Intelligent self-optimization prediction method and system for connection pre-tightening force of rusted high-strength bolt

Abstract

The invention discloses a rusted high-strength bolt connection pretightening force intelligent self-optimization prediction method and system, and belongs to the technical field of bolt pretightening force detection.The method specifically comprises the steps that a finite element database of bolt connection pretightening force loss is established, and a machine learning prediction model based on a neural network is trained; when the recognition accuracy of the machine learning prediction model reaches a preset value, pre-tightening force loss of the target bolt is monitored in real time, and real-time geometric feature information of connection of the target bolt is obtained; when the similarity between the real-time geometric feature information of the target bolt connection and a finite element model in the finite element database is greater than or equal to a preset value, calling the pre-tightening force loss of the existing finite element database; when the similarity is lower than a preset value, the trained machine learning prediction model is utilized to output actual bolt connection pre-tightening force loss in real time; the bolt looseness detection device realizes automatic and marker-free bolt looseness detection, is high in bolt image acquisition angle and distance tolerance, and can meet bolt looseness detection at different positions.

Description

Technical Field

[0001] The present invention belongs to the technical field of bolt preload detection, and in particular relates to an intelligent self-optimization prediction method and system for the preload of a corroded high-strength bolt connection. Background Art

[0002] As a key connection element in the steel structure system, bolts are widely used in the construction and installation of various steel structure projects such as buildings, bridges, towers, etc. due to their excellent stress performance, fatigue resistance, earthquake resistance, high-strength connection, construction convenience and maintainability. However, with the increase in service time, especially in the severe marine atmospheric corrosion environment and harsh industrial atmospheric corrosion environment, the rust phenomenon of bolt connection nodes has become more and more common and serious, becoming a major hidden danger affecting the structural performance and safety of steel structures.

[0003] High-strength bolts that are exposed to corrosive environments for a long time, especially those with a service life of 10 years or more, often show large-scale and deep corrosion, such as obvious bubbling, cracking, peeling and other phenomena. Taking the Nanpanjiang Bridge as an example, 10 years after its completion, about 80% of its main truss bolt connection nodes showed moderate or above corrosion, and the corrosion condition was obviously more serious than that of the rods. This corrosion phenomenon not only destroys the surface material of the bolts and reduces the effective cross-sectional area of ​​the structural material, but also directly leads to a significant loss of bolt preload. Preload is the core element to maintain the stability and bearing capacity of high-strength bolt connections, and the degree of its reduction is closely related to the degree of corrosion of the bolt head and nut. Once the preload drops significantly, it will directly affect the stiffness, bearing capacity and fatigue life of the connection node, thereby endangering the stability and safety of the entire steel structure system.

[0004] Faced with the severe challenges brought by corroded bolt connections, existing detection methods often fail to meet the needs of efficiency, accuracy and economy. Traditional detection methods such as manual visual inspection, hammer test, torque wrench measurement, etc., although simple to operate, are inefficient, rely on manual experience, are easily affected by subjective factors, and pose safety hazards in bolt detection at high places or inaccessible areas. Although the emerging acoustic, electrical, and magnetic sensor detection technologies can theoretically provide more detailed data, the equipment is expensive, sensitive to environmental conditions, and the effect is not ideal in actual applications. Image recognition-based detection technology also faces problems such as fragile markers and limited image acquisition angles, and cannot adapt to complex field conditions. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method and system for intelligent self-optimization prediction of the preload force of corroded high-strength bolt connections in response to the deficiencies in the above-mentioned prior art, which is used to solve the technical problem that there are limitations in the existing detection of preload force loss of various corroded bolts, realize automatic and marker-free detection of bolt looseness, have a high tolerance for bolt image acquisition angles and distances, and can meet the requirements of bolt looseness detection at different positions.

