A method for testing and analyzing the bonding strength of tile adhesive based on big data

Abstract

The present invention belongs to the technical field of the bonding strength test of tile adhesives, and specifically discloses a method for testing and analyzing the bonding strength of tile adhesives based on big data. The method includes: selecting each test sample of each type of test tile; importing the planned test parameters of the tile adhesive to be tested currently; importing the reference pulling force of the tile adhesive to be tested under the reference test temperature and reference test humidity before import; conducting a bonding strength test, recording the test data, and conducting a test analysis of the bonding strength of the tile adhesive according to the test data; outputting a test analysis report on the bonding strength of the tile adhesive and feeding it back to the production management department. By analyzing the inventory time difference degree and inventory environment difference degree based on the inventory data of the test tile types and the planned number of test samples, and then selecting the test samples, the present invention effectively solves the problem of insufficient representativeness in the selection of current test samples. At the same time, by considering the influence of the test environment on the test results, the accuracy of the test results is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of tile adhesive bonding strength testing. Specifically, it relates to a method for testing and analyzing the bonding strength of tile adhesives based on big data. Background Art

[0002] Tile adhesives, also known as tile binders or tile mortars, are tested for their bonding strength to ensure that tiles can firmly adhere to the substrate after installation and will not easily fall off. This is very important in the construction and decoration industries as it directly relates to the construction quality and safety.

[0003] As an existing technology, a bonding strength testing device disclosed in a Chinese invention patent application with the application publication number CN108445657A converts a traditional bonding strength testing device into a thrust testing device by applying a thrust to the sample to be tested by a force applying component, and uses the thrust to evaluate the bonding performance of the material, more accurately simulating the external forces received by the product during film tearing or other operations, and reasonably evaluating the bonding and sealing performance of the material.

[0004] Regarding the above two technical solutions, obviously, the current testing and analysis of the bonding strength of tile adhesives mainly focuses on the testing method, and lacks consideration of the testing object, that is, the external influencing factors of the test. There are also the following deficiencies: 1. Insufficient attention is paid to the representativeness of the test samples. The performance of tiles in different inventory situations may have slight deviations, and currently, targeted bonding tests are not combined with different inventory situations, resulting in insufficient representativeness of the test results.

[0005] 2. There are certain deviations in the accuracy of the test results. Currently, less consideration is given to the test environment, and there are certain errors in the method of directly using the test data to obtain the analysis results. Summary of the Invention

[0006] In view of this, to solve the problems raised in the above background art, a method for testing and analyzing the bonding strength of tile adhesives based on big data is proposed.

[0007] The object of the present invention can be achieved by the following technical solutions: The present invention provides a method for testing and analyzing the bonding strength of tile adhesives based on big data, including: S1. Selection of test samples: Extract the types of test tiles of the tile adhesive to be tested currently, the inventory data of each type of test tile, and the planned number of test samples, and select each test sample of each type of test tile.

[0008] S2. Import of test parameters: Import the planned test parameters of the tile adhesive to be tested currently, including the test temperature and test humidity.

[0009] S3. Import of test performance: Import the reference pull-out force of the tile adhesive to be tested before the test under the reference test temperature and reference test humidity.

[0010] S4. Test Execution and Result Analysis: Based on the test parameters, perform bond strength tests on each test sample of each test tile type, record the test data, and conduct test analysis on the bond strength of the tile adhesive according to the test data.

[0011] S5. Test Result Output: Based on the test analysis results of the tile bond strength, output a test analysis report on the bond strength of the tile adhesive and feedback it to the production management department.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By analyzing the inventory time difference degree and inventory environment difference degree based on the inventory data of the test tile types and the planned number of test samples, and then selecting test samples, the present invention effectively solves the problem of insufficient representativeness in the selection of current test samples, fully considers the external influencing factors of the test, facilitates targeted adhesion tests according to different inventory situations, and thus improves the representativeness and persuasiveness of subsequent test results.

[0013] (2) By combining the inventory time difference degree and inventory environment difference degree to select test samples, the present invention can comprehensively understand the performance and stability of tiles under different time periods and different inventory environments, thereby improving the comprehensiveness of the test. At the same time, it can increase the diversity and representativeness of the samples, making the test results more accurately reflect the actual performance under different conditions, and thus improving the reliability of the test results.

[0014] (3) When analyzing the inventory environment difference degree, by setting the tile environment interference factor according to the tile packaging method and setting the environment deviation compensation factor according to the difference situation of the environmental monitoring values, the present invention can more deeply understand the specific performance of tiles in various environments under different packaging methods, thereby improving the accuracy and effectiveness of the difference analysis results in the inventory link and avoiding the one-sidedness of only conducting single-value comparison.

