Method for testing strength of boiler steel plate with weld defects

Abstract

The invention discloses a method for testing the strength of a boiler steel plate with weld defects, which relates to the technical field of steel plate data analysis, and comprises the following steps: collecting steel plate test samples, testing the strength of the test samples, obtaining tensile strength test results, numbering the test samples, and obtaining weld characteristic data of the test samples. Analyzing the adaptation degree between the test samples with the same welding seam defect type, analyzing the reference accuracy of test data, establishing a steel plate test selection model, obtaining welding seam characteristic data of the current to-be-processed steel plate, confirming the welding seam defect type and number of the current to-be-processed steel plate, and comparing the welding seam characteristics with the test samples with the same type. According to the method, the adaptation degree between the current to-be-machined steel plate and the test sample is analyzed, the actual data of the current to-be-machined steel plate is input into the model, the mode of obtaining the tensile strength data of the current to-be-machined steel plate is planned, the accuracy of the steel plate data needed in the actual machining process is improved, and unnecessary strength test work is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of steel plate data analysis, in particular to a strength testing method for a boiler steel plate with weld defects. Background Art

[0002] Boiler steel plate mainly refers to hot-rolled medium and thick plate materials used to manufacture superheaters, main steam pipes and boiler fire chamber heating surfaces. Boiler steel plate is a key material in boiler manufacturing. In order to ensure the safe operation of the boiler and extend its service life, it is necessary to conduct a strength test on the boiler steel plate. The strength test can ensure that the boiler steel plate has good physical properties and machinability. The tensile strength of the steel plate refers to the maximum tensile force that the steel plate can withstand during the stretching process. When conducting a tensile strength test on the steel plate, there is a method in the prior art for conducting a tensile strength test on the complete component. However, in the actual application scenario, the weld may have some defects during the welding process. The tensile strength of the steel plate with weld defects is different from that of the steel plate without weld defects. Therefore, it is necessary to conduct a tensile strength test on the steel plate with weld defects to obtain a more accurate tensile strength of the steel plate with different weld defects, so as to set appropriate working parameters in the later processing of the steel plate to achieve the purpose of steel plate quality control;

[0003] After multiple tests, the tensile strength data of steel plates with different weld defects can be collected. The collected data can be used as reference data when setting working parameters for actual processing of steel plates. If a steel plate identical to the test sample appears in actual processing later, the tensile strength of the test sample obtained by the test can be used as the tensile strength data of the steel plate in actual processing. However, during the strength test, although the type of weld defects is the same, the tensile strength of steel plates with different weld profiles may also be different, and the test data collected in the early stage may not be perfect. During the actual processing, steel plates with weld profiles that are significantly different from those of the test samples may appear. For steel plates with the same type of weld defects as the test samples but significantly different weld profiles, if the tensile strength of the test sample is still used as the tensile strength of the current actual steel plate, the data obtained may not be accurate enough. The prior art cannot obtain the tensile strength of steel plates with weld defects in actual situations in the best way, and cannot improve the accuracy of the tensile strength data of the steel plates obtained during the actual processing. Summary of the invention

[0004] The object of the present invention is to provide a strength testing method for a boiler steel plate with weld defects, so as to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for testing the strength of a boiler steel plate with a weld defect, characterized in that it comprises the following steps:

[0006] S1: Collect steel plate test samples, perform strength tests on the test samples, and obtain tensile strength test results;

[0007] S2: number the test samples, obtain the weld characteristic data of the test samples, and analyze the fit between the test samples with the same weld defect type;

[0008] S3: Count the total number of test samples with the same fitness and the number of test samples with the same fitness and the same tensile strength obtained in the test, and analyze the reference accuracy of the test data;

[0009] S4: Fitting training is performed on the sample number, fitness and reference accuracy data to establish a steel plate test selection model;

[0010] S5: obtaining weld feature data of the current steel plate to be processed, confirming the weld defect type and number of the current steel plate to be processed, comparing the weld features with the test sample of the same type, and analyzing the compatibility between the current steel plate to be processed and the test sample;

[0011] S6: Inputting the serial number and fitness of the current steel plate to be processed into the steel plate test selection model, and planning a method for obtaining the tensile strength data of the current steel plate to be processed.

