Crack Detection Method, Electronic Device, and Computer-Readable Storage Medium

Abstract

The present application is applicable to the field of structural health detection technology, and provides a crack detection method, an electronic device and a computer-readable storage medium. The method determines the slope of the strain value change rate corresponding to each sampling position by using the strain values of the target structure collected by distributed optical fiber sensing at each sampling position; determines the strain observation sequence corresponding to each sampling position according to each slope; determines the target observation sequence according to the strain distribution characteristics corresponding to all strain observation sequences; determines the target position where the crack is located based on the strain values in the target observation sequence; determines the target integration interval according to the target position, and uses the adaptive Simpson algorithm to perform a progressive integration operation on the continuous strain function within the target integration interval with the midpoint of the interval as the interval dividing line, and determines the sum of the obtained multiple target integration values as the width of the crack, which can not only realize the automatic detection and width calculation of the crack, but also improve the detection efficiency and accuracy of the crack.

Description

Technical Field

[0001] This application belongs to the technical field of structural health monitoring, and particularly relates to a crack detection method, an electronic device, and a computer-readable storage medium. Background Art

[0002] During the service life of structures such as bridges, road surfaces, or tunnels, cracks will inevitably occur due to factors such as loads, environmental erosion, or material aging. Cracks are the initial signs of the decline in the durability of the structure and even insufficient bearing capacity. The continuous expansion of micro-cracks will weaken the integrity and stability of the structure, and may even cause safety accidents in severe cases. Therefore, timely and accurate detection of cracks in the structure can reduce potential safety risks.

[0003] One traditional crack detection mainly relies on manual regular inspections and visual observations, which is not only inefficient but also unable to achieve real-time monitoring of cracks. Another traditional crack detection method is to locate the crack position by performing threshold analysis and simple gradient calculation on the strain data of the structure to be detected. However, this method is sensitive to environmental noise, resulting in low accuracy of crack detection. Summary of the Invention

[0004] In view of this, the embodiments of this application provide a crack detection method, an electronic device, and a computer-readable storage medium to solve the technical problems that the existing crack detection methods have low crack detection efficiency and accuracy and cannot achieve real-time monitoring of cracks.

[0005] In a first aspect, the embodiments of this application provide a crack detection method, including:

[0006] Determine the slope of the strain value change rate corresponding to each sampling position according to the strain values of the target structure at each sampling position collected by distributed optical fiber sensing;

[0007] Determine the strain observation sequence corresponding to each sampling position according to the slopes of the strain value change rates; the strain observation sequence includes the strain values corresponding to a continuous plurality of sampling positions;

[0008] Determine the strain distribution characteristics corresponding to each strain observation sequence, and determine the target observation sequence from the strain observation sequences according to the strain distribution characteristics corresponding to all the strain observation sequences;

[0009] Based on the strain values in the target observation sequence, determine the target position where the crack is located;

[0010] Determine a target integral interval according to the target position, and use the adaptive Simpson algorithm to perform a progressive integration operation on the continuous strain function within the target integral interval with the midpoint of the interval as the interval dividing line, and determine the sum of multiple target integral values obtained by the integration as the width of the crack; the continuous strain function is constructed according to the target observation sequence.

[0011] In an alternative implementation manner of the first aspect, according to the strain values of the target structure at each sampling position collected by the distributed optical fiber sensing, determining the slope of the strain value change rate corresponding to each sampling position includes:

[0012] For the i th sampling position, according to the strain values of the target structure at the i th sampling position, the i +1th sampling position, and the i +2th sampling position, calculate the second derivative of the strain value corresponding to the i th sampling position, and determine the second derivative as the slope of the strain value change rate corresponding to the i th sampling position; 1 ≤ i ≤ n -2, n is the number of sampling positions, i is an integer.

[0013] In an alternative implementation manner of the first aspect, according to the slopes of the strain value change rates, determining the strain observation sequence corresponding to each sampling position includes:

[0014] For the 1st sampling position, determine the first strain value sequence composed of the strain values corresponding to consecutive first number of sampling positions including the 1st sampling position as the strain observation sequence corresponding to the 1st sampling position;

[0015] For the j th sampling position, when the slope of the strain value change rate corresponding to the j th sampling position is negative and the slope of the strain value change rate corresponding to the j -1th sampling position is positive, determine the second strain value sequence composed of the strain values corresponding to consecutive second number of sampling positions including the j th sampling position as the strain observation sequence corresponding to the j th sampling position;

[0016] For the j th sampling position, when the slope corresponding to the j th sampling position and the slope corresponding to the j-1 sampling position corresponding to the slope difference is less than the first preset threshold, the first j The third strain value sequence composed of strain values corresponding to the third number of consecutive sampling positions including the sampling positions is determined as the first j The strain observation sequence corresponding to each sampling position;

[0017] Among them, 2≤ j ≤ n , n is the number of sampling locations, j is an integer; the second number is greater than the first number, and the third number is less than the first number.

[0018] In an optional implementation of the first aspect, determining a target observation sequence from the strain observation sequences according to strain distribution characteristics corresponding to all the strain observation sequences includes:

[0019] Performing dimensionality reduction processing on the strain distribution characteristics corresponding to each of the strain observation sequences to obtain low-dimensional strain data corresponding to each of the strain observation sequences;

[0020] Clustering is performed on all the low-dimensional strain data, and the strain observation sequence under the target category in the clustering result is determined as the target observation sequence; the target category refers to the category containing crack information.

[0021] In an optional implementation of the first aspect, determining a target position of a crack based on each strain value in the target observation sequence includes:

[0022] Calculating a first-order backward difference sequence of the target observation sequence;

[0023] Processing the first-order backward difference sequence using a unit step function to obtain a binary sequence corresponding to the first-order backward difference sequence; each element in the binary sequence corresponds to each element in the first-order backward difference sequence, and the value of each element in the binary sequence is 0 or 1;

[0024] Shifting all elements in the binary sequence right by one position to obtain a right-shifted sequence corresponding to the binary sequence, and performing an XOR operation on the binary sequence and the right-shifted sequence to obtain a target sequence;

[0025] The sampling position corresponding to the element with a value of 1 in the target sequence is determined as the target position.

[0026] In an optional implementation of the first aspect, determining a target integration interval according to the target position includes:

[0027] The moving average algorithm is used to smooth each strain value in the target observation sequence, and a smoothed strain value sequence corresponding to the target observation sequence is obtained;

[0028] A continuous strain function corresponding to the smoothed strain value sequence is constructed;

[0029] A target integration interval including the target position is selected from the sampling position interval corresponding to the continuous strain function.

[0030] In an optional implementation manner of the first aspect, the adaptive Simpson algorithm is used to perform progressive integration operations on the continuous strain function within the target integration interval with the midpoint of the interval as the interval dividing line, including:

[0031] The adaptive Simpson algorithm is used to perform integration operations on the continuous strain function within the target integration interval to obtain an integration value corresponding to the target integration interval;

[0032] With the midpoint of the target integration interval as the interval dividing line, the target integration interval is divided into two sub-intervals, and the adaptive Simpson algorithm is used to perform integration operations on the continuous strain function within the two sub-intervals respectively to obtain integration values corresponding to the two sub-intervals respectively;

[0033] When the first difference between the integration value corresponding to the target integration interval and the sum of the integration values corresponding to the two sub-intervals is less than a second preset threshold, stop further dividing the sub-intervals, and determine the integration values corresponding to the two sub-intervals respectively as the target integration value;

[0034] When the first difference is greater than or equal to the second preset threshold, with the midpoint of each sub-interval as the interval dividing line, each sub-interval is further divided respectively, and the adaptive Simpson algorithm is used to calculate the integration values corresponding to the sub-intervals obtained by the further division until the first difference between the sum of the integration values corresponding to each two sub-intervals obtained by the further division and the integration value corresponding to the corresponding parent interval is less than the second preset threshold, stop the interval further division, and determine the integration values corresponding to the sub-intervals with the corresponding first difference less than the second preset threshold as the target integration value.

