Tank bottom defect evaluation method, system, electronic device and storage medium

Abstract

The application discloses a kind of storage tank bottom plate defect evaluation method and system, the method includes the following steps: A, acquisition storage tank bottom plate defect signal and obtain the relative value data of the magnetic flux of receiving coil under different sampling interval conditions;B, the relative value data of the magnetic flux of receiving coil is based on superposition different power noise signal, obtains defect signal containing noise;C, based on three-peak exponential decay cosine model, parameter estimation and fitting are carried out to defect signal containing noise;D, according to the signal generated by fitting detection statistics, generate ROC curve under different sampling interval, different PSNR and SNR conditions using the detection statistics;E, according to detection statistics and ROC curve, the relationship between detection decision threshold value and detection probability is constructed;F, on the basis of selected sampling interval, PSNR and SNR condition, for actual storage tank bottom plate defect signal, using the determined detection decision threshold value, the storage tank bottom plate defect is evaluated.The application can determine the key parameters such as storage tank bottom plate defect position, size.

Description

Technical Field

[0001] This invention relates to the field of defect detection technology, and in particular to a method, system, electronic device and storage medium for assessing defects in the bottom plate of a storage tank. Background Technology

[0002] Storage tanks play a crucial role in the storage and transportation of oil and natural gas. With the increasing demand for oil and natural gas from industries such as petroleum and chemicals, the safety of storage tanks has become increasingly important. Because large storage tanks typically store large quantities of flammable and explosive materials, leaks and other accidents can cause significant property damage, casualties, and environmental pollution, with extremely serious consequences. The environment in which the tank bottom plate is located is extremely harsh, subject to many adverse factors such as complex stress and electrochemical corrosion, leading to various defects that can easily develop into leaks and other accidents, seriously endangering public safety. Therefore, the assessment and detection of defects in tank bottom plates is an issue that cannot be ignored.

[0003] Existing technologies include solutions for detecting defects in tank bottom plates. For example, Chinese patent application CN111157612A discloses a device and method for detecting defects in tank bottom plates. This method uses a hardware detection device to scan the surface of the object being inspected, maintaining a constant lift-off value between the detection device and the object during the scanning process. If the signal value output by the PCB board with the chip decreases during the scanning, it is determined that there is a corrosion pit on the upper surface of the object at the current location; if the output signal value increases, it is determined that there is a weld bead on the upper surface of the object at the current location; if the signal value output by the PCB board with the chip remains constant, but the sensor strip detects a magnetic leakage signal, it is determined that there is a corrosion pit or weld bead on the lower surface of the object at the current location. However, this type of solution can only perform preliminary detection of defects in the tank bottom plate, and the detection results have significant uncertainty.

[0004] The inventors discovered two key problems in the detection of defects in tank bottom plates: "weak signal" and "parameter estimation." "Weak signal" refers to the low signal-to-noise ratio (SNR) of the acquired defect detection signal; therefore, detection performance under low SNR conditions is a crucial indicator in tank bottom plate defect detection. "Parameter estimation" refers to whether the constructed algorithm model and corresponding evaluation and detection methods can determine the location, size, and other key parameters of the tank bottom plate defects.

[0005] Therefore, there is an urgent need for a method that can accurately assess defects in the bottom plate of storage tanks and determine key parameters such as the location and size of defects under low signal-to-noise ratio conditions.

[0006] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide a defect assessment method and system that can determine key parameters such as the location and size of defects in the bottom plate of a storage tank based on a three-peak exponential decay cosine model under low signal-to-noise ratio conditions.

