Bolt state parameter determination method and device, equipment, medium and product

Abstract

The invention provides a bolt state parameter determination method and device, equipment, a medium and a product, and relates to the technical field of structural bolt health state automatic detection.The method specifically comprises the steps that a target detection model is determined based on the detection requirement of a to-be-detected bolt, and bolt information of the to-be-detected bolt is input into the target detection model, obtaining first bolt image information of the to-be-detected bolt; the to-be-detected bolt comprises at least one bolt; performing image calibration processing on the first bolt image information to obtain second bolt image information; the marking line orientation of the bolt in the second bolt image information is a preset orientation; based on the second bolt image information, state parameters of the to-be-detected bolt are determined, and the state parameters are the bolt loosening angle and / or the bolt pre-tightening force. According to the invention, automatic non-contact nondestructive detection of the loosening angle and the pre-tightening force of the bolt can be realized, and the efficiency and the accuracy of bolt health state detection are improved to a great extent.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic detection of the health status of structural bolts, and in particular to a method, device, equipment, medium and product for determining bolt status parameters. Background Art

[0002] Bolt connection has the advantages of convenient construction, safety and reliability, and is widely used to connect various structural components. In the actual operation and use of bolted structures, they are subject to the coupling of complex environments and reciprocating dynamic loads, and are in a state of vibration and load for a long time. Bolts are prone to loosening or even fatigue fracture, which in turn reduces the bolt preload and even affects the bearing capacity and safety of the structure. Therefore, the detection of bolt state parameters is a problem that has received much attention in the field of automation.

[0003] At present, the methods for determining bolt status parameters are mainly manual on-site detection and sensor detection. The manual on-site detection method mainly includes the marking calibration detection method, the torque wrench method and the strain measurement method. This method has low detection efficiency, high missed detection rate and false detection rate, low operation safety, and the detection results are highly dependent on the detection experience of the detection personnel; the sensor detection method is to install various sensors at the bolts, combine various data analysis methods to analyze the sensor data, and then evaluate the connection status of the bolts, but the sensor data has strong nonlinearity and randomness, the accuracy of the data analysis results is low, and the analysis process also involves a large number of complex calculations, which has high requirements for analysts and processing equipment, high detection costs, and low universality. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a method, device, equipment, medium and product for determining bolt state parameters. The parameter determination method of the present invention is a bolt loosening and preload detection method based on computer vision.

[0005] In order to solve the above technical problems, the present invention proposes a method for determining bolt state parameters, which specifically includes: Based on the detection requirements of the bolts to be detected, a target detection model is determined, and the bolt information of the bolts to be detected is input into the target detection model to obtain first bolt image information of the bolts to be detected; wherein the bolt information of the bolts to be detected is image data of the bolts to be detected determined by computer vision, and the bolts to be detected include at least one bolt; Performing image calibration processing on the first bolt image information to obtain second bolt image information; wherein the orientation of the marking line of the bolt in the second bolt image information is a preset orientation; Based on the second bolt image information, state parameters of the bolt to be detected are determined, wherein the state parameters are the bolt loosening angle and / or the bolt preload, the bolt loosening angle is determined based on the marking line angle of each bolt, the bolt preload is determined based on the bolt loosening angle or the bolt length increment of each bolt, and the marking line angle, the bolt loosening angle and the bolt length increment of each bolt are determined according to the orientation of each bolt in the second bolt image information.

[0006] Compared with the prior art, the advantages of the present invention are: it can realize the automatic contactless non-destructive detection of the loose angle and preload of the bolt, greatly improving the efficiency and accuracy of the bolt health status detection. Secondly, the present invention uses computer vision to automatically identify the bolt marking method, replacing the manual inspection work, which greatly improves the efficiency. It only needs to take pictures to complete batch detection, which simplifies the workload and saves costs. Furthermore, the present invention uses computer vision to calculate the bolt length increment, and then completes the calculation of the bolt preload increment. Compared with the existing contact bolt preload measurement method, it saves a large number of expensive sensors and eliminates the complex analysis process of a large amount of sensor data. It can realize non-contact non-destructive automatic detection, greatly improves the level of intelligence, and reduces costs.

[0007] Based on the above method, the present invention also provides a device for determining bolt state parameters, an electronic device, a computer-readable storage medium and a computer program product.

[0008] The device for determining the state parameters of a bolt is used to implement the method for determining the state parameters of a bolt in any embodiment of the present invention, and the device comprises: A first determination module is used to determine a target detection model based on the detection requirements of the bolts to be detected, and input the bolt information of the bolts to be detected into the target detection model to obtain first bolt image information of the bolts to be detected; wherein the bolt information of the bolts to be detected is image data of the bolts to be detected determined by computer vision, and the bolts to be detected include at least one bolt; A second determination module is used to perform image calibration processing on the first bolt image information using a preset image calibration rule to obtain second bolt image information; wherein the orientation of the bolt mark line in the second bolt image information is a preset orientation; A parameter determination module is used to determine the state parameters of the bolt to be detected based on the second bolt image information, wherein the state parameters are the bolt loosening angle and / or the bolt preload, the bolt loosening angle is determined based on the marking line angle of each bolt, the bolt preload is determined based on the bolt loosening angle or the bolt length increment of each bolt, and the marking line angle, bolt loosening angle and bolt length increment of each bolt are determined according to the orientation of each bolt in the second bolt image information.

