Rapid non-contact type sensing detection method for bolt loosening defect of power transmission line tower

Abstract

The invention relates to a rapid non-contact sensing detection method for a bolt loosening defect of a power transmission line tower, and belongs to the technical field of power system maintenance and monitoring. The method comprises the following steps: constructing a bolt vibration characteristic dynamic database, and detecting the bolt loosening condition by adopting an impact elastic wave principle; recognizing the bolt loosening condition of the power transmission line tower by using an acoustic imaging technology, an infrared imaging technology and an electromagnetic field detection technology; a power transmission line tower bolt looseness early warning model is constructed, and the power transmission line tower bolt looseness condition is obtained by adopting detection data of real-time acoustic imaging, infrared imaging and electromagnetic field detection; and finally, early warning of looseness of the bolt of the power transmission line tower. By combining multiple detection means, the detection efficiency and accuracy of the bolt loosening defect of the power transmission line tower are improved, so that the maintenance cost is reduced, and the reliability and safety of a power transmission system are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of power system maintenance and monitoring, and in particular relates to a method for rapid non-contact sensing and detection of loose bolt defects in a transmission line tower. Background Art

[0002] Transmission line towers are crucial elements in the power system, and their stability is directly related to the safe and reliable operation of the entire power system. Bolts are a key part of the assembly and connection of towers, and their tightening state is crucial to the structural safety of the towers.

[0003] The tower structure of the transmission tower is often connected by bolts or welds. Long-term wind and structural vibration may cause the connection parts to loosen or crack. If not discovered and handled in time, it may cause serious accidents and secondary disasters, which will have a huge impact on social life and safety. At the same time, key components such as wire clamps and bolts in the transmission line are also very susceptible to interference from environmental factors. Especially in areas with changeable climates, strong winds, rainstorms and drastic temperature changes will aggravate the wear and looseness of components, increasing the difficulty of inspection and maintenance. In some remote mountainous areas, due to complex terrain and inconvenient transportation, inspection work is more difficult, thereby increasing the risk of transmission line failure. At present, the use of single infrared and ultraviolet temperature measurement technology cannot accurately detect hardware defects. Therefore, it is an important task to quickly and accurately detect bolt loosening defects and repair them in time to ensure the safe operation of transmission lines.

[0004] In the research on detecting bolt loosening, sensing technology and monitoring technology are constantly developing. Non-contact sensing technology is gradually being used in health monitoring of bolt loosening due to its advantages such as high efficiency, reliability and strong real-time performance.

[0005] In the prior art, a method for detecting bolt loosening using computer vision technology first uses a smartphone to take a picture of the bolt to obtain image information, and then performs preprocessing operations on the image, which includes perspective transformation and converting the image into a grayscale image. Next, the Canny algorithm (edge ​​detection algorithm) is used for edge detection, and the Hough transform is used to extract the straight line features in the image. Finally, by analyzing the angle change of the identified straight line, it is determined whether the bolt is loose. This detection method can effectively identify the loosening of the bolt, showing the advantages of cost-effectiveness and ease of operation. However, its detection is strongly dependent on ambient light and background. Under different lighting conditions or complex backgrounds, it may be difficult to obtain a clear image, thereby affecting the accuracy of the detection; at the same time, this method determines whether the bolt is loose by identifying the angle change of the straight line, which requires that the edge of the bolt in the image must be clearly visible. For bolts that are partially blocked or unevenly illuminated, this method may not be able to accurately measure the angle change; although algorithms such as edge detection and Hough transform are widely used, they may not be able to accurately distinguish bolts from other objects with similar features in complex scenes, which may lead to false detection or missed detection.