[0015] (4) When the number of test samples is inconsistent with the number of regions, by setting the selection rules for the inventory regions, the present invention can ensure the selection of test samples from each representative region, avoid concentrating only on certain specific regions, resulting in the test results being unable to comprehensively reflect the overall inventory situation. At the same time, it can make the selection process more orderly and efficient, reduce unnecessary time and resource waste, and also improve the rationality of the selection of test samples.

[0016] (5) By considering the pull-out force under different test temperatures and different test humidities, and then conducting bond conformity analysis and calculating the passing degree of the tile adhesive bond strength test, the present invention fully considers the influence of the test environment on the test results, thereby ensuring the accuracy of the test results, avoiding the errors in direct test data analysis, and improving the reference value of the test results. Description of the Drawings

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a schematic flowchart of the implementation steps of the method of the present invention.

[0019] Figure 2 It is a schematic flowchart for confirming the selection rules of the inventory area of the present invention. Specific implementation manners

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0021] Please refer to Figure 1 As shown, the present invention provides a method for testing and analyzing the bonding strength of tile adhesive based on big data. The method includes: S1. Selection of test samples: Extract the types of test tiles of the currently to-be-tested tile adhesive, the inventory data of each type of test tile, and the planned number of test samples, and select each test sample of each type of test tile.

[0022] Specifically, the inventory data includes the inventory duration corresponding to each inventory area, the tile packaging method, and the monitoring environment data corresponding to each monitoring day. Among them, the monitoring environment data includes, but is not limited to, the highest monitoring temperature and the highest monitoring humidity.

[0023] S2. Import of test parameters: Import the planned test parameters of the currently to-be-tested tile adhesive, including the test temperature and the test humidity.

[0024] Exemplarily, the test temperature, the test humidity, and the test pulling force are all set according to actual needs and standard requirements. The test temperature is, for example, 0°C, 23°C, and 50°C, and the test humidity is, for example, 40%rh, 60%rh, and 80%rh.

[0025] S3. Import of test performance: Import the reference pulling force of the to-be-tested tile adhesive under the reference test temperature and the reference test humidity.

[0026] S4. Test Execution and Result Analysis: Based on the test parameters, perform bond strength tests on each test sample of each test tile type, record the test data, and conduct test analysis on the bond strength of the tile adhesive according to the test data.

[0027] Specifically, the test data includes, but is not limited to, the pull-out forces of each test sample corresponding to each test tile type at each test temperature and each test humidity.

[0028] S5. Test Result Output: Based on the test analysis results of the tile bond strength, output a test analysis report on the bond strength of the tile adhesive and feedback it to the production management department.

[0029] Specifically, in step S1, selecting each test sample of each test tile type includes: S11. Extract the inventory duration, tile packaging method, and monitoring environment data corresponding to each inventory area on each monitoring day from the inventory data.

[0030] S12. Calculate the standard deviation of the inventory duration of each inventory area corresponding to the same test tile type, and use the calculation result of the standard deviation as the inventory time difference degree.

[0031] S13. Based on the monitoring environment data corresponding to each monitoring day, calculate the inventory environment difference degree of each test tile type.

[0032] S14. If both the inventory time difference degree and the inventory environment difference degree of a certain test tile type are less than or equal to the set corresponding reference difference degree, then record this test tile type as a uniformly stocked tile type.

[0033] S15. Count the number of inventory areas of the uniformly stocked tile types, compare the number of inventory areas with the planned number of test samples. If the two are equal, randomly select one tile from each inventory area to form each test sample. If the two are not equal, confirm the selection rules for each inventory area, and randomly select each test sample based on the selection rules.

[0034] S16. If either the inventory time difference degree or the inventory environment difference degree of a certain test tile type is greater than the set corresponding reference difference degree, then record this planned tile type as an increased test tile type, confirm the appropriate number of test samples for the increased test tile type, and perform corresponding selection according to the selection method of each test sample corresponding to the uniformly stocked tile types based on the appropriate number of test samples for the increased test tile type.

[0035] In a specific embodiment, when the number of inventory areas is an integer multiple of the planned number of test samples, the corresponding selection can also be carried out in the manner described in step S15. When the number of inventory areas is not an integer multiple of the planned number of test samples, each test sample can be randomly selected with reference to the selection rules. An example of a single selection given in the present invention is the description when the number of inventory areas is equal to the planned number of test samples, and when the two are not equal and neither exceeds one time of the other, that is, the number of inventory areas does not exceed one time of the planned number of test samples or the planned number of test samples does not exceed one time of the number of inventory areas.

