly connected to a connecting block by screws. Through the arrangement of the first hydraulic rod, connecting block, and other structures, activating the first hydraulic rod causes the connecting block to slide, which in turn causes the rack to rotate, which in turn causes the first threaded rod to rotate, causing the first internal threaded cylinder, long sliding block, and top sliding block to slide. This allows the top sliding block to slide, bringing the device into contact with the curtain wall. Furthermore, rubber pads are provided on one side of both the top sliding block and the movable base to prevent the movable base and mounting plate from colliding with the curtain wall and causing damage. This technical solution solves the problems of easy damage to curtain walls and limited usability in existing technologies.

[0005] It is evident that the existing technology still has the following problems:

[0006] In practice, the method for testing curtain wall materials in use is single-point testing. However, the intensity of use and the environment can cause different areas of the material to change to varying degrees. Single-point testing can lead to deviations in the test results and cannot reflect abnormalities in a timely manner, while full-area testing can reduce testing efficiency. Summary of the Invention

[0007] To address this, the present invention provides a rebound strength testing system for energy-saving and environmentally friendly curtain wall materials. This system overcomes the problem that in practice, the testing of curtain wall materials in use is done through single-point testing. However, the intensity of use and the environment can cause different degrees of change in different areas of the material. Single-point testing can lead to deviations in the test results and cannot reflect abnormal situations in a timely manner, while full-area testing can reduce testing efficiency.

[0008] To achieve the above objectives, the present invention provides a rebound strength testing system for energy-saving and environmentally friendly curtain wall materials, comprising:

[0009] The data acquisition module is used to obtain the hardness value of the material to be tested during use, obtain the ozone concentration in the environment, obtain the ambient temperature to determine the temperature difference, and obtain the compression duration of the material to be tested.

[0010] The prior analysis module, which is connected to the data acquisition module, selects any point in the material to be tested as a test point, determines the hardness coefficient and compression coefficient based on the hardness value and compression duration at the test point, calculates the rebound anomaly characterization value of the test point, and compares and analyzes it with the adjacent rebound anomaly characterization value of the adjacent test point to determine the state of the material to be tested and to determine whether it is necessary to divide the area.

[0011] The rebound detection module is connected to the prior analysis module. In response to the need to divide the region, it determines the rebound detection area of ​​the material to be tested, performs multi-point detection on the rebound detection area, determines the region's rebound anomaly characterization value, and analyzes the anomaly trend.

[0012] A rebound analysis module, connected to the rebound detection module, is used to determine the environmental impact characterization value based on the temperature difference and the ozone concentration based on the analysis results of strong anomaly tendency, and to calculate the regional anomaly characteristic value by combining the regional rebound anomaly characterization value to determine the state of the material to be tested.

[0013] The detection and early warning module is connected to the prior analysis module and the rebound analysis module. Based on the state of the material to be detected, it determines whether to issue a material replacement early warning signal.

[0014] Furthermore, the prior analysis module determines the hardness coefficient and compressibility coefficient, including,

[0015] The ratio of the hardness value at the test point to the reference hardness value is determined as the hardness coefficient;

[0016] The ratio of the compression duration at the detection point to the reference compression duration is determined as the compression coefficient.

[0017] Further, the prior analysis module calculates the rebound anomaly characterization value of the detection point, including,

[0018] The ratio of the hardness coefficient to the reference hardness coefficient is determined as the first influencing factor;

[0019] The ratio of the compression factor to the benchmark compression factor is determined as the second influencing factor;

[0020] The weighted sum of the first influence factor and the second influence factor is determined to be the rebound mutation characterization value.

[0021] Furthermore, the prior analysis module determines the state of the material to be tested and determines whether region division is necessary, wherein...

[0022] If the rebound mutation characterization value and the adjacent rebound mutation characterization value do not meet the preset conditions, then the state of the material to be tested is determined to be the state to be analyzed, and no region division is required.

[0023] If the rebound mutation characterization value and the adjacent rebound mutation characterization value meet the preset conditions, then the state of the material to be tested is determined to be non-uniform, and region division is required.

[0024] The preset condition is that the rebound variation characterization value and the adjacent rebound variation characterization value are both less than the rebound variation characterization value threshold, and the absolute value of the difference between the rebound variation characterization value and the adjacent rebound variation characterization value is greater than the preset tolerance threshold.