[0008] To achieve the above objectives, according to a first aspect of the present invention, the present invention provides a method for evaluating defects in the bottom plate of a storage tank, comprising the following steps: A. Acquiring defects in the bottom plate of the storage tank and obtaining relative values ​​of the magnetic flux of the receiving coil under different sampling intervals; B. Superimposing noise signals of different powers on the relative values ​​of the magnetic flux of the receiving coil to obtain a defect signal containing noise; C. Estimating and fitting parameters of the defect signal containing noise based on a three-peak exponential decay cosine model; D. Generating a detection statistic based on the fitted signal, and using the detection statistic to generate ROC curves under different sampling intervals, different PSNRs, and different SNRs; ​​E. Constructing a relationship between a detection decision threshold and a detection probability based on the detection statistic and the ROC curves; F. Evaluating the defects in the bottom plate of the storage tank using the determined detection decision threshold, based on the selected sampling interval, PSNR, and SNR conditions, for the actual defects in the bottom plate of the storage tank.

[0009] Furthermore, in the above technical solution, the defect signal of the tank bottom plate in step A can be collected by a detection robot; the sampling interval can be set to 0.5mm, 1mm, 1.5mm and 2mm, etc.

[0010] Furthermore, in the above technical solution, step A may specifically include: A1, simulating the actual working conditions using finite element analysis software; A2, using the received signal when the tank bottom plate is defect-free as a reference value, and obtaining relative values ​​of the magnetic flux of the receiving coil under different sampling interval conditions.

[0011] Furthermore, in the above technical solution, step B may specifically include: B1, calculating the noise power under different PSNR and SNR conditions; B2, superimposing Gaussian white noise of different powers on the relative value data of the magnetic flux of the receiving coil through Monte Carlo simulation as a defect signal containing noise.

[0012] Furthermore, in the above technical solution, step B1 can be calculated using the following formula:

[0013]

[0014]

[0015] Among them, P N1 P is the noise power calculated based on PSNR. N2 Here, s is the noise power calculated based on SNR, and s0 is the relative value of the magnetic flux of the receiving coil. Let s0 be the power.

[0016] Furthermore, in the above technical solution, step C may specifically include:

[0017] C1. Construct a three-peaked exponentially decaying cosine model; the specific model is as follows:

[0018] s(n)=Ae γ|n| cos(wn) Formula (3);

[0019] Where n = -N, -N+1, ..., 0, ..., N-1, N are sampling points, A is the peak value of the main peak, γ is the attenuation coefficient of the exponential function, and w is the angular frequency of the cosine function;

[0020] C2. The nonlinear least squares method is used as the estimation method for the three-peak exponential decay cosine model to estimate the parameters of the noisy defect signal.

[0021] C3. Generate a fitted signal using the estimated values ​​of the parameters A, γ, and w, and the fitted model; the fitted model is specifically:

[0022]

[0023] Furthermore, in the above technical solution, step C2 can be specifically described as follows:

[0024] The parameter estimates of γ and w are calculated using the following formula (5):

[0025]

[0026] Where x represents the sampled data at certain distances, and,

[0027] h(i) = e γ|i| cos(wi); and,

[0028] pass Calculate the parameter estimates for A.

[0029] Furthermore, in the above technical solution, step D may specifically include:

[0030] D1. The detection statistic is generated based on the average power of the fitted signal, as shown in the following formula:

[0031]

[0032] in, Let x be the fitted signal, and let x be the defect signal containing noise.

[0033] D2. The average power of the defect signal containing noise is used as the normalization parameter of the detection statistic.

[0034] Furthermore, in the above technical solution, step E may specifically include: E1, obtaining the corresponding false alarm probability and detection probability through the ROC curve; E2, obtaining the relationship between the detection decision threshold and the detection probability through the detection statistics.

[0035] Furthermore, in the above technical solution, the evaluation of the tank bottom plate defect in step F specifically involves: when the detection statistic exceeds the detection decision threshold, it is determined that the sampling point interval has a defect signal; and the size of the corresponding defect is determined by the specific value of the detection statistic.