[0036] It should be added that the storage time of the tiles in the inventory may affect their surface state. Long-term storage may cause dust adsorption or minor chemical changes on the tile surface, thereby affecting the bonding effect of the tile adhesive. At the same time, the temperature and humidity conditions of the storage environment will affect the state of the tiles. For example, in a high-humidity environment, the tile surface may absorb moisture, affecting the bonding force of the tile adhesive. Therefore, the difference analysis is carried out from two aspects of the inventory time and the storage environment, and the test samples are selected to ensure the accuracy and representativeness of the test sample selection.

[0037] In the embodiment of the present invention, by combining the inventory time difference degree and the inventory environment difference degree for test sample selection, the performance and stability of the tiles under different time periods and different inventory environments can be comprehensively understood, thereby improving the comprehensiveness of the test. At the same time, the diversity and representativeness of the samples can be increased, so that the test results can more truly reflect the actual performance under different conditions, thereby improving the reliability of the test results.

[0038] Further, calculating the inventory environment difference degree of each test tile type in step S13 includes: J1. Based on the tile packaging method, set the tile environment interference factor corresponding to each test tile type in each inventory area, denoted as μ ij , where i represents the test tile type number, i = 1, 2,... n, and j represents the inventory area number, j = 1, 2,... m.

[0039] J2. Extract the highest monitored temperature and the highest monitored humidity from the monitored environment data, and compare the highest monitored temperature and the highest monitored humidity with the set suitable storage temperature and the set suitable storage humidity correspondingly.

[0040] J3. If the highest monitored temperature corresponding to a certain monitoring day exceeds the set suitable storage temperature or the highest monitored humidity exceeds the set suitable storage humidity, mark this monitoring day as a storage deviation day.

[0041] J4. Count the number of storage deviation days and the number of monitoring days, and use the ratio of the two as the storage deviation ratio. At the same time, set the environmental deviation compensation factor.

[0042] J5. Denote the storage deviation ratio and environmental deviation compensation factor corresponding to each inventory area for each type of test tile as k. ij and Take as the target deviation ratio corresponding to each inventory area for each type of test tile.

[0043] J6. Calculate the standard deviation of the target deviation ratios corresponding to each inventory area for the same type of test tile, and denote the calculation result of the standard deviation as the inventory environmental difference degree, so as to obtain the inventory environmental difference degree for each type of test tile.

[0044] It can be understood that in step J1, setting the tile environmental interference factors corresponding to each inventory area for each type of test tile includes: J11. Traverse the tile packaging methods corresponding to each inventory area for each type of test tile.

[0045] J12. If the tile packaging method in a certain inventory area is sealed packaging, denote the tile environmental interference factor of this inventory area as ε 0 .

[0046] J13. If the tile packaging method in a certain inventory area is open packaging, then denote the tile environmental interference factor of this inventory area as ε 1 , so as to obtain the tile environmental interference factors μ corresponding to each inventory area for each type of test tile ij , μ ij takes values of ε 0 or ε 1 , ε 0 + ε 1 = 1 and ε 0 < ε 1 .

[0047] In a specific embodiment, when the tile packaging method is open packaging, it is in closer contact with the environment, and it is easy for dust and impurities to adhere to the tile surface. Especially when stored for a long time, the tile surface may become dirty, affecting the subsequent bonding effect. At the same time, temperature and humidity will also directly affect the surface performance of the tile. Therefore, set the tile environmental interference factor of sealed packaging to be less than that of open packaging. Exemplarily, ε 0 can take a value of 0.3, and ε 1 can take a value of 0.7.

[0048] It can also be understood that in step J4 of setting the environmental deviation compensation factor, it includes: J41. If the difference between the highest monitored temperature on a certain storage deviation day and the set suitable storage temperature is within the set reference difference range, then take 0 as the temperature deviation factor for this storage deviation day. Otherwise, denote the highest monitored temperature on this storage deviation day as T 0 , denote the set suitable storage temperature as T 1 , take As the temperature deviation factor for the storage deviation day, denoted as α, the temperature deviation factors for each storage deviation day are obtained and denoted as φ t , φ t takes a value of 0 or α, α > 0, e is the natural constant, t represents the storage deviation day number, t = 1, 2,......h.

[0049] J42. Similarly, according to the setting method of the temperature deviation factors for each storage deviation day, the humidity deviation factors for each storage deviation day are set and denoted as

[0050] J43. Calculate the mean values of the temperature deviation factors and humidity deviation factors corresponding to each storage deviation day to obtain the average temperature deviation factor and the average humidity deviation factor. Set the weights of temperature and humidity, and obtain the environmental deviation compensation factor through weighted summation.