[0012] Further, step S1 includes: butt welding two steel plate samples to form a weld with a width of d, preparing and collecting steel plate test samples with weld defects and different weld defect types, wherein the weld defect types include pores, slag inclusions, lack of fusion, lack of penetration and cracks, collecting a total of m steel plate test samples, the weld widths of the m steel plate test samples are all d, setting the test environment temperature to T, performing a tensile test on the test samples under the corresponding test environment, and obtaining the tensile strength of the test samples obtained by the test;

[0013] Taking into account that some defects may occur in the weld under actual circumstances, the strength of the steel plate has decreased at this time, and it can no longer be processed according to the previous strength test results of the steel plate without weld defects. The relevant parameters of the working conditions, such as temperature and pressure, should be lowered. Therefore, the steel plate test sample with welding defects is placed in a high temperature environment for tensile testing to test the tensile strength of the steel plate. The tensile strength of the steel plates with different types of welding defects obtained by the test can help reasonably adjust the working condition parameters according to the strength test results of the steel plate test samples with welding defects in the actual processing process. Taking into account the impact of weld defects on the strength of the steel plate, it is helpful to avoid the problem of affecting the quality and service life of the steel plate due to improper setting of working condition parameters.

[0014] Further, step S2 includes: randomly extracting samples with different weld defect types from the m test samples as samples to be calibrated: each weld defect type corresponds to a sample to be calibrated, numbering the remaining steel plate test samples collected after the extraction process, and the steel plate test samples with the same weld defect type have the same number, and the number set of the remaining n steel plate test samples is obtained as A={A1, A2, ..., An}, and there are the same numbers in the number set, that is, some of the n steel plate test samples have the same number, and samples representing a random type of weld defect are retrieved from the n steel plate test samples, and the number of retrieved samples is counted as k, and weld images of the k retrieved samples are photographed, and the Canny edge detection technology is used to identify k The weld contour of the sample is obtained by taking weld images of the sample to be calibrated with the same weld defect type as the k samples, identifying the weld contour of the corresponding sample to be calibrated, and extracting weld contour feature data using OpenCV technology, wherein the feature data includes the area of ​​the area surrounded by the weld contour, the boundary length of the weld contour, and the area of ​​the smallest circle that can surround the weld contour. The area set of the area surrounded by the weld contours of the k samples is {R1, R2, ..., Rk}, the boundary length set of the weld contour is {L1, L2, ..., Lk}, and the area set of the smallest circle that can surround the weld contour is {r1, r2, ..., rk}. The weld contour feature data of the sample to be calibrated with the same weld defect type as the k samples are R ’ , L ’ and r ’ , analyze the degree of fit Wi between a random sample among k samples and the sample to be calibrated:

[0015] Wi=1 / (|R ’ -Ri|+|L ’ -Li|+|r ’ -ri|);

[0016] Since there are high probability differences between weld profiles in actual situations, |R ’ -Ri|+|L ’ -Li|+|r ’ -ri|≠0, the set of fitness between k samples and the sample to be calibrated is W={W1, W2, ..., Wi, ..., Wk}, and there are equal fitness in the set W, where Ri represents the area of ​​the area enclosed by the weld contour of the i-th sample among the k samples, Li represents the boundary length of the weld contour of the i-th sample among the k samples, and ri represents the minimum circle area that can enclose the weld contour of the i-th sample;

[0017] Considering that the test sample data collected in the early stage may not be perfect, steel plates with weld profiles different from those of the test samples may appear in the actual processing process. Therefore, the samples to be calibrated are randomly selected from the test samples collected in the early stage, and the remaining steel plate test samples with the same weld defect type as the samples to be calibrated are compared with the weld feature data of the samples to be calibrated to analyze the degree of fit between the two. The higher the degree of fit, the smaller the difference in weld characteristics between the two samples.

[0018] Further, step S3 includes: counting the number of samples with the same fitness W1 as the sample to be calibrated among the k samples as a, the number of samples with the same tensile strength as the sample to be calibrated among the a samples as b, calculating the reference accuracy P1 of the tensile strength test data of the sample when the fitness between the steel plate to be processed and the sample is W1 for the sample representing a random type of weld defect, P1=b / a, and obtaining the reference accuracy set {P1, P2, ..., Pk}, Pk represents the reference accuracy P1 of the tensile strength test data of the sample when the fitness between the steel plate to be processed and the sample is W1 for the sample representing a random type of weld defect The sample of the type weld defect, when the fitness between the steel plate to be processed and the sample is Wk, the reference accuracy of the tensile strength test data of the sample is obtained, the fitness set between the n test samples representing all types of weld defects and the corresponding types of the same to-be-calibrated samples is obtained as w={w1, w2, ..., wn}, where the set w contains the set W, and the reference accuracy set of the tensile strength test data of the sample when the fitness between the steel plate to be processed and the sample is respectively the fitness in the set w is obtained as p={p1, p2, ..., pn};