[0036] According to a second aspect of the present invention, a tank bottom plate defect assessment system is provided, comprising: a sampling data acquisition module for acquiring tank bottom plate defect signals and obtaining relative values ​​of receiving coil magnetic flux under different sampling interval conditions; a defect signal acquisition module for superimposing noise signals of different powers on the relative values ​​of receiving coil magnetic flux to acquire a noise-containing defect signal; a parameter estimation and fitting module for performing parameter estimation and fitting on the noise-containing defect signal based on a three-peak exponential decay cosine model; a statistics generation module for generating detection statistics based on the fitted signal and using the detection statistics to generate ROC curves under different sampling intervals, different PSNR and SNR conditions; a relationship construction module for constructing a relationship between a detection decision threshold and a detection probability based on the detection statistics and the ROC curves; and a defect assessment module for assessing the tank bottom plate defect using the detection decision threshold, based on selected sampling intervals, PSNR and SNR conditions, for the actual tank bottom plate defect signal.

[0037] According to a third aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform a tank bottom plate defect assessment method as described in any of the above technical solutions.

[0038] According to a fourth aspect of the present invention, the present invention provides a non-transitory computer-readable storage medium storing computer-executable instructions for causing a computer to perform a tank bottom plate defect assessment method as described in any of the above technical solutions.

[0039] Compared with the prior art, the present invention has one or more of the following beneficial effects:

[0040] 1) The tank bottom plate defect assessment method of the present invention is based on the three-peak exponential decay cosine model studied by the inventor to estimate and detect defect signals, and has the characteristics of low signal-to-noise ratio, false alarm probability, and high detection probability.

[0041] 2) The method of the present invention can accurately analyze the location, size and other parameters of the defect signal of the tank bottom plate.

[0042] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, and to make the above and other objects, technical features and advantages of the present invention easier to understand, one or more preferred embodiments are listed below and described in detail with reference to the accompanying drawings. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the method for assessing defects in the tank bottom plate according to Embodiment 1 of the present invention.

[0044] Figure 2 This is a schematic diagram of the data acquisition process in the tank bottom plate defect assessment method of the present invention.

[0045] Figure 3 The received signal (i.e., the relative value data of the magnetic flux of the receiving coil) is obtained by numerical calculation under the condition of a sampling interval of 0.5 mm in the tank bottom plate defect assessment method of the present invention.

[0046] Figure 4 It is the received signal (i.e., the relative value data of the magnetic flux of the receiving coil) obtained by numerical calculation under the condition of a sampling interval of 1 mm in the tank bottom plate defect assessment method of the present invention.

[0047] Figure 5 The received signal (i.e., the relative value data of the magnetic flux of the receiving coil) is obtained by numerical calculation under the condition of a sampling interval of 1.5 mm in the tank bottom plate defect assessment method of the present invention.

[0048] Figure 6 This is the received signal (i.e., the relative value data of the magnetic flux of the receiving coil) obtained by numerical calculation under the condition of a sampling interval of 2 mm in the tank bottom plate defect assessment method of the present invention.

[0049] Figure 7 This invention relates to the method for assessing defects in the tank bottom plate. Figure 5 A schematic diagram comparing the received signal before and after fitting.

[0050] Figure 8This is the ROC curve under the condition of a sampling interval of 0.5 mm in the tank bottom plate defect assessment method of the present invention.

[0051] Figure 9 This is the ROC curve under the condition of a sampling interval of 1 mm in the tank bottom plate defect assessment method of the present invention.

[0052] Figure 10 This is the ROC curve under the condition of a sampling interval of 1.5 mm in the tank bottom plate defect assessment method of the present invention.

[0053] Figure 11 This is the ROC curve under the condition of a sampling interval of 2 mm in the tank bottom plate defect assessment method of the present invention.

[0054] Figure 12 This is a schematic diagram illustrating the relationship between the detection decision threshold and the detection probability in the tank bottom plate defect assessment method of the present invention.

[0055] Figure 13 This is a specific example of the actual received signal in the tank bottom plate defect assessment method of the present invention.

[0056] Figure 14 Is Figure 13 The detection statistics provided are based on this.

[0057] Figure 15 This is a schematic diagram of the structure of the tank bottom plate defect assessment system in Embodiment 2 of the present invention.

[0058] Figure 16 This is a schematic diagram of the hardware structure of the electronic device for assessing defects in the tank bottom plate according to Embodiment 5 of the present invention. Detailed Implementation

[0059] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0060] Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprises" shall be understood to include the stated elements or components without excluding other elements or other components.