[0051] In a specific embodiment, the weights of temperature and humidity can be set to equal weights, that is, both the weight of temperature and the weight of humidity can take a value of 0.5.

[0052] It should be added that the environmental deviation compensation factor is to compensate for the differences in the environmental impacts suffered by different types of tiles under different storage conditions, reduce the error of only considering the overall time level and ignoring the specific deviation numerical degree at present. It is a factor set based on the tile type and storage conditions, used to adjust the storage deviation ratio, so that it can better reflect the performance of the tiles under actual storage conditions, and thus improve the rationality and reliability of the analysis results of the inventory environment difference degree.

[0053] Exemplarily, for the convenience of analysis, assume that there are two planned test tile types (i = 1, 2), stored in two inventory areas respectively (j = 1, 2), and the packaging method of the first planned test tile type is sealed packaging, and the packaging method of the second planned test tile type is also sealed packaging. The set suitable storage temperature is 25°C, and the set suitable storage humidity is 50%rh. Then the specific calculation example process for calculating the inventory environment difference degree of each planned test tile type is as follows: Step 1. Set the environmental interference factors, where the environmental interference factor for sealed packaging: 0.3, and the environmental interference factor for open packaging: 0.7.

[0054] Step 2. Extract the highest monitored temperature and the highest monitored humidity corresponding to each monitored day in each inventory cycle for each planned test tile type in each inventory area, as shown in Table 1 and Table 2.

[0055] Step 3: Calculate the storage deviation days: Assume that the number of storage deviation days for the first planned test tile type in storage area 1 is 30, and in storage area 1 is 0. Assume that the number of storage deviation days for the second planned test tile type in storage area 1 is 20, and in storage area 1 is 5.

[0056] Step 4: Count the number of monitoring days: Assume that the total number of monitoring days for each storage area is 100 days.

[0057] Step 5: Calculate the storage deviation ratio: The storage deviation ratio of the first planned test tile type in storage area 1 is: The storage deviation ratio of the first planned test tile type in storage area 1 is 0, and the storage deviation ratio of the second planned test tile type in storage area 1 is The storage deviation ratio of the second planned test tile type in storage area 1 is

[0058] Step 6: Set the environmental deviation compensation factor: Assume that the environmental deviation compensation factor of the first planned test tile type in storage area 1 is 0.5, and in storage area 2 is 0.3. Assume that the environmental deviation compensation factor of the second planned test tile type in storage area 1 is 0.6, and in storage area 2 is 0.4.

[0059] Step 7: Calculate the target deviation ratio: The target deviation ratio of the first planned test tile type in storage area 1 is: 0.3 * (1 + 0.5) * 0.3 = 0.135, and in storage area 1 is: 0 * (1 + 0.3) * 0.3 = 0. The target deviation ratio of the first planned test tile type in storage area 1 is: 0.2 * (1 + 0.6) * 0.7 = 0.224, and in storage area 1 is: 0.05 * (1 + 0.4) * 0.7 = 0.049.

[0060] Step 8: Calculate the standard deviation of the target deviation ratio: The average value of the target deviation ratio of the first planned test tile type is 0.0675. Denote the standard deviation of the target deviation ratio of the first planned test tile type as σ 1 , The average value of the target deviation ratio of the second planned test tile type is 0.01365. Denote the standard deviation of the target deviation ratio of the second planned test tile type as σ 2 ≈0.2133.

[0061] Table 1 Monitoring data table of the first planned test tile type

[0062]

[0063] Monitoring Data Table of the Second Plan's Test Tile Types in Table 2

[0064]

[0065] In the above table, g represents the number of monitoring days. In combination with the example described herein, i.e., g = 100.

[0066] Furthermore, please refer to Figure 2 As shown, in step S15, the selection rules for each inventory area are confirmed, including: R1. If the number of inventory areas is greater than the number of test samples, based on the highest monitored temperature and highest monitored humidity corresponding to each inventory area on each monitoring day, the inventory environment similarity between each inventory area is calculated by a similarity method.

[0067] R2. If the inventory environment similarity between a certain inventory area and another inventory area is greater than the set reference similarity, then the other inventory area is used as the similar area of this inventory area.

[0068] R3. The difference between the number of inventory areas and the number of test samples is used as the number of inventory areas to be removed, denoted as M.

[0069] R4. Count the number of similar areas of each inventory area, sort each inventory area in descending order according to the number of its similar areas, and use the top M inventory areas as the inventory areas to be excluded. The remaining inventory areas after exclusion are used as the selected inventory areas, and the sample number of each selected inventory area is set to 1.