[0006] The present invention adopts the following technical solutions:

[0007] An intelligent self-optimization prediction method for preload force of corroded high-strength bolt connections, comprising the following steps:

[0008] Establish a finite element database of bolt connection preload loss and train a neural network-based machine learning prediction model;

[0009] Determine whether the recognition accuracy of the obtained machine learning prediction model reaches a preset value;

[0010] When the recognition accuracy of the machine learning prediction model reaches the preset value, the preload loss of the target bolt is monitored in real time to obtain the real-time geometric feature information of the target bolt connection;

[0011] Compare the similarity between the obtained real-time geometric feature information of the target bolt connection and the finite element model in the finite element database. When the similarity is greater than or equal to a preset value, call the preload loss in the existing finite element database.

[0012] When the similarity is lower than the preset value, the trained machine learning prediction model is used to output the actual bolt connection preload loss in real time.

[0013] Preferably, establishing a bolt connection preload loss database is specifically as follows:

[0014] Establish a three-dimensional solid geometry model of the uncorroded bolt connection;

[0015] Performing unit division and applying boundary conditions to the three-dimensional solid geometric model of the non-corroded bolt connection to obtain a first analysis model;

[0016] Apply the first step load to the first analysis model and calculate the preload force of the first analysis model;

[0017] Randomly define components as corrosion units based on the size of uncorroded bolt connections and the actual bolt corrosion principle;

[0018] Randomly set the physical properties related to bolt connection according to the actual bolt corrosion principle;

[0019] The modified unit is killed by using the birth and death unit method to obtain the second analysis model;

[0020] Apply the second step load to the second analysis, calculate the preload of the second analysis model, and calculate the preload loss value of the corroded bolt;

[0021] Record the relevant corroded bolt size information, physical information and preload loss value;

[0022] Repeat the above steps, organize the data obtained each time, establish a bolt connection preload loss database, and train a neural network-based machine learning prediction model.

[0023] Preferably, the neural network-based prediction model uses a particle swarm algorithm to optimize the BP neural network.

[0024] Preferably, when the recognition accuracy of the prediction model does not reach a preset value, a finite element database is added and the training of the neural network-based machine learning prediction model is returned.

[0025] Preferably, obtaining the real-time geometric feature information of the bolt connection is specifically as follows:

[0026] Performing grayscale processing on the obtained bolt connection image to obtain a grayscale image;

[0027] The grayscale image is denoised by Gaussian filtering to obtain a denoised image;

[0028] The minimum error method is used to determine the binarization threshold, and the denoised image is binarized to obtain a binary image.

[0029] Using Harris corner point detection, identify and mark the center of the bolt head;

[0030] Use edge detection technology to accurately determine the overall profile of the bolt, ensuring that the bolt boundary can still be accurately defined under the influence of corrosion;

[0031] Arrange and export the geometric dimensions of each bolt connection, such as diameter, length, corrosion degree, etc.

[0032] Preferably, the real-time geometric feature information of the target bolt connection obtained by comparison is compared with the similarity of the finite element model in the finite element database. When the similarity is greater than or equal to a preset value, the preload loss of the existing finite element database is called as follows:

[0033] Compare the size information recorded in the database according to the geometric features of the target bolt connection, and obtain the comparison similarity;

[0034] Set the target comparison similarity threshold, and judge the similarity between the model and the real-time bolt connection based on the comparison similarity;

[0035] If it is higher than the threshold, the preload loss in the existing finite element database is directly called as the prediction result.

[0036] Preferably, the trained machine learning prediction model is used to output the actual bolt connection preload loss in real time as follows:

[0037] The geometric features and related physical information of the target bolt are normalized and regularized and input into the machine learning model for preload prediction. If the preload loss exceeds the set value, a signal is sent to notify the maintenance personnel to repair the target bolt. Otherwise, normal data will be detected in real time for archiving and backup.

[0038] For the obtained preload results, the finite element model is established by combining the corresponding geometric features and physical information, and the finite element database is increased.