[0025] Furthermore, the springback detection module determines the springback detection area of ​​the material to be tested, including,

[0026] The rebound mutation characterization value and the adjacent rebound mutation characterization value are sorted in descending order to determine the first detection point;

[0027] The first detection point is determined as the midpoint, and the distance between the detection point and the adjacent detection point is determined as the extended line segment;

[0028] Construct a sphere with the extended line segment as its radius and the midpoint as its center;

[0029] The overlapping area between the sphere and the material to be tested is determined as the rebound detection area.

[0030] Furthermore, the rebound detection module determines the regional rebound anomaly characterization value and analyzes the anomaly trend, including,

[0031] The rebound detection area is divided into several identical sub-regions, several sub-detection points are determined, and several sub-rebound anomaly characterization values ​​are calculated.

[0032] The average value of each sub-rebound anomaly characterization value is determined as the regional rebound anomaly characterization value;

[0033] If the rebound mutation characterization value of the region is greater than the rebound mutation characterization value threshold, then the rebound detection region is determined to have a strong mutation tendency.

[0034] If the rebound mutation characterization value of the region is less than or equal to the rebound mutation characterization value threshold, then the rebound detection region is classified as having a strong mutation tendency.

[0035] Furthermore, the rebound analysis module determines environmental impact characterization values ​​based on the temperature difference and the ozone concentration, including:

[0036] The ratio of the temperature difference to the reference temperature difference is determined as the first environmental factor;

[0037] The ratio of the ozone concentration to the reference ozone concentration is determined as the second environmental factor;

[0038] The summation of the first environmental factor and the second environmental factor is determined as the environmental impact characterization value.

[0039] Furthermore, the rebound analysis module calculates regional anomaly characteristic values, including:

[0040] The ratio of the region's rebound variation characterization value to the baseline rebound variation characterization value is determined as the rebound influence factor;

[0041] The ratio of the environmental impact characterization value to the baseline environmental impact characterization value is determined as the environmental impact factor;

[0042] The weighted sum of the rebound impact factor and the environmental impact factor is determined to be the regional variation characteristic value.

[0043] Furthermore, the springback analysis module determines the state of the material to be tested, wherein,

[0044] If the regional anomaly characteristic value is greater than the regional anomaly characteristic value threshold, then the state of the material to be detected is determined to be abnormal.

[0045] If the regional variation characteristic value is less than or equal to the regional variation characteristic value threshold, then the state of the material to be tested is determined to be normal.

[0046] Furthermore, the detection and early warning module determines whether to issue a material replacement warning signal, wherein,

[0047] If the condition of the material to be tested meets the warning conditions, a material replacement warning signal is issued;

[0048] If the condition of the material to be tested does not meet the warning conditions, no material replacement warning signal will be issued;

[0049] The warning condition is that the state of the material to be tested is the abnormal state, or that either the rebound mutation characterization value or the adjacent rebound mutation characterization value is greater than the rebound mutation characterization value threshold.

[0050] Compared with existing technologies, this invention sets up a data acquisition module, a priori analysis module, a rebound detection module, a rebound analysis module, and a detection early warning module. By determining detection points, analyzing the rebound anomaly characterization values ​​of the detection points and adjacent detection points, the state of the material to be tested is determined. Rebound detection areas are divided, corresponding regional rebound anomaly characterization values ​​are determined, and anomaly tendencies are analyzed. For analysis results with strong anomaly tendencies, combined with environmental impact characterization values ​​and the regional rebound anomaly characterization values, regional anomaly characteristic values ​​are calculated to determine the state of the material to be tested, thereby determining whether to issue a material replacement early warning signal. This invention performs rebound strength analysis on materials in use, including single-point detection and regional detection based on single-point detection, to reduce the number of tests and improve detection efficiency and accuracy.

[0051] In particular, by conducting prior analysis and considering the inherent characteristics of the material under test, the rebound anomaly characterization value of the test point is calculated. It is understood that rebound strength testing is mostly aimed at the sealing materials of curtain walls. In the actual testing process, obvious aging or deformation can usually be judged manually or directly detected by existing equipment. However, since some sealing materials are under pressure and the visible and accessible range is limited, traditional methods are difficult to effectively test them. If the rebound strength of such areas is directly evaluated based on equipment readings, it is easy to introduce significant errors. Based on this, the present invention obtains the hardness value and compression duration of the material under test and calculates the rebound anomaly characterization value of the test point, providing a data basis for subsequent comparison with adjacent test points, so as to analyze the state of the material under test, issue a material replacement warning signal, and improve the testing efficiency and accuracy.