[0070] R5. If the number of inventory areas is less than the number of test samples, the difference between the number of test samples and the number of inventory areas is used as the number of additional samples.

[0071] R6. The number of inventory areas is used as the number of samples selected at one time. Random test samples are selected from each inventory area one by one, and each inventory area is sorted in ascending order according to the number of its similar areas. Based on the sorting order, the selection and distribution of additional samples are carried out in sequence.

[0072] It can be understood that the similarity method described in step R1 includes, but is not limited to, Euclidean distance or cosine similarity in a specific embodiment. For the convenience of analysis, Euclidean distance can be selected here for similarity calculation.

[0073] Exemplarily, assume that we have two inventory areas, inventory area 1 and inventory area 2. Calculate the average highest monitored temperature T′ and average highest monitored humidity rh′ of inventory area 1. Assume T′ = 28°C and rh′ = 60%.

[0074] Calculate the average maximum monitored temperature T″ and the average maximum monitored humidity rh″ of inventory area 2, assuming T″ = 27 °C and rh″ = 58%.

[0075] Calculate the Euclidean distance between inventory area 1 and inventory 2:

[0076] Assume that the maximum Euclidean distance between all pairs of inventory areas is 10, and calculate the similarity between inventory area 1 and inventory 2, denoted as ξ (1,2) ,

[0077] It should be added that the Euclidean distances between all pairs of inventory areas are confirmed according to specific calculations, and the values given here are only for example analysis.

[0078] Exemplarily, when the number of inventory areas is greater than the number of test samples, assume there are 5 inventory areas (A, B, C, D, E) and the number of test samples is 3.

[0079] Calculate the similarity between each pair of inventory areas, and set the reference similarity to 0.7. Assume the calculated similarity results are as follows: the similarity between inventory area A and inventory area B is 0.8, the similarity between inventory area A and inventory area C is 0.5, the similarity between inventory area A and inventory area D is 0.3, the similarity between inventory area A and inventory area E is 0.6, the similarity between inventory area B and inventory area C is 0.4, the similarity between inventory area B and inventory area D is 0.2, the similarity between inventory area B and inventory area E is 0.7, the similarity between inventory area C and inventory area D is 0.3, the similarity between inventory area C and inventory area E is 0.5, and the similarity between inventory area D and inventory area E is 0.4.

[0080] Count the number of similar areas as follows: there are 2 for inventory area A, 3 for inventory area B, 2 for inventory area C, 1 for inventory area D, and 3 for inventory area E.

[0081] Sort the inventory areas: inventory area D, inventory area A, inventory area C, inventory area B, inventory area E.

[0082] The number of inventory areas M to be removed 0 = 5 - 3 = 2, that is, remove inventory area D and inventory area A, and take inventory area C, inventory area B, and inventory area E as the selected inventory areas.

[0083] Another example is that when the number of inventory areas is less than the number of test samples. Suppose there are 2 inventory areas (A and B) and the number of test samples is 3. The number of samples to be added is 2. Taking 2 as the number of samples selected at one time, a test sample is randomly selected from the randomly selected inventory areas A and B. Suppose the number of similar areas in inventory area A is 1 and the number of similar areas in inventory area B is 0, then another test sample is randomly selected from inventory area B.

[0084] In the embodiment of the present invention, when the number of test samples and the number of areas are inconsistent, by setting the selection rules for inventory areas, it is possible to ensure that test samples are selected from each representative area, avoiding the situation where the test results cannot comprehensively reflect the overall inventory situation due to concentration only in certain specific areas. At the same time, the selection process can be made more orderly and efficient, reducing unnecessary time and resource waste, and also improving the rationality of test sample selection.

[0085] Furthermore, in step S16, confirming the appropriate number of test samples for adding test tile types includes: 1) respectively denoting the inventory time difference degree and inventory environment difference degree of adding test tile types as γ 0 and δ 0 .

[0086] 2) Setting a test sample compensation factor, denoted as λ, γ′ and δ′ are respectively the set inventory time reference difference degree and set inventory environment reference difference degree.

[0087] 3) Denoting the planned number of test samples for adding test tile types as F 0 , calculating the appropriate number of test samples F′ for adding test tile types, F′ = F 0 *(1 + λ).

[0088] In a specific embodiment, the specific values of the set inventory time reference difference degree and set inventory environment reference difference degree depend on multiple factors, including but not limited to the sensitivity of tile types, expected changes in storage conditions, past experience data, etc. Exemplarily, assuming that the average storage time of most tiles is one month, then the inventory time reference difference degree can be set to 10% of the average time, that is, the set inventory time reference difference degree can specifically take the value of 3, and the set inventory environment reference difference degree can specifically take the value of 0.2.