[0039] In a second aspect, an embodiment of the present invention provides an intelligent self-optimization prediction system for preload force of corroded high-strength bolt connections, comprising:

[0040] Data module, to establish a finite element database of bolt connection preload loss and train a machine learning prediction model based on a neural network;

[0041] A judgment module determines whether the recognition accuracy of the machine learning prediction model reaches a preset value;

[0042] The monitoring module monitors the preload loss of the target bolt in real time and obtains the real-time geometric feature information of the target bolt connection when the recognition accuracy of the machine learning prediction model reaches the preset value;

[0043] A comparison module compares the real-time geometric feature information of the target bolt connection with the finite element model in the finite element database. When the similarity is greater than or equal to a preset value, the preload loss in the existing finite element database is called;

[0044] The prediction module, when the similarity is lower than the preset value, uses the trained machine learning prediction model to output the actual bolt connection preload loss in real time.

[0045] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for intelligent self-optimization prediction of preload force of corroded high-strength bolt connections when executing the computer program.

[0046] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for intelligent self-optimization prediction of preload force of corroded high-strength bolt connections.

[0047] Compared with the prior art, the present invention has at least the following beneficial effects:

[0048] An intelligent self-optimization prediction method for the preload of corroded high-strength bolt connections. First, a preload loss database is established and a prediction model is trained using neural network technology, aiming to capture the complex correlation between features and improve prediction accuracy. Accuracy verification is then used to ensure the reliability of the model, and bolt geometric features are monitored and extracted in real time. Combined with database comparison, rapid prediction can be made or machine learning models can be called for analysis. This process not only optimizes resource allocation and reduces safety risks through a rapid response mechanism, but also continuously integrates new data into the database to continuously enhance the generalization ability of the model, forming a closed-loop system of continuous learning and optimization, and fundamentally improving the scientificity and foresight of corroded bolt monitoring. The method aims to build an efficient and adaptive intelligent self-optimization prediction system for the preload of corroded high-strength bolt connections to ensure structural safety and maintenance efficiency.

[0049] Furthermore, by establishing a database of bolt connection preload loss, we first create a three-dimensional model of an uncorroded bolt and perform finite element analysis, then simulate the effect of the corrosion process on the mechanical properties of the model, record the change in preload before and after corrosion, and finally accumulate to form a database; this step is designed to capture the specific effect of corrosion on bolt preload through precise simulation and data analysis. Its core advantage is that it provides a quantitative evaluation method that can accurately predict and prevent preload loss caused by bolt corrosion to ensure structural safety. In principle, it combines computer-aided design, finite element analysis and material corrosion science, and reflects the degradation mechanism of bolt performance in a real environment by gradually applying loads and adjusting corrosion parameters. The establishment of the database provides a solid data foundation for further statistical analysis, prediction model optimization and practical engineering applications.

[0050] Furthermore, the particle swarm algorithm is used to optimize the BP neural network (PSO-BP) as the neural network used in the prediction model, aiming to utilize the global search advantage of PSO and the powerful fitting ability of BP neural network to significantly improve the accuracy and efficiency of intelligent prediction of preload of corroded high-strength bolt connections. The PSO algorithm simulates swarm intelligence to guide the efficient optimization of network weights, overcomes the limitation of traditional BP neural network that is prone to fall into local optimality, and achieves the approximation of the global optimal solution. Under the optimization of the particle swarm algorithm, the BP neural network can better learn the complex patterns implicit in the input data, especially the nonlinear mapping relationship between corrosion characteristics and preload loss through multi-level feedforward and back propagation. Different from CNN, which relies on convolutional layers and pooling layers to capture local features and reduce dimensions, the PSO-BP neural network focuses on the in-depth learning of the overall data, and dynamically adjusts the weight matrix through particle swarms to achieve effective integration and optimization of features. This process not only enhances the sensitivity of the model to corrosion characteristics, but also enables the model to learn and adapt to various corrosion conditions in a large number of training samples through continuous weight updates, ensuring the wide applicability and high reliability of the prediction results, and providing a scientific basis for structural safety assessment and maintenance decisions.

