Alloy steel wire rope damage feature recognition and extraction method and equipment

Abstract

The invention discloses an alloy steel wire rope damage feature identification and extraction method and equipment, and relates to the technical field of steel wire rope damage feature identification, and the method comprises the following steps: obtaining a signal diagram of a to-be-detected alloy steel wire rope based on magnetic flux leakage detection equipment, and marking the signal diagram as a real-time detection signal diagram; acquiring a first signal threshold value and a second signal threshold value based on the signal diagram of the non-destructive alloy steel wire rope; acquiring a real-time abnormal wave band based on the real-time detection signal diagram, the first signal threshold value and the second signal threshold value; constructing a real-time classification characteristic value based on the real-time abnormal wave band; acquiring a classification feature threshold based on the signal diagram of the broken alloy steel wire rope; acquiring different damage signals based on the real-time classification feature value and the classification feature threshold; the method is used for solving the problem that in the prior art, damage characteristic values cannot be further constructed based on a magnetic flux leakage detection method, then different damage types cannot be analyzed based on the damage characteristic values, and the damage types cannot be obtained through the magnetic flux leakage detection method.

Description

Technical Field

[0001] This invention relates to the field of steel wire rope damage feature recognition technology, specifically to a method and equipment for identifying and extracting damage features of alloy steel wire ropes. Background Technology

[0002] As a core load-bearing component in the engineering field, alloy steel wire ropes are used for a long time under complex alternating loads, harsh environments and extreme working conditions. The internal steel wires are prone to various damages such as wire breakage, corrosion and fatigue. The insidious accumulation of these damages can significantly weaken the overall mechanical properties of the wire rope. Once a critical state is reached, it may lead to sudden fracture, causing catastrophic equipment failure and major safety accidents. Therefore, it is necessary to identify and extract the damage characteristics of alloy steel wire ropes.

[0003] Existing methods for detecting damage in alloy steel wire ropes include image detection and magnetic flux leakage detection. Image detection can only detect surface damage and cannot detect internal damage. Furthermore, image detection is affected by ambient light. For example, patent application CN119399114A discloses a method and device for detecting surface damage in steel wire ropes, but this method can only detect damage on the surface of the steel wire rope. Existing magnetic flux leakage detection only detects whether there is damage, but fails to further establish damage feature values ​​and analyze different damage types based on these feature values. In other words, existing technologies fail to further construct damage feature values ​​based on magnetic flux leakage detection methods and then analyze different damage types based on these feature values, making it impossible to obtain the damage type through magnetic flux leakage detection methods. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It involves acquiring a signal image of an alloy steel wire rope to be tested using a magnetic flux leakage detection device, marking it as a real-time detection signal image; obtaining a first signal threshold and a second signal threshold based on the signal image of the undamaged alloy steel wire rope; obtaining real-time abnormal bands based on the real-time detection signal image, the first signal threshold, and the second signal threshold; constructing real-time classification feature values ​​based on the real-time abnormal bands; obtaining classification feature thresholds based on the signal image of the broken alloy steel wire rope; and obtaining different damage types based on the real-time classification feature values ​​and the classification feature thresholds. This addresses the problem in the prior art that it fails to further construct damage feature values ​​based on the magnetic flux leakage detection method and then analyze different damage types based on the damage feature values, thus making it impossible to obtain the damage type using the magnetic flux leakage detection method.

[0005] To achieve the above objectives, this application provides a method for identifying and extracting damage features of alloy steel wire ropes, comprising the following steps: The signal image of the alloy steel wire rope to be tested is obtained based on the leakage magnetic flux detection equipment and marked as the real-time detection signal image; The first and second signal thresholds are obtained from the signal map of the non-destructive alloy steel wire rope. Real-time abnormal bands are obtained based on the real-time detection signal map, the first signal threshold, and the second signal threshold. Constructing real-time classification feature values ​​based on real-time anomaly bands; Classification feature thresholds are obtained from the signal map of alloy steel wire rope with broken wires; Different damage types are obtained based on real-time classification feature values ​​and classification feature thresholds.

[0006] Furthermore, acquiring the signal diagram of the alloy steel wire rope to be tested based on the magnetic flux leakage detection equipment includes the following sub-steps: The magnetic flux leakage detection equipment starts the test from one end of the alloy steel wire rope to be tested; Mark the distance from the starting endpoint as the real-time detection location; The voltage of the sensor corresponding to the real-time detection position is marked as the real-time detection voltage; A Cartesian coordinate system is established with the real-time detection position as the horizontal axis and the real-time detection voltage as the vertical axis, and this system is marked as the real-time detection coordinate system. The real-time detection location and the corresponding real-time detection voltage are plotted in the real-time detection coordinate system to obtain a graph, which is then labeled as the real-time detection signal graph.