[0061] In this document, for ease of description, spatial relative terms such as “below,” “under,” “down,” “above,” “above,” “up,” etc., are used to describe the relationship of one element or feature to another element or feature in the accompanying drawings. It should be understood that spatial relative terms are intended to encompass different orientations of an object in use or operation, in addition to those depicted in the figures. For example, if an object in the figure is flipped, an element described as “below” or “under” another element or feature would be oriented “above” that element or feature. Thus, the exemplary term “below” can encompass both the downward and upward orientations. An object may also have other orientations (rotated 90 degrees or other orientations), and the spatial relative terms used herein should be interpreted accordingly.

[0062] In this document, the terms "first," "second," etc., are used to distinguish two different elements or parts, and are not used to define specific positions or relative relationships. In other words, in some embodiments, the terms "first," "second," etc., can also be used interchangeably.

[0063] The methods, systems, electronic devices, and storage media of the present invention are described in more detail below by way of specific embodiments. It should be understood that the embodiments are merely exemplary and the present invention is not limited thereto.

[0064] Example 1

[0065] like Figure 1 As shown, this embodiment provides a method for assessing defects in the bottom plate of a storage tank. It primarily estimates and detects defect signals in the tank bottom plate based on a three-peak exponential decay cosine model, and includes the following steps:

[0066] Step S101: Acquire the defect signal of the tank bottom plate and obtain the relative value data of the magnetic flux of the receiving coil under different sampling interval conditions. Specifically, the actual working condition can be simulated first using finite element analysis software; then, the received signal when the tank bottom plate is defect-free can be used as the reference value to obtain (through numerical calculation) the relative value data of the magnetic flux of the receiving coil under different sampling interval conditions.

[0067] Further as Figure 2 As shown, this step involves a detection robot moving along the bottom plate of the storage tank to collect samples. A signal is emitted via an excitation coil below the magnet, and received by a receiving coil. The detection robot collects magnetic flux data from the receiving coil at specific sampling intervals. Considering practical needs, the sampling interval can be set to 0.5mm, 1mm, 1.5mm, or 2mm.

[0068] To simulate the defect detection scenario of the tank bottom plate using finite element analysis software, it is necessary to set the relevant parameters of the excitation signal, magnet, coil and tank bottom plate. The relevant parameters are shown in Table 1.

[0069] Table 1. Relevant parameters for numerical calculations using finite element analysis software.

[0070]

[0071]

[0072] Finite element analysis was performed, using the received signal when the tank bottom plate was defect-free as the reference value. The numerical calculation results of the relative value of the magnetic flux of the receiving coil were used as the research object. Numerical calculations were conducted under sampling intervals of 0.5 mm, 1 mm, 1.5 mm, and 2 mm to obtain the received signals under different sampling intervals, such as... Figures 3-6 As shown.

[0073] Step S102 involves superimposing noise signals of different powers onto the relative value data of the coil magnetic flux received in step S101 to obtain a defect signal containing noise. Specifically, this step obtains the noisy signal through Monte Carlo simulation.

[0074] First, the noise power under different PSNR (peak signal-to-noise ratio) and SNR (signal-to-noise ratio) conditions is calculated using the following formulas (1) and (2):

[0075]

[0076]

[0077] Among them, P N1 P is the noise power calculated based on PSNR. N2 Here, s is the noise power calculated based on SNR, and s0 is the relative value of the magnetic flux of the receiving coil. Let s0 be the power.

[0078] Secondly, the number of simulations in the Monte Carlo simulation was set to 10. 3 Under these conditions, Gaussian white noise of different powers is superimposed on the relative value data of the magnetic flux of the receiving coil to serve as a defect signal containing noise.

[0079] Step S103: Based on the three-peak exponential decay cosine model, the parameters of the defect signal containing noise in step S102 are estimated and fitted.