[0052] In particular, by comparing the rebound anomaly characterization value of the detection point with that of adjacent detection points, a data basis is provided for subsequent region division. In practice, most materials to be tested are tested using single-point or full-area testing methods. However, single-point testing is difficult to fully reflect the spatial heterogeneity of the material state, which can easily lead to the omission of local defects and affect the accuracy of the results. While full-area testing provides comprehensive coverage, its testing efficiency is low. Based on this, this invention constructs a region consistency criterion by analyzing the difference in rebound anomaly characterization values ​​between the detection point and adjacent detection points. For regions with significant differences in characterization values ​​and clear states, a warning signal is directly generated. For ambiguous regions, further region division is implemented to achieve more refined state discrimination and analysis, thereby improving testing efficiency and accuracy.

[0053] In particular, considering that even if the rebound anomaly characterization value and the adjacent rebound anomaly characterization value are both within the threshold range of the material to be tested, the significant difference between their values ​​may still cause abnormal rebound behavior, thereby affecting the sealing performance. This is a potential failure risk in assessing such spatial inconsistencies. Based on this, the present invention further divides the region, merges the rebound anomaly characterization values ​​of all detection points in the region, calculates the region's rebound anomaly characterization value, and determines the anomaly tendency of the region accordingly. This identifies regions with strong anomaly tendency due to discrete characterization value distribution and poor coordination. Even if there are detection points in the region that do not exceed the upper limit of the threshold, it provides a theoretical basis and data support for subsequent key analysis and early warning, improving detection efficiency and detection accuracy.

[0054] In particular, targeted analysis was conducted on areas with strong anomaly tendencies. Multiphysics coupling analysis was performed, combining environmental temperature difference and ozone concentration, to analyze the regional anomaly characteristics of these areas. This provides a data basis for subsequent determination of whether to issue a material replacement warning signal. In reality, environmental temperature differences can cause significant changes in the internal shear stress of materials. The greater the temperature difference, the more prominent the thermal mismatch effect caused by the difference in the thermal expansion coefficients of different materials. For example, there is a significant difference in the thermal expansion coefficients between aluminum profiles and glass. Under temperature fluctuations, the difference in expansion and contraction between the aluminum frame and the glass will be entirely borne by the sealant. If the sealant's displacement capacity and elasticity are insufficient, huge shear stress will be generated at the bonding interface, leading to bonding failure. Furthermore, ozone... Oxygen concentration is a key environmental factor that accelerates the aging of rubber-based sealing materials, especially in industrial areas or after thunderstorms when concentrations rise significantly. Compared to general oxidative aging, ozone corrosion is more specific and severe. It can react with the double bonds in unsaturated carbon chain rubber molecules (such as natural rubber and styrene-butadiene rubber), causing molecular chain breakage, microcrack initiation, and surface pulverization, reducing the ductility and sealing durability of the material. Based on this, this invention introduces environmental temperature difference and ozone concentration as coupled correction variables to identify areas with strong anomalies. It comprehensively analyzes the anomalies in areas with strong anomaly tendencies, enabling accurate identification and early warning of potential failure areas, and timely issuance of material replacement warning signals, thereby improving detection efficiency and accuracy. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the structure of a rebound strength testing system for energy-saving and environmentally friendly curtain wall materials according to an embodiment of the invention.

[0056] Figure 2 A logic block diagram for determining whether region division is required in an embodiment of the invention;

[0057] Figure 3 A logic block diagram for analyzing the tendency of regional rebound mutation characterization values ​​in the embodiments of the invention;

[0058] Figure 4 A logic block diagram for determining the state of the material to be tested according to an embodiment of the invention;

[0059] Figure 5 This is a logic block diagram illustrating how an invention embodiment determines whether to issue a material replacement warning signal. Detailed Implementation

[0060] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0061] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0062] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0063] Please see Figure 1 , Figure 1 This is a schematic diagram of a rebound strength testing system for energy-saving and environmentally friendly curtain wall materials, according to an embodiment of the invention. The rebound strength testing system for energy-saving and environmentally friendly curtain wall materials of the present invention includes:

[0064] The data acquisition module is used to obtain the hardness value of the material to be tested during use, obtain the ozone concentration in the environment, obtain the ambient temperature to determine the temperature difference, and obtain the compression duration of the material to be tested.