[0007] Furthermore, obtaining the first signal threshold and the second signal threshold based on the signal map of the undamaged alloy steel wire rope includes the following sub-steps: Signal images of undamaged alloy steel wire ropes obtained using magnetic flux leakage detection equipment are marked as historical normal signal images; Mark the vertical axis values ​​in the historical normal signal graph as historical normal voltages; Obtain the first number of historical normal voltages; The number of identical historical normal voltages is marked as the number of identical voltages; A Cartesian coordinate system was established with historical normal voltage as the horizontal axis data and the number of identical voltages as the vertical axis data, and it was marked as the voltage distribution coordinate system. Plot the historical normal voltage and the corresponding number of the same voltage as data points on the voltage distribution coordinate system; Mark the data points in the voltage distribution coordinate system as voltage distribution coordinate points; Obtain the range length of the x-coordinate of all voltage distribution coordinate points, and label it H1; Create a line segment of length H2 on the horizontal axis of the voltage distribution coordinate points that can move left and right, and mark it as the first moving line segment; The first number threshold is calculated as: F1 = b1 × (H2 ÷ H1) × S1; where F1 is the first number threshold, b1 is the first number ratio, and S1 is the first number.

[0008] Furthermore, obtaining the first and second signal thresholds based on the signal map of the undamaged alloy steel wire rope also includes the following sub-steps: Mark the sum of the historical normal voltages corresponding to all voltage distribution coordinate points directly above the first moving line segment as the first moving number; On the horizontal axis of the voltage distribution coordinate system, make the horizontal coordinate of the left endpoint of the first moving segment equal to the horizontal coordinate of the minimum voltage distribution coordinate point. Then, move the first number of moves to the right and judge the size of the first number of moves and the first number threshold in real time. When the first number of moves is greater than or equal to the first number threshold, stop moving the first moving segment. Obtain the historical normal voltage corresponding to the left endpoint of the first moving segment at this time and mark it as the first signal threshold. On the horizontal axis of the voltage distribution coordinate system, make the horizontal coordinate of the right endpoint of the first moving segment equal to the horizontal coordinate of the largest voltage distribution coordinate point. Then, move the first number of moves to the left and judge the size of the first number of moves and the first number threshold in real time. When the first number of moves is greater than or equal to the first number threshold, stop moving the first moving segment. Obtain the historical normal voltage corresponding to the right endpoint of the first moving segment at this time and mark it as the second signal threshold.

[0009] Furthermore, obtaining the real-time abnormal band based on the real-time detection signal map, the first signal threshold, and the second signal threshold includes the following sub-steps: The bands in the real-time detection signal graph where the real-time detection voltage exceeds the first signal threshold to the second signal threshold are identified and marked as real-time abnormal bands.

[0010] Furthermore, constructing real-time classification feature values ​​based on real-time anomaly bands includes the following sub-steps: Obtain the peak value of the real-time abnormal band and mark it as the first starting point; On the real-time anomaly band, starting from the first starting point, draw two equally spaced points to the left and right respectively, and label them as the first left point, the second left point, the first right point, and the second right point; Connect the first starting point and the first left point to obtain a line segment, which is marked as the first left line segment; connect the first left point and the second left point to obtain a line segment, which is marked as the second left line segment; connect the first starting point and the first right point to obtain a line segment, which is marked as the first right line segment; connect the first right point and the second right point to obtain a line segment, which is marked as the second right line segment; Obtain the slopes of the first left line segment, the second left line segment, the first right line segment, and the second right line segment, and label them as the first left slope, the second left slope, the first right slope, and the second right slope, respectively. The real-time classification feature value is obtained as follows: N = (|0-L1|+|L1-L2|+|0-R1|+|R1-R2|)÷4; where N is the real-time classification feature value, L1 is the first left slope, L2 is the second left slope, R1 is the first right slope, and R2 is the first right slope.