[0035] In a second aspect, an embodiment of the present application provides an electronic device, including:

[0036] A first determination unit, configured to determine the slope of the strain value change rate corresponding to each sampling position according to the strain values of the target structure at each sampling position collected by distributed optical fiber sensing;

[0037] A second determination unit, configured to determine a strain observation sequence corresponding to each sampling position according to the slope of the change rate of each of the strain values; the strain observation sequence includes the strain values corresponding to a plurality of consecutive sampling positions;

[0038] A third determination unit, configured to determine the strain distribution characteristics corresponding to each strain observation sequence, and determine a target observation sequence from the strain observation sequences according to the strain distribution characteristics corresponding to all the strain observation sequences;

[0039] A fourth determination unit, configured to determine a target position where the crack is located based on each of the strain values in the target observation sequence;

[0040] A fifth determination unit, configured to determine a target integration interval according to the target position, and use an adaptive Simpson's algorithm to perform a progressive integration operation on the continuous strain function within the target integration interval with the midpoint of the interval as the interval dividing line, and determine the sum of a plurality of target integral values obtained by the integration as the width of the crack; the continuous strain function is constructed according to the target observation sequence.

[0041] In a third aspect, an embodiment of the present application provides another electronic device, including a memory and a computer program stored in the memory and executable on a processor, where when the processor executes the computer program, the method described in any optional implementation manner of the first aspect above is implemented.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the crack detection method described in any optional implementation manner of the first aspect above is implemented.

[0043] In a fifth aspect, an embodiment of the present application provides a computer program product, where when the computer program product runs on an electronic device, the electronic device is enabled to implement the crack detection method described in any optional implementation manner of the first aspect.

[0044] Implementing the crack detection method, electronic device, computer-readable storage medium, and computer program product provided by the embodiments of the present application has the following beneficial effects:

[0045] The crack detection method provided by the embodiments of the present application can roughly determine the position interval where the crack is located by determining the slope of the strain value change rate corresponding to each sampling position. On this basis, in order to more accurately locate the crack, all strain values are divided into multiple strain observation sequences according to each slope. Since the strain distribution characteristics corresponding to the strain observation sequences can accurately reflect the strain value distribution in the strain observation sequences, by determining the strain distribution characteristics corresponding to each strain observation sequence and based on the strain distribution characteristics corresponding to all strain observation sequences, the target observation sequence containing crack information can be accurately determined from the strain observation sequences. Furthermore, based on the strain values in the target observation sequence, the target position where the crack is located can be accurately determined, thereby improving the accuracy and efficiency of crack detection. At the same time, by determining the target integration interval where the target position is located and using the adaptive Simpson algorithm to perform progressive integration operations on the continuous strain function within the target integration interval with the midpoint of the interval as the interval dividing line, the estimation accuracy of the integral value can be improved. Since the crack width is the sum of multiple target integral values obtained by integration, the accuracy of crack width calculation can be improved.

[0046] In addition, since this crack detection method only needs to deploy distributed optical fiber sensors on the target structure to realize automatic monitoring of cracks on the target structure without manual regular inspection, it not only improves the crack monitoring efficiency but also reduces the labor cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0048] Figure 1 It is a schematic structural diagram of a crack detection system provided by the embodiments of the present application;

[0049] Figure 2 It is a schematic flowchart of a crack detection method provided by the embodiments of the present application;

[0050] Figure 3 It is a schematic diagram of the interface of an electronic device provided by the embodiments of the present application;

[0051] Figure 4 It is a schematic diagram of the implementation process of S203 in a crack detection method provided by the embodiments of the present application;

[0052] Figure 5Schematic structural diagram of a preset encoder provided by an embodiment of the present application;

[0053] Figure 6 Schematic implementation flowchart of S204 in a crack detection method provided by an embodiment of the present application;

[0054] Figure 7 Schematic implementation flowchart of S205 in a crack detection method provided by an embodiment of the present application;

[0055] Figure 8 Schematic structural diagram of an electronic device provided by an embodiment of the present application;

[0056] Figure 9 Schematic structural diagram of an electronic device provided by another embodiment of the present application. Detailed implementation manners

[0057] The following embodiments are only used to illustrate the technical solutions of the present application more clearly, so they are only examples and cannot be used to limit the protection scope of the present application.

[0058] In the description of the embodiments of the present application, technical terms such as "include", "comprise", "have" and any of their deformations all mean "including but not limited to", unless otherwise specifically emphasized in other ways. In the description of the embodiments of the present application, unless otherwise stated, the technical term "plurality" means two or more than two, and the technical terms "at least one", "one or more" mean one, two or more than two. Technical terms such as "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order or primary and secondary relationship of the indicated technical features. The technical term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0059] The embodiments of the present application first provide a crack detection system. Please refer to Figure 1 , Schematic structural diagram of a crack detection system provided by an embodiment of the present application. As Figure 1 shown, the crack detection system may include an electronic device 11 and a collection device 12. A communication connection is established between the electronic device 11 and the collection device 12, and this communication connection may be a wireless communication connection or a wired communication connection. The embodiments of the present application do not limit the communication connection manner between the electronic device 11 and the collection device 12.

[0060] Exemplarily, the electronic device 11 may include devices such as a mobile phone, a tablet computer, a laptop computer, a desktop computer, or a server. The embodiments of the present application do not limit the type of the electronic device 11.

[0061] In some embodiments, the acquisition device 12 may include a distributed optical fiber sensor 121 and an optical fiber grating demodulator 122. The optical fiber grating demodulator 122 may be connected to the distributed optical fiber sensor 121 and the electronic device 11.

[0062] Exemplarily, the distributed optical fiber sensor 121 may include, from the inside to the outside: a fused silica optical fiber core, a fused silica optical fiber cladding, and a protective coating made of multiple layers of polymers.

[0063] Among them, multiple equally spaced fiber Bragg gratings (FBGs) may be integrated on the optical fiber core of the distributed optical fiber sensor 121, and the interval between every two adjacent FBGs may be set according to actual requirements. For example, in order to enable the distributed optical fiber sensor 121 to have a high spatial resolution, the interval between every two adjacent FBGs may be set to 5 millimeters (mm) to 10 mm.

[0064] In practical applications, when it is necessary to detect the health state of a target structure (such as whether there are cracks), the distributed optical fiber sensor 121 may be disposed on the surface or inside of the key stress area of the target structure. Exemplarily, the target structure may include concrete structures such as buildings, bridges, tunnels, or road surfaces, and may also include structures made of other materials (such as wood structures or ceramic structures, etc.). The embodiments of the present application do not limit the specific type of the target structure.

[0065] Optionally, when the distributed optical fiber sensor 121 is disposed on the surface of the target structure, the distributed optical fiber sensor 121 may be bonded to the surface of the target structure by using epoxy resin or other suitable adhesives, so that the distributed optical fiber sensor 121 is closely attached to the surface of the target structure, thereby effectively transmitting the strain of the target structure, reducing the measurement error of the distributed optical fiber sensor, and improving the accuracy of crack detection.

[0066] It should be noted that different FBGs in the distributed optical fiber sensor 121 may be used to reflect light of different wavelengths. Since the distributed optical fiber sensor 121 is disposed on the surface or inside of the target structure, each FBG in the distributed optical fiber sensor 121 may correspond to a sampling position on the target structure, and is used to collect the strain value of the target structure at this sampling position. The multiple FBGs on the optical fiber core form an FBG array along the optical fiber axis, and can realize multi-point distributed monitoring of the strain value of the target structure.

[0067] Based on this, the fiber Bragg grating demodulator 122 can emit light to the distributed fiber optic sensor 121, receive the light reflected back by the distributed fiber optic sensor 121 (i.e., the reflected light), and analyze the actual wavelength of the reflected light, so as to obtain the strain values of the target structure at each sampling position. Among them, the strain values can be used to represent the relative deformation degree of the target structure.

[0068] Exemplarily, the sampling frequency of the acquisition device 12 can be set according to actual requirements. For example, in order to meet the high time resolution requirements of the strain changes of the target structure during the crack propagation process, the sampling frequency of the acquisition device 12 can be set to 100 Hertz (Hz) to 1000 Hz.

[0069] Optionally, after the fiber Bragg grating demodulator 122 obtains the strain values of the target structure at each sampling position, it can store the strain values of the target structure at each sampling position in a local server or database for subsequent application in the crack detection of the target structure. Exemplarily, the storage format of the strain values of the target structure at each sampling position can be set according to actual requirements, and the embodiments of the present application do not limit it.

[0070] Optionally, when the fiber Bragg grating demodulator 122 receives a strain value acquisition instruction from the electronic device 11, it can also send the strain values of the target structure at each sampling position stored in the local server or database to the electronic device 11, so that the electronic device 11 can determine the target position where the crack is located and calculate the width of the crack according to the strain values of the target structure at each sampling position.