[0065] The prior analysis module, which is connected to the data acquisition module, selects any point in the material to be tested as a test point, determines the hardness coefficient and compression coefficient based on the hardness value and compression duration at the test point, calculates the rebound anomaly characterization value of the test point, and compares and analyzes it with the adjacent rebound anomaly characterization value of the adjacent test point to determine the state of the material to be tested and to determine whether it is necessary to divide the area.

[0066] The rebound detection module is connected to the prior analysis module. In response to the need to divide the region, it determines the rebound detection area of ​​the material to be tested, performs multi-point detection on the rebound detection area, determines the region's rebound anomaly characterization value, and analyzes the anomaly trend.

[0067] A rebound analysis module, connected to the rebound detection module, is used to determine the environmental impact characterization value based on the temperature difference and the ozone concentration based on the analysis results of strong anomaly tendency, and to calculate the regional anomaly characteristic value by combining the regional rebound anomaly characterization value to determine the state of the material to be tested.

[0068] The detection and early warning module is connected to the prior analysis module and the rebound analysis module. Based on the state of the material to be detected, it determines whether to issue a material replacement early warning signal.

[0069] Specifically, there are no restrictions on the method of collecting hardness values. In practice, for example, hardness can be measured directly using a hardness tester. Of course, those skilled in the art can also use other methods to measure hardness, as long as they are reasonable. This will not be elaborated further.

[0070] Specifically, there are no restrictions on the method of obtaining ozone concentration. In practice, for example, ozone sensors can be deployed to collect concentration data. Of course, those skilled in the art can also use other methods to detect ozone concentration, as long as they are reasonable. This will not be elaborated further.

[0071] Specifically, there are no restrictions on the method of obtaining temperature. For example, it can be obtained directly through a temperature sensor or through weather forecast data released by the meteorological department. Any reasonable method is acceptable. The temperature difference is defined as the difference between the highest and lowest temperatures of the day, which will not be elaborated further.

[0072] Specifically, the compression duration characterizes the time during which the sealing material is compressed. There is no limitation on the method of obtaining the compression duration. For example, the parameter can be determined by the time information recorded by a pre-set monitoring camera. Of course, those skilled in the art can also use other methods to obtain the compression duration, as long as they are reasonable. This will not be elaborated further.

[0073] Understandably, the testing points should follow the principle of random distribution, and it is only necessary to ensure that they are located within the effective testing area of ​​the material.

[0074] Understandably, most enclosed materials are regular hexahedrons with a longest and shortest side. Specifically, the method for determining the location of adjacent detection points is not limited; in implementation...

[0075] A virtual dividing line is constructed to divide the closed material into large and small regions, and the virtual dividing line is parallel to the shortest side of the material to be tested.

[0076] Determine which adjacent detection points to select within a large area;

[0077] Ensure that the line connecting adjacent detection points is parallel to the longest side of the material to be tested;

[0078] The distance between each detection point and its adjacent detection point is set to 30% of the total length of the material.

[0079] Specifically, the prior analysis module determines the hardness coefficient and compressibility coefficient, including...

[0080] The ratio of the hardness value at the test point to the reference hardness value is determined as the hardness coefficient;

[0081] The ratio of the compression duration at the detection point to the reference compression duration is determined as the compression coefficient.

[0082] Specifically, the reference hardness value is the hardness record of the material to be tested when it was first put into storage, or the typical value in the product standard, which will not be elaborated further.

[0083] Specifically, the baseline compression duration is determined by historical data. The historical compression durations of several materials to be tested are obtained in advance, and the average of each historical compression duration is determined as the baseline compression duration.

[0084] Specifically, the prior analysis module calculates the rebound anomaly characterization value of the detection point, including,

[0085] The ratio of the hardness coefficient to the reference hardness coefficient is determined as the first influencing factor;

[0086] The ratio of the compression factor to the benchmark compression factor is determined as the second influencing factor;

[0087] The weighted sum of the first influence factor and the second influence factor is determined to be the rebound mutation characterization value.

[0088] Specifically, the reference hardness coefficient is calculated in advance. Several historical hardness coefficients of the materials to be tested are obtained in advance, and the average value of each historical hardness coefficient is determined as the reference hardness coefficient.