[0011] Furthermore, obtaining the classification feature threshold based on the signal map of the broken alloy steel wire rope includes the following sub-steps: The signal image of the alloy steel wire rope with broken wires is regarded as a real-time detection signal image to obtain real-time classification feature values, which are then marked as historical broken wire feature values. Obtain the second number of historical broken wire feature values; The number of identical historical broken wire feature values ​​is marked as the number of identical feature values; A Cartesian coordinate system is established with historical wire breakage characteristics as the horizontal axis data and the number of identical characteristic values ​​as the vertical axis data, and this system is marked as the characteristic value distribution coordinate system. Plot the historical broken wire characteristic values ​​and the corresponding number of identical characteristic values ​​as data points on the characteristic value distribution coordinate system; Mark the data points in the eigenvalue distribution coordinate system as eigenvalue distribution coordinate points; Obtain the range length of the x-coordinates of all feature value distribution points, and label it as H3; Create a line segment of length H4 on the horizontal axis of the eigenvalue distribution coordinate points that can move left and right, and mark it as the second moving line segment; The second threshold is calculated as: F2 = b2 × (H4 ÷ H3) × S2; where F2 is the second threshold, b2 is the proportion of the second number, and S2 is the second number.

[0012] Furthermore, obtaining the classification feature threshold based on the signal map of the broken alloy steel wire rope also includes the following sub-steps: The sum of the number of historical broken wire feature values ​​corresponding to all feature value distribution coordinate points directly above the second moving line segment is marked as the second moving number; On the horizontal axis of the feature value distribution coordinate system, make the horizontal coordinate of the left endpoint of the second moving segment equal to the horizontal coordinate of the smallest feature value distribution coordinate point. Then, move the second number of moves to the right and judge the size of the second number of moves and the second number threshold in real time. When the second number of moves is greater than or equal to the second number threshold, stop moving the second moving segment. Obtain the historical broken wire feature corresponding to the left endpoint of the second moving segment at this time and mark it as the classification feature threshold.

[0013] Furthermore, obtaining different damage types based on real-time classification feature values ​​and classification feature thresholds includes the following sub-steps: If the real-time classification feature value is greater than or equal to the classification feature threshold, the damage to the alloy steel wire rope at the real-time abnormal band is marked as the first type of damage; if the real-time classification feature value is less than the classification feature threshold, the damage to the alloy steel wire rope at the real-time abnormal band is marked as the second type of damage.

[0014] This application provides an electronic device, including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method described above are performed.

[0015] The beneficial effects of this invention are as follows: This invention acquires the signal image of the alloy steel wire rope to be tested based on a magnetic flux leakage detection device and marks it as a real-time detection signal image; it obtains a first signal threshold and a second signal threshold based on the signal image of the undamaged alloy steel wire rope; it obtains real-time abnormal bands based on the real-time detection signal image, the first signal threshold, and the second signal threshold; it constructs real-time classification feature values ​​based on the real-time abnormal bands; it obtains classification feature thresholds based on the signal image of the alloy steel wire rope with broken wires; and it obtains different damage types based on the real-time classification feature values ​​and the classification feature thresholds. The advantage lies in further establishing damage feature values, analyzing different damage types based on the damage feature values, reducing subsequent manual damage type judgment, and enabling timely acquisition of damage types. This invention constructs real-time classification feature values ​​based on real-time abnormal bands. Its advantage lies in constructing a real-time classification feature value based on real-time detection signal map, analyzing different damage types based on damage feature values, reducing subsequent manual damage type judgment, and enabling timely acquisition of damage types. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of the method of the present invention; Figure 2 This is a schematic diagram of the real-time detection signal of the present invention; Figure 3 This is a schematic diagram of the electrical distribution coordinate points of the present invention; Figure 4 This is a schematic diagram of the real-time abnormal band of the present invention; Figure 5 This is a schematic diagram of the coordinate points of the feature value distribution of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown, this application provides a method for identifying and extracting damage features of alloy steel wire ropes, comprising the following steps: Step S1: Obtain the signal image of the alloy steel wire rope to be tested based on the magnetic flux leakage detection device, and mark it as the real-time detection signal image; Step S1 includes the following sub-steps: Step S101: The magnetic flux leakage detection device starts the detection from one starting end of the alloy steel wire rope to be tested; the magnetic flux leakage detection method is that magnetic flux leakage will occur when damage is encountered, and the change in magnetic flux leakage is detected by a sensor, wherein the data detected by the sensor is voltage; Step S102: Mark the distance from the starting endpoint as the real-time detection position; Step S103: Mark the voltage of the sensor corresponding to the real-time detection position as the real-time detection voltage; Step S104: Establish a Cartesian coordinate system with the real-time detection position as the horizontal axis and the real-time detection voltage as the vertical axis, and mark it as the real-time detection coordinate system; Step S105: Plot the real-time detection position and the corresponding real-time detection voltage in the real-time detection coordinate system to obtain a graph, which is marked as a real-time detection signal graph; the real-time detection signal graph makes it easy to observe whether the alloy steel wire rope is damaged. For practical applications, please refer to Figure 2 The image shows the acquired real-time detection signal.