[0006] In the prior art, there is also a bolt loosening detection method that combines deep learning target detection and digital image processing. First, by collecting and learning a certain number of bolt image samples, a deep learning network model that can identify the bolt area in the image is trained. Using this model, the system can accurately locate and crop the area containing the bolt from the image. Subsequently, a series of digital image processing operations are performed on these cropped bolt areas, including filtering, edge detection, and Hough line transform, to extract the bolt marking line and the bolt fixing surface marking line. By calculating the angle between the two marking lines, the loosening angle of the bolt can be further calculated to determine whether the bolt has loosened and the degree of loosening. However, in this method, the performance of the deep learning model usually depends on the quality and quantity of the training data. If the training data set is not large or diverse enough, the model may not be able to accurately recognize new or rare postures, resulting in poor generalization ability; deep learning models are usually regarded as "black boxes" and it is difficult to explain their internal decision-making process. This may lead to trust and transparency issues, especially in safety-critical applications.

[0007] In summary, although the existing technologies are all non-contact detection, they will face challenges in detection accuracy, environmental adaptability, cost-effectiveness, etc. under certain conditions. There are many uncertainties and deviations in actual applications. Therefore, how to overcome the shortcomings of existing technologies is an urgent problem to be solved in the field of power system maintenance and monitoring technology. Summary of the invention

[0008] The purpose of the present invention is to solve the shortcomings of the prior art and provide a method for rapid non-contact sensing and detection of loose bolt defects in power transmission line towers. By combining a variety of detection means, the detection efficiency and accuracy of loose bolt defects in power transmission line towers are improved, thereby reducing maintenance costs and improving the reliability and safety of the power transmission system.

[0009] To achieve the above purpose, the technical solution adopted by the present invention is as follows: The method for rapid non-contact sensing and detecting loose bolt defects of transmission line towers comprises the following steps: Step (1), constructing a dynamic database of bolt vibration characteristics and using the impact elastic wave principle to detect bolt loosening; Step (2), using acoustic imaging technology, infrared imaging technology and electromagnetic field detection technology to identify the looseness of the transmission line tower bolts; Step (3), using the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection and the corresponding transmission line tower bolt loosening conditions, to construct a transmission line tower bolt loosening early warning model; Step (4), using the transmission line tower bolt loosening early warning model and using real-time acoustic imaging, infrared imaging and electromagnetic field detection data to obtain the transmission line tower bolt loosening situation; Step (5): if any of the detection results of step (1), step (2) and step (4) is that the bolts are loose, a loose bolt warning of the transmission line tower is issued.

[0010] Furthermore, preferably, the specific method of step (1) is: Establish an accurate model of the tower bolt connection in finite element software, including bolts, nuts, contact surfaces and surrounding structures; Define material properties for each component in the model; Meshing the model; Set boundary conditions to simulate the actual working environment and apply external loads or shocks to the model to simulate operating conditions that may cause bolt loosening; The simulation simulates the propagation of elastic waves generated by the bolt after being impacted, and calculates the dynamic response at different time points; Focus on the reflection and transmission of impact elastic waves at bolted joints, record the waveform changes when the bolts are normally fixed and in different loose states, extract the bolt vibration feature data, and build a dynamic database of bolt vibration features; The impact elastic wave waveform of the bolt connection to be tested is collected in real time, and the bolt vibration characteristic data is obtained. The data is compared with the data in the bolt vibration characteristic dynamic database to obtain the looseness of the bolt.

[0011] Further, preferably, the material properties include elastic modulus, Poisson's ratio and density.

[0012] Further, preferably, the bolt vibration characteristic data includes vibration frequency, amplitude and waveform.

[0013] Further, preferably, in step (2), the specific method for identifying the looseness of the transmission line tower bolts using acoustic imaging technology is: A microphone array is used to capture the sound waves at the bolt connections of transmission line towers. If the sound waves of the detected bolts are inconsistent with the sound waves of the bolts when they are normally fixed, it is considered that the bolts may be loose. These sound wave information are then converted into visual images so that the operator can intuitively see the possible loose location and degree.

[0014] Further, preferably, in step (2), the specific method of using infrared imaging technology to identify the looseness of the transmission line tower bolts is: An infrared thermal imager is used to collect thermal images of the bolted joints of the transmission line tower, and the thermal images are analyzed to obtain the temperature distribution. The temperature distribution is compared with the temperature distribution when the bolts are normally fixed. If obvious abnormal hot spots appear, it is considered that the bolts may be loose.