[0089] Specifically, the baseline compression coefficient is calculated in advance. Several historical compression coefficients of the materials to be tested are obtained in advance, and the average value of each historical compression coefficient is determined as the baseline compression coefficient.

[0090] Specifically, the sum of the weighting coefficients of the first influence factor and the second influence factor is 1. When adjusting the weighting coefficients, considering that hardness can more clearly show the state of the material to be tested, the weighting coefficient of the first influence factor is set to 0.6 and the weighting coefficient of the second influence factor is set to 0.4.

[0091] Specifically, by conducting prior analysis and considering the inherent characteristics of the material under test, the rebound anomaly characterization value of the test point is calculated. Rebound strength testing is mostly aimed at the sealing materials of curtain walls. In actual testing, obvious aging or deformation can usually be judged manually or directly detected by existing equipment. However, since some sealing materials are under pressure and the visible and accessible range is limited, traditional methods are difficult to effectively detect them. If the rebound strength of such areas is directly evaluated based on equipment readings, it is easy to introduce significant errors. Based on this, the present invention obtains the hardness value and compression duration of the material under test and calculates the rebound anomaly characterization value of the test point. This provides a data basis for subsequent comparison with adjacent test points, analyzes the state of the material under test, issues a material replacement warning signal, and improves the testing efficiency and accuracy.

[0092] Please see Figure 2 , Figure 2 This is a logic block diagram illustrating the determination of whether region division is necessary according to an embodiment of the invention. Specifically, the prior analysis module determines the state of the material to be detected and determines whether region division is necessary, wherein...

[0093] If the rebound mutation characterization value and the adjacent rebound mutation characterization value do not meet the preset conditions, then the state of the material to be tested is determined to be the state to be analyzed, and no region division is required.

[0094] If the rebound mutation characterization value and the adjacent rebound mutation characterization value meet the preset conditions, then the state of the material to be tested is determined to be non-uniform, and region division is required.

[0095] The preset condition is that the rebound variation characterization value and the adjacent rebound variation characterization value are both less than the rebound variation characterization value threshold, and the absolute value of the difference between the rebound variation characterization value and the adjacent rebound variation characterization value is greater than the preset tolerance threshold.

[0096] Specifically, the rebound anomaly characterization threshold represents a boundary where anomalies occur at the detection point. It is calculated in advance by acquiring several materials that have experienced anomalies, recording the corresponding rebound anomaly characterization values, and determining the product of each rebound anomaly characterization value and the accuracy coefficient as the rebound anomaly characterization threshold. The accuracy coefficient is selected within the range [0.8, 0.97]. In practice, to improve the calculation accuracy, the accuracy coefficient is determined to be 0.9.

[0097] Specifically, the tolerance threshold is calculated in advance. Several abnormal materials are obtained in advance, and the absolute value of the difference between the rebound anomaly characterization value of each detection point in the material and the adjacent detection point is recorded. The average value of the absolute values ​​of each difference is determined as the tolerance threshold.

[0098] Specifically, the springback variation characterization value of the detection point is compared with the adjacent springback variation characterization value of the adjacent detection point to provide a data basis for subsequent region division. In practice, most materials to be tested are tested using single-point or full-area testing methods. However, single-point testing is difficult to fully reflect the spatial heterogeneity of the material state, which can easily lead to the omission of local defects and affect the accuracy of the results. While full-area testing has comprehensive coverage, its testing efficiency is low. Based on this, this invention constructs a region consistency criterion by analyzing the difference in springback variation characterization values ​​between the detection point and adjacent detection points. For regions with significant differences in characterization values ​​and clear states, a warning signal is directly generated. For ambiguous regions, further region division is implemented to achieve more refined state discrimination and analysis, thereby improving testing efficiency and accuracy.

[0099] Specifically, the springback detection module determines the springback detection area of ​​the material to be tested, including,

[0100] The rebound mutation characterization value and the adjacent rebound mutation characterization value are sorted in descending order to determine the first detection point;

[0101] The first detection point is determined as the midpoint, and the distance between the detection point and the adjacent detection point is determined as the extended line segment;

[0102] Construct a sphere with the extended line segment as its radius and the midpoint as its center;

[0103] The overlapping area between the sphere and the material to be tested is determined as the rebound detection area.

[0104] It is understandable that the point corresponding to the first representation value is the first detection point.