[0019] Step S2 involves obtaining a first signal threshold and a second signal threshold based on the signal map of the undamaged alloy steel wire rope. Step S2 includes the following sub-steps: Step S201: Obtain the signal image of the undamaged alloy steel wire rope based on the magnetic flux leakage detection device, and mark it as a historical normal signal image; Step S202: Mark the vertical axis value in the historical normal signal graph as the historical normal voltage; that is, the normal voltage fluctuation range. Step S203: Obtain a first number of historical normal voltages; in order to obtain the normal range of historical normal voltages, for example, the first number is 500. Step S204: Mark the number of identical historical normal voltages as the same voltage quantity; Step S205: Establish a Cartesian coordinate system with historical normal voltage as the horizontal axis data and the number of identical voltages as the vertical axis data, and label it as the voltage distribution coordinate system; the voltage distribution coordinate system is established to observe the distribution of historical normal voltage. Step S206: Plot the historical normal voltage and the corresponding number of the same voltage as data points on the voltage distribution coordinate system; Step S207: Mark the data points in the voltage distribution coordinate system as voltage distribution coordinate points; Step S208: Obtain the range length of the abscissa of all voltage distribution coordinate points, marked as H1; please refer to... Figure 3 As shown, for example, the horizontal axis length from 2.71 to 2.77 is 24cm, then H1 is 24cm; Step S209: Create a line segment of length H2 on the horizontal axis of the voltage distribution coordinate points, which can move left and right, and mark it as the first moving line segment; in order to observe the distribution of historical normal voltage by moving the first moving line segment, H2 is set to be less than H1, for example, H2 is 1cm; Step S210, calculate the first number threshold as: F1=b1×(H2÷H1)×S1; where F1 is the first number threshold, b1 is the first number ratio, and S1 is the first number; the first number threshold is to obtain the area with less distribution of historical normal voltage, and (H2÷H1)×S1 represents the average number of historical normal voltages directly above the first moving line segment, so b1 should be less than 1, for example, b1 is 0.2; In practical applications, the threshold for the first number is calculated as: F1 = 0.2 × (1 ÷ 24) × 500 = 4.1; the result is rounded to one decimal place, so the threshold for the first number is 4.1.

[0020] Step S211: Mark the sum of the historical normal voltage counts corresponding to all voltage distribution coordinate points directly above the first moving line segment as the first moving number; Step S212: On the horizontal axis of the voltage distribution coordinate system, make the horizontal coordinate of the left endpoint of the first moving segment equal to the horizontal coordinate of the minimum voltage distribution coordinate point. Then, move the first number of moves to the right and continuously compare the first number of moves with the first number threshold. Stop moving the first moving segment when the first number of moves is greater than or equal to the first number threshold. Obtain the historical normal voltage corresponding to the left endpoint of the first moving segment at this time and mark it as the first signal threshold. Delete abnormally small historical normal voltages to obtain the accurate minimum value of the historical normal voltage. Step S213: On the horizontal axis of the voltage distribution coordinate system, make the horizontal coordinate of the right endpoint of the first moving segment equal to the horizontal coordinate of the largest voltage distribution coordinate point. Then, move the first number of moves to the left and continuously compare the first number of moves with the first number threshold. Stop moving the first moving segment when the first number of moves is greater than or equal to the first number threshold. Obtain the historical normal voltage corresponding to the right endpoint of the first moving segment at this time and mark it as the second signal threshold. Delete abnormally large historical normal voltages to obtain the accurate maximum value of the historical normal voltage. For practical applications, please refer to Figure 3As shown, on the horizontal axis of the voltage distribution coordinate system, the horizontal coordinate of the left endpoint of the first moving segment is made equal to the horizontal coordinate of the smallest voltage distribution coordinate point. Then, the first number of moves is moved to the right, and the size of the first number of moves and the first threshold number are judged in real time. When the first number of moves is 13, which is greater than 4.1, the movement of the first moving segment is stopped. The historical normal voltage corresponding to the left endpoint of the first moving segment at this time is 2.72V, so the first signal threshold is 2.72V. On the horizontal axis of the voltage distribution coordinate system, the horizontal coordinate of the right endpoint of the first moving segment is made equal to the horizontal coordinate of the largest voltage distribution coordinate point. Then, the first number of moves is moved to the left, and the size of the first number of moves and the first threshold number are judged in real time. When the first number of moves is 13, which is greater than 4.1, the movement of the first moving segment is stopped. The historical normal voltage corresponding to the right endpoint of the first moving segment at this time is 2.76V, so the second signal threshold is 2.76V.