[0019] After analyzing the degree of fit between the remaining samples and the samples to be calibrated, the number of samples with the same degree of fit as the samples to be calibrated and the number of samples with the same degree of fit and the same tensile strength are counted from the remaining samples. The ratio between the two numbers of samples is calculated to analyze the reference accuracy of the test data of the reference sample as the tensile strength data of the actual steel plate to be processed when the degree of fit between the actual steel plate to be processed and the sample is different. For example, when the degree of fit is W1, the higher the proportion of the number of samples with the same tensile strength as the sample to be calibrated, the more likely it is that the tensile strength of the steel plate to be processed with a degree of fit W1 with the sample is the tensile strength of the corresponding sample. At this time, the sample test data is used as the tensile strength of the actual steel plate to be processed. The more accurate the strength data of the actual steel plate to be processed is, the higher the reference accuracy is.

[0020] Further, step S4 includes: forming a training data set {(A1, w1, p1), (A2, w2, p2), ..., (An, wn, pn)} with sample numbers, fitness and reference accuracy data, performing fitting training on the training data, and establishing a steel plate test selection model:

[0021] z=ζ0*x+ζ1*y+ζ2;

[0022] Among them, x represents the first variable representing the serial number in the steel plate test selection model, y represents the second variable representing the fitness in the model, z represents the dependent variable representing the reference accuracy in the model, and ζ0, ζ1, and ζ2 represent the fitting coefficients.

[0023] Further, step S5 includes: obtaining the weld defect type of the currently to-be-processed steel plate, and obtaining that the weld defect type of the currently to-be-processed steel plate is the same as that of the test sample numbered A ’ , A ’ ∈ A, and there are e test samples numbered A ’ . Calculate the fitness between the currently to-be-processed steel plate and the e test samples respectively. The calculation method of the fitness between the currently to-be-processed steel plate and the e test samples is the same as that of Wi.

[0024] Further, step S6 includes: comparing the fitness magnitudes between the currently to-be-processed steel plate and the e test samples, and obtaining that the highest fitness among the e test samples with the currently to-be-processed steel plate is Q max . Input A ’ and Q max into the steel plate test selection model: Let x = A ’ , y = Q max . Predict that the reference accuracy of using the tensile strength test data of the sample with the highest fitness with the currently to-be-processed steel plate among the e test samples as the reference data for the tensile strength of the currently to-be-processed steel plate is: ζ0 * A ’ + ζ1 * Q max + ζ2. Set the reference accuracy threshold as J, and compare ζ0 * A ’ + ζ1 * Q max + ζ2 and J: If ζ0 * A ’ + ζ1 * Q max + ζ2 ≥ J, plan the method for obtaining the tensile strength data of the currently to-be-processed steel plate as: taking the tensile strength of the test sample with a fitness of Q max with the currently to-be-processed steel plate as the tensile strength of the currently to-be-processed steel plate; if ζ0 * A ’ + ζ1 * Q max + ζ2 < J, plan the method for obtaining the tensile strength data of the currently to-be-processed steel plate as: placing the currently to-be-processed steel plate in a test environment for tensile testing, taking the actual test result as the tensile strength of the currently to-be-processed steel plate, and at the same time, after obtaining the actual test result, supplementing the tensile strength test result data of the currently to-be-processed steel plate to the test database to enrich the test sample data;