[0080] The inventors discovered that the detection signal when the inspection robot passes a defect has the following characteristics: 1) When the inspection robot approaches the defect, the detection signal first decreases slightly, then increases significantly; 2) When the inspection robot moves away from the defect, the detection signal first decreases significantly from the peak value, then increases slightly back to near zero. In other words, the defect signal of the tank bottom plate has three peaks, including one positive primary peak and two negative secondary peaks symmetrical about zero (see reference). Figure 7 (Illustrative). Therefore, this invention uses an exponential function with absolute value to simulate the attenuation of the signal peak and a cosine function to simulate the positive and negative signs of the signal, thereby constructing the three-peak exponential attenuation cosine model of this invention.

[0081] Based on the above research, this step includes the following sub-steps:

[0082] 1) Construct a three-peaked exponentially decaying cosine model; see the following formula (3) for details:

[0083] s(n)=Ae γ|n| cos(wn) Formula (3);

[0084] Where n = -N, -N+1, ..., 0, ..., N-1, N are sampling points, A is the peak value of the main peak, γ is the attenuation coefficient of the exponential function, and w is the angular frequency of the cosine function.

[0085] 2) The nonlinear least squares method is used as the estimation method for the three-peak exponential decay cosine model to estimate the parameters of the defect signal containing noise (the parameters include the peak value A of the main peak, the decay coefficient γ of the exponential function and the angular frequency w of the cosine function).

[0086] First, the parameter estimates of γ and w are calculated using the following formula (5):

[0087]

[0088] Where x represents the sampled data at certain distances, and,

[0089]

[0090]

[0091]

[0092] h(i) = e γi cos(wi).

[0093] Secondly, through Calculate the parameter estimates for A.

[0094] Next, a fitted signal is generated using the estimated values ​​of parameters A, γ, and w, and the fitted model; the fitted model is as follows:

[0095]

[0096] For example, when using the nonlinear least squares method to... Figure 5 When the signal shown is fitted, a three-peaked exponentially decaying cosine model signal (i.e., the fitted signal) can be obtained, along with the tank bottom defect signal (i.e., the relative value data of the receiving coil magnetic flux when the sampling interval is 1.5 mm). Figure 7 As shown.

[0097] Step S104: Generate detection statistics based on the fitted signal, and use the detection statistics to generate ROC curves under different sampling intervals, different PSNR and SNR conditions. The ROC curve is the receiver operating characteristic curve.

[0098] Specifically, this step involves Monte Carlo simulations under different conditions (10 3 The detection statistics obtained (from the previous sampling) are used to generate ROC curves under different sampling intervals, PSNR, and SNR conditions, as shown below. Figures 8-11 As shown.

[0099] First, the detection statistic is generated based on the average power of the fitted signal, as shown in formula (6):

[0100]

[0101] in, Let x be the aforementioned fitted signal, and let x be the aforementioned original defect signal containing noise.

[0102] Secondly, the average power of the defect signal containing noise is used as the normalization parameter of the detection statistic.

[0103] Step S105: Construct the relationship between the detection decision threshold and the detection probability based on the detection statistics and ROC curve obtained in step S104. Specifically, by selecting specific sampling intervals, PSNR, and SNR conditions, the corresponding false alarm probability and detection probability can be obtained through the ROC curve; the relationship between the detector decision threshold and the detection probability can be obtained using the detection statistics, refer to... Figure 12 By setting the detection probability, the specific value of the detection decision threshold is obtained by utilizing the relationship between the detection decision threshold and the detection probability.

[0104] The following is a specific example to illustrate this:

[0105] The experiment in this example uses data acquisition with a sampling interval of approximately 0.5 mm. Under these conditions, when the PSNR is 3 dB and the SNR is -5.25 dB, the detection probability reaches 87.8% with a false alarm probability of 1%, and 94.5% with a false alarm probability of 5%. When the PSNR is 9 dB and the SNR is 0.75 dB, the detection probability reaches 100% with a false alarm probability of 1%. As mentioned earlier, based on the selected sampling interval, PSNR, and SNR conditions, the Monte Carlo simulation under the corresponding conditions is performed using the aforementioned steps (10...). 3 The detection statistics obtained (from (times)) yield the relationship between the detection decision threshold and the detection probability, as shown in the example. Figure 12 As shown. The detection probability is set to 98%, according to... Figure 12 The relationship between the detection decision threshold and the detection probability shown can be used to obtain a corresponding decision threshold of 0.40.