[0021] Step S3: Obtain the real-time abnormal band based on the real-time detection signal map, the first signal threshold, and the second signal threshold; Step S3 includes the following sub-steps: Step S301: Obtain the band in the real-time detection signal graph where the real-time detection voltage exceeds the first signal threshold to the second signal threshold, and mark it as a real-time abnormal band; the real-time abnormal band indicates magnetic leakage and damage.

[0022] Step S4: Construct real-time classification feature values ​​based on real-time anomaly bands; Step S4 includes the following sub-steps: Step S401: Obtain the peak of the real-time abnormal band and mark it as the first starting point; Step S402: On the real-time abnormal band, starting from the first starting point, draw two points with equal intervals to the left and right respectively, and mark them as the first left point, the second left point, the first right point, and the second right point; Step S403: Connect the first starting point and the first left point to obtain a line segment, and mark it as the first left line segment; connect the first left point and the second left point to obtain a line segment, and mark it as the second left line segment; connect the first starting point and the first right point to obtain a line segment, and mark it as the first right line segment; connect the first right point and the second right point to obtain a line segment, and mark it as the second right line segment. Step S404: Obtain the slopes of the first left line segment, the second left line segment, the first right line segment, and the second right line segment, and label them as the first left slope, the second left slope, the first right slope, and the second right slope, respectively; construct the degree of change of the peak slope based on the multiple slopes of the first left slope, the second left slope, the first right slope, and the second right slope to improve the representativeness of the real-time classification feature values; Step S402, obtain the real-time classification feature values: N = (|0-L1|+|L1-L2|+|0-R1|+|R1-R2|)÷4; where N is the real-time classification feature value, L1 is the first left slope, L2 is the second left slope, R1 is the first right slope, and R2 is the first right slope. If the alloy steel wire rope breaks, the magnetic field changes rapidly, so the peak of the real-time anomaly band is sharper, and the real-time classification feature value is larger. If corrosion and fatigue occur, the magnetic field changes relatively smoothly, so the peak of the real-time anomaly band is relatively gentle, and the real-time classification feature value is smaller. For practical applications, please refer to Figure 4 As shown, the obtained first left slope, second left slope, first right slope, and second right slope are 1.54, 3.01, -1.96, and -6.31, respectively. Then, N = (|0-1.54|+|1.54-3.01|+|0-(-1.96)|+|(-1.96)-(-6.31)|)÷4 = 2.33; that is, the real-time classification feature value is 2.33.

[0023] Step S5: Obtain the classification feature threshold based on the signal map of the broken alloy steel wire rope; Step S5 includes the following sub-steps: Step S501: Obtain the signal image of the alloy steel wire rope with broken wires as a real-time detection signal image, obtain real-time classification feature values, and mark them as historical broken wire feature values. Step S502: Obtain a second number of historical wire breakage feature values; to obtain the range of historical wire breakage feature values, for example, the second number is 400. Step S503: Mark the number of identical historical broken wire feature values ​​as the number of identical feature values; Step S504: Establish a Cartesian coordinate system with historical wire breakage feature values ​​as the horizontal axis data and the number of identical feature values ​​as the vertical axis data, and label it as the feature value distribution coordinate system; the feature value distribution coordinate system is established to observe the distribution of historical wire breakage feature values. Step S505: Plot the historical broken wire feature values ​​and the corresponding number of identical feature values ​​as data points on the feature value distribution coordinate system; Step S505: Mark the data points in the eigenvalue distribution coordinate system as eigenvalue distribution coordinate points; Step S506: Obtain the range length of the x-coordinates of all feature value distribution points, labeled as H3; please refer to [link to relevant documentation]. Figure 5 As shown, for example, the length of the horizontal axis from 1.0 to 2.2 is 24cm; Step S507: Create a line segment of length H4 on the horizontal axis of the feature value distribution coordinate points, which can move left and right, and mark it as the second moving line segment; set the second moving line segment to be movable so as to facilitate the observation of the distribution of historical broken wire feature values, so H4 should be less than H3, for example, H4 is 2cm; Step S508, calculate the second number threshold as: F2 = b2 × (H4 ÷ H3) × S2; where F2 is the second number threshold, b2 is the second number ratio, and S2 is the second number; the second number threshold is to obtain the area with fewer historical broken wire feature values, and (H4 ÷ H3) × S2 represents the average number of historical broken wire feature values ​​directly above the second moving line segment, so b2 should be less than 1, for example, b1 is 0.2; In practical applications, the threshold for the second number is calculated as: F2 = 0.2 × (2 ÷ 24) × 400 = 6.6. The result is rounded to one decimal place, so the threshold for the second number is 6.6.