[0025] A steel plate test selection model is established by fitting and training the numbered data, fitness data and reference accuracy data obtained from the previous analysis. The number represents the weld defect type of the steel plate, the fitness represents the difference between the welds of the steel plate, and the reference accuracy represents the accuracy of using the tensile strength test data of the specified sample as the tensile strength data of the actual steel plate to be processed when the actual steel plate with the same weld defect type and the fitness between the sample are the corresponding fitness. The fitness between the current steel plate to be processed and the sample with the same type of weld defect is analyzed, and the number corresponding to the weld defect type and the highest fitness data are selected to be input into the steel plate test selection model. According to the output results, the method of obtaining the tensile strength data of the current steel plate to be processed is selected and planned: when the predicted reference accuracy is not lower than the threshold, it means that the tensile strength of the sample with the highest fitness is most likely equal to the tensile strength of the current steel plate to be processed. Therefore, the tensile strength test data of the sample corresponding to the highest fitness is selected as The tensile strength of the current steel plate to be processed reduces unnecessary testing work while ensuring the accuracy of the tensile strength data of the actual steel plate; when the predicted reference accuracy is lower than the threshold, the current steel plate to be processed is selected for strength testing to obtain the tensile strength. The reason for selecting the highest fitness input model is that the higher the fitness, the smaller the difference between the current steel plate to be processed and the sample. When the accuracy of the sample with the highest fitness as the reference data of the tensile strength data of the current steel plate to be processed is lower than the threshold, it means that the accuracy of the remaining sample data as a reference will only be lower. The method of inputting fitness one by one is not adopted, which reduces unnecessary data input operations. The above method is selected to plan the method of obtaining the actual strength data of the steel plate to be processed, which improves the accuracy of the tensile strength data of the steel plate required in the actual processing process while reducing unnecessary strength testing work. At the same time, it gradually enriches the steel plate test sample data to a certain extent and improves the perfection of the test sample data.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention places a steel plate test sample with welding defects in a high temperature environment for a tensile test to test the tensile strength of the steel plate. The tensile strength of the steel plate with different types of welding defects obtained by the test can help reasonably adjust the working condition parameters according to the strength test results of the steel plate test sample with welding defects during the actual processing process, taking into account the influence of the weld defects on the strength of the steel plate, which is conducive to avoiding the problem of affecting the quality and service life of the steel plate due to improper setting of the working condition parameters;

[0028] Taking into account the influence of weld profile characteristics on steel plate strength data, samples to be calibrated are randomly selected from the test samples collected in the early stage, and the remaining steel plate test samples with the same weld defect type as the samples to be calibrated are compared with the weld feature data of the samples to be calibrated, and the degree of fit between the two is analyzed. After the degree of fit between the remaining samples and the samples to be calibrated is analyzed, the number of samples with the same degree of fit as the samples to be calibrated and the number of samples with the same degree of fit and the same tensile strength are counted from the remaining samples. The ratio between the two sample numbers is calculated to analyze the test data of the reference sample when the degree of fit between the actual steel plate to be processed and the sample is different as the tensile strength of the actual steel plate to be processed. The reference accuracy of the strength data is determined by fitting the number data, fitness data and reference accuracy data obtained from the previous analysis to establish a steel plate test selection model, analyze the fitness between the current steel plate to be processed and the sample with the same type of weld defects, and select the number corresponding to the weld defect type and the highest fitness data to input into the steel plate test selection model. According to the output results, the method of obtaining the tensile strength data of the current steel plate to be processed is selected and planned. While improving the accuracy of the tensile strength data of the steel plate required in the actual processing process, unnecessary strength testing work is reduced. At the same time, the steel plate test sample data is gradually enriched to a certain extent, and the perfection of the test sample data is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The figure is a schematic flow chart of a method for testing the strength of a boiler steel plate with weld defects according to the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Embodiment 1:

[0032] like Figure 1As shown, this embodiment provides a strength test method for a boiler steel plate with weld defects, including: S1: collecting steel plate test samples, performing strength tests on the test samples, and obtaining tensile strength test results: butt-welding two steel plate samples to form a weld with a width of d, preparing and collecting steel plate test samples with weld defects and different weld defect types, the weld defect types including pores, slag inclusions, lack of fusion, lack of penetration and cracks, collecting a total of m steel plate test samples, the weld widths of the m steel plate test samples are all d, setting the test environment temperature range to T, performing a tensile test on the test samples under the corresponding test environment, and obtaining the tensile strength of the test samples obtained by the test;