[0009] The electronic device includes: at least one processor; and a memory connected to the at least one processor in communication; the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the bolt state parameters in any embodiment of the present invention.

[0010] The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the bolt state parameter in any embodiment of the present invention when the processor executes the computer instructions.

[0011] The computer program product comprises a computer program, and when the computer program is executed by a processor, the method for determining the bolt state parameter of any embodiment of the present invention is implemented.

[0012] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention will be described in more detail below based on embodiments and with reference to the accompanying drawings, wherein: Figure 1 It is a flow chart of a method for determining bolt state parameters provided in the first embodiment of the present invention; Figure 2 Schematic diagram of a YOLOv8 network structure provided by Embodiment 1 of the present invention; Figure 3 is a schematic diagram of a group of bolt images provided by the first embodiment of the present invention; Figure 4 is a structural diagram of an attention mechanism model provided in Embodiment 1 of the present invention; Figure 5 is a schematic diagram of a channel attention structure provided by the first embodiment of the present invention; Figure 6 is a schematic diagram of a structure of spatial attention provided by Embodiment 1 of the present invention; Figure 7 is a schematic diagram of a target detection model based on transfer learning provided in Example 1 of the present invention; Figure 8 is a schematic diagram of a learning rate decay curve provided in Example 1 of the present invention; Fig. 9 is a schematic diagram of another set of bolt images provided in the first embodiment of the present invention; Fig.10 is a schematic diagram of a group of uncalibrated bolt images provided by Embodiment 1 of the present invention; Fig.11is a schematic diagram of a set of calibrated bolt images provided in the first embodiment of the present invention; Fig.12 is a schematic diagram of a group of bolt images with marking lines provided in the first embodiment of the present invention; Fig.13 is a schematic diagram of a group of bolt images from which marker line contours are extracted, provided in Embodiment 1 of the present invention; Fig.14 is a schematic diagram of the minimum circumscribed rectangle of a group of marking lines of bolts provided in the first embodiment of the present invention; Fig.15 is a schematic diagram of a marker line directed vector provided by the first embodiment of the present invention; Fig.16 is a schematic diagram of a bolt structure provided in Embodiment 1 of the present invention; Fig.17 It is a schematic diagram of calculating a bolt length increment provided in the first embodiment of the present invention; Fig.18 It is a structural schematic diagram of a device for determining bolt state parameters provided in the second embodiment of the present invention; Fig.19 It is a structural schematic diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0014] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereby.

[0015] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0016] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0017] In the present invention, unless otherwise clearly specified and limited, the terms "assemble", "connect", "connect", "fix" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0018] In recent years, the rapid rise of computer vision technology has provided new ideas for bolt loosening and preload assessment. Many scholars have used machine learning and image processing technology to assess bolt status, including support vector machines, threshold segmentation, edge detection, and Hough (a feature extraction technology) line detection. This method has improved to a certain extent compared with manual on-site detection methods, but the detection results are greatly affected by the external environment and require manual feature extraction. The accuracy and intelligence level need to be improved. The deep learning method has the ability to directly extract features from images and is an advanced data analysis method. It is feasible to apply the deep learning method to the assessment of bolt connection status. Some scholars directly extract the distance feature between the screw and the connecting plate, and then classify the loose and non-loose bolts. However, the minimum recognition distance of this method is about 4mm, which is suitable for situations where there is obvious looseness. For the slight looseness of the bolt, some scholars calculate the loosening angle of the bolt by detecting the coordinates of the six key points of the bolt, but this method is only suitable for bolt identification with a loosening angle range of 0~60°, and cannot reflect the change of bolt preload. It has certain limitations for bolts with looseness exceeding 60°, and the overall detection accuracy needs to be improved. The present invention combines computer vision to analyze the angle between the current marking line and the ideal marking line of the marked bolt, thereby determining the loose state of the bolt, and can also further determine the change in the bolt preload force based on the angle and length increment of the bolt. The present invention is a contactless, non-destructive, automated detection method, which can significantly improve the efficiency and accuracy of bolt health status assessment.

[0019] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Embodiment 1: Figure 1 This is a flow chart of a method for determining bolt state parameters provided by the present invention. This embodiment can be applied to determine bolt loosening angle and bolt preload with high precision and low cost. This method can be executed by a bolt state parameter determination device provided by the present invention. The device can be implemented in the form of hardware and / or software. In a specific embodiment, the device can be integrated into an electronic device. The following embodiments will be described by taking the device integrated into an electronic device as an example. Figure 1, the method specifically comprises the following steps: S101. Based on the detection requirements of the bolts to be detected, determine a target detection model, and input the bolt information of the bolts to be detected into the target detection model to obtain first bolt image information of the bolts to be detected.

[0021] The method of the present invention can be used to detect the state parameters of a single bolt, and can also be used to detect the state parameters of a batch of bolts, that is, the bolts to be detected include at least one bolt, the bolt information of the bolts to be detected is the image data of the bolts to be detected determined by computer vision, the first bolt image information can be understood as an image representing the current state of the bolt to be detected, the bolt information can be understood as the image data of the bolt to be detected, the target detection model can be understood as an algorithm for processing the bolt information to obtain the first bolt image information, and the detection requirement is used to indicate the target of the current bolt detection work so as to perform the detection work more in line with user needs.