[0051] Furthermore, when the prediction model recognition accuracy is lower than the preset standard, the finite element database is added and the training process is re-entered to enrich the data diversity and improve the model generalization ability. This strategy uses the real physical data of finite element analysis to enhance the model's understanding of complex corrosion conditions, optimize model parameters to reduce overfitting, and further improve the prediction accuracy to the desired level. This process combines the advantages of physical simulation and machine learning to ensure that the intelligent self-optimization prediction of the preload force of corroded high-strength bolt connections is more accurate and effective.

[0052] Furthermore, the image graying, noise reduction, binarization, corner detection and edge recognition technologies used in the step of obtaining real-time geometric feature information of bolt connections are designed to automatically extract the geometric dimensions and corrosion status of the bolts through precise image processing technology to ensure the real-time and accuracy of the data. This series of processes not only speeds up the detection process and improves monitoring efficiency, but also provides solid data support for the preload loss prediction model by accurately defining the bolt profile and evaluating the degree of corrosion. Through step-by-step image processing to reduce environmental interference, accurately identify key features, and achieve fine measurement of bolt diameter, length and corrosion area, it provides a scientific basis for maintenance decisions and ensures structural safety and reliability.

[0053] Furthermore, by comparing the real-time detected bolt geometry features with database records, the preload loss can be predicted quickly and accurately, improving the prediction efficiency and practicality. The principle is to use feature matching technology to quantify the similarity between the real-time acquired bolt connection geometry data and the model data in the database, and set threshold distinction rules and processing methods: when the similarity is higher than the threshold, the preload loss value of the matching item in the database is directly called as the prediction result, and the decision-making process is accelerated by using past data to ensure the reliability and timeliness of the prediction; it not only optimizes resource allocation and concentrates efforts to solve complex problems, but also greatly improves the overall efficiency and adaptability of the prediction system through intelligent screening mechanisms.

[0054] Furthermore, by inputting the normalized characteristics and physical information of specific bolts into the machine learning model to predict the preload loss, immediate warning of abnormal situations and archiving of routine data can be achieved, ensuring structural safety while accumulating valuable maintenance data; further, the predicted results and actual characteristics are used to build a finite element model and expand the database, which not only optimizes the accuracy of the immediate prediction, but also promotes model optimization; intelligent technology is used to improve maintenance efficiency, and through a continuous data feedback loop, the understanding of the preload loss mechanism of corroded bolts and the response strategy are deepened, fundamentally enhancing the practical value and long-term benefits of the prediction system.

[0055] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0056] In summary, the present invention achieves efficient and accurate prediction of bolt corrosion, greatly enhances the timeliness and accuracy of structural safety monitoring, can respond quickly and provide a scientific basis for maintenance decision-making, while ensuring the long-term reliability and practicality of the prediction.

[0057] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the process of the present invention;

[0059] Figure 2 A schematic diagram of a computer device provided by an embodiment of the present invention;

[0060] Figure 3 A block diagram of a chip provided according to an embodiment of the present invention;

[0061] Figure 4 The particle swarm algorithm used in the present invention optimizes the BP neural network structure diagram;

[0062] Figure 5A schematic diagram of a process for obtaining a target bolt connection size and outputting a preload force using an industrial camera provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0065] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0066] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0067] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0068] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0069] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0070] The present invention provides an intelligent self-optimization prediction method for the preload of corroded high-strength bolt connections. By constructing an intelligent self-optimization prediction system for the preload of corroded high-strength bolt connections based on neural networks and finite element analysis, efficient and accurate prediction of bolt corrosion is achieved, greatly enhancing the timeliness and accuracy of structural safety monitoring. By using deep learning technology to automatically extract features and combining real-time monitoring and image processing technology, the system can respond quickly and provide a scientific basis for maintenance decisions. At the same time, through continuous learning mechanisms and database optimization, the generalization ability of the model in complex corrosion conditions is continuously improved, ensuring the long-term reliability and practicality of the prediction.