[0033] S2: number the test samples, obtain weld feature data of the test samples, analyze the fitness between the test samples with the same weld defect type: randomly extract samples with different weld defect types from the m test samples as samples to be calibrated: each weld defect type corresponds to a sample to be calibrated, number the remaining steel plate test samples collected after the extraction process, the steel plate test samples with the same weld defect type have the same number, and the number set of the remaining n steel plate test samples is obtained as A={A1, A2, ..., An}, there are the same numbers in the number set, that is, some of the n steel plate test samples have the same number, retrieve samples representing a random type of weld defect from the n steel plate test samples, count the number of retrieved samples to be k, and take weld images of the k retrieved samples. The weld contours of k samples are photographed using Canny edge detection technology, weld images of samples to be calibrated with the same weld defect type as the k samples are photographed, the weld contours of the corresponding samples to be calibrated are identified, and weld contour feature data are extracted using OpenCV technology. The feature data includes the area of ​​the area surrounded by the weld contour, the boundary length of the weld contour, and the area of ​​the smallest circle that can surround the weld contour. The area set of the area surrounded by the weld contours of the k samples is {R1, R2, ..., Rk}, the boundary length set of the weld contour is {L1, L2, ..., Lk}, and the area set of the smallest circle that can surround the weld contour is {r1, r2, ..., rk}. The weld contour feature data of the samples to be calibrated with the same weld defect type as the k samples are R ’ , L ’ and r ’ , analyze the degree of fit Wi between a random sample among k samples and the sample to be calibrated:

[0034] Wi=1 / (|R ’ -Ri|+|L ’ -Li|+|r ’ -ri|);

[0035] The fitness set between the k samples and the sample to be calibrated is obtained as W = {W1, W2, ..., Wi, ..., Wk}, and there are equal fitness in the set W, where Ri represents the area of ​​the area surrounded by the weld contour of the i-th sample among the k samples, Li represents the boundary length of the weld contour of the i-th sample among the k samples, and ri represents the minimum circle area that can surround the weld contour of the i-th sample;

[0036] S3: Count the total number of test samples with the same fitness and the number of test samples with the same fitness and the same tensile strength, and analyze the reference accuracy of the test data: Count the number of samples with the fitness W1 equal to the sample to be calibrated among the k samples as a, and the number of samples with the same tensile strength as the sample to be calibrated among the a samples as b. Calculate the reference accuracy P1 of the tensile strength test data of the sample representing a random type of weld defect when the fitness between the steel plate to be processed and the sample is W1, P1 = b / a, and the reference accuracy set is {P1, P2 , ..., Pk}, Pk represents the reference accuracy of the tensile strength test data of the sample when the degree of fit between the steel plate to be processed and the sample is Wk for a sample representing a random type of weld defect, and the set of fits between the n test samples representing all types of weld defects and the corresponding types of samples to be calibrated is w = {w1, w2, ..., wn}, where the set w contains the set W, and the set of reference accuracy of the tensile strength test data of the sample when the degrees of fit between the steel plate to be processed and the sample are the degrees of fit in the set w is p = {p1, p2, ..., pn};

[0037] S4: Fitting training is performed on the sample number, fitness and reference accuracy data to establish a steel plate test selection model: the sample number, fitness and reference accuracy data are combined into a training data set {(A1, w1, p1), (A2, w2, p2), ..., (An, wn, pn)}, and fitting training is performed on the training data to establish a steel plate test selection model:

[0038] z=ζ0*x+ζ1*y+ζ2;

[0039] Where * represents a multiplication sign, x represents the first variable representing the number in the steel plate test selection model, y represents the second variable representing the fitness in the model, z represents the dependent variable representing the reference accuracy in the model, ζ0, ζ1 and ζ2 represent the fitting coefficients;

[0040] S5: Obtain the weld feature data of the currently to-be-processed steel plate, confirm the weld defect type and number of the currently to-be-processed steel plate, compare the weld features with the test samples of the same type, and analyze the fitness between the currently to-be-processed steel plate and the test samples: Obtain the weld defect type of the currently to-be-processed steel plate, and get that the weld defect type of the currently to-be-processed steel plate is the same as that of the test sample with the number A ’ There are e test samples with the weld defect type of A ’ ∈A, and calculate the fitness between the currently to-be-processed steel plate and the e test samples respectively. The calculation method of the fitness between the currently to-be-processed steel plate and the e test samples is the same as that of Wi; ’