[0106] In step S106, based on the selected sampling interval, PSNR, and SNR conditions (e.g., the sampling interval of 0.5 mm, PSNR of 3 dB, and SNR of -5.25 dB in step S105), the tank bottom plate defects are evaluated using the determined detection decision threshold (e.g., the decision threshold of 0.40 in the example of step S105) for the actual tank bottom plate defect signal.

[0107] This step estimates and detects the actual received signal based on a three-peak exponential attenuation cosine model to determine the corresponding parameters of the tank bottom defect signal. For example, under the condition that the detection window length is set to 100, for example... Figure 13 The actual received signal is estimated and detected based on a three-peak exponential attenuation cosine model, and the result of the detection statistic T1 is as follows: Figure 14 As shown, since the peak value of the defect signal is positive, when the estimated peak parameter is negative, the corresponding detection statistic T1 can be set to zero. Figure 14 It can be seen that the detection statistic T1 exceeds the decision threshold of 0.4 in the sampling point intervals [286,300], [365,374], [512,520], and [717,723], indicating that a defect signal has occurred in these four sampling point intervals. Furthermore, the size and other parameters of the corresponding defect can be determined by the value of the detection statistic T1.

[0108] The tank bottom plate defect assessment method in this embodiment is based on the three-peak exponential decay cosine model researched by the inventor to estimate and detect defect signals. It has the characteristics of low signal-to-noise ratio, false alarm probability, and high detection probability. The method in this embodiment can accurately analyze the location, size and other relevant parameters of the tank bottom plate defect signal.

[0109] Example 2

[0110] Combination Figure 15 As shown, this embodiment provides a tank bottom plate defect assessment system. The system includes: a sampling data acquisition module 201, a defect signal acquisition module 202, a parameter estimation and fitting module 203, a statistics generation module 204, a relationship construction module 205, and a defect assessment module 206. The sampling data acquisition module 201 is used to collect the defect signal of the tank bottom plate and obtain the relative value data of the magnetic flux of the receiving coil under different sampling interval conditions; the defect signal acquisition module 202 is used to superimpose noise signals of different powers on the relative value data of the magnetic flux of the receiving coil to obtain the defect signal containing noise; the parameter estimation and fitting module 203 is used to perform parameter estimation and fitting on the defect signal containing noise based on the three-peak exponential decay cosine model; the statistics generation module 204 is used to generate detection statistics based on the fitted signal, and use the detection statistics to generate ROC curves under different sampling intervals, different PSNR and SNR conditions; the relationship construction module 205 is used to construct the relationship between the detection decision threshold and the detection probability based on the detection statistics and ROC curves; the defect evaluation module 206 is used to evaluate the tank bottom plate defect based on the selected sampling interval, PSNR and SNR conditions, using the detection decision threshold to evaluate the tank bottom plate defect.

[0111] The system in this embodiment is a virtual device corresponding to the method in Embodiment 1, and can achieve the same technical effect as in Embodiment 1.

[0112] Example 3

[0113] This embodiment provides a non-transient (non-volatile) computer storage medium storing computer-executable instructions. These instructions can execute the tank bottom plate defect assessment method in any of the above-described method embodiments 1 and achieve the same technical effect. The method includes the following steps: A. Acquiring tank bottom plate defect signals and obtaining relative values ​​of the receiving coil flux under different sampling intervals; B. Superimposing noise signals of different powers onto the relative values ​​of the receiving coil flux to obtain a noisy defect signal; C. Estimating and fitting parameters of the noisy defect signal based on a three-peak exponential decay cosine model; D. Generating a detection statistic based on the fitted signal, and using this detection statistic to generate ROC curves under different sampling intervals, PSNR, and SNR conditions; E. Constructing a relationship between the detection decision threshold and the detection probability based on the detection statistic and the ROC curve; F. Based on the selected sampling interval, PSNR, and SNR conditions, evaluating the tank bottom plate defect using the determined detection decision threshold for the actual tank bottom plate defect signal.