[0071] See also Figure 1 The present invention provides an intelligent self-optimization prediction method for the preload force of a corroded high-strength bolt connection, comprising the following steps:

[0072] S1. Establish a bolt connection preload loss database and use the existing data in the database to train the neural network model;

[0073] The establishment of bolt connection preload loss database is as follows:

[0074] S101. Use finite element software to establish a three-dimensional solid geometry model of an uncorroded bolt connection;

[0075] S102, performing unit division on the three-dimensional solid geometric model of the non-corroded bolt connection and applying boundary conditions to obtain a first analysis model;

[0076] S103, applying the first step load to the first analysis model, and calculating the preload force of the first analysis model;

[0077] S104, randomly define components as corrosion units according to the size of non-corroded bolt connections and the actual bolt corrosion principle;

[0078] S105. Randomly set physical properties related to bolt connection according to actual bolt corrosion principle;

[0079] S106, using the birth and death unit method, killing the modified unit to obtain a second analysis model;

[0080] S107, applying the second step load to the second analysis, calculating the preload force of the second analysis model, and calculating the preload force loss value of the corroded bolt;

[0081] S108. Record the size information, physical information and preload loss value of the relevant corroded bolts;

[0082] S109, repeat the above steps, organize the data obtained each time, and establish a bolt connection preload loss database, wherein ANSYS finite element software is used to preliminarily establish a database with 1000 finite element models.

[0083] Among them, the neural network model uses the particle swarm algorithm to optimize the BP neural network. The neural network structure selects a three-layer network. The number of input neurons is the number of target bolt connection characteristics, the number of hidden layer neurons is 5, and the number of output neurons is 1.

[0084] When simulating actual corrosion using finite element software, it is necessary to consider as much dimensional information as possible to ensure that the finite element simulation results are realistic.

[0085] The preload loss simulated in the finite element method is used as output, and multiple physical information is used as input. The training is performed by dividing the set into training set, validation set, and test set to obtain a machine learning model.

[0086] S2, determine whether the recognition accuracy of the prediction model reaches a preset value, if so, proceed to step S3, otherwise, add a finite element database and return to step S1;

[0087] S3, real-time monitoring of the target bolt preload loss is performed through an image collector and a nondestructive detector to obtain real-time geometric feature information of the bolt connection;

[0088] In the embodiment of the present invention, the image collector adopts a CCD industrial camera, which has a relatively low cost. The image collector is installed near the target bolt connection, and the picture is clear with a slight angle, so that the relevant geometric dimensions of the bolt connection can be fully captured.

[0089] In the embodiment of the present invention, the image collector can obtain bolt connection pictures when the light in the setting area is good, such as collecting one picture each at 8:00, 10:00, 12:00, 14:00, 16:00, and 18:00 and transmitting it to the remote server.

[0090] To obtain the real-time geometric feature information of bolt connections, please refer to:

[0091] S301, performing grayscale processing on the acquired image of the bolt connection to obtain a grayscale image;

[0092] S302, reducing noise on the grayscale image by Gaussian filtering to obtain a denoised image;

[0093] S303, using a minimum error method to determine a binarization threshold, and performing a binarization operation on the denoised image to obtain a binary image;

[0094] S304, using Harris corner point detection to identify and mark the center of the bolt head;

[0095] S305. Use edge detection technology (such as improved Canny operator or active contour model) to accurately determine the overall contour of the bolt, ensuring that the bolt boundary can still be accurately defined under the influence of corrosion;

[0096] S306. Arrange and output the geometric dimensions of each bolt connection, such as diameter, length, degree of corrosion, etc.

[0097] S4, comparing the real-time bolt connection with the finite element model, if the similarity is lower than a preset