[0105] Please see Figure 3 , Figure 3 A logic block diagram for determining the regional springback anomaly characterization value and analyzing the anomaly trend in an embodiment of the invention. Specifically, the springback detection module determines the regional springback anomaly characterization value and analyzes the anomaly trend, including:

[0106] The rebound detection area is divided into several identical sub-regions, several sub-detection points are determined, and several sub-rebound anomaly characterization values ​​are calculated.

[0107] The average value of each sub-rebound anomaly characterization value is determined as the regional rebound anomaly characterization value;

[0108] If the rebound mutation characterization value of the region is greater than the rebound mutation characterization value threshold, then the rebound detection region is determined to have a strong mutation tendency.

[0109] If the rebound variation characterization value of the region is less than or equal to the rebound variation characterization value threshold, then the rebound detection region is determined to have a weak variation tendency.

[0110] Specifically, the shape of the sub-region is not limited. In practice, the shape of the sub-region is determined to be square. Of course, those skilled in the art can determine the shape according to the actual situation, as long as it can cover the rebound detection area. This will not be elaborated further.

[0111] Specifically, considering that even if the rebound anomaly characterization value and the adjacent rebound anomaly characterization value are both within the threshold range of the material to be tested, the significant difference between their values ​​may still cause abnormal rebound behavior, thereby affecting the sealing performance. This is a potential failure risk in assessing such spatial inconsistencies. Based on this, the present invention further divides the region, merges the rebound anomaly characterization values ​​of all detection points in the region, calculates the region's rebound anomaly characterization value, and determines the anomaly tendency of the region accordingly. This identifies regions with strong anomaly tendency due to discrete characterization value distribution and poor coordination. Even if there are detection points in the region that do not exceed the upper limit of the threshold, it provides a theoretical basis and data support for subsequent key analysis and early warning, improving detection efficiency and detection accuracy.

[0112] Specifically, the rebound analysis module determines environmental impact characterization values ​​based on the temperature difference and the ozone concentration, including:

[0113] The ratio of the temperature difference to the reference temperature difference is determined as the first environmental factor;

[0114] The ratio of the ozone concentration to the reference ozone concentration is determined as the second environmental factor;

[0115] The summation of the first environmental factor and the second environmental factor is determined as the environmental impact characterization value.

[0116] Specifically, the reference temperature difference is calculated in advance by obtaining several historical temperature differences of the environment in which the material to be tested is located, and determining the average value of each historical temperature difference as the reference temperature difference.

[0117] Specifically, the baseline ozone concentration is determined by obtaining several historical ozone concentrations of the environment in which the material to be tested is located, and the average of these historical ozone concentrations is used as the baseline ozone concentration.

[0118] Specifically, the rebound analysis module calculates regional anomaly characteristic values, including,

[0119] The ratio of the region's rebound variation characterization value to the baseline rebound variation characterization value is determined as the rebound influence factor;

[0120] The ratio of the environmental impact characterization value to the baseline environmental impact characterization value is determined as the environmental impact factor;

[0121] The weighted sum of the rebound impact factor and the environmental impact factor is determined to be the regional variation characteristic value.

[0122] Specifically, the benchmark springback distortion characterization value is the springback distortion characterization value corresponding to the benchmark hardness coefficient and the benchmark compression coefficient.

[0123] Specifically, the baseline environmental impact characterization value is calculated in advance. Several historical environmental impact characterization values ​​of the environment in which the material to be tested is located are obtained in advance, and the average value of each historical environmental impact characterization value is determined as the baseline environmental impact characterization value.

[0124] Specifically, the sum of the weighting coefficients of the rebound impact factor and the environmental impact factor is 1. When adjusting the weighting coefficients, considering that environmental factors are the more dominant and proactive variables affecting the abnormality of the tested material, the weighting coefficient of the rebound impact factor is set to 0.4, and the weighting coefficient of the environmental impact factor is set to 0.6.