[0114] Example 4

[0115] This embodiment provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, which, when executed by a computer, cause the computer to perform the tank bottom plate defect assessment method described in the above aspects and achieve the same technical effect. The method includes: A) acquiring tank bottom plate defect signals and obtaining relative values ​​of the magnetic flux of the receiving coil under different sampling interval conditions; B) superimposing noise signals of different powers on the relative values ​​of the magnetic flux of the receiving coil to obtain a defect signal containing noise; C) estimating and fitting parameters of the defect signal containing noise based on a three-peak exponential decay cosine model; D) generating a detection statistic based on the fitted signal, and using the detection statistic to generate ROC curves under different sampling intervals, different PSNR and SNR conditions; E) constructing a relationship between the detection decision threshold and the detection probability based on the detection statistic and the ROC curve; F) evaluating the tank bottom plate defects using the determined detection decision threshold, based on the selected sampling interval, PSNR and SNR conditions, for the actual tank bottom plate defect signal.

[0116] Example 5

[0117] Figure 16 This is a schematic diagram of the hardware structure of the electronic device in this embodiment. The device includes one or more processors 610 and a memory 620. Taking one processor 610 as an example, the device may also include an input device 630 and an output device 640.

[0118] The processor 610, memory 620, input device 630 and output device 640 can be connected by a bus or other means.

[0119] The memory 620, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 610 executes various functional applications and data processing of the electronic device by running the non-transitory software programs, instructions, and modules stored in the memory 620, thereby implementing the processing method of the above-described method embodiments.

[0120] The memory 620 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; the data storage area may store data, etc. Furthermore, the memory 620 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 620 may optionally include memory remotely located relative to the processor 610, and these remote memories may be connected to the processing device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0121] Input device 630 can receive input digital or character information and generate signal input. Output device 640 may include display devices such as a display screen.

[0122] The one or more modules are stored in the memory 620. When executed by the one or more processors 610, they perform the following: A. Acquire the tank bottom plate defect signal and obtain the relative value data of the magnetic flux of the receiving coil under different sampling interval conditions; B. Superimpose noise signals of different powers on the relative value data of the magnetic flux of the receiving coil to obtain the defect signal containing noise; C. Estimate and fit the parameters of the defect signal containing noise based on the three-peak exponential decay cosine model; D. Generate a detection statistic based on the fitted signal, and use the detection statistic to generate ROC curves under different sampling intervals, different PSNR and SNR conditions; E. Construct the relationship between the detection decision threshold and the detection probability based on the detection statistic and the ROC curve; F. Based on the selected sampling interval, PSNR and SNR conditions, evaluate the tank bottom plate defect using the determined detection decision threshold for the actual tank bottom plate defect signal.

[0123] The above-described product can execute the methods provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the methods. Technical details not described in detail in this embodiment can be found in the methods provided in other embodiments of the present invention.

[0124] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0126] The foregoing description of specific exemplary embodiments of the present invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. Any simple modifications, equivalent changes, and alterations made to the foregoing exemplary embodiments should fall within the scope of protection of the present invention.

Claims

1. A method for assessing defects in the bottom plate of a storage tank, characterized in that, Includes the following steps: A. Collect defect signals from the bottom plate of the storage tank and obtain relative values ​​of the magnetic flux of the receiving coil under different sampling interval conditions; B. By superimposing noise signals of different powers onto the relative value data of the magnetic flux of the receiving coil, a defect signal containing noise is obtained; C. Estimating and fitting parameters of the noisy defect signal based on a three-peak exponential decay cosine model; step C specifically includes: C1. Construct a three-peaked exponentially decaying cosine model; the specific model is as follows: ； in, For sampling points, The peak value of the main peak. The decay coefficient of the exponential function is . ω is the angular frequency of the cosine function; C2. The nonlinear least squares method is used as the estimation method for the three-peak exponential decay cosine model to estimate the parameters of the noisy defect signal. C3, through the above , , The parameter estimates and the fitting model are used to generate the fitted signal; the fitting model is specifically: ； D. Generate a detection statistic based on the fitted signal, and use this detection statistic to generate ROC curves under different sampling intervals, PSNR, and SNR conditions; step D specifically includes: D1. The detection statistic is generated based on the average power of the fitted signal, as shown in the following formula: ； in, The fitted signal, The defect signal containing noise; D2. The average power of the defect signal containing noise is used as the normalization parameter of the detection statistic. E. Construct the relationship between the detection decision threshold and the detection probability based on the aforementioned detection statistics and ROC curve; F. Based on the selected sampling interval, PSNR, and SNR conditions, the tank bottom plate defects are evaluated using the determined detection decision threshold value for the actual tank bottom plate defect signal.