[0015] Further, preferably, in step (2), the specific method of using electromagnetic field detection technology to identify the looseness of transmission line tower bolts is: The AC electromagnetic field detection technology is used to detect the electromagnetic fluctuations at the bolt connections of the transmission line towers, and the electromagnetic fluctuations are compared with the electromagnetic fluctuations when the bolts are normally fixed. If there are obvious abnormal changes in the electromagnetic field, it is considered that the bolts may be loose.

[0016] Furthermore, preferably, the specific method of step (3) is: With the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection as input and the actual loosening of the corresponding transmission line tower bolts as output, the convolutional neural network is trained to obtain a transmission line tower bolt loosening early warning model; among which, the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection are the sound wave, thermal image and electromagnetic fluctuation data of the bolt connection of the transmission line tower, respectively.

[0017] Furthermore, preferably, in step (4), when a loose bolt warning is issued for a transmission line tower, the loose bolts are displayed to prompt corresponding operators.

[0018] In order to further quickly and accurately detect the signs of loosening of transmission tower bolts, the present invention combines acoustic imaging technology, infrared temperature measurement technology and electromagnetic field detection technology to jointly detect whether the transmission tower bolts are loose and the degree of looseness. This is conducive to reducing the impact of external environment such as climate change and terrain, and improving the intelligent level of transmission line operation and maintenance and inspection.

[0019] The rapid non-contact sensing detection method for loose bolt defects in transmission line towers studied in the present invention can effectively improve the accuracy of bolt loosening detection, and has strong anti-interference ability during detection, which can significantly improve the stable operation and safety level of the power transmission system.

[0020] The present invention combines a series of technical measures to detect the looseness of transmission tower bolts and wire clamp bolts, significantly improving the accuracy of detection and the quality of work. The specific technical effects are as follows: 1. Improve detection speed and accuracy: By building a comprehensive dynamic database of bolt vibration characteristics, a reliable reference standard is provided for detection work, which significantly improves the speed and accuracy of detection.

[0021] 2. Implement diversified detection strategies: Combining three technologies, acoustic imaging, infrared imaging and electromagnetic field detection, to form a comprehensive detection method that can quickly identify and locate potential bolt loosening defects remotely and non-contact.

[0022] 3. Promote the rapidity and effectiveness of operation and maintenance response: Use drones equipped with relevant detection equipment to conduct large-scale and rapid monitoring, which greatly reduces the risk of line accidents caused by bolt problems and ensures the stable operation of the power grid and the safety of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Flow chart for detecting bolt loosening by using the impact elastic wave principle; Figure 2 It is the flow chart of beamforming algorithm; Figure 3 Detection process for infrared imaging technology Figure 4 This is a technical flow chart for AC electromagnetic field detection; Figure 5 It is the early warning flow chart; Figure 6 This is the overall functional architecture diagram. DETAILED DESCRIPTION

[0024] The present invention is further described in detail below in conjunction with embodiments.

[0025] Those skilled in the art will appreciate that the following examples are only used to illustrate the present invention and should not be considered to limit the scope of the present invention. If no specific techniques or conditions are specified in the examples, the techniques or conditions described in the literature in the art or the product specifications are used. If the manufacturer of the materials or equipment used is not specified, they are all conventional products that can be purchased.

[0026] The method for rapid non-contact sensing and detecting loose bolt defects of transmission line towers comprises the following steps: Step (1), constructing a dynamic database of bolt vibration characteristics and using the impact elastic wave principle to detect bolt loosening; Step (2), using acoustic imaging technology, infrared imaging technology and electromagnetic field detection technology to identify the looseness of the transmission line tower bolts; Step (3), using the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection and the corresponding transmission line tower bolt loosening conditions, to construct a transmission line tower bolt loosening early warning model; Step (4), using the transmission line tower bolt loosening early warning model and using real-time acoustic imaging, infrared imaging and electromagnetic field detection data to obtain the transmission line tower bolt loosening situation; Step (5): if any of the detection results of step (1), step (2) and step (4) is that the bolts are loose, a loose bolt warning of the transmission line tower is issued.