[0024] Step S509: Mark the sum of the number of historical broken wire feature values ​​corresponding to all feature value distribution coordinate points directly above the second moving line segment as the second moving number; Step S510: On the horizontal axis of the feature value distribution coordinate system, make the horizontal coordinate of the left endpoint of the second moving segment equal to the horizontal coordinate of the minimum feature value distribution coordinate point. Then, move the second number of moves to the right and continuously compare the second number of moves with the second threshold. Stop moving the second moving segment when the second number of moves is greater than or equal to the second threshold. Obtain the historical broken wire feature corresponding to the left endpoint of the second moving segment at this time and mark it as the classification feature threshold. Delete the historical broken wire features that are abnormally small, and then obtain the minimum value of the accurate historical broken wire feature. For practical applications, please refer to Figure 5 As shown, on the horizontal axis of the feature value distribution coordinate system, the horizontal coordinate of the left endpoint of the second moving segment is made equal to the horizontal coordinate of the minimum feature value distribution coordinate point. Then, the second moving number is moved to the right, and the size of the second moving number and the second threshold number are judged in real time. When the second moving number is 11, which is greater than 6.6, the second moving segment is stopped. The historical broken wire feature corresponding to the left endpoint of the second moving segment at this time is obtained as 1.10, and the classification feature threshold is 1.10.

[0025] Step S6 involves obtaining different damage types based on real-time classification feature values ​​and classification feature thresholds; Step S6 includes the following sub-steps: Step S601: If the real-time classification feature value is greater than or equal to the classification feature threshold, the damage to the alloy steel wire rope at the real-time abnormal band is marked as the first type of damage; if the real-time classification feature value is less than the classification feature threshold, the damage to the alloy steel wire rope at the real-time abnormal band is marked as the second type of damage; if the alloy steel wire rope is broken, the magnetic field changes rapidly, so the peak of the real-time abnormal band is sharper, and the real-time classification feature value is larger; if corrosion and fatigue occur, the magnetic field changes relatively smoothly, so the peak of the real-time abnormal band is relatively gentle, and the real-time classification feature value is smaller; at the same time, the classification feature threshold is the minimum value of the real-time classification feature value corresponding to the broken wire; therefore, the first type of damage is very likely to be broken wire, and the second type of damage is very likely to be corrosion and fatigue. In practical applications, if the real-time classification feature value of 2.33 is greater than the classification feature threshold of 1.10, the damage to the alloy steel wire rope at the real-time abnormal band is marked as the first type of damage.

[0026] Example 2: This application also provides an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps of a method for identifying and extracting damage features of alloy steel wire ropes are performed to achieve the following functions: acquiring a signal image of the alloy steel wire rope to be detected based on a magnetic flux leakage detection device, and marking it as a real-time detection signal image; acquiring a first signal threshold and a second signal threshold based on the signal image of the undamaged alloy steel wire rope; acquiring a real-time abnormal band based on the real-time detection signal image, the first signal threshold, and the second signal threshold; constructing a real-time classification feature value based on the real-time abnormal band; acquiring a classification feature threshold based on the signal image of the alloy steel wire rope with broken wires; and acquiring different damage types based on the real-time classification feature value and the classification feature threshold.

[0027] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0028] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a method for identifying and extracting damage features of alloy steel wire ropes provided by the above methods. This method includes: acquiring a signal image of the alloy steel wire rope to be detected based on a magnetic flux leakage detection device, and marking it as a real-time detection signal image; acquiring a first signal threshold and a second signal threshold based on the signal image of the undamaged alloy steel wire rope; acquiring a real-time abnormal band based on the real-time detection signal image, the first signal threshold, and the second signal threshold; constructing a real-time classification feature value based on the real-time abnormal band; acquiring a classification feature threshold based on the signal image of the alloy steel wire rope with broken wires; and acquiring different damage types based on the real-time classification feature value and the classification feature threshold.