2. The method for assessing defects in the tank bottom plate according to claim 1, characterized in that, The defect signal of the tank bottom plate in step A is collected by a detection robot; the sampling interval is 0.5mm, 1mm, 1.5mm and 2mm.

3. The method for assessing defects in the tank bottom plate according to claim 2, characterized in that, Step A specifically includes: A1. Simulate actual working conditions using finite element analysis software; A2. Using the received signal when the tank bottom plate is defect-free as a reference value, obtain the relative value data of the magnetic flux of the receiving coil under the different sampling interval conditions.

4. The method for assessing defects in the tank bottom plate according to claim 1, characterized in that, Step B specifically includes: B1. Calculate the noise power under different PSNR and SNR conditions; B2. Gaussian white noise of different powers is superimposed on the relative value data of the magnetic flux of the receiving coil through Monte Carlo simulation as the defect signal containing noise.

5. The method for assessing defects in the tank bottom plate according to claim 4, characterized in that, Step B1 is calculated using the following formula: ； ； in, The noise power is calculated based on PSNR. The noise power is calculated based on the SNR. To receive relative values ​​of the magnetic flux of the receiving coil, for The power.

6. The method for assessing defects in the tank bottom plate according to claim 1, characterized in that, Step C2 specifically involves: The following formula (5) is used to calculate the... and Parameter estimates: ； in, The data is sampled at intervals of a certain distance, and... ; ; ; ; and, through Calculate the The parameter estimates.

7. The method for assessing defects in the tank bottom plate according to claim 1, characterized in that, Step E specifically includes: E1. Obtain the corresponding false alarm probability and detection probability through the ROC curve; E2. Obtain the relationship between the detection decision threshold and the detection probability through the detection statistics.

8. The method for assessing defects in the tank bottom plate according to claim 1, characterized in that, The evaluation of the tank bottom plate defects in step F specifically involves: when the detection statistic exceeds the detection decision threshold, it is determined that there is a defect signal in the sampling point interval; and the size of the corresponding defect is determined by the specific value of the detection statistic.

9. A tank bottom plate defect assessment system, characterized in that, The method described in any one of claims 1 to 8 includes: The sampling data acquisition module is used to collect defect signals from the bottom plate of the storage tank and obtain relative values ​​of the magnetic flux of the receiving coil under different sampling interval conditions. The defect signal acquisition module is used to superimpose noise signals of different powers on the relative value data of the magnetic flux of the receiving coil to acquire a defect signal containing noise. The parameter estimation and fitting module is used to perform parameter estimation and fitting on the noisy defect signal based on the three-peak exponential decay cosine model. The statistics generation module is used to generate detection statistics based on the fitted signal, and to generate ROC curves under different sampling intervals, different PSNR and SNR conditions using the detection statistics; The relationship construction module is used to construct the relationship between the detection decision threshold and the detection probability based on the detection statistics and ROC curve. The defect assessment module is used to assess the defects of the tank bottom plate based on the selected sampling interval, PSNR, and SNR conditions, using the detection decision threshold value.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to cause the at least one processor to perform the tank bottom plate defect assessment method as described in any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer-executable instructions for causing the computer to perform the tank bottom plate defect assessment method as described in any one of claims 1 to 8.

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