[0027] The specific method of step (1) is: Establish an accurate model of the tower bolt connection in finite element software, including bolts, nuts, contact surfaces and surrounding structures; Define material properties for each component in the model; Meshing the model; Set boundary conditions to simulate the actual working environment and apply external loads or shocks to the model to simulate operating conditions that may cause bolt loosening; The simulation simulates the propagation of elastic waves generated by the bolt after being impacted, and calculates the dynamic response at different time points; Focus on the reflection and transmission of impact elastic waves at bolted joints, record the waveform changes when the bolts are normally fixed and in different loose states, extract the bolt vibration feature data, and build a dynamic database of bolt vibration features; The impact elastic wave waveform of the bolt connection to be tested is collected in real time, and the bolt vibration characteristic data is obtained. The data is compared with the data in the bolt vibration characteristic dynamic database to obtain the looseness of the bolt.

[0028] Material properties include elastic modulus, Poisson's ratio, and density.

[0029] Bolt vibration characteristic data include vibration frequency, amplitude and waveform.

[0030] In step (2), the specific method of using acoustic imaging technology to identify the looseness of transmission line tower bolts is: A microphone array is used to capture the sound waves at the bolt connections of transmission line towers. If the sound waves of the detected bolts are inconsistent with the sound waves of the bolts when they are normally fixed, it is considered that the bolts may be loose. These sound wave information are then converted into visual images so that the operator can intuitively see the possible loose location and degree.

[0031] In step (2), the specific method of using infrared imaging technology to identify the looseness of the transmission line tower bolts is: An infrared thermal imager is used to collect thermal images of the bolted joints of the transmission line tower, and the thermal images are analyzed to obtain the temperature distribution. The temperature distribution is compared with the temperature distribution when the bolts are normally fixed. If obvious abnormal hot spots appear, it is considered that the bolts may be loose.

[0032] In step (2), the specific method of using electromagnetic field detection technology to identify the looseness of transmission line tower bolts is: The AC electromagnetic field detection technology is used to detect the electromagnetic fluctuations at the bolt connections of the transmission line towers, and the electromagnetic fluctuations are compared with the electromagnetic fluctuations when the bolts are normally fixed. If there are obvious abnormal changes in the electromagnetic field, it is considered that the bolts may be loose.

[0033] The specific method of step (3) is: With the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection as input and the actual loosening of the corresponding transmission line tower bolts as output, the convolutional neural network is trained to obtain a transmission line tower bolt loosening early warning model; among which, the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection are the sound wave, thermal image and electromagnetic fluctuation data of the bolt connection of the transmission line tower, respectively.

[0034] In step (4), when a loose bolt warning is issued for a transmission line tower, the loose bolts are displayed to prompt the corresponding operator.

[0035] The present invention starts from understanding the loosening characteristics of transmission tower bolts, and then studies the detection method based on these characteristics. The loosening characteristics of transmission tower bolts include: (1) When the bolts of a transmission line tower loosen, the natural frequency of the tower material to which it is connected will change, thereby affecting the vibration characteristics of the entire structure; (2) Loose bolts may also increase the local contact resistance of the transmission tower, which will cause the temperature of the area to rise due to the heat generated when the current passes through. (3) When the transmission line is operating normally, a stable electromagnetic field distribution will be generated around it. When the bolts are loosened, the position of the conductor may change slightly, thereby affecting the distribution of the local electromagnetic field.

[0036] Based on these characteristics, the present invention first studies the vibration frequency and characteristics of the transmission tower bolts, with the aim of identifying and distinguishing the vibration frequency and characteristic modes of the bolts in the normal fixed state and various loose states; secondly, integrates the vibration frequency characteristic data of various hardware models and specifications in different states, builds a database, and uses the database as a reference standard for detection, which helps to improve the accuracy and efficiency of detection; then, combines acoustic imaging, infrared imaging and electromagnetic field detection technology, and proposes a set of comprehensive detection strategies that can quickly identify and locate potential bolt loosening defects remotely and non-contactly. Finally, the transmission tower bolts are tested by using drones equipped with relevant detection equipment, and the data obtained from these tests are fused and processed to obtain the test results, realizing the rapid non-contact perception and detection of loose bolt defects in transmission line towers.