[0022] The target detection model of the present invention includes a single-stage algorithm model and a two-stage algorithm model, which are respectively used to perform detection tasks with different requirements, and the detection requirements include detection speed requirements and detection accuracy requirements.

[0023] The single-stage algorithm model directly parses the input image to obtain the location and category of each target in the input image, including but not limited to you only look once (YOLO series), RetinaNet, Single Shot MultiBoxDetector (SSD), CenterNet, EfficientDet and Swin Transformer; the two-stage algorithm model parses the input image to obtain multiple candidate regions, classifies and locates each candidate region respectively, and then determines the location and category of each target in the input image, including but not limited to RCNN, Fast-RCNN, Faster-RCNN, Mask-RCNN, SPP-Net, FPN, R-FCN and DetectoRS. Different target detection models can be selected for different application scenarios (i.e. detection requirements). Specifically, the two-stage target detection model has high accuracy, and the single-stage target detection model has high speed. When the detection accuracy is high but the detection speed is not required, the two-stage target detection model can be selected; when the detection speed is high but the detection accuracy is not required, the single-stage target detection model can be selected. Therefore, based on the detection requirements of the bolts to be detected, the target detection model is determined, including: when the detection requirement is a detection speed requirement, the target detection model is determined to be a single-stage algorithm model; when the detection requirement is a detection accuracy requirement, the target detection model is determined to be a two-stage algorithm model.

[0024] The present invention can select the single-stage target detection network YOLOv8 to build a bolt target detection model. Figure 2is a schematic diagram of a YOLOv8 network structure provided by the first embodiment of the present invention, from Figure 2 It can be seen that the YOLOv8 network structure is mainly composed of three parts: feature extraction network, feature fusion network and decoupled detection head. Determining the target detection model includes: obtaining the initial detection model and the initial bolt image data set; using the pre-set data set expansion plan to supplement the initial bolt image data set to obtain the target bolt image data set; using the target bolt image data set to train the initial detection model, and when the initial detection model meets the preset model convergence conditions, determining the trained initial detection model as the target detection model.

[0025] The initial bolt image dataset can be understood as the labeled bolt images taken by smart devices in multiple ways, and the target bolt image dataset can be understood as the expanded bolt image dataset, for example, a dataset that is created by augmenting and expanding the acquired dataset using a variety of conventional and deep learning methods and annotating the images. The dataset expansion scheme includes expanding the dataset using the basic method and expanding the dataset using the deep learning method. The basic method can be understood as the commonly used conventional dataset expansion method.

[0026] Smart devices for collecting images include smartphones, cameras, drones, etc. There is no limit to the devices, but when taking images, you should consider changing different angles, distances, and light intensities to ensure the representativeness and diversity of the image dataset. It is worth noting that in order to ensure the training effect of the model, at least 500 images must be taken.

[0027] The methods for data set expansion include but are not limited to augmenting and expanding the collected data set using conventional methods and augmenting and expanding the collected data set using deep learning methods. The methods for augmenting and expanding the collected data set using conventional methods include randomly rotating the image, randomly scaling, adding random noise, randomly blocking the image, changing the color temperature and convolution calculation. The methods for augmenting and expanding the collected data set using deep learning methods include but are not limited to generating augmented data sets using RandAugment, AutoAugment, TrivialAugment, Generative Adversarial Network (GAN) and various variants. The dataset is augmented using a deep convolutional generative adversarial network, and the detailed network parameters of the method used are shown in Table 1.

[0028]

[0029] When bolt images are annotated to create a dataset, the augmented dataset should be at least 10 3Specifically, the image annotation software LabelImg can be used to annotate the comprehensive augmented data set, including two categories: bolt targets with marked lines and bolt targets without marked lines. The data set folder includes image files in jpg format and bolt target position files in txt format. The bolt image can be, for example, Figure 3 , Figure 3 is a schematic diagram of a group of bolt images provided by the first embodiment of the present invention, the image includes 9 bolts, from Figure 3 It can be seen from the figure that the three-dimensional structure and marking line direction of each bolt, the triangle on the bolt represents the bolt marking line, and the direction of the marking line away from the center of the bolt is the marking line direction of the bolt.

[0030] When training the model, a detection model (i.e., initial detection model) is built based on the deep learning platform, the data set is divided and input into the initial detection model, and the transfer learning method is combined to accelerate model training to obtain the target detection model (i.e., the optimal detection model obtained through training). The optimal target detection model obtained through training can also be used for image reasoning to obtain bolt target and marked bolt target detection results.

[0031] The deep learning platform of the present invention can select TensorFlow, PyTorch, Keras, etc. After the detection is selected, the detection model can also be improved, for example, replacing the feature extraction module, adding a channel attention mechanism or a spatial attention mechanism, etc., to enhance the performance of the detection model. The present invention can add a CBAM (Convolutional Block Attention Module) attention mechanism to the YOLOv8 feature extraction network. The structure of the attention mechanism model is as follows Figure 4 As shown in Figure 2, CBAM is a model that combines channel attention and spatial attention, which can enhance the convolutional neural network's ability to focus on images. The structure of the channel attention module is shown in Figure 2. Figure 5 As shown in Figure 2, the structure of the spatial attention module is as follows: Figure 6As shown. Dataset partitioning refers to dividing images into training set, validation set and test set in a certain ratio, which are used for model training, validation and testing respectively. The present invention can divide the training set, validation set and test set in a ratio of 7:2:1. Transfer learning (TL) is to transfer knowledge from one field (i.e., source field) to another field (i.e., target field) so that the target field can achieve better learning results. The source field can select various large-scale data sets, such as COCO, ImageNet, CIFAR-10 and other data sets. The target domain is the bolt image data set. The feature extraction network that has been pre-trained in the source domain is migrated to the target domain, and then a new classifier is trained on the target domain. In order to make the source domain and the target domain have a higher correlation, the present invention selects the COCO data set as the source domain and the bolt image data set as the target domain. The target detection model based on transfer learning is as follows. Figure 7 As shown in the figure, the data sets are: COCO data set and bolt image data set.