[0071] In some other embodiments, the acquisition device 12 may further include a temperature sensor. The temperature sensor can be connected to the electronic device 11. In actual applications, the temperature sensor can be arranged in the key stress areas of the target structure to collect the actual temperature of the key stress areas of the target structure and send the actual temperature to the electronic device 11, so that the electronic device 11 can perform temperature compensation on the strain values of the target structure at each sampling position based on the actual temperature subsequently, thereby reducing the influence of the temperature change of the target structure on the measurement accuracy of the strain values and improving the accuracy of crack detection.

[0072] It should be noted that the specific processes of the electronic device 11 for temperature compensation of strain values, determining the target position where the crack is located, and calculating the width of the crack can refer to the relevant descriptions in the subsequent method embodiments, and will not be elaborated here.

[0073] The embodiments of the present application also provide a crack detection method, and the execution subject of this crack detection method can be the electronic device 11 in the above crack detection system. Exemplarily, please refer to Figure 2 , which is a schematic flowchart of a crack detection method provided by the embodiments of the present application. AsFigure 2 As shown, in some embodiments, the crack detection method may include S201 to S205, which are described in detail as follows:

[0074] S201. Determine the slope of the strain value change rate corresponding to each sampling position according to the strain values of the target structure collected by the distributed optical fiber sensing at each sampling position.

[0075] Among them, the slope of the strain value change rate corresponding to each sampling position can be used to represent the change speed of the strain value change rate at each sampling position.

[0076] In an alternative implementation, when receiving a crack detection instruction, the electronic device may obtain the strain values of the target structure collected by the distributed sensor at each sampling position from the fiber grating demodulator.

[0077] Among them, the crack detection instruction may be manually triggered by the user. Exemplarily, please refer to Figure 3 , which is a schematic diagram of the interface of an electronic device provided in an embodiment of the present application. As Figure 3 shown, a crack detection control 31 may be configured on the electronic device. The user can issue a crack detection instruction to the electronic device by clicking the crack detection control 31. Based on this, when detecting that the crack detection control 31 is clicked, the electronic device may determine that it has received the crack detection instruction and obtain the strain values of the target structure collected by the distributed sensor at each sampling position from the fiber grating demodulator.

[0078] In another alternative implementation, the electronic device may automatically obtain the strain values of the target structure collected by the distributed sensor at each sampling position from the fiber grating demodulator every first duration. Among them, the first duration may be set according to actual needs, and the embodiments of the present application do not limit it.

[0079] Optionally, in the case where the crack detection system does not include a temperature sensor, in order to reduce the computational complexity, the electronic device may directly determine the slope of the strain value change rate corresponding to each sampling position according to the strain values of the target structure at each sampling position. In order to reduce the strain value fluctuation caused by environmental changes, the electronic device may perform denoising processing on the strain values of the target structure at each sampling position and determine the slope of the strain value change rate corresponding to each sampling position according to the denoised strain values of each sampling position.

[0080] Optionally, when the crack detection system includes a temperature sensor, the electronic device can further perform temperature compensation on the strain values at each sampling position after denoising based on the actual temperature of the key stress area of the target structure collected by the temperature sensor, and determine the slope of the strain value change rate corresponding to each sampling position according to the strain values at each sampling position after temperature compensation. This can reduce the influence of temperature changes on the measurement accuracy of strain values and improve the accuracy of crack detection.

[0081] In a specific implementation, the electronic device can determine the slope of the strain value change rate corresponding to each sampling position through step 1.1, which is described in detail as follows:

[0082] Step 1.1, for the i th sampling position, according to the strain values of the target structure at the i th sampling position, the i +1th sampling position, and the i +2th sampling position, calculate the second derivative of the strain value corresponding to the i th sampling position, and determine the second derivative as the slope of the strain value change rate corresponding to the i th sampling position.

[0083] Among them, 1 ≤ i ≤ n -2, n is the number of sampling positions, i is an integer.

[0084] In an alternative implementation, the electronic device can specifically calculate the second derivative of the strain value corresponding to the i th sampling position through the following formula (1):

[0085] Gradient’ ( i ) = Strain ( i +2) - 2 Strain ( i +1) + Strain ( i )] / Δ x 2 ; Formula (1)

[0086] Among them, Gradient’ ( i ) is the second derivative of the strain value corresponding to the i th sampling position, Strain ( i ) is the strain value of the target structure at the i th sampling position, Strain ( i +1) is the strain value of the target structure at thei The strain value at +1 sampling position, Strain ( i +2) is the strain value of the target structure at the i +2nd sampling position, Δ x is the interval between every two adjacent sampling positions.

[0087] The second derivative of the strain value corresponding to each sampling position can be used to measure the curvature (i.e., the degree of bending) of the strain curve at that sampling position. Among them, the strain curve can be a continuous curve obtained by connecting the strain values of the target structure at each sampling position in spatial order, which can reflect the strain distribution of the target structure.

[0088] It can be understood that when a crack occurs in the target structure, the material at the crack will break and separate, resulting in relative displacement of the structures on both sides of the crack. Since the distributed fiber optic sensor is closely attached to the target structure, the corresponding part of the outermost protective coating of the distributed fiber optic sensor will be forced to stretch to adapt to the opening of the crack. This stretching will cause the corresponding part of the protective coating to bear a very high strain, and the protective coating will transmit this high strain to the FBG at the corresponding position. In this way, not only can the FBG at the corresponding position sense this high strain, but also the probability of the distributed fiber optic sensor being torn is greatly reduced, making the distributed fiber optic sensor have a long service life. It should be noted that the above high strain will cause a peak in the strain curve, that is, the peak region in the strain curve usually corresponds to the crack position of the target structure.

[0089] It can also be understood that since the curvature of the strain curve is large near the peak, the second derivative of the strain value near the peak is usually negative, and the strain value reaches the minimum (the largest absolute value) at the peak; since the strain curve is close to a straight line in the region with gentle changes, the second derivative of the strain value in this region is usually close to 0. Based on this, the location of the crack can be determined according to the sign change of the second derivative of the strain value corresponding to each sampling position.

[0090] S202. Determine the strain observation sequence corresponding to each sampling position according to the slope of the change rate of each strain value; the strain observation sequence includes the strain values corresponding to a continuous plurality of sampling positions.

[0091] To improve the accuracy of crack detection, when positioning the target position where the crack is located, multiple strain observation sequences can be determined according to the sampling positions. Each strain observation sequence can be composed of the strain values corresponding to a continuous plurality of sampling positions. For example, the strain observation sequence corresponding to each sampling position can be determined according to the slope of the change rate of the strain value corresponding to each sampling position, so as to obtain n strain observation sequences.

[0092] In an alternative implementation, the electronic device can determine the strain observation sequence corresponding to each sampling position through steps 2.1 to 2.3, which are described in detail as follows:

[0093] Step 2.1, for the first sampling position, determine the first strain value sequence composed of the strain values corresponding to the first consecutive number of sampling positions including the first sampling position as the strain observation sequence corresponding to the first sampling position.

[0094] Step 2.2, for the j th sampling position, when the slope of the strain value change rate corresponding to the j th sampling position is negative and the slope of the strain value change rate corresponding to the j -1th sampling position is positive, determine the second strain value sequence composed of the strain values corresponding to the second consecutive number of sampling positions including the j th sampling position as the strain observation sequence corresponding to the j th sampling position.

[0095] Step 2.3, for the j th sampling position, when the difference between the slope corresponding to the j th sampling position and the slope corresponding to the j -1th sampling position is less than the first preset threshold, determine the third strain value sequence composed of the strain values corresponding to the third consecutive number of sampling positions including the j th sampling position as the strain observation sequence corresponding to the j th sampling position.

[0096] Wherein, 2 ≤ j ≤ n , n is the number of sampling positions, j is an integer.

[0097] The second quantity can be greater than the first quantity, and the third quantity can be less than the first quantity. For example, the first quantity can be 60, the second quantity can be 90, and the third quantity can be 30, that is, the first strain value sequence can be composed of 60 strain values, the second strain value sequence can be composed of 90 strain values, and the third strain value sequence can be composed of 30 strain values.

[0098] In the embodiments of the present application, by using a smaller strain observation sequence for fast data processing in the area where the strain value changes gently, and using a larger strain observation sequence for stable and comprehensive data analysis in the area where the strain value changes violently (i.e., the area containing crack information), the size of the strain observation sequence can be matched with the local complexity of the data, which can not only accurately detect the location of the crack, but also reduce the computational complexity and save computational resources.

[0099] S203, determine the strain distribution characteristics corresponding to each strain observation sequence, and determine the target observation sequence from the strain observation sequences according to the strain distribution characteristics corresponding to all strain observation sequences.