[0089] In the embodiment of the present invention, by analyzing the inventory time difference degree and inventory environment difference degree based on the inventory data of test tile types and the planned number of test samples, and then selecting test samples, the problem of insufficient representativeness in current test sample selection is effectively solved, fully considering the external influencing factors of the test, facilitating targeted bonding tests according to different inventory situations, and thus improving the representativeness and persuasiveness of subsequent test results.

[0090] Specifically, the tile adhesive bonding strength test and analysis described in step S4 include: S41. Extract the pulling forces of each test tile type corresponding to each test sample at each test temperature and each test humidity from the test data, and analyze the bonding compliance of each test tile type corresponding to each test sample.

[0091] S42. Denote the test samples with a bonding compliance greater than the set reference bonding compliance as compliant samples, count the number of compliant samples corresponding to each test tile type, and compare it with the number of its test samples to obtain the sample test compliance ratio.

[0092] S43. Calculate the mean value of the sample test compliance ratios of each test tile type to obtain the average sample test compliance ratio, denoted as k. b 。

[0093] S44. Denote the test tile types with a test compliance ratio greater than the set reference test compliance ratio as compliant tile types, count the number of compliant tile types, and compare it with the number of test tile types. Take the ratio as the tile type compliance ratio, denoted as k. z 。

[0094] S451. Calculate the passing degree ψ of the tile adhesive bonding strength test, ψ = f 1 *k b +f 2 *k z ,f 2 and f 1 respectively represent the weights of the sample test compliance ratio and the tile type compliance ratio, f 1 >f 2 ,and f 2 +f 1 =1. Take ψ as the result of the tile adhesive bonding strength test analysis.

[0095] It should be added that the sample test compliance ratio reflects the consistency performance of the same tile adhesive sample under different test conditions, such as temperature and humidity. Specifically, it examines whether the bonding performance of the same tile adhesive sample is stable under different environmental conditions. The sample test compliance ratio targets the performance of a single sample under different conditions and more specifically and directly reflects the actual use situation of the tile adhesive. The tile type compliance ratio, on the other hand, examines the overall performance consistency of different tile adhesive types at a macroscopic level. It focuses on the overall performance of different types of tile adhesives. In actual applications, the consistency performance of specific samples is often more important because it directly affects the actual experience of users. Therefore, f 1 >f 2 ,and exemplarily, f 1 can specifically take the value of 0.7, f 2Specifically, it can take the value of 0.3.

[0096] It should also be added that the set reference test matching ratio can specifically take the value of 0.7.

[0097] Furthermore, analyzing the bonding conformity of each test tile type corresponding to each test sample in step S41 includes: based on the reference pulling force, calculating the pulling force difference degrees of each test tile type corresponding to each test sample at each test temperature and the pulling force difference degrees at each test humidity, and performing mean value calculation to obtain the average temperature pulling force difference degree and the average humidity pulling force difference degree corresponding to each test tile type and each test sample, which are respectively denoted as χ id and χ i ′ d , where d represents the test sample number, d = 1, 2,......x.

[0098] Calculating the bonding conformity of each test tile type corresponding to each test sample χ 0 and Δχ are respectively the pulling force difference degree of the set reference and the deviation of the pulling force difference degree.

[0099] It should be added that calculating the pulling force difference degrees of each test tile type corresponding to each test sample at each test temperature and the pulling force difference degrees at each test humidity is the same calculation method. Among them, calculating the pulling force difference degrees of each test tile type corresponding to each test sample at each test temperature includes: denoting the pulling force of each test tile type corresponding to each test sample at each test temperature as N idq , where q represents the q-th test temperature, q = 1, 2,......y.

[0100] Calculating the pulling force difference degrees of each test tile type corresponding to each test sample at each test temperature

[0101] It should also be added that setting the max(·) calculation method is to avoid the influence of the calculation value signs of the temperature pulling force difference degree and the humidity pulling force difference degree on the overall analysis result during calculation, and to maintain the singularity and stability of the analysis result.

[0102] In a specific embodiment, for the convenience of analysis, χ 0 and Δχ can both take the value of 0.5 megapascals.

[0103] The embodiment of the present invention analyzes the bonding conformity and calculates the passing degree of the tile adhesive bonding force test by considering the pulling forces at different test temperatures and different test humidities, fully considers the influence of the test environment on the test result, thereby ensuring the accuracy of the test result, avoiding the errors existing in the direct test data analysis, and improving the reference value of the test result.