[0032] Specifically, during model training, the hyperparameters of the network need to be set in advance, and the values ​​of the hyperparameters will affect the performance of the model after training. Common hyperparameters include input image size, initial learning rate, optimizer, number of epochs for iterative training, batch size, etc. The larger the input image size, the higher the model accuracy, but it will take up more video memory and have higher requirements on the computer; the initial learning rate is an important hyperparameter for adjusting the network weights through the gradient of the loss function. If it is set too large, oscillations may occur easily, and if it is set too small, the convergence speed may be too slow. Therefore, the initial learning rate should not be set too large, preferably between 0.01 and 0.001, and should decay with the number of iterations during training. The initial learning rate of the present invention decays according to a cosine function, and the learning rate decay curve is as follows: Figure 8 The optimizer is used to optimize the network neuron parameter values. Reasonable settings can make the model converge to the optimal solution faster and more accurately. The number of iterative training is the total number of model trainings. One training of all data sets is an epoch. The batch size is the number of samples selected for each training batch. The size of this parameter will affect the convergence speed and optimization degree of the model. It is generally set to 2N. The training hyperparameters of the YOLOv8 target detection model of the present invention are shown in Table 2.

[0033]

[0034] In this invention, the loss function of YOLOv8 is composed of three weighted parts: detection box loss, confidence loss and classification loss. The total loss function is , represents the detection box loss, represents the confidence loss, represents the classification loss, Represents the weight coefficient of the detection box loss, Represents the weight coefficient of confidence loss, Represents the weight coefficient of classification loss. In the process of iterative network training, it is necessary to continuously update the network parameters according to the loss value of the model to minimize the loss value. The corresponding model parameters are the optimal parameters. In order to determine the optimal parameters when the loss value reaches the minimum, the gradient descent method is often used to update the weights and bias parameters of each layer of the network. The update method is: , Representative Layer network; are the weight matrix and bias vector before updating, is the learning rate, is the loss function; are the updated weight matrix and bias vector respectively.

[0035] After the model training is completed, the model performance needs to be evaluated to determine whether the trained model has good performance. Common deep learning model evaluation indicators include precision, recall, average precision, etc. In the present invention, precision p, recall r and F1 score indicators are selected to evaluate the model performance. Specifically, precision , recall rate , F1 score , TP means that the truly positive samples are predicted to be positive, FP means that the truly negative samples are predicted to be positive, and FN means that the truly positive samples are predicted to be negative.

[0036] After the model is trained and successfully tested, the image with detected bolts can be input into the trained optimal target detection model to obtain the detection results of bolt targets with and without marked lines. The detection results include the location information and confidence of the predicted target, such as Fig. 9 As shown, Fig. 9 This is a schematic diagram of another group of bolt images provided in the first embodiment of the present invention, which is essentially the image information output by the target detection model, from which information such as the predicted box position and confidence level of each bolt can be seen.

[0037] S102: Perform image calibration processing on the first bolt image information to obtain second bolt image information.

[0038] The second bolt image information can be understood as the corrected first bolt image information, and the marking line of the bolt in the second bolt image information is oriented in a preset direction. The correction work can be to align the coordinates of the bolt center point based on the target detection result, calculate the perspective transformation matrix, and complete the image correction.

[0039] In one embodiment, S102 may specifically include: 1) inputting the first bolt image information into the target detection model for processing to obtain bolt prediction frame position information, the bolt prediction frame position information includes at least one bolt prediction frame, and the bolt prediction frame corresponds to the bolt one-to-one; for example, the bolt image may be input into the optimal target detection model obtained by training to obtain the bolt target detection result without the marking line, and then obtain the prediction frame position information in the detection result. 2) Based on at least one bolt prediction frame, determine the target center point coordinates of at least one bolt; for example, calculate the center point coordinates of each bolt according to the prediction frame position information of each bolt, and determine the center point coordinates of the bolt in the corrected image. 3) Based on the target center point coordinates and the initial center point coordinates of at least one bolt, determine the calibration matrix; for example, calculate the perspective transformation matrix using the perspective transformation formula according to the position relationship between the coordinate points before and after correction. 4) Process the first bolt image information based on the calibration matrix to obtain the second bolt image information; for example, reproject the distorted image coordinate points using the perspective transformation matrix to complete the correction of the distorted image.

[0040] The initial center point coordinates are the center point coordinates of the uncorrected bolts, that is, the center point coordinates of each bolt in the first bolt image information, which are used to represent the current state of the bolts. The target center point coordinates are the center point coordinates of the corrected bolts, that is, the center point coordinates of each bolt in the second bolt image information, which are used to represent the ideal state of the bolts. The calibration matrix can be determined based on the current state and the ideal state of the bolts.