[0125] Specifically, targeted analysis is conducted on areas with strong anomaly tendencies. Multiphysics coupling analysis is performed, combining environmental temperature difference and ozone concentration, to analyze the regional anomaly characteristics of these areas. This provides a data basis for subsequently determining whether to issue a material replacement warning signal. In reality, environmental temperature differences can cause significant changes in the internal shear stress of materials. The greater the temperature difference, the more prominent the thermal mismatch effect caused by the difference in the thermal expansion coefficients of different materials. For example, there is a significant difference in the thermal expansion coefficients between aluminum profiles and glass. Under temperature fluctuations, the difference in expansion and contraction between the aluminum frame and the glass will be entirely borne by the sealant. If the sealant's displacement capacity and elasticity are insufficient, huge shear stress will be generated at the bonding interface, leading to bonding failure. Furthermore… Ozone concentration is a key environmental factor that accelerates the aging of rubber-based sealing materials, especially in industrial areas or after thunderstorms when concentrations rise significantly. Compared to general oxidative aging, ozone corrosion is more specific and severe. It can react with the double bonds in unsaturated carbon chain rubber molecules (such as natural rubber and styrene-butadiene rubber), causing molecular chain breakage, microcrack initiation, and surface pulverization, reducing the ductility and sealing durability of the material. Based on this, this invention introduces environmental temperature difference and ozone concentration as coupled correction variables to identify areas with strong anomalies. By comprehensively analyzing the anomalies in areas with strong anomaly tendencies, it can accurately identify potential failure areas and provide early warnings, promptly issuing material replacement warning signals and improving detection efficiency and accuracy.

[0126] Please see Figure 4 , Figure 4 This is a logic block diagram illustrating the determination of the state of the material to be tested according to an embodiment of the invention. Specifically, the springback analysis module determines the state of the material to be tested, wherein...

[0127] If the regional anomaly characteristic value is greater than the regional anomaly characteristic value threshold, then the state of the material to be detected is determined to be abnormal.

[0128] If the regional variation characteristic value is less than or equal to the regional variation characteristic value threshold, then the state of the material to be tested is determined to be normal.

[0129] Specifically, the regional anomaly characteristic value threshold characterizes a boundary where the material to be detected in the region is abnormal. It is calculated in advance, the abnormal material is obtained in advance, several regions where the anomaly occurs are recorded, several regional anomaly characteristic values ​​are determined, and the product of each regional anomaly characteristic value and the regional precision coefficient is determined as the regional anomaly characteristic value threshold. The regional precision coefficient is selected in the interval [0.8, 0.95]. In practice, in order to improve the calculation accuracy, the precision coefficient is determined to be 0.95.

[0130] Please see Figure 5 , Figure 5 This is a logic block diagram illustrating whether a material replacement warning signal should be issued, according to an embodiment of the invention. Specifically, the warning detection module determines whether a material replacement warning signal should be issued, wherein...

[0131] If the condition of the material to be tested meets the warning conditions, a material replacement warning signal is issued;

[0132] If the condition of the material to be tested does not meet the warning conditions, no material replacement warning signal will be issued;

[0133] The warning condition is that the state of the material to be tested is the abnormal state, or that either the rebound mutation characterization value or the adjacent rebound mutation characterization value is greater than the rebound mutation characterization value threshold.

[0134] It is understood that the warning signal can be either an audible signal or a visual signal, as long as it can achieve the warning effect. Those skilled in the art can determine the appropriate signal based on the actual situation, which will not be elaborated further here.

[0135] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0136] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A rebound strength testing system for energy-saving and environmentally friendly curtain wall materials, characterized in that, include: The data acquisition module is used to obtain the hardness value of the material to be tested during use, obtain the ozone concentration in the environment, obtain the ambient temperature to determine the temperature difference, and obtain the compression duration of the material to be tested. The prior analysis module, which is connected to the data acquisition module, selects any point in the material to be tested as a test point, determines the hardness coefficient and compression coefficient based on the hardness value and compression duration at the test point, calculates the rebound anomaly characterization value of the test point, and compares and analyzes it with the adjacent rebound anomaly characterization value of the adjacent test point to determine the state of the material to be tested and to determine whether it is necessary to divide the area. The rebound detection module is connected to the prior analysis module. In response to the need to divide the region, it determines the rebound detection area of ​​the material to be tested, performs multi-point detection on the rebound detection area, determines the region's rebound anomaly characterization value, and analyzes the anomaly trend. A rebound analysis module, connected to the rebound detection module, is used to determine the environmental impact characterization value based on the temperature difference and the ozone concentration based on the analysis results of strong anomaly tendency, and to calculate the regional anomaly characteristic value by combining the regional rebound anomaly characterization value to determine the state of the material to be tested. The detection and early warning module is connected to the prior analysis module and the rebound analysis module. Based on the state of the material to be detected, it determines whether to issue a material replacement early warning signal. The prior analysis module calculates the rebound anomaly characterization value of the detection point, including, The ratio of the hardness coefficient to the reference hardness coefficient is determined as the first influencing factor; The ratio of the compression coefficient to the benchmark compression coefficient is determined as the second influencing factor; The weighted sum of the first influence factor and the second influence factor is determined to be the rebound variation characterization value; The prior analysis module determines the state of the material to be tested and determines whether region division is necessary. If the rebound mutation characterization value and the adjacent rebound mutation characterization value do not meet the preset conditions, then the state of the material to be tested is determined to be the state to be analyzed, and no region division is required. If the rebound mutation characterization value and the adjacent rebound mutation characterization value meet the preset conditions, then the state of the material to be tested is determined to be non-uniform, and region division is required. The preset condition is that the rebound variation characterization value and the adjacent rebound variation characterization value are both less than the rebound variation characterization value threshold, and the absolute value of the difference between the rebound variation characterization value and the adjacent rebound variation characterization value is greater than the preset tolerance threshold. The rebound detection module determines the regional rebound anomaly characterization value and analyzes the anomaly trend, including: The rebound detection area is divided into several identical sub-regions, several sub-detection points are determined, and several sub-rebound anomaly characterization values ​​are calculated. The average value of each sub-rebound anomaly characterization value is determined as the regional rebound anomaly characterization value; If the rebound mutation characterization value of the region is greater than the rebound mutation characterization value threshold, then the rebound detection region is determined to have a strong mutation tendency. If the rebound variation characterization value of the region is less than or equal to the rebound variation characterization value threshold, then the rebound detection region is determined to have a weak variation tendency.