[0104] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology can make various modifications, supplements, or use similar methods to substitute for the specific embodiments described, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for testing and analyzing the adhesion of tile adhesive based on big data, characterized in that: include: S1. Test sample selection: extract the test tile types of the tile adhesive to be tested, the inventory data of each test tile type, and the planned number of test samples, and select each test sample of each test tile type; If the inventory time difference and inventory environment difference of a certain test tile type are both less than or equal to the set corresponding reference difference, then the test tile type is recorded as a uniform inventory tile type; Count the number of stock areas with uniform stock of tile types, compare the number of stock areas with the planned number of test samples, if the two are equal, randomly select one tile from each stock area to form each test sample, if the two are not equal, confirm the selection rule of each stock area, and randomly select each test sample based on the selection rule; If the inventory time difference or inventory environment difference of a certain test tile type is greater than the set corresponding reference difference, the test tile type is recorded as an added test tile type, and the appropriate number of test samples for the added test tile type is determined. Based on the appropriate number of test samples for the added test tile type, corresponding selection is performed according to the selection method of each test sample corresponding to the uniform inventory tile type; S2. Test parameter import: import the planned test parameters of the tile adhesive to be tested, including test temperature and test humidity; S3. Test performance introduction: reference pull-out force of the tile adhesive to be tested at the benchmark test temperature and benchmark test humidity before introduction; S4, test execution and result analysis: performing an adhesion test on each test sample of each test tile type based on the test parameters, recording the test data, and performing tile adhesive adhesion test analysis based on the test data; S5. Test result output: Based on the tile adhesion test analysis results, output the tile adhesive adhesion test analysis report and feedback to the production management department.

2. A method for testing and analyzing the adhesion of tile adhesive based on big data as claimed in claim 1, characterized in that: The step of selecting test samples of each test tile type comprises: Extract the inventory duration, tile packaging method and monitoring environment data corresponding to each inventory area on each monitoring day from the inventory data; Calculate the standard deviation of the inventory time of each inventory area for the same test tile type, and use the standard deviation calculation result as the inventory time difference; Based on the monitoring environment data corresponding to each monitoring day, the inventory environment difference of each test tile type is calculated.

3. A method for testing and analyzing the adhesion of tile adhesive based on big data as claimed in claim 2, characterized in that: The calculation of the inventory environment difference of each test tile type includes: Based on the tile packaging method, set the tile environmental interference factor for each test tile type corresponding to each inventory area, denoted as μ ij , i represents the test tile type number, i=1,2,......n, j represents the inventory area number, j=1,2,......m; Extracting the highest monitored temperature and the highest monitored humidity from the monitored environment data, and comparing the highest monitored temperature and the highest monitored humidity with the set suitable storage temperature and the set suitable storage humidity; If the highest monitored temperature corresponding to a monitoring day exceeds the set suitable storage temperature or the highest monitored humidity exceeds the set suitable storage humidity, the monitoring day will be recorded as a storage deviation day; Count the number of storage deviation days and monitoring days, use the ratio of the two as the storage deviation ratio, and set the environmental deviation compensation factor; The storage deviation ratio and environmental deviation compensation factor of each inventory area corresponding to the test tile type are denoted as k ij and Will as the target deviation ratio for each test tile type corresponding to each stock area; The standard deviation of the target deviation ratio of each inventory area for the same test tile type is calculated, and the standard deviation calculation result is recorded as the inventory environment difference, thereby obtaining the inventory environment difference of each test tile type.

4. A method for testing and analyzing the adhesion of tile adhesive based on big data as claimed in claim 3, characterized in that: The setting of the tile environmental interference factor for each test tile type corresponding to each inventory area includes: Traverse the tile packaging methods for each test tile type corresponding to each inventory area; If the packaging method of tiles in a certain inventory area is sealed packaging, the environmental interference factor of tiles in this inventory area is recorded as ε0; If the packaging method of tiles in a certain inventory area is open packaging, the tile environmental interference factor of the inventory area is recorded as ε1, so as to obtain the tile environmental interference factor μ of each inventory area corresponding to each test tile type. ij , μ ij The value is ε0 or ε1, ε0+ε1=1 and ε0<ε1.