[0041] In the present invention, the calibration matrix (i.e., the perspective transformation matrix) is solved as follows: , and They are the corresponding coordinate points of the views before and after correction, represents the initial center point coordinates, The first axis value representing the coordinates of the initial center point of the at least one bolt, that is, the coordinate value in the x-axis direction, The second axis value representing the coordinates of the initial center point of the at least one bolt, that is, the coordinate value in the y-axis direction, represents the coordinates of the target center point, a first axis value representing the target center point coordinates of the at least one bolt, a second axis value representing the target center point coordinates of the at least one bolt, represents the perspective transformation matrix, , ,…, all represent the element values ​​of the perspective transformation matrix.

[0042] Fig.10 is a schematic diagram of a group of uncalibrated bolt images provided by the first embodiment of the present invention, Fig.11is a schematic diagram of a set of calibrated bolt images provided in the first embodiment of the present invention, Fig.11 After the calibration matrix conversion Fig.10 , combined with Fig.10 and Fig.11 It can be seen that the calibration method of the present invention can well complete the correction of the image so as to perform subsequent parameter detection work, and the direction of the marking line is the preset direction, which can be understood as aligning the bolt in one direction.

[0043] S103: Determine state parameters of the bolt to be inspected based on the second bolt image information.

[0044] Among them, the state parameters are the bolt loosening angle and / or the bolt preload. Determining the state parameters of the bolt to be detected based on the second bolt image information can be understood as determining the bolt loosening angle and / or bolt preload of each bolt based on the second bolt image information. Specifically, the bolt loosening angle is determined based on the marking line angle of each bolt, and the bolt preload is determined based on the bolt loosening angle or bolt length increment of each bolt. The marking line angle, bolt loosening angle and bolt length increment of each bolt are determined according to the orientation of each bolt in the second bolt image information.

[0045] On the one hand, based on the second bolt image information, the bolt loosening angle of the bolt to be detected is determined, including: based on the orientation of the marking lines of each bolt in the second bolt image information, determining the first marking line and the second marking line of at least one bolt; based on the angle information of the first marking line and the second marking line of at least one bolt, determining the bolt loosening angle of at least one bolt.

[0046] The present invention extracts the straight line feature information of the marking line on the bolt with marking line, calculates the angle between the two straight lines to determine the loosening angle of the bolt, one straight line is the current marking line of the bolt, and the other straight line is the ideal marking line of the bolt. The angle between the two straight lines is the offset angle of the bolt, which can be used to indicate the degree of loosening of the bolt.

[0047] Specifically, the process of determining the bolt loosening angle includes: 1) inputting the corrected bolt image containing both the first marking line and the second marking line into the trained optimal target detection model to obtain the detection result of the bolt with the marking line, and cropping the bolt with the marking line according to the prediction box to obtain the bolt sub-image with the marking line (see Fig.12 ). 2) The bolt sub-image with the marked line is grayed, binarized, eroded and expanded in sequence, and then the contour information of the marked line is extracted using the edge detection algorithm to obtain the marked line contour image (see Fig.13 ). 3) Use the minimum convex hull principle to calculate the minimum enclosing rectangle of the marker line contour in step 2) and obtain the minimum enclosing rectangle (see Fig.14). 4) By calculating the midpoint of the short side of the rectangle, we get two points on the marking line, and then determine the order according to the distance between the two coordinate points and the center point of the bolt, and construct a directed direction vector from the near point to the far point (see Fig.15 5) Calculate the angle between the directional vectors on the two marking lines. , get the bolt loosening angle.

[0048] Further, combined with Fig.15 , O is the coordinate of the center point of the bolt, which is calculated by the target detection frame; O1 and O2 can determine the straight line L1, O1 is closer to point O than O2, so the directed vector is O1 pointing to O2, and similarly, the directed vector from O3 to O4 can be obtained. After obtaining the equations of the two marked lines, the bolt loosening angle , and are the direction vectors of the two lines respectively; and They correspond to the modulus of the direction vector respectively.

[0049] On the other hand, based on the second bolt image information, the bolt preload of the bolt to be detected is determined, including: determining the nut surface loosening angle and the nut surface loosening angle of at least one bolt based on the second bolt image information; processing the second bolt image information based on the target detection model to obtain the nut surface prediction frame information and the nut surface prediction frame information of at least one bolt; determining the measured length of at least one bolt based on the nut surface prediction frame information and the nut surface prediction frame information of at least one bolt, and determining the bolt length increment of at least one bolt based on the measured length and the preset length; determining the bolt preload of at least one bolt based on the stiffness of the connector, the stiffness of the bolt, the looseness angle of the nut surface and the looseness angle of the nut surface of at least one bolt; or, determining the bolt preload of at least one bolt based on the stiffness of the connector, the stiffness of the bolt, the looseness angle of the nut surface of the at least one bolt and the bolt length increment.