2. The rebound strength testing system for energy-saving and environmentally friendly curtain wall materials according to claim 1, characterized in that, The prior analysis module determines the hardness coefficient and compressibility coefficient, including, The ratio of the hardness value at the test point to the reference hardness value is determined as the hardness coefficient; The ratio of the compression duration at the detection point to the reference compression duration is determined as the compression coefficient.

3. The rebound strength testing system for energy-saving and environmentally friendly curtain wall materials according to claim 1, characterized in that, The springback detection module determines the springback detection area of ​​the material to be tested, including: The rebound mutation characterization value and the adjacent rebound mutation characterization value are sorted in descending order to determine the first detection point; The first detection point is determined as the midpoint, and the distance between the detection point and the adjacent detection point is determined as the extended line segment; Construct a sphere with the extended line segment as its radius and the midpoint as its center; The overlapping area between the sphere and the material to be tested is determined as the rebound detection area.

4. The rebound strength testing system for energy-saving and environmentally friendly curtain wall materials according to claim 1, characterized in that, The rebound analysis module determines environmental impact characterization values ​​based on the temperature difference and the ozone concentration, including: The ratio of the temperature difference to the reference temperature difference is determined as the first environmental factor; The ratio of the ozone concentration to the reference ozone concentration is determined as the second environmental factor; The summation of the first environmental factor and the second environmental factor is determined as the environmental impact characterization value.

5. The rebound strength testing system for energy-saving and environmentally friendly curtain wall materials according to claim 1, characterized in that, The rebound analysis module calculates regional anomaly characteristic values, including: The ratio of the region's rebound variation characterization value to the baseline rebound variation characterization value is determined as the rebound influence factor; The ratio of the environmental impact characterization value to the baseline environmental impact characterization value is determined as the environmental impact factor; The weighted sum of the rebound impact factor and the environmental impact factor is determined to be the regional variation characteristic value.

6. The rebound strength testing system for energy-saving and environmentally friendly curtain wall materials according to claim 1, characterized in that, The rebound analysis module determines the state of the material to be tested, wherein, If the regional anomaly characteristic value is greater than the regional anomaly characteristic value threshold, then the state of the material to be detected is determined to be abnormal. If the regional variation characteristic value is less than or equal to the regional variation characteristic value threshold, then the state of the material to be tested is determined to be normal.

7. The rebound strength testing system for energy-saving and environmentally friendly curtain wall materials according to claim 6, characterized in that, The detection and early warning module determines whether to issue a material replacement warning signal, wherein... If the condition of the material to be tested meets the warning conditions, a material replacement warning signal is issued; If the condition of the material to be tested does not meet the warning conditions, no material replacement warning signal will be issued; The warning condition is that the state of the material to be tested is the abnormal state, or that either the rebound mutation characterization value or the adjacent rebound mutation characterization value is greater than the rebound mutation characterization value threshold.

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