[0037] (1) In-depth study of the vibration characteristics of hardware, such as bolts and wire clamps, to ensure the stability and safety of the power transmission system. The main goal is to identify the vibration frequency and mode of hardware in normal fixing and different loosening states. Finite element software is used to perform simulation tests and transient dynamic analysis of bolted connections before and after complete loosening. This involves using the impact elastic wave principle to detect bolt loosening and analyzing the response of bolted connections under various load conditions to evaluate their performance in different situations.

[0038] The impact elastic wave principle is that when an object is impacted, stress waves (elastic waves) are generated, which will propagate within the material. When these waves encounter an interface, such as a bolt connection, they will undergo a reflection and transmission process. By analyzing the characteristics of these reflected waves, the condition of the interface can be determined, such as whether there is looseness. The implementation process of detecting bolt loosening using the impact elastic wave principle is as follows Figure 1 shown.

[0039] Through these simulations, the changes in structural vibration characteristics when bolts loosen can be predicted, providing data support for establishing a database and providing important frequency domain information for acoustic imaging.

[0040] The bolt loosening characteristics obtained from the simulation test are sorted out, and combined with the collected on-site bolt vibration data to build a comprehensive, accurate and reliable dynamic database of bolt vibration characteristics, providing solid basic data for subsequent detection. At the same time, the database is continuously updated and expanded through the data obtained from later tests to ensure that it covers new hardware types and loosening modes to adapt to the ever-changing detection needs. In the integrated database, the vibration frequency range of some transmission line hardware is shown in Table 1. If the detected vibration frequency exceeds the maximum frequency value, it is considered to be loose.

[0041] Table 1 Vibration frequency range of transmission line hardware

[0042] Based on this database, reliable and constantly updated benchmark data can be provided for subsequent testing work, thereby greatly improving the accuracy and efficiency of testing. In order to ensure the accuracy, reliability and convenient queryability of information, while ensuring the safe storage and rapid access of information, it is necessary to perform necessary maintenance and expansion of the database periodically to ensure that testing needs are met and the accuracy of testing is improved.

[0043] (3) Rapid identification of loose bolt defects on transmission line towers requires a combination of acoustic imaging, infrared imaging, and electromagnetic field detection technologies. These technologies can capture acoustic information of hardware from a distance, detect tiny temperature changes, and monitor abnormal fluctuations in the electromagnetic field, thereby discovering potential fault points. By using drones equipped with detection equipment and using machine learning algorithms to compare the collected data with feature databases, the health status of hardware and towers can be evaluated, achieving early warning and fault diagnosis.

[0044] The specific detection is achieved by using acoustic imaging, infrared imaging and electromagnetic field detection technology as follows: ① Use acoustic imaging technology to detect loose bolt defects on transmission line towers. Acoustic imaging technology is a technology that uses sound waves to capture and convert sound information into visual images. It usually uses a microphone array to receive sounds from a specific object or environment from a long distance. In the application scenario of loose bolt defect detection on transmission line towers, when the bolts begin to loosen, specific sound waves are generated, which can be captured by the microphone array. Subsequently, through image processing technology, these sound wave information is converted into visual images, allowing operators to intuitively see the possible loose location and degree.

[0045] The beamforming algorithm is used for signal processing. By adjusting and optimizing the phase and amplitude of the signals from each microphone in the microphone array, the sound source signal in a specific direction can be enhanced while suppressing noise and interference in other directions. The flowchart of the beamforming algorithm is shown in the figure below. Figure 2 shown.