[0041] S6: Input the number and fitness of the currently to-be-processed steel plate into the steel plate test selection model, and plan the method for obtaining the tensile strength data of the currently to-be-processed steel plate: Compare the fitness sizes between the currently to-be-processed steel plate and the e test samples, and get that the highest fitness between the currently to-be-processed steel plate and the e test samples is Q max , input A ’ and Q max into the steel plate test selection model, and predict that the reference accuracy of using the tensile strength test data of the sample with the highest fitness with the currently to-be-processed steel plate among the e test samples as the reference data for the tensile strength of the currently to-be-processed steel plate is: ζ0*A ’ +ζ1*Q max +ζ2. Set the reference accuracy threshold as J, and compare ζ0*A ’ +ζ1*Q max +ζ2 and J: If ζ0*A ’ +ζ1*Q max +ζ2≥J, plan the method for obtaining the tensile strength data of the currently to-be-processed steel plate as: Use the tensile strength of the test sample with the fitness of Q max with the currently to-be-processed steel plate as the tensile strength of the currently to-be-processed steel plate; If ζ0*A ’ +ζ1*Q max +ζ2<J, plan the method for obtaining the tensile strength data of the currently to-be-processed steel plate as: Place the currently to-be-processed steel plate in the test environment for tensile testing, and use the actual test result as the tensile strength of the currently to-be-processed steel plate;

[0042] For example: Establish a steel plate test selection model: z = 0.15x + 1.89y - 0.15, and get that the weld defect type of the currently to-be-processed steel plate is the same as that of the test sample with the number A ’ = 1, and there are 5 test samples with the number 1. Get that the highest fitness between the 5 test samples and the currently to-be-processed steel plate is Q max ​=0.05, let x=1, y=0.45, and predict that the reference accuracy of using the tensile strength test data of the sample with the highest adaptability to the current steel plate to be processed among the five test samples as the reference data of the tensile strength of the current steel plate to be processed is 0.85, and the reference accuracy threshold is set to J=0.80, 0.85>0.80, ζ0*A ’ +ζ1*Q max +ζ2>J, the method of planning to obtain the tensile strength data of the current steel plate to be processed is: the degree of fit between the current steel plate to be processed is Q max The tensile strength of the test sample is taken as the tensile strength of the steel plate to be processed.

[0043] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for testing the strength of boiler steel plates with weld defects, characterized in that: The following steps are involved: S1: Collect steel plate test samples, perform strength tests on the test samples, and obtain tensile strength test results; S2: number the test samples, obtain the weld characteristic data of the test samples, and analyze the fit between the test samples with the same weld defect type; S3: Count the total number of test samples with the same fitness and the number of test samples with the same fitness and the same tensile strength obtained in the test, and analyze the reference accuracy of the test data; S4: Fitting training is performed on the sample number, fitness and reference accuracy data to establish a steel plate test selection model; S5: obtaining weld feature data of the current steel plate to be processed, confirming the weld defect type and number of the current steel plate to be processed, comparing the weld features with the test sample of the same type, and analyzing the compatibility between the current steel plate to be processed and the test sample; S6: Inputting the serial number and fitness of the current steel plate to be processed into the steel plate test selection model, and planning a method for obtaining the tensile strength data of the current steel plate to be processed.

2. The strength testing method for boiler steel plates with weld defects according to claim 1, characterized in that: Step S1 includes: butt welding two steel plate samples to form a weld with a width of d, making and collecting steel plate test samples with weld defects and different weld defect types, collecting a total of m steel plate test samples, setting the test environment temperature range to T, performing a tensile test on the test samples under the corresponding test environment, and obtaining the tensile strength of the test samples obtained by the test.

3. The strength testing method of boiler steel plate with weld defects according to claim 2, characterized in that: Step S2 includes: randomly extracting samples with different weld defect types from m test samples as samples to be calibrated: each weld defect type corresponds to a sample to be calibrated, numbering the remaining steel plate test samples collected after the extraction process, and the steel plate test samples with the same weld defect type have the same number, and the number set of the remaining n steel plate test samples is obtained as A={A1, A2, ..., An}, retrieving samples representing a random type of weld defect from the n steel plate test samples, counting the number of retrieved samples to be k, taking weld images of the k retrieved samples, identifying the weld contours of the k samples using Canny edge detection technology, taking weld images of the samples to be calibrated with the same weld defect type as the k samples, identifying the weld contours of the corresponding samples to be calibrated, and extracting weld contour feature data using OpenCV technology.