[0100] Among them, the number of strain distribution characteristics can be multiple. Different strain distribution characteristics can be used to reflect the strain value distribution in the strain observation sequence from different angles.

[0101] The target observation sequence can refer to the strain observation sequence containing crack information.

[0102] The strain distribution characteristics can include: basic statistical characteristics, slope distribution characteristics of the change rate, strain difference characteristics, distribution fitting characteristics, peak characteristics, direction characteristics, tail characteristics, and comprehensive calculation characteristics, etc.

[0103] The basic statistical characteristics can be used to represent the central tendency, dispersion degree, strain condition at the starting sampling position, and strain condition at the ending sampling position of the strain value distribution in the strain observation sequence. Exemplarily, the basic statistical characteristics can include: the maximum strain value in the strain observation sequence, the standard deviation of the strain value, the first quartile of the strain value, the third quartile of the strain value, the strain starting value, and the strain ending value.

[0104] Among them, the first quartile of the strain value in the strain observation sequence can be the strain value at the first 1 / 4 position after sorting all the strain values in the strain observation sequence in ascending order. Similarly, the third quartile of the strain value in the strain observation sequence can be the strain value at the first 3 / 4 position after sorting all the strain values in the strain observation sequence in ascending order. The strain starting value in the strain observation sequence can refer to the strain value corresponding to the first sampling position in the strain observation sequence. The strain ending value in the strain observation sequence can refer to the strain value corresponding to the last sampling position in the strain observation sequence.

[0105] The slope distribution characteristics can be used to represent the change trend of the strain value change rate in the strain observation sequence. Exemplarily, the slope distribution characteristics can include: the sampling position corresponding to the maximum strain value in the strain observation sequence, the slope of the strain value change rate before the maximum strain value (abbreviated as the forward slope), and the slope of the strain value change rate after the maximum strain value (abbreviated as the backward slope).

[0106] Optionally, the electronic device may perform linear regression fitting on the strain values corresponding to several sampling positions before the maximum strain value in the strain observation sequence, and determine the slope of the first curve obtained by fitting as the forward slope of the maximum strain value. Optionally, the electronic device may also perform linear regression fitting on the strain values corresponding to several sampling positions after the maximum strain value in the strain observation sequence, and determine the slope of the second curve obtained by fitting as the backward slope of the maximum strain value.

[0107] The strain difference feature can be used to represent the difference situation of the strain values in the strain observation sequence. Exemplarily, the strain difference feature may include: the difference between the maximum strain value and the minimum strain value in the strain observation sequence, the difference between the accumulated relative strain values before and after the maximum strain value, and the difference between the strain start value and the strain end value.

[0108] Among them, the difference between the accumulated relative strain values before and after the maximum strain value in the strain observation sequence may refer to the difference between the sum of the strain values corresponding to all sampling positions before the sampling position corresponding to the maximum strain value and the sum of the strain values corresponding to all sampling positions after the sampling position corresponding to the maximum strain value.

[0109] The distribution fitting feature can be used to represent the distribution characteristics of the strain values in the strain observation sequence, and can provide a basis for crack identification. Exemplarily, the distribution fitting feature may include: the difference between the true distribution of the strain values in the strain observation sequence and the fitted normal distribution.

[0110] Optionally, the electronic device may calculate the difference between the true distribution of the strain values in the strain observation sequence and the fitted normal distribution through the following steps 3.1 to 3.5:

[0111] Step 3.1, calculate the average value s 1 and the standard deviation δ1 of all strain values in the strain observation sequence.

[0112] Step 3.2, construct a normal distribution with the average value s 1 as the mean and the standard deviation δ1 as the standard deviation ( s 1, δ1 2 )

[0113] Step 3.3, calculate the first empirical cumulative distribution function ECDF 1( x )

[0114] Step 3.4, calculate the second empirical cumulative distribution function s 1, δ1 2 corresponding to the normal distribution ( ECDF 2( x )

[0115] Step 3.5, according to the formula D = max x | ECDF 1( x ) - ECDF 2( x )|, calculate the difference measure between the first empirical cumulative distribution function ECDF 1( x ) and the second empirical cumulative distribution function ECDF 2( x ), and determine this difference measure D as the difference between the true distribution of the strain values in the strain observation sequence and the fitted normal distribution. D

[0116] Exemplarily, the peak feature may include the number of peaks in the strain observation sequence. Since cracks will cause peaks in the strain distribution, the peak feature can be used to assist in identifying the location of the cracks.

[0117] The direction feature can be used to evaluate the symmetry of the strain values in the strain observation sequence to judge the direction of the strain distribution. Exemplarily, the direction feature may include the skewness coefficient. Optionally, the skewness coefficient may be, for example, the Fisher - Pearson skewness coefficient. It should be noted that the specific calculation method of the Fisher - Pearson skewness coefficient can refer to the relevant descriptions in the prior art and will not be elaborated here.

[0118] The tail feature can be used to represent the ending feature of the strain distribution corresponding to the strain observation sequence. Exemplarily, the tail feature may include the kurtosis coefficient corresponding to the strain observation sequence. It should be noted that the specific calculation method of the kurtosis coefficient can refer to the relevant descriptions in the prior art and will not be elaborated here.

[0119] Exemplarily, the comprehensive calculation features may include: the sum of all strain values in the strain observation sequence, the test statistic of the null hypothesis test corresponding to the strain observation sequence, the p - value corresponding to the test statistic, and the full width at half maximum of the strain value. Among them, the sum of all strain values in the strain observation sequence can reflect the cumulative situation of the strain values as a whole. The test statistic of the null hypothesis test corresponding to the strain observation sequence and the p - value corresponding to the test statistic are used to test whether the strain values in the strain observation sequence follow a normal distribution to assist in judging the distribution characteristics of the strain values. The full width at half maximum of the strain value can be used to represent the width feature of the peak.

[0120] It should be noted that the specific calculation methods of the sum of all strain values in the strain observation sequence, the test statistic of the null hypothesis test corresponding to the strain observation sequence, the p - value corresponding to the test statistic, and the full width at half maximum of the strain value can refer to the relevant descriptions in the prior art and will not be elaborated here.

[0121] In a specific implementation, the electronic device can, for example, Figure 4 as shown in S2031 - S2032, determine the target observation sequence from the strain observation sequence, which is described in detail as follows:

[0122] S2031, perform dimensionality reduction processing on the strain distribution characteristics corresponding to each strain observation sequence to obtain the low - dimensional strain data corresponding to each strain observation sequence.

[0123] Optionally, the electronic device can input the strain distribution characteristics corresponding to each strain observation sequence into a preset encoder for processing to obtain the low - dimensional strain data corresponding to each strain observation sequence.

[0124] Exemplarily, please refer to Figure 5 , which is a schematic structural diagram of a preset encoder provided in an embodiment of the present application. As Figure 5 shown, the preset encoder can include a plurality of cascaded feature extraction networks 51. Each feature extraction network 51 can include a fully - connected layer and an activation layer connected to the output end of the fully - connected layer.

[0125] Among them, the fully - connected layer can be used to map the high - dimensional strain to low - dimensional strain data and send the low - dimensional strain data to the activation layer. The activation layer can be used to perform a non - linear transformation on the received low - dimensional strain data using a preset activation function. It should be noted that the data output by the activation layer of the last feature extraction network 51 is the low - dimensional strain data corresponding to the strain observation sequence.

[0126] Exemplarily, the preset activation function can be the hyperbolic tangent function, or other types of activation functions. The embodiment of the present application does not limit the type of the preset activation function.

[0127] In the embodiment of the present application, by setting a plurality of cascaded feature extraction networks 51 in the preset encoder, the input multiple strain distribution characteristics can be gradually compressed into low - dimensional strain data, so as to realize the dimensionality reduction processing and feature extraction of the strain distribution characteristics and reduce the computational complexity of subsequent steps.

[0128] S2032, perform clustering processing on all the low - dimensional strain data, and determine the strain observation sequences under the target category in the clustering result as the target observation sequences.

[0129] Exemplarily, the electronic device can use the K - means clustering algorithm to perform clustering processing on the low - dimensional strain data corresponding to all strain observation sequences, so as to cluster the low - dimensional strain data into K categories. Exemplarily, K can be 2, and the two categories can be the target category containing crack information and the non - target category not containing crack information. Among them, the similarity between the low - dimensional strain data in the same category is relatively high, and the similarity between the low - dimensional strain data in different categories is relatively low.

[0130] In practical applications, the specific types of the above two categories can be determined according to the test statistic of the null hypothesis test corresponding to the strain observation sequence in each category and the p-value corresponding to the test statistic. For example, when both the test statistic of the null hypothesis test corresponding to the strain observation sequence in a certain category and the p-value corresponding to the test statistic are close to 0, the electronic device can determine that this category is a non-target category that does not contain crack information, and determine the other category as the target category that contains crack information.

[0131] It should be noted that the target category can include one or more strain observation sequences.

[0132] S204. Based on each strain value in the target observation sequence, determine the target position where the crack is located.

[0133] Among them, the target position where the crack is located can be the sampling position corresponding to one or more strain values in the target observation sequence, that is, the number of target positions where the crack is located can be one or more.

[0134] Optionally, S204 can be implemented through S2041 to S2044 as shown in Figure 6 and are described in detail as follows:

[0135] S2041. Calculate the first-order backward difference sequence of the target observation sequence.

[0136] Among them, the first-order backward difference sequence can be used to represent the difference between the second to the m th strain values in the target observation sequence and their previous strain values respectively. m is the number of strain values included in the target observation sequence.

[0137] Optionally, the electronic device can shift all the strain values in the target observation sequence one bit to the left to obtain the left-shifted sequence corresponding to the target observation sequence, and calculate the difference values (i.e., the differences) between each strain value in the target observation sequence and each strain value in the left-shifted sequence, and determine the sequence composed of all the difference values as the first-order backward difference sequence of the target observation sequence.

[0138] Exemplarily, assume that the target observation sequence is [2, 3, 5, 6, 10, 6, 5, 3, 2]. Then, the shifted sequence obtained by shifting all the strain values in the target observation sequence one position to the left can be [3, 5, 6, 10, 6, 5, 3, 2, X1], where X1 is the out-of-range data and can be ignored when calculating the first-order backward difference sequence. Based on this, the first-order backward difference sequence can be [2 - 3, 3 - 5, 5 - 6, 6 - 10, 10 - 6, 6 - 5, 5 - 3, 3 - 2], that is, [-1, -2, -1, -4, 4, 1, 2, 1]. It can be seen that -4 and 4 in the first-order backward difference sequence are relatively prominent compared to other values, indicating that cracks may have occurred at the positions corresponding to -4 and 4 in the target structure.

[0139] S2042. Process the first-order backward difference sequence using the unit step function to obtain the binary sequence corresponding to the first-order backward difference sequence.

[0140] The purpose of the electronic device to process the first-order backward difference sequence using the unit step function is to remove the negative part of the first-order backward difference sequence and only retain the positive part that may have mutations.

[0141] Optionally, the electronic device can assign all negative values in the first-order backward difference sequence to 0 and all positive values to 1, thereby obtaining the binary sequence corresponding to the first-order backward difference sequence. That is, each element in the binary sequence corresponds to each element in the first-order backward difference sequence, and the value of each element in the binary sequence is 0 or 1.

[0142] Exemplarily, assume that the first-order backward difference sequence is [-1, -2, -1, -4, 4, 1, 2, 1]. Then, the binary sequence obtained by processing the first-order backward difference sequence using the unit step function can be [0, 0, 0, 0, 1, 1, 1, 1].

[0143] S2043. Shift all elements in the binary sequence one position to the right to obtain the shifted sequence corresponding to the binary sequence, and perform an exclusive OR operation on the binary sequence and the shifted sequence to obtain the target sequence.

[0144] Exemplarily, assume that the binary sequence is [0, 0, 0, 0, 1, 1, 1, 1]. Then, the shifted sequence obtained by shifting all elements in the binary sequence one position to the right can be [X2, 0, 0, 0, 0, 1, 1, 1], where X2 is an unknown value and can be ignored or replaced with 0. Based on this, the target sequence obtained by performing an exclusive OR operation on the binary sequence and the shifted sequence can be [0, 0, 0, 0, 1, 0, 0, 0].

[0145] S2044. Determine the acquisition positions corresponding to the elements with a value of 1 in the target sequence as the target positions.

[0146] Exemplarily, assuming that the 1 in the target sequence [0, 0, 0, 0, 1, 0, 0, 0] corresponds to the 5th sampling position, it can be illustrated that there is a crack at the 5th sampling position, that is, the target position where the crack is located is the 5th sampling position.

[0147] S205. Determine a target integration interval according to the target position, and use the adaptive Simpson algorithm to perform a progressive integration operation on the continuous strain function within the target integration interval with the midpoint of the interval as the interval dividing line, and determine the sum of multiple target integration values obtained by the integration as the width of the crack.

[0148] Among them, the target integration interval refers to a continuous position interval including the target position where the crack is located.

[0149] The continuous strain function can be constructed according to the target observation sequence.

[0150] Optionally, in S205, determining the target integration interval according to the target position and using the adaptive Simpson algorithm to perform a progressive integration operation on the continuous strain function within the target integration interval with the midpoint of the interval as the interval dividing line may include S2051 to S2057 as shown in Figure 7 shown below, which are described in detail as follows:

[0151] S2051. Use the moving average algorithm to smooth each strain value in the target observation sequence to obtain a smoothed strain value sequence corresponding to the target observation sequence.

[0152] Since the strain values within the target integration interval (i.e., the crack area) usually have large fluctuations, the moving average algorithm can be used to preprocess the strain values within the target integration interval to reduce noise interference. For example, the electronic device can use a moving window of size h to perform a moving average process on the strain values in the target observation sequence to obtain a smoothed strain value sequence corresponding to the target observation sequence. Among them, h can be set according to actual needs. For example, h can be 3.

[0153] Exemplarily, the value of each element in the smoothed strain value sequence can be the mean of all strain values within the moving window corresponding to the corresponding sampling position in the target observation sequence. That is, y q =( x q-1 + x q + x q+1 ) / 3, where y q is the value of the q th element in the smoothed strain value sequence.x q-1 is the strain value corresponding to the q -1th sampling position in the target observation sequence, x q is the strain value corresponding to the q th sampling position in the target observation sequence, x q+1 is the strain value corresponding to the q +1th sampling position in the target observation sequence, 1 ≤ q ≤ m .

[0154] It should be noted that for the sampling positions corresponding to the boundary strain values in the target observation sequence, the missing strain values in the moving window corresponding to the sampling position can be determined by mirror extension (i.e., mirror symmetry supplementation). For example, for the sampling position corresponding to the first strain value in the target observation sequence, the strain value x 0 in its corresponding moving window can be determined according to the second strain value x 2 in the target observation sequence. For the sampling position corresponding to the m th strain value in the target observation sequence, the strain value x m+1 in its corresponding moving window can be determined according to the m -1th strain value x m-1 in the target observation sequence.

[0155] S2052. Construct a continuous strain function corresponding to the smoothed strain value sequence.

[0156] It can be understood that since the adaptive Simpson algorithm repeatedly calculates the integral value of the function in a continuous interval, it is necessary to construct a continuous strain function corresponding to the smoothed strain value sequence to meet the calculation requirements of the adaptive Simpson algorithm.

[0157] Exemplarily, the electronic device can use the cubic spline interpolation method to convert each discrete strain value in the smoothed strain value sequence into a continuous strain function. Specifically, the electronic device can construct a continuous strain function through the following steps 4.1 to 4.3, which are described in detail as follows:

[0158] Step 4.1. Construct m -1 cubic polynomials according to the smoothed strain value sequence.

[0159] Among them, m is the number of strain values included in the smoothed strain value sequence.

[0160] m-1 cubic polynomials can be cubic polynomials corresponding to the sampling position intervals corresponding to every two adjacent strain values in the smoothed strain value sequence.

[0161] For example, the electronic device can use each strain value in the smoothed strain value sequence as a vertical coordinate value (i.e. y 1, y 2,……, y m ), the sampling position corresponding to each strain value in the smoothed strain value sequence is used as a corresponding horizontal coordinate value (i.e. x 1, x 2,……, x m ), thus obtaining m Discrete coordinate points: ( x 1, y 1), ( x 2, y 2),……,( x m , y m ). Electronic devices can be based on this m discrete coordinate points, constructed using cubic spline interpolation m -1 cubic polynomial S d ( x ). In which, each polynomial S d ( x ) corresponds to a coordinate point interval [ x d , x d+1 ], 1≤ d ≤ m -1. For example, the electronic device can convert each cubic polynomial S d ( x ) is defined as: S d ( x )= a d + b d ( x - x d )+ c d ( x - x d ) 2 + d d ( x -x d ) 3 . Among them, a d 、 b d 、 c d and d d are the coefficients of the cubic polynomial S d ( x ).

[0162] Step 4.2, Solve the coefficients of each cubic polynomial to obtain the complete function expression corresponding to each cubic polynomial.

[0163] For each coordinate point interval x d , x d+1 , the function value x = x d at the starting position (i.e., S d ( x d ) is y d . To make the curve of the finally obtained continuous strain function continuous and smooth, the electronic device can make the function values, first derivatives, and second derivatives equal at the junction points of every two adjacent coordinate point intervals (i.e., x = x d+1 ), that is, S d ( x d+1 )= S d+1 ( x d+1 ), S d ’ ( x d+1 )= S d+1 ’ ( x d+1 ), and S d ’ ( x d+1 )= S d+1 ’ ( x d+1), and setting the second-order derivatives of all coordinate points at the starting and ending coordinates equal to 0. This makes the curve of the continuous strain function smoother at the beginning and end. The electronic device solves the simultaneous equations based on the above conditions to obtain the coefficients of each cubic polynomial.

[0164] In step 4.3, the functions described by the complete function expressions corresponding to all cubic polynomials are determined as continuous strain functions.

[0165] S2053: Select a target integration interval including the target position from the sampling position interval corresponding to the continuous strain function.

[0166] The target integration interval may be the interval between the starting position and the ending position of the crack.

[0167] S2054: An adaptive Simpson algorithm is used to perform an integration operation on the continuous strain function within the target integration interval to obtain an integral value corresponding to the target integration interval.

[0168] For example, assuming the target integration interval is [ a , b ], the electronic device can use the following adaptive Simpson algorithm to perform integration operation on the continuous strain function within the target integration interval to obtain the integral value corresponding to the target integration interval:

[0169] ;

[0170] in, S ( a , b ) is the integral value corresponding to the target integral interval, f is a continuous strain function, ( a + b ) / 2 is the midpoint of the target integration interval.

[0171] S2055: Using the midpoint of the target integral interval as the interval dividing line, the target integral interval is divided into two subintervals, and the adaptive Simpson algorithm is used to perform integration operations on the continuous strain functions in the two subintervals respectively to obtain the integral values corresponding to the two subintervals.

[0172] For example, assuming the target integration interval is [ a , b ], the electronic device can set the target interval [ a , b ] is divided into the midpoint ( a + b ) / 2 is the interval dividing line, and the target interval [ a , b ] is divided into two subintervals[a , r and r , b , where r= ( a + b ) / 2. The electronic device can adopt the above adaptive Simpson algorithm to separately integrate the continuous strain function within the sub - intervals a , r and r , b , and obtain the integral values a , r corresponding to the sub - interval S ( a , r ) and the integral values r , b corresponding to the sub - interval S ( r , b ).

[0173] S2056. When the first difference between the integral value corresponding to the target integral interval and the sum of the integral values corresponding to the two sub - intervals is less than the second preset threshold, stop further dividing the sub - intervals, and determine the integral values corresponding to the two sub - intervals respectively as the target integral values.

[0174] S2057. When the first difference is greater than or equal to the second preset threshold, take the mid - point of each sub - interval as the interval dividing line, further divide each sub - interval respectively, and use the adaptive Simpson algorithm to calculate the integral values corresponding to the sub - intervals obtained by the further division until the first difference between the sum of the integral values corresponding to every two sub - intervals obtained by the further division and the integral value corresponding to the corresponding parent interval is less than the second preset threshold, then stop the interval further division, and determine the integral values corresponding to the sub - intervals with the corresponding first difference less than the second preset threshold as the target integral values.

[0175] Among them, the second preset threshold can be determined according to actual needs, and the embodiments of the present application do not limit it.

[0176] It should be noted that the above first difference is the absolute value of the difference.

[0177] Optionally, in the above S ( a , b ) and S ( a , r ) + S ( r , bWhen the first difference of ( a , r ) is less than the second preset threshold, it indicates that the calculation accuracy of the current integral value has reached the preset requirement. Therefore, the electronic device can stop further dividing the sub-interval and determine the integral value corresponding to the sub-interval S ( a , r ) and the integral value corresponding to the sub-interval r , b ) as the target integral value, and determine the sum of the target integral values S ( r , b ) + S ( a , r ) as the crack width. S ( r , b )

[0178] Optionally, when the first difference between S ( a , b ) and S ( a , r ) + S ( r , b ) is greater than or equal to the second preset threshold, it indicates that the calculation accuracy of the current integral value has not reached the preset requirement. Therefore, the electronic device can perform operations such as further dividing, integrating, and comparing integral differences on the sub-intervals a , r and r , b until the first difference between the sum of the integral values corresponding to each pair of sub-intervals obtained by further division and the integral value corresponding to the corresponding parent interval is less than the second preset threshold, and then stop further dividing the interval, and determine the integral values corresponding to the sub-intervals with the first difference less than the second preset threshold as the target integral values.

[0179] Exemplarily, for the sub-interval a , r , the electronic device can perform the following operations on it:

[0180] Calculate the midpoint a , r of the sub-interval r , a + r ) / 2, and based on the midpoint a , r of the sub-interval r 1, divide the sub-interval into two sub-intervals a ,r 1] and r 1, r ; The above adaptive Simpson's algorithm is used to integrate the continuous strain function in the sub-intervals a , r 1] and r 1, r respectively, and the integral values corresponding to the sub-intervals a , r 1] are obtained S ( a , r 1) and the integral values corresponding to the sub-intervals r 1, r ; When the sum of the integral value corresponding to the sub-interval S ( r 1, r ) and the integral value corresponding to the sub-interval a , r 1] and the first difference between the integral value corresponding to the sub-interval r 1, r is less than the second preset threshold, it indicates that the calculation accuracy of the current integral value has reached the preset requirement. Therefore, the electronic device can stop re-dividing the sub-intervals S ( a , r 1)+ S ( r 1, r ) and the integral value corresponding to the corresponding parent interval a , r . a , r 1] and r 1, r .

[0181] Exemplarily, for the sub-interval r , b , the electronic device can perform the following operations on it:

[0182] Calculate the midpoint r , b of the sub-interval r 2 = ( r + b ) / 2, and according to the midpoint r , b of the sub-interval r 2, re-divide the sub-interval into two sub-intervals r , r 2] and r 2, b ; The above adaptive Simpson's algorithm is used to integrate the sub-intervals r , r2] and r 2, b Integrate the continuous strain function within to obtain the sub - interval r , r 2] corresponding integral value S ( r , r 2) and the sub - interval r 2, b corresponding integral value S ( r 2, b );In the sub - interval r , r 2] corresponding integral value and the sub - interval r 2, b corresponding integral value sum S ( r , r 2)+ S ( r 2, b ) and the corresponding integral value of the corresponding parent interval r , b When the first difference is less than the second preset threshold, it indicates that the calculation accuracy of the current integral value has reached the preset requirement. Therefore, the electronic device can stop re - dividing the sub - sub - intervals r , r 2] and r 2, b .

[0183] Based on this, the electronic device can S ( a , r 1), S ( r 1, r )、 S ( r , r 2) and S ( r 2, b ) Determine as the target integral value, and the sum of the target integral values S ( a , r 1)+ S ( r 1, r )+ S ( r , r 2)+ S ( r 2, b ) Determine as the crack width.

[0184] By performing the progressive integration operation in this way, the accuracy of the integral value estimation can be gradually improved. When all the first differences are less than the second preset threshold, the sum of the integral values corresponding to each sub-interval where the corresponding first difference is less than the second threshold is determined as the crack width, which can improve the accuracy of crack width calculation.

[0185] As can be seen from the above, in the embodiment of the present application, by determining the slope of the strain value change rate corresponding to each sampling position, the position interval where the crack is located can be roughly determined according to the signs of the slopes; on this basis, in order to more accurately locate the crack, all the strain values are divided into multiple strain observation sequences according to the slopes. Since the strain distribution characteristics corresponding to the strain observation sequences can accurately reflect the strain value distribution in the strain observation sequences, by determining the strain distribution characteristics corresponding to each strain observation sequence and according to the strain distribution characteristics corresponding to all the strain observation sequences, the target observation sequence containing crack information can be accurately determined from the strain observation sequences. Furthermore, based on the strain values in the target observation sequence, the target position where the crack is located can be accurately determined, thereby improving the accuracy and efficiency of crack detection. At the same time, by determining the target integration interval where the target position is located and using the adaptive Simpson algorithm to perform a progressive integration operation on the continuous strain function in the target integration interval with the midpoint of the interval as the interval dividing line, the estimation accuracy of the integral value can be improved; since the crack width is the sum of multiple target integral values obtained by integration, the accuracy of crack width calculation can be improved.