[0050] The structure of the bolt detected by the present invention is detailed in Fig.16. Specifically, the process of determining the bolt preload includes: 1) determining the nut face angle looseness value and the nut face angle looseness value in turn according to the calculation method of the bolt looseness angle, subtracting the nut face angle looseness value and the nut face angle looseness value to obtain the relative looseness angle increment of the bolt. 2) inputting the bolt image into the optimal target detection model for processing, obtaining the nut face prediction frame information and the nut face prediction frame information, obtaining the vertex coordinates of each detection frame, calculating the distance between the farthest vertices of the upper and lower prediction frames, obtaining the initial length of the bolt in the tightened state (which can be understood as the preset length), and then subtracting the initial length from the calculated bolt length (ie, the measured length) to obtain the bolt length increment. 3) obtaining the stiffness of the connection and the stiffness of the bolt itself, and then jointly calculating the preload change value (ie, the bolt preload) by the connection stiffness, the bolt stiffness, the bolt length increment or the relative looseness angle increment of the bolt.

[0051] Bolt loosening angle increment , Indicates the loose angle of the nut surface, Indicates the loose angle of the nut surface. Fig.17 is a schematic diagram of calculating the bolt length increment provided in the first embodiment of the present invention, with reference to Fig.17 , the coordinates of the four vertices of the nut surface prediction box are P 1 ~P 4 , the coordinates of the four vertices of the nut face prediction box are P 5 ~P 8 , the distances between the farthest vertices of the two prediction boxes are P 1 With P 7 The distance between 2 With P 8 The bolt length increment is calculated by the combined formula , , and .

[0052] Among them, the predicted box vertex The coordinates of , is the length calculation error, the theoretical value is 0, is the error correction factor, which is equal to the length calculation error corresponding; is the calculated bolt length, the length unit is pixel; It is the conversion coefficient between pixel length and real length, and its unit is mm / pixel. The conversion coefficient can be obtained by calibrating the marker block. The bolt length converted into real value in millimeters; is the initial length of the bolt in the tightened state; The calculated bolt length increment.

[0053] The bolt preload is calculated as: , .

[0054] in, is the bolt preload in the current state, is the preload force of the bolt in the initial state of tightening, is the change in bolt preload, is the thread pitch of the bolt, is the loose angle of the nut surface, is the loosening angle of the nut surface, is the bolt loosening angle increment, is the bolt length increment, is the bolt stiffness; is the stiffness of the connector, is the friction coefficient, which reflects the inhibitory effect of friction on the change of preload. The technical solution of this embodiment includes: 1) constructing an image data set, that is, using smart devices to take images of marked bolts in multiple ways, using a variety of conventional and deep learning methods to augment and expand the collected data set, and finally annotating the image to produce a data set. 2) Model training and reasoning, that is, building a target detection model based on a deep learning platform, dividing the data set and inputting it into the model, and combining the transfer learning method to accelerate model training, using the trained optimal target detection model to perform image reasoning, and obtain bolt target and marked bolt target detection results. 3) Perspective transformation to correct the image, that is, based on the target detection result, the coordinates of the center point of the bolt are aligned, and the perspective transformation matrix is ​​calculated to complete the image correction. 4) Calculating the bolt loosening angle, that is, extracting the feature information of the marking line on the bolt with a marked line, and calculating the angle between the two straight lines to determine the bolt loosening angle. 5) Calculation of bolt preload: First, calculate the loose angles of the nut face and the nut face respectively, and subtract the two to obtain the relative loose angle increment of the bolt. Secondly, determine the bolt length increment according to the target detection results of the nut and the screw. Finally, calculate the preload change value by combining the stiffness of the connector, the stiffness of the bolt, the bolt length increment or the relative loose angle increment of the bolt. The aim is to achieve automatic non-contact non-destructive testing of the bolt loose angle and preload, and improve the efficiency and accuracy of bolt health status detection. Secondly, the present invention uses computer vision to automatically identify the bolt marking method, replacing manual inspection work, greatly improving efficiency, and only needs to take pictures to complete batch detection, simplifying the workload and saving costs. Finally, the present invention uses computer vision to calculate the bolt length increment, and then completes the calculation of the bolt preload increment. Compared with the existing contact bolt preload measurement method, it saves a large number of expensive sensors and eliminates the complex analysis process of a large amount of sensor data. It can realize non-contact non-destructive automatic detection, greatly improving the level of intelligence and reducing costs.

[0055] Embodiment 2: Fig.18 Schematic diagram of the structure of a device for determining bolt state parameters provided by the present invention. Fig.18 As shown, the device includes: a first determination module 201, a second determination module 202 and a parameter determination module 203.

[0056] The first determination module 201 is used to determine the target detection model based on the detection requirements of the bolts to be detected, and input the bolt information of the bolts to be detected into the target detection model to obtain the first bolt image information of the bolts to be detected; wherein the bolt information of the bolts to be detected is the image data of the bolts to be detected determined by computer vision, and the bolts to be detected include at least one bolt.

[0057] The second determination module 202 is used to perform image calibration processing on the first bolt image information by using a preset image calibration rule to obtain second bolt image information; wherein the orientation of the marking line of the bolt in the second bolt image information is a preset orientation.

[0058] The parameter determination module 203 is used to determine the state parameters of the bolt to be detected based on the second bolt image information, wherein the state parameters are the bolt loosening angle and / or the bolt preload, the bolt loosening angle is determined based on the marking line angle of each bolt, the bolt preload is determined based on the bolt loosening angle or the bolt length increment of each bolt, and the marking line angle, bolt loosening angle and bolt length increment of each bolt are determined according to the orientation of each bolt in the second bolt image information.