5. A method for testing and analyzing the adhesion of tile adhesive based on big data as claimed in claim 3, characterized in that: The setting of the environmental deviation compensation factor includes: If the difference between the highest monitored temperature and the set suitable storage temperature on a storage deviation day is within the set reference difference range, 0 is used as the temperature deviation factor for the storage deviation day. Otherwise, the highest monitored temperature on the storage deviation day is recorded as T0, the set suitable storage temperature is recorded as T1, and As the temperature deviation factor of the storage deviation day, it is recorded as α, and the temperature deviation factor of each storage deviation day is obtained, which is recorded as φ t ,φ t The value is 0 or α, α>0, e is a natural constant, t represents the storage deviation day number, t=1,2,......h; The humidity deviation factor of each storage deviation day is set in the same way as the temperature deviation factor of each storage deviation day, which is recorded as Calculate the mean of the temperature deviation factor and humidity deviation factor corresponding to each storage deviation day to obtain the average temperature deviation factor and the average humidity deviation factor, set the weights of temperature and humidity, and obtain the environmental deviation compensation factor through weighted summation.

6. A method for testing and analyzing the adhesion of tile adhesive based on big data as claimed in claim 2, characterized in that: The selection rules for confirming each inventory area include: If the number of inventory areas is greater than the number of test samples, the inventory environment similarity between the inventory areas is calculated using a similarity method based on the highest monitored temperature and the highest monitored humidity of each inventory area on each monitoring day; If the inventory environment similarity between a certain inventory area and another inventory area is greater than the set reference similarity, the other inventory area is used as a similar area of ​​the inventory area; The difference between the number of inventory areas and the number of test samples is taken as the number of inventory areas to be removed, denoted as M; Count the number of similar areas in each inventory area, sort the inventory areas from large to small according to the number of similar areas, take the top M inventory areas as the eliminated inventory areas, take the remaining inventory areas after elimination as the selected inventory areas, and set the number of samples of each selected inventory area to 1; If the number of inventory areas is less than the number of test samples, the difference between the number of test samples and the number of inventory areas is used as the number of additional samples; The number of inventory areas is used as the number of samples to be selected at one time, and random test samples are selected from each inventory area in turn. Each inventory area is sorted from small to large according to the number of similar areas, and the number of samples is increased based on the sorting order.

7. A method for testing and analyzing the adhesion of tile adhesive based on big data as claimed in claim 2, characterized in that: The confirmation of increasing the number of appropriate test samples for the types of tested tiles includes: Set the test sample compensation factor, denoted as λ; The planned number of test samples for increasing the types of test tiles is recorded as F0, and the appropriate number of test samples for increasing the types of test tiles is calculated as F′, F′=F0*(1+λ).

8. A method for testing and analyzing the adhesion of tile adhesive based on big data as claimed in claim 7, characterized in that: The step of setting the test sample compensation factor includes: The inventory time difference and inventory environment difference of adding test tile types are denoted as γ0 and δ0 respectively; Set the test sample compensation factor λ, γ′ and δ′ are the reference difference of the set inventory time and the reference difference of the set inventory environment, respectively.

9. The method for testing and analyzing the adhesion of tile adhesive based on big data according to claim 3, characterized in that: The tile adhesive bonding test analysis comprises: Extracting the pull-out force of each test sample corresponding to each test tile type at each test temperature and each test humidity from the test data, and analyzing the bonding conformity of each test sample corresponding to each test tile type; The test samples whose bonding degree is greater than the set reference bonding degree are recorded as matching samples, and the number of matching samples corresponding to each test tile type is counted and compared with the number of test samples to obtain the sample test matching ratio; The sample test matching ratio of each test tile type is averaged to obtain the average sample test matching ratio, which is denoted as k b ; The test tile types whose test matching ratio is greater than the set reference test matching ratio are recorded as matching tile types. The number of matching tile types is counted and compared with the number of test tile types. The ratio is taken as the tile type matching ratio, recorded as k z ; Calculate the tile adhesive adhesion test standard ψ, ψ=f1*k b +f2*k z , f2 and f1 represent the weights of the sample test matching ratio and the tile type matching ratio respectively, f1>f2, and f2+f1=1, and ψ is taken as the analysis result of the tile adhesive adhesion test.

10. A method for testing and analyzing the adhesion of tile adhesive based on big data as claimed in claim 8, characterized in that: The analysis of the bonding conformity of each test tile type corresponding to each test sample includes: Based on the reference pull-out force, the pull-out force difference of each test tile type corresponding to each test sample at each test temperature and the pull-out force difference at each test humidity are calculated, and the mean is calculated to obtain the average temperature pull-out force difference and the average humidity pull-out force difference corresponding to each test tile type and each test sample, which are respectively denoted as χ id and χ′ id , d represents the test sample number, d = 1, 2, ... x; Calculate the bonding fit of each test tile type to each test sample χ0 and Δχ are the pull-out force difference and pull-out force difference deviation of the set reference, respectively.

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