[0046] ② Use infrared imaging technology to detect loose bolt defects on transmission line towers. When the bolts in the transmission line are loose, it will cause changes in local resistance, resulting in a difference in temperature between the location and the surrounding environment. This temperature difference can be effectively detected by infrared thermal imaging technology, which is based on the relationship between the temperature of an object and its infrared radiation. Any object will emit infrared radiation as long as its temperature is greater than absolute zero. Therefore, by capturing the infrared radiation emitted from the surface of an object, the temperature distribution of the object can be detected.

[0047] During the inspection, the thermal imager should be fixed steadily to avoid any shaking caused by environmental factors such as wind, so as to ensure the clarity of the image and the accuracy of the data; then the data collection is carried out. This stage is easily affected by environmental factors such as temperature, humidity, and wind speed. When analyzing the data, it is necessary to consider the possible impact of these external factors on the temperature measurement results; then the thermal imaging technology is used to generate a thermal image, which can clearly show the heat distribution. The red area usually represents a high temperature area, which means that the bolts there may be loose or have other problems. The inspection process is as follows: Figure 3 shown.

[0048] ③ Use electromagnetic field detection technology to detect loose bolt defects on transmission line towers. Considering that loose bolts will affect the distribution of the surrounding electromagnetic field, AC electromagnetic field detection technology is used to detect the area around the hardware and monitor abnormal electromagnetic fluctuations. This can capture changes in the electromagnetic field caused by loose bolts. AC electromagnetic field detection technology generates alternating current in the conductor, thereby generating a changing electromagnetic field in the surrounding space. When there are defects in the conductor or the bolts around it, it will affect the distribution of the magnetic field. The detection implementation process is as follows: Figure 4 shown.

[0049] Combine acoustic imaging technology, infrared imaging technology and electromagnetic field detection technology, make full use of their respective unique advantages, and jointly detect the loosening defects of transmission tower bolts. At this time, it is necessary to use early warning technology to realize the integration of detection technology, which is achieved by using convolutional neural algorithm. First, the data detected in three different aspects need to be fused and preprocessed, which not only increases the detection dimension, but also improves the accuracy and reliability of the entire monitoring system; then the convolutional neural network design is carried out, which needs to be adjusted and optimized according to specific problems and data sets; then the model is trained, combined with the dynamic database of bolt vibration characteristics, to enhance the training data and improve the accuracy of the model; finally, the trained model is used for detection and identification, and the newly acquired data and analysis results are fed back to the bolt vibration feature database. When a loose bolt problem or potential problem is detected, there will be a warning display, and finally the efficient detection of loose bolt defects of transmission line towers can be achieved. The early warning display implementation process is as follows Figure 5 shown.

[0050] Combining the above technical research points, by using drones equipped with sensors and other equipment, it is possible to quickly detect loose bolt defects in complete transmission line towers. First, the vibration frequency and mode of the transmission tower bolts in different states are deeply analyzed to distinguish between normal fixed and loose states. Next, the vibration data of various hardware models and specifications in various states are integrated to build a comprehensive database to provide a reference standard for detection work, significantly improving the detection speed and accuracy. In addition, this technology integrates acoustic imaging, infrared imaging, and electromagnetic field detection methods to form a diversified detection strategy. Using drones equipped with these devices can efficiently cover a wide area and achieve rapid and extensive monitoring. The system can also automatically adjust the detection parameters to adapt to different environments and conditions, thereby enhancing the flexibility and adaptability of the detection technology. The overall functional implementation architecture of the present invention is as follows Figure 6 shown.

[0051] By using the technical research of the present invention, loose bolt defects can be discovered and handled in a timely manner, greatly reducing the risk of line accidents caused by bolt problems, thereby ensuring the stable operation of the power grid and the safety of power supply. Combining multiple detection technologies and database support, the detection rate and accuracy are improved, making the operation and maintenance response more rapid and effective.