[0059] Optionally, the target detection model includes a single-stage algorithm model and a two-stage algorithm model, and the detection requirements include a detection speed requirement and a detection accuracy requirement.

[0060] Optionally, the first determination module 201 is specifically used to determine that the target detection model is a single-stage algorithm model when the detection requirement is a detection speed requirement. The single-stage algorithm model is used to directly parse the input image to obtain the position and category of each target in the input image; when the detection requirement is a detection accuracy requirement, the target detection model is determined to be a two-stage algorithm model. The two-stage algorithm model is used to parse the input image to obtain multiple candidate areas, and each candidate area is classified and located to determine the position and category of each target in the input image.

[0061] Optionally, the second determination module 202 is specifically used to input the first bolt image information into the target detection model for processing to obtain bolt prediction frame position information, the bolt prediction frame position information includes at least one bolt prediction frame, and the bolt prediction frame corresponds to the bolt one-to-one; based on the at least one bolt prediction frame, determine the target center point coordinates of at least one bolt; based on the target center point coordinates and initial center point coordinates of at least one bolt, determine the calibration matrix; process the first bolt image information based on the calibration matrix to obtain second bolt image information.

[0062] Optionally, the correlation relationship between the calibration matrix, the target center point coordinates of at least one bolt, and the initial center point coordinates is: ; in, The first axis value representing the coordinates of the initial center point of at least one bolt, The second axis value representing the coordinates of the initial center point of at least one bolt, The first axis value representing the coordinates of the target center point of at least one bolt, The second axis value representing the coordinates of the target center point of at least one bolt, represents the calibration matrix, , , , , , , , and is the element value of the calibration matrix, =1.

[0063] Optionally, the parameter determination module 203 is specifically used to determine the first marking line and the second marking line of at least one bolt based on the marking line orientation of each bolt in the second bolt image information; the first marking line and the second marking line are two vector lines determined based on the marking line orientation of each bolt in the second bolt image information and the center point of the bolt; based on the angle information between the first marking line and the second marking line of at least one bolt, determine the bolt loosening angle of at least one bolt.

[0064] Optionally, the parameter determination module 203 is specifically used to determine the nut surface loosening angle and the nut surface loosening angle of at least one bolt based on the second bolt image information; process the second bolt image information based on the target detection model to obtain the nut surface prediction frame information and the nut surface prediction frame information of at least one bolt; determine the measured length of at least one bolt based on the nut surface prediction frame information and the nut surface prediction frame information of at least one bolt, and determine the bolt length increment of at least one bolt based on the measured length and the preset length; determine the bolt preload force of the at least one bolt based on the connector stiffness, the bolt stiffness, the nut surface loosening angle and the nut surface loosening angle of the at least one bolt; or, determine the bolt preload force of the at least one bolt based on the connector stiffness, the bolt stiffness, the nut surface loosening angle and the bolt length increment.

[0065] Optionally, the first determination module 201 is also used to obtain an initial detection model and an initial bolt image data set; use a pre-set data set expansion scheme to supplement the initial bolt image data set to obtain a target bolt image data set, and the data set expansion scheme includes expanding the data set using a basic method and expanding the data set using a deep learning method; use the target bolt image data set to train the initial detection model, and when the initial detection model meets the preset model convergence conditions, determine that the trained initial detection model is the target detection model.

[0066] The device for determining bolt state parameters provided in this embodiment can execute the method for determining bolt state parameters provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0067] Embodiment three: Fig.19: is a schematic diagram of the structure of an electronic device provided by the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0068] like Fig.19 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (also called random access memory, RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0069] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0070] The processor 11 may be a variety of general and / or dedicated processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for determining bolt state parameters.

[0071] In some embodiments, the method for determining the bolt state parameters may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the bolt state parameters described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for determining the bolt state parameters in any other appropriate manner (e.g., by means of firmware).

[0072] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0073] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0074] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0075] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0076] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server) or a computing system that includes middleware components (e.g., an application server) or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein) or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by digital data communications (e.g., a communications network) of any form or medium. Examples of communications networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0077] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0078] In one embodiment, the present invention further includes a computer program product, the computer program product includes a computer program, and when the computer program is executed by a processor, the method for determining the bolt state parameter of any embodiment of the present invention is implemented.

[0079] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages ​​and conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0080] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0081] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

[0082] Although the present invention has been described with reference to preferred embodiments, various modifications may be made thereto and parts thereof may be replaced by equivalents without departing from the scope of the present invention. In particular, the various technical features mentioned in the various embodiments may be combined in any manner as long as there are no structural conflicts. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for determining bolt state parameters, characterized in that: include: Based on the detection requirements of the bolts to be detected, a target detection model is determined, and the bolt information of the bolts to be detected is input into the target detection model to obtain first bolt image information of the bolts to be detected; wherein the bolt information of the bolts to be detected is image data of the bolts to be detected determined by computer vision, and the bolts to be detected include at least one bolt; Performing image calibration processing on the first bolt image information to obtain second bolt image information; wherein the orientation of the bolt marking line in the second bolt image information is a preset orientation; Based on the second bolt image information, the state parameters of the bolt to be detected are determined, wherein the state parameters are the bolt loosening angle and / or the bolt preload, the bolt loosening angle is determined based on the marking line angle of each bolt, the bolt preload is determined based on the bolt loosening angle or the bolt length increment of each bolt, and the marking line angle, bolt loosening angle and bolt length increment of each bolt are determined according to the orientation of each bolt in the second bolt image information.