4. The strength testing method of boiler steel plate with weld defects according to claim 3, characterized in that: The characteristic data include the area of ​​the area surrounded by the weld contour, the boundary length of the weld contour and the area of ​​the smallest circle that can surround the weld contour. The area set of the area surrounded by the weld contours of the k samples is {R1, R2, ..., Rk}, the boundary length set of the weld contour is {L1, L2, ..., Lk}, and the smallest circle area set that can surround the weld contour is {r1, r2, ..., rk}. The weld contour characteristic data of the samples to be calibrated with the same weld defect type as the k samples are R ’ , L ’ and r ’ , analyze the degree of fit Wi between a random sample among k samples and the sample to be calibrated: Wi=1 / (|R ’ -Ri|+|L ’ -Li|+|r ’ -ri|); The obtained fitness set between the k samples and the sample to be calibrated is W = {W1, W2, ..., Wi, ..., Wk}, where Ri represents the area of ​​the region enclosed by the weld contour of the i-th sample among the k samples, Li represents the boundary length of the weld contour of the i-th sample among the k samples, and ri represents the minimum circle area that can enclose the weld contour of the i-th sample.

5. The strength testing method for boiler steel plate with weld defects according to claim 4, characterized in that: Step S3 includes: counting the number of samples with the same fitness W1 as the sample to be calibrated among the k samples as a, the number of samples with the same tensile strength as the sample to be calibrated among the a samples as b, calculating the reference accuracy P1 of the tensile strength test data of the sample when the fitness between the steel plate to be processed and the sample is W1 for the sample representing a random type of weld defect, P1=b / a, and obtaining the reference accuracy set {P1, P2, ..., Pk}, Pk represents the tensile strength test data of the sample representing a random type of weld defect The defective sample, when the fitness between the steel plate to be processed and the sample is Wk, the reference accuracy of the tensile strength test data of the sample is obtained, and the fitness set between the n test samples representing all types of weld defects and the corresponding types of samples to be calibrated is w = {w1, w2, ..., wn}, where the set w contains the set W, and the reference accuracy set of the tensile strength test data of the sample when the fitness between the steel plate to be processed and the sample is the fitness in the set w is p = {p1, p2, ..., pn}.

6. The strength testing method of boiler steel plate with weld defects according to claim 5, characterized in that: Step S4 includes: forming a training data set {(A1, w1, p1), (A2, w2, p2), ..., (An, wn, pn)} with sample numbers, fitness and reference accuracy data, performing fitting training on the training data, and establishing a steel plate test selection model: z=ζ0*x+ζ1*y+ζ2; Among them, x represents the first variable representing the number in the steel plate test selection model, y represents the second variable representing the fitness in the model, z represents the dependent variable representing the reference accuracy in the model, and ζ0, ζ1 and ζ2 represent the fitting coefficients.

7. The strength testing method for boiler steel plates with weld defects according to claim 6, characterized in that: Step S5 includes: obtaining the weld defect type of the current steel plate to be processed, obtaining the weld defect type of the current steel plate to be processed and the weld defect type numbered A ’ The weld defect types of the test samples are the same, A ’ ∈A, there are e numbers numbered A ’ The degree of fit between the current steel plate to be processed and the e test samples is calculated respectively.

8. The strength testing method for boiler steel plate with weld defects according to claim 7, characterized in that: Step S6 includes: comparing the degree of fit between the current steel plate to be processed and the e test samples, and obtaining the highest degree of fit between the e test samples and the current steel plate to be processed as Q max , A ’ and Q max Input to the steel plate test selection model: Let x = A ’ , y=Q max , the prediction results show that the reference accuracy of using the tensile strength test data of the sample with the highest adaptability to the current steel plate to be processed among the e test samples as the reference data of the tensile strength of the current steel plate to be processed is: ζ0*A ’ +ζ1*Q max +ζ2, set the reference accuracy threshold to J, compare ζ0*A ’ +ζ1*Q max +ζ2 and J, get the comparison results.

9. The strength testing method for boiler steel plates with weld defects according to claim 8, characterized in that: If ζ0*A ’ + ζ1*Q max + ζ2 ≥ J, the method for planning to obtain the tensile strength data of the currently to-be-processed steel plate is: taking the tensile strength of the test sample with a fitness of Q max with the currently to-be-processed steel plate as the tensile strength of the currently to-be-processed steel plate; if ζ0*A ’ + ζ1*Q max + ζ2 < J, the method for planning to obtain the tensile strength data of the currently to-be-processed steel plate is: placing the currently to-be-processed steel plate in a test environment for a tensile test, and taking the actual test result as the tensile strength of the currently to-be-processed steel plate.

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