[0052] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A fast non-contact sensing and detection method for loose bolt defects of transmission line towers, characterized in that: The steps include: Step (1), constructing a dynamic database of bolt vibration characteristics and using the impact elastic wave principle to detect bolt loosening; Step (2), using acoustic imaging technology, infrared imaging technology and electromagnetic field detection technology to identify the looseness of the transmission line tower bolts; Step (3), using the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection and the corresponding transmission line tower bolt loosening conditions, to construct a transmission line tower bolt loosening early warning model; Step (4), using the transmission line tower bolt loosening early warning model and using real-time acoustic imaging, infrared imaging and electromagnetic field detection data to obtain the transmission line tower bolt loosening situation; Step (5): if any of the detection results of step (1), step (2) and step (4) is that the bolts are loose, a loose bolt warning of the transmission line tower is issued.

2. The method for rapid non-contact detection of loose bolt defects in power transmission line towers according to claim 1 is characterized in that: The specific method of step (1) is: Establish an accurate model of the tower bolt connection in finite element software, including bolts, nuts, contact surfaces and surrounding structures; Define material properties for each component in the model; Meshing the model; Set boundary conditions to simulate the actual working environment and apply external loads or shocks to the model to simulate operating conditions that may cause bolt loosening; The simulation simulates the propagation of elastic waves generated by the bolt after being impacted, and calculates the dynamic response at different time points; Focus on the reflection and transmission of impact elastic waves at bolted joints, record the waveform changes when the bolts are normally fixed and in different loose states, extract the bolt vibration feature data, and build a dynamic database of bolt vibration features; The impact elastic wave waveform of the bolt connection to be tested is collected in real time, and the bolt vibration characteristic data is obtained. The data is compared with the data in the bolt vibration characteristic dynamic database to obtain the looseness of the bolt.

3. The method for rapid non-contact detection of loose bolt defects in power transmission line towers according to claim 2 is characterized in that: Material properties include elastic modulus, Poisson's ratio, and density.

4. The method for rapid non-contact sensing and detecting loose bolt defects of power transmission line towers according to claim 2 is characterized in that: Bolt vibration characteristic data include vibration frequency, amplitude and waveform.

5. The method for rapid non-contact sensing and detecting loose bolt defects of power transmission line towers according to claim 1 is characterized in that: In step (2), the specific method of using acoustic imaging technology to identify the looseness of transmission line tower bolts is: A microphone array is used to capture the sound waves at the bolt connections of transmission line towers. If the sound waves of the detected bolts are inconsistent with the sound waves of the bolts when they are normally fixed, it is considered that the bolts may be loose. These sound wave information are then converted into visual images so that the operator can intuitively see the possible loose location and degree.

6. The method for rapid non-contact sensing and detecting loose bolt defects of power transmission line towers according to claim 1 is characterized in that: In step (2), the specific method of using infrared imaging technology to identify the looseness of the transmission line tower bolts is: An infrared thermal imager is used to collect thermal images of the bolted joints of the transmission line tower, and the thermal images are analyzed to obtain the temperature distribution. The temperature distribution is compared with the temperature distribution when the bolts are normally fixed. If obvious abnormal hot spots appear, it is considered that the bolts may be loose.

7. The method for rapid non-contact sensing and detecting loose bolt defects of power transmission line towers according to claim 1 is characterized in that: In step (2), the specific method of using electromagnetic field detection technology to identify the looseness of transmission line tower bolts is: The AC electromagnetic field detection technology is used to detect the electromagnetic fluctuations at the bolt connections of the transmission line towers, and the electromagnetic fluctuations are compared with the electromagnetic fluctuations when the bolts are normally fixed. If there are obvious abnormal changes in the electromagnetic field, it is considered that the bolts may be loose.

8. The method for rapid non-contact sensing and detecting loose bolt defects of power transmission line towers according to claim 1 is characterized in that: The specific method of step (3) is: With the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection as input and the actual loosening of the corresponding transmission line tower bolts as output, the convolutional neural network is trained to obtain a transmission line tower bolt loosening early warning model; among which, the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection are the sound wave, thermal image and electromagnetic fluctuation data of the bolt connection of the transmission line tower, respectively.

9. The method for rapid non-contact sensing and detecting loose bolt defects of power transmission line towers according to claim 1, characterized in that: In step (4), when a loose bolt warning is issued for a transmission line tower, the loose bolts are displayed to prompt the corresponding operator.

Images