2. The method for determining bolt state parameters according to claim 1, characterized in that: The target detection model includes a single-stage algorithm model and a two-stage algorithm model. The detection requirements include detection speed requirements and detection accuracy requirements. The target detection model is determined based on the detection requirements of the bolts to be detected, including: When the detection requirement is the detection speed requirement, determining that the target detection model is the single-stage algorithm model, wherein the single-stage algorithm model is used to directly parse the input image to obtain the position and category of each target in the input image; When the detection requirement is the detection accuracy requirement, the target detection model is determined to be the two-stage algorithm model, wherein the two-stage algorithm model is used to parse the input image, obtain multiple candidate areas, and classify and locate each candidate area to determine the position and category of each target in the input image.

3. The method for determining bolt state parameters according to claim 1, characterized in that: The performing image calibration processing on the first bolt image information to obtain second bolt image information includes: Inputting the first bolt image information into the target detection model for processing to obtain bolt prediction frame position information, wherein the bolt prediction frame position information includes at least one bolt prediction frame, and the bolt prediction frame corresponds to the bolt one by one; Determining target center point coordinates of the at least one bolt based on the at least one bolt prediction frame; determining a calibration matrix based on the target center point coordinates and the initial center point coordinates of the at least one bolt; The correlation relationship between the calibration matrix, the target center point coordinates of the at least one bolt and the initial center point coordinates is: ； in, a first axis value representing the coordinates of the initial center point of the at least one bolt, a second axis value representing the coordinates of the initial center point of the at least one bolt, a first axis value representing the target center point coordinates of the at least one bolt, a second axis value representing the target center point coordinates of the at least one bolt, represents the calibration matrix, , , , , , , , and is the element value of the calibration matrix, =1; The first bolt image information is processed based on the calibration matrix to obtain the second bolt image information.

4. The method for determining bolt state parameters according to claim 1, characterized in that: Determining a bolt loosening angle of the bolt to be detected based on the second bolt image information includes: Determine a first marking line and a second marking line of the at least one bolt based on the marking line orientation of each bolt in the second bolt image information, wherein the first marking line and the second marking line are two vector lines determined based on the marking line orientation of each bolt in the second bolt image information and the center point of the bolt; Based on the angle information between the first marking line and the second marking line of the at least one bolt, a bolt loosening angle of the at least one bolt is determined.

5. The method for determining bolt state parameters according to claim 1, characterized in that: Determining the bolt preload force of the bolt to be detected based on the second bolt image information includes: Determining a nut face loosening angle and a nut face loosening angle of the at least one bolt based on the second bolt image information; Processing the second bolt image information based on the target detection model to obtain nut surface prediction frame information and nut surface prediction frame information of the at least one bolt; Determining a measured length of the at least one bolt based on the nut face prediction frame information and the nut face prediction frame information of the at least one bolt, and determining a bolt length increment of the at least one bolt based on the measured length and a preset length; Determine the bolt preload of the at least one bolt based on the connector stiffness, the bolt stiffness, the nut face loosening angle and the nut face loosening angle of the at least one bolt; or, determine the bolt preload of the at least one bolt based on the connector stiffness, the bolt stiffness, the nut face loosening angle and the bolt length increment.

6. The method for determining bolt state parameters according to claim 5, characterized in that: The calculation expression of the bolt preload force of the bolt is: ， ， ； ， in, is the bolt preload in the current state, is the preload force of the bolt in the initial state of tightening, is the change in bolt preload, is the loose angle of the nut surface, is the loosening angle of the nut surface, is the bolt loosening angle increment, is the bolt length increment, is the bolt stiffness; is the stiffness of the connector, is the thread pitch of the bolt, is the initial length of the bolt in the tightened state, To convert the bolt length into real value, is the calculated bolt length, is the conversion coefficient between pixel length and real length, is the error correction coefficient, is the friction coefficient, which reflects the inhibitory effect of friction on the change of preload force.

7. A device for determining bolt state parameters, characterized in that: The method for determining the bolt state parameter according to any one of claims 1 to 6 is used, wherein the device for determining the bolt state parameter comprises: A first determination module is used to determine a target detection model based on the detection requirements of the bolts to be detected, and input the bolt information of the bolts to be detected into the target detection model to obtain first bolt image information of the bolts to be detected; wherein the bolt information of the bolts to be detected is the image data of the bolts to be detected determined by computer vision, and the bolts to be detected include at least one bolt; A second determination module is used to perform image calibration processing on the first bolt image information using a preset image calibration rule to obtain second bolt image information; wherein the orientation of the bolt mark line in the second bolt image information is a preset orientation; A parameter determination module is used to determine the state parameters of the bolt to be detected based on the second bolt image information, wherein the state parameters are the bolt loosening angle and / or the bolt preload, the bolt loosening angle is determined based on the marking line angle of each bolt, the bolt preload is determined based on the bolt loosening angle or the bolt length increment of each bolt, and the marking line angle, bolt loosening angle and bolt length increment of each bolt are determined according to the orientation of each bolt in the second bolt image information.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the bolt state parameters described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining bolt state parameters as described in any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the method for determining the bolt state parameter according to any one of claims 1 to 6.

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