[0029] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps of the above-described method for identifying and extracting damage features of alloy steel wire ropes to achieve the following functions: acquiring a signal image of the alloy steel wire rope to be detected based on a magnetic flux leakage detection device, and marking it as a real-time detection signal image; acquiring a first signal threshold and a second signal threshold based on the signal image of the undamaged alloy steel wire rope; acquiring a real-time abnormal band based on the real-time detection signal image, the first signal threshold, and the second signal threshold; constructing real-time classification feature values ​​based on the real-time abnormal bands; acquiring classification feature thresholds based on the signal image of the alloy steel wire rope with broken wires; and acquiring different damage types based on the real-time classification feature values ​​and the classification feature thresholds.

[0030] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0031] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions 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 this application.

Claims

1. A method for identifying and extracting damage features of an alloy steel wire rope, characterized by, The method comprises the following steps: Obtaining a signal graph of the alloy steel wire to be detected based on the magnetic flux leakage detection device, and marking it as a real-time detection signal graph; Obtaining a first signal threshold and a second signal threshold based on the signal graph of the undamaged alloy steel wire; Obtaining a real-time abnormal wave band based on the real-time detection signal graph, the first signal threshold and the second signal threshold; Constructing a real-time classification characteristic value based on the real-time abnormal wave band; Obtaining a classification characteristic threshold based on the signal graph of the broken-wire alloy steel wire; Obtaining different damage types based on the real-time classification characteristic value and the classification characteristic threshold.

2. The method of claim 1, wherein the method further comprises: The step of obtaining the signal graph of the alloy steel wire to be detected based on the magnetic flux leakage detection device comprises the following sub-steps: The magnetic flux leakage detection device starts detection from a starting end point of the alloy steel wire to be detected; The length from the starting end point is marked as a real-time detection position; The voltage of the sensor corresponding to the real-time detection position is marked as a real-time detection voltage; A plane rectangular coordinate system is established with the real-time detection position as the horizontal axis value and the real-time detection voltage as the vertical axis value, and is marked as a real-time detection coordinate system; The real-time detection position and the corresponding real-time detection voltage are plotted in the real-time detection coordinate system to obtain a graph, which is marked as a real-time detection signal graph.

3. The method of claim 2, wherein the method further comprises: The step of obtaining a first signal threshold and a second signal threshold based on the signal graph of the undamaged alloy steel wire comprises the following sub-steps: Obtaining a signal graph of the undamaged alloy steel wire based on the magnetic flux leakage detection device, and marking it as a historical normal signal graph; The vertical axis value in the historical normal signal graph is marked as a historical normal voltage; A first number of historical normal voltages is obtained; The number of the same historical normal voltages is marked as the same voltage number; A plane rectangular coordinate system is established with the historical normal voltage as the horizontal axis data and the same voltage number as the vertical axis data, and is marked as a voltage distribution coordinate system; The historical normal voltage and the corresponding same voltage number are plotted as data points in the voltage distribution coordinate system; The data points in the voltage distribution coordinate system are marked as voltage distribution coordinate points; The range length of the horizontal coordinates of all voltage distribution coordinate points is obtained, and is marked as H1; A line segment with a length of H2 and capable of moving left and right is established on the horizontal axis of the voltage distribution coordinate points, and is marked as a first moving line segment; The first number threshold is calculated as: F1 = b1 × (H2 ÷ H1) × S1; Wherein F1 is the first number threshold, b1 is the first number ratio, and S1 is the first number.

4. The method of claim 3, wherein the method further comprises: The step of obtaining a first signal threshold and a second signal threshold based on the signal graph of the undamaged alloy steel wire further comprises the following sub-steps: The sum of the number of historical normal voltages corresponding to all voltage distribution coordinate points directly above the first moving line segment is marked as a first moving number; The horizontal coordinate of the left end point of the first moving line segment is equal to the horizontal coordinate of the smallest voltage distribution coordinate point on the horizontal axis of the voltage distribution coordinate system, and then the first moving number is moved to the right, and the size of the first moving number and the first number threshold is determined in real time, and the first moving line segment is stopped when the first moving number is greater than or equal to the first number threshold; The historical normal voltage corresponding to the left end point of the first moving line segment at this time is obtained, and is marked as the first signal threshold; The horizontal coordinate of the right end point of the first moving line segment is equal to the horizontal coordinate of the maximum voltage distribution coordinate point on the horizontal axis of the voltage distribution coordinate system, and then the first moving line segment is moved to the left, and the size of the first moving number and the first number threshold is judged in real time, and the first moving line segment is stopped when the first moving number is greater than or equal to the first number threshold; The historical normal voltage corresponding to the right end point of the first moving line segment at this time is obtained and marked as the second signal threshold.