[0186] In addition, since this crack detection method only needs to deploy distributed optical fiber sensors on the target structure to realize the automatic monitoring of cracks on the target structure without the need for manual regular inspections, it not only improves the crack monitoring efficiency but also reduces the labor cost.

[0187] It can be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0188] Based on the crack detection method provided in the above embodiments, the embodiments of the present application further provide an embodiment of an electronic device for implementing the above method embodiments. Please refer to Figure 8 , which is a schematic structural diagram of an electronic device provided in an embodiment of the present application. For the convenience of description, only the parts related to this embodiment are shown. As Figure 8 shown, the electronic device 80 may include: a first determination unit 801, a second determination unit 802, a third determination unit 803, a fourth determination unit 804, and a fifth determination unit 805. Among them:

[0189] The first determination unit 801 is configured to determine the slope of the strain value change rate corresponding to each sampling position according to the strain values of the target structure at each sampling position collected by distributed optical fiber sensing.

[0190] The second determination unit 802 is configured to determine the strain observation sequence corresponding to each sampling position according to the slope of the strain value change rate of each; the strain observation sequence includes the strain values corresponding to a continuous plurality of sampling positions.

[0191] The third determination unit 803 is configured to determine the strain distribution characteristics corresponding to each strain observation sequence, and determine the target observation sequence from the strain observation sequences according to the strain distribution characteristics corresponding to all the strain observation sequences.

[0192] The fourth determination unit 804 is configured to determine the target position where the crack is located based on the strain values in the target observation sequence.

[0193] The fifth determination unit 805 is configured to determine the target integration interval according to the target position, and use the adaptive Simpson algorithm to perform a progressive integration operation on the continuous strain function within the target integration interval with the midpoint of the interval as the interval division line, and determine the sum of the obtained multiple target integral values as the width of the crack; the continuous strain function is constructed according to the target observation sequence.

[0194] Optionally, the first determination unit 801 is specifically configured to:

[0195] For the i th sampling position, according to the strain values of the target structure at the i th sampling position, the i +1th sampling position, and the i +2th sampling position, calculate the second derivative of the strain value corresponding to the i th sampling position, and determine the second derivative as the slope of the strain value change rate corresponding to the i th sampling position; 1 ≤ i ≤ n -2, n is the number of sampling positions, i is an integer.

[0196] Optionally, the second determination unit 802 is specifically configured to:

[0197] For the 1st sampling position, determine the first strain value sequence composed of the strain values corresponding to the continuous first number of sampling positions including the 1st sampling position as the strain observation sequence corresponding to the 1st sampling position;

[0198] For the j th sampling position, at thej The slope of the change rate of the strain value corresponding to the j -1st sampling position is positive, when the slope of the change rate of the strain value corresponding to the j th sampling position is negative, a second strain value sequence composed of strain values corresponding to a consecutive second number of sampling positions including the j th sampling position is determined as the strain observation sequence corresponding to the

[0199] For the j th sampling position, when the difference between the slope corresponding to the j th sampling position and the slope corresponding to the j -1st sampling position is less than a first preset threshold, a third strain value sequence composed of strain values corresponding to a consecutive third number of sampling positions including the j th sampling position is determined as the strain observation sequence corresponding to the j th sampling position;

[0200] Wherein, 2 ≤ j ≤ n , n is the number of sampling positions, j is an integer; the second number is greater than the first number, and the third number is less than the first number.

[0201] Optionally, the third determination unit 803 is specifically configured to:

[0202] Perform dimensionality reduction processing on the strain distribution characteristics corresponding to each of the strain observation sequences to obtain low-dimensional strain data corresponding to each of the strain observation sequences;

[0203] Perform clustering processing on all the low-dimensional strain data, and determine the strain observation sequences under the target category in the clustering result as the target observation sequences; the target category refers to the category containing crack information.

[0204] Optionally, the fourth determination unit 804 is specifically configured to:

[0205] Calculate the first-order backward difference sequence of the target observation sequence;

[0206] Process the first-order backward difference sequence by using a unit step function to obtain a binary sequence corresponding to the first-order backward difference sequence; each element in the binary sequence corresponds to each element in the first-order backward difference sequence, and the value of each element in the binary sequence is 0 or 1;

[0207] Shift all elements in the binary sequence one position to the right to obtain the right-shifted sequence corresponding to the binary sequence, and perform an exclusive OR operation on the binary sequence and the right-shifted sequence to obtain the target sequence;

[0208] Determine the sampling positions corresponding to the elements with a value of 1 in the target sequence as the target positions.

[0209] Optionally, the fifth determination unit 805 is specifically configured to:

[0210] Use a moving average algorithm to smooth each strain value in the target observation sequence to obtain a smoothed strain value sequence corresponding to the target observation sequence;

[0211] Construct a continuous strain function corresponding to the smoothed strain value sequence;

[0212] Select a target integration interval including the target position from the sampling position interval corresponding to the continuous strain function.

[0213] Optionally, the fifth determination unit 805 is further specifically configured to:

[0214] Use an adaptive Simpson's algorithm to perform an integration operation on the continuous strain function within the target integration interval to obtain an integral value corresponding to the target integration interval;

[0215] Take the midpoint of the target integration interval as the interval division line, divide the target integration interval into two sub-intervals, and use the adaptive Simpson's algorithm to perform an integration operation on the continuous strain function within the two sub-intervals respectively to obtain integral values corresponding to the two sub-intervals respectively;

[0216] When the first difference between the integral value corresponding to the target integration interval and the sum of the integral values corresponding to the two sub-intervals is less than a second preset threshold, stop further dividing the sub-intervals, and determine the integral values corresponding to the two sub-intervals respectively as the target integral value;

[0217] When the first difference is greater than or equal to the second preset threshold, take the midpoint of each sub-interval as the interval division line, respectively re-divide each sub-interval, and use the adaptive Simpson's algorithm to calculate the integral values corresponding to the re-divided sub-intervals until the first difference between the sum of the integral values corresponding to each pair of re-divided sub-intervals and the integral value corresponding to the corresponding parent interval is less than the second preset threshold, then stop the interval re-division, and determine the integral values corresponding to the sub-intervals with the corresponding first difference less than the second preset threshold as the target integral value.

[0218] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit is used as an example. In actual applications, the above functions can be allocated to different functional units as needed, that is, the internal structure of the electronic device is divided into different functional units to complete all or part of the functions described above. Each functional unit in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of each unit in the above electronic device can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0219] Please refer to Figure 9 , Figure 9 which is a schematic structural diagram of an electronic device provided in another embodiment of this application. As Figure 9 shown, the electronic device 9 provided in this embodiment may include: a processor 90, a memory 91, and a computer program 92 stored in the memory 91 and executable on the processor 90, such as a program corresponding to the crack detection method. When the processor 90 executes the computer program 92, the steps in the above crack detection method embodiment are implemented, such as Figure 2 the S201~S205 shown. Or when the processor 90 executes the computer program 92, the functions of each module / unit in the above electronic device embodiment are implemented, such as Figure 8 the functions of the units 801~805 shown.

[0220] Exemplarily, the computer program 92 can be divided into one or more modules / units. One or more modules / units are stored in the memory 91 and executed by the processor 90 to complete this application. One or more modules / units can be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 92 in the electronic device 9. For example, the computer program 92 can be divided into a first determination unit, a second determination unit, a third determination unit, a fourth determination unit, and a fifth determination unit. For the specific functions of each unit, please refer to Figure 8 the relevant descriptions in the corresponding embodiments and will not be elaborated here.

[0221] Those skilled in the art can understand that Figure 9 is only an example of the electronic device 9 and does not constitute a limitation on the electronic device 9. It may include more or fewer components than shown in the figure, or combine some components, or different components.

[0222] The processor 90 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0223] The memory 91 may be an internal storage unit of the electronic device 9, such as the hard disk or memory of the electronic device 9. The memory 91 may also be an external storage device of the electronic device 9, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, or a flash card equipped on the electronic device 9, etc. Further, the memory 91 may also include both the internal storage unit and the external storage device of the electronic device 9. The memory 91 is used to store computer programs and other programs and data required by the electronic device. The memory 91 may also be used to temporarily store the data that has been output or is to be output.

[0224] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements each step in the crack detection method in the above method embodiment.

[0225] The embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, it enables the electronic device to implement the steps in each of the above method embodiments.

[0226] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0227] It should be noted that, unless otherwise specified, all technical terms used in the embodiments of the present application have the same meanings as those commonly understood by those skilled in the technical field to which the present application belongs. The technical terms used in the embodiments of the present application are only used to explain the specific embodiments of the present application, rather than to limit the present application.

[0228] As used in the description of the embodiments of the present application, the term "embodiment" means that the specific features, structures, or characteristics described in connection with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive of other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0229] Those of ordinary skill in the art will appreciate that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0230] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A crack detection method, characterized in that, Including: Determine the slope of the strain value change rate corresponding to each sampling position according to the strain values of the target structure collected by distributed optical fiber sensing at each sampling position; Determine the strain observation sequence corresponding to each sampling position according to the slopes of the strain value change rates; The strain observation sequence includes the strain values corresponding to a continuous plurality of sampling positions; Determine the strain distribution characteristics corresponding to each strain observation sequence, and determine the target observation sequence from the strain observation sequences according to the strain distribution characteristics corresponding to all the strain observation sequences; Based on the strain values in the target observation sequence, determine the target position where the crack is located; Determine the target integration interval according to the target position, and use the adaptive Simpson algorithm to perform a progressive integration operation on the continuous strain function in the target integration interval with the midpoint of the interval as the interval dividing line, and determine the sum of the multiple target integral values obtained by integration as the width of the crack; The continuous strain function is constructed according to the target observation sequence; Using the adaptive Simpson algorithm to perform a progressive integration operation on the continuous strain function in the target integration interval with the midpoint of the interval as the interval dividing line includes: Perform an integration operation on the continuous strain function in the target integration interval using the adaptive Simpson algorithm to obtain the integral value corresponding to the target integration interval; Taking the midpoint of the target integration interval as the interval dividing line, divide the target integration interval into two sub-intervals, and use the adaptive Simpson algorithm to perform integration operations on the continuous strain function in the two sub-intervals respectively to obtain the integral values corresponding to the two sub-intervals respectively; When the first difference between the integral value corresponding to the target integration interval and the sum of the integral values corresponding to the two sub-intervals is less than the second preset threshold, stop further dividing the sub-intervals, and determine the integral values corresponding to the two sub-intervals respectively as the target integral values; When the first difference is greater than or equal to the second preset threshold, take the midpoint of each sub-interval as the interval dividing line, divide each sub-interval respectively, and use the adaptive Simpson algorithm to calculate the integral values corresponding to the sub-intervals obtained by the further division until the first difference between the sum of the integral values corresponding to each two sub-intervals obtained by the further division and the integral value corresponding to the corresponding parent interval is less than the second preset threshold, stop the interval further division, and determine the integral values corresponding to the sub-intervals with the corresponding first difference less than the second preset threshold as the target integral values.

2. The method according to claim 1, wherein Determine the slope of the strain value change rate corresponding to each sampling position according to the strain values of the target structure collected by distributed optical fiber sensing at each sampling position, including: For the i th sampling position, according to the strain values at the i th sampling position, the i +1 th sampling position and the i +2 th sampling position, calculate the second derivative of the strain value corresponding to the i th sampling position, and determine the second derivative as the slope of the strain value change rate corresponding to the i th sampling position; 1 ≤ i ≤ n -2, n is the number of sampling positions, i is an integer.

3. The method according to claim 1, wherein Determine the strain observation sequence corresponding to each sampling position according to the slopes of the strain value change rates, including: For the first sampling position, determine the first strain value sequence composed of the strain values corresponding to the continuous first number of sampling positions including the first sampling position as the strain observation sequence corresponding to the first sampling position; For the j th sampling position, when the slope of the strain value change rate corresponding to the j th sampling position is negative and the slope of the strain value change rate corresponding to the j -1th sampling position is positive, the second strain value sequence composed of the strain values corresponding to the consecutive second number of sampling positions including the j th sampling position is determined as the strain observation sequence corresponding to the j th sampling position; For the j th sampling position, when the difference between the slope corresponding to the j th sampling position and the slope corresponding to the j -1 th sampling position is less than the first preset threshold, a third strain value sequence composed of strain values corresponding to a continuous third number of sampling positions including the j th sampling position is determined as the strain observation sequence corresponding to the j th sampling position; where 2 ≤ j ≤ n , n is the number of sampling positions, j is an integer; the second quantity is greater than the first quantity, and the third quantity is less than the first quantity.

4. The method according to claim 1, wherein Determining a target observation sequence from the strain observation sequences according to the strain distribution characteristics corresponding to all the strain observation sequences, including: Performing dimensionality reduction processing on the strain distribution characteristics corresponding to each strain observation sequence to obtain low-dimensional strain data corresponding to each strain observation sequence; Performing clustering processing on all the low-dimensional strain data, and determining the strain observation sequences under the target category in the clustering result as the target observation sequence; the target category refers to the category containing crack information.

5. The method according to claim 1, characterized in that, Based on each strain value in the target observation sequence, determining the target position where the crack is located, including: Calculating a first-order backward difference sequence of the target observation sequence; Processing the first-order backward difference sequence by using a unit step function to obtain a binary sequence corresponding to the first-order backward difference sequence; each element in the binary sequence corresponds to each element in the first-order backward difference sequence, and the value of each element in the binary sequence is 0 or 1; Shifting all elements in the binary sequence one bit to the right to obtain a right-shifted sequence corresponding to the binary sequence, and performing an exclusive OR operation on the binary sequence and the right-shifted sequence to obtain a target sequence; Determining the sampling positions corresponding to the elements with a value of 1 in the target sequence as the target position.

6. The method according to any one of claims 1-5, characterized in that, Determining a target integration interval according to the target position, including: Performing smoothing processing on each strain value in the target observation sequence by using a moving average algorithm to obtain a smoothed strain value sequence corresponding to the target observation sequence; Constructing a continuous strain function corresponding to the smoothed strain value sequence; Selecting a target integration interval containing the target position from the sampling position interval corresponding to the continuous strain function.

7. An electronic device, characterized in that, Including: A first determination unit, configured to determine the slope of the strain value change rate corresponding to each sampling position according to the strain values of the target structure collected by distributed optical fiber sensing at each sampling position; A second determination unit, configured to determine the strain observation sequence corresponding to each sampling position according to the slope of each strain value change rate; The strain observation sequence includes the strain values corresponding to a continuous plurality of sampling positions; A third determination unit, configured to determine the strain distribution characteristics corresponding to each strain observation sequence, and determine a target observation sequence from the strain observation sequences according to the strain distribution characteristics corresponding to all the strain observation sequences; A fourth determination unit, configured to determine the target position where the crack is located based on each strain value in the target observation sequence; A fifth determination unit, configured to determine a target integration interval according to the target position, and perform progressive integration operation on the continuous strain function within the target integration interval with the midpoint of the interval as the interval dividing line by using an adaptive Simpson algorithm, and determining the sum of multiple target integration values obtained by integration as the width of the crack; the continuous strain function is constructed according to the target observation sequence; The fifth determination unit is specifically configured to: Performing integration operation on the continuous strain function within the target integration interval by using an adaptive Simpson algorithm to obtain an integration value corresponding to the target integration interval; Taking the midpoint of the target integration interval as the interval division line, dividing the target integration interval into two sub-intervals, and using the adaptive Simpson's algorithm to perform integral operations on the continuous strain function in the two sub-intervals respectively to obtain the integral values corresponding to the two sub-intervals; When the first difference between the integral value corresponding to the target integration interval and the sum of the integral values corresponding to the two sub-intervals is less than the second preset threshold, stop further dividing the sub-intervals, and determine the integral values corresponding to the two sub-intervals as the target integral values; When the first difference is greater than or equal to the second preset threshold, taking the midpoint of each sub-interval as the interval division line, respectively re-divide each sub-interval, and use the adaptive Simpson's algorithm to calculate the integral values corresponding to the re-divided sub-intervals. Stop the interval re-division until the first difference between the sum of the integral values corresponding to every two re-divided sub-intervals and the integral value corresponding to the corresponding parent interval is less than the second preset threshold, and determine the integral values corresponding to the sub-intervals where the corresponding first difference is less than the second preset threshold as the target integral values.

8. An electronic device, characterized in that, It includes a memory and a computer program stored in the memory and executable on a processor. When the processor executes the computer program, it implements the method according to any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the crack detection method according to any one of claims 1-6.

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