5. The method of claim 4, wherein the method further comprises: The real-time abnormal wave band is obtained based on the real-time detection signal graph, the first signal threshold and the second signal threshold, including the following sub-steps: The wave band between the real-time detection voltage exceeding the first signal threshold and the second signal threshold in the real-time detection signal graph is obtained and marked as the real-time abnormal wave band.

6. The method of claim 5, wherein the method further comprises: The real-time classification feature value is constructed based on the real-time abnormal wave band, including the following sub-steps: The peak point of the real-time abnormal wave band is obtained and marked as the first starting point; Two points with equal intervals are drawn to the left and right of the first starting point on the real-time abnormal wave band respectively, and are marked as the first left point, the second left point, the first right point and the second right point respectively; The line segment connecting the first starting point and the first left point is obtained and marked as the first left line segment; The line segment connecting the first left point and the second left point is obtained and marked as the second left line segment; The line segment connecting the first starting point and the first right point is obtained and marked as the first right line segment; The line segment connecting the first right point and the second right point is obtained and marked as the second right line segment; The slopes of the first left line segment, the second left line segment, the first right line segment and the second right line segment are obtained and marked as the first left slope, the second left slope, the first right slope and the second right slope respectively; The real-time classification feature value is obtained as: N=(|0-L1|+|L1-L2|+|0-R1|+|R1-R2|)÷4;Wherein N is the real-time classification feature value, L1 is the first left slope, L2 is the second left slope, R1 is the first right slope, R1 is the first right slope.

7. The method of claim 6, wherein the method further comprises: The classification feature threshold value is obtained based on the signal graph of the broken alloy steel wire rope, including the following sub-steps: The signal graph of the alloy steel wire rope with broken wire is obtained as the real-time detection signal graph to obtain the real-time classification feature value, which is marked as the historical broken wire feature value; The second number of historical broken wire feature values is obtained; The number of the same historical broken wire feature values is marked as the same feature value number; A plane rectangular coordinate system is established with the historical broken wire feature value as the horizontal axis data and the same feature value number as the vertical axis data, which is marked as the feature value distribution coordinate system; The historical broken wire feature value and the corresponding same feature value number are plotted as data points in the feature value distribution coordinate system; The data points in the feature value distribution coordinate system are marked as feature value distribution coordinate points; The range length of the horizontal coordinates of all feature value distribution coordinate points is obtained and marked as H3; A line segment with a length of H4 and movable left and right is established on the horizontal axis of the feature value distribution coordinate point, which is marked as the second moving line segment; The second number threshold value F2 is calculated as: F2=b2×(H4÷H3)×S2;Wherein F2 is the second number threshold value, b2 is the second number ratio, and S2 is the second number.

8. The method of claim 7, wherein the method further comprises: The classification feature threshold value is obtained based on the signal graph of the broken alloy steel wire rope, including the following sub-steps: The sum of the number of historical broken wire characteristic values corresponding to all characteristic value distribution coordinate points above the second moving line segment is marked as a second moving number; The horizontal coordinate of the left end point of the second moving line segment is equal to the horizontal coordinate of the smallest characteristic value distribution coordinate point on the horizontal axis of the characteristic value distribution coordinate system, and then the second moving number is moved to the right, and the size of the second moving number and the second number threshold is judged in real time, and the second moving line segment is stopped when the second moving number is greater than or equal to the second number threshold; The historical broken wire characteristic corresponding to the left end point of the second moving line segment at this time is obtained and marked as a classification characteristic threshold.

9. The method of claim 8, wherein the method further comprises: Based on the real-time classification characteristic value and the classification characteristic threshold, different damage types are obtained, including the following sub-steps: If the real-time classification characteristic value is greater than or equal to the classification characteristic threshold, the damage of the alloy steel wire rope at the real-time abnormal wave band is marked as the first type of damage; if the real-time classification characteristic value is less than the classification characteristic threshold, the damage of the alloy steel wire rope at the real-time abnormal wave band is marked as the second type of damage.

10. An electronic device, comprising: The method comprises a processor and a memory, and the memory stores computer readable instructions, when the computer readable instructions are executed by the processor, the steps in the method of any one of claims 1-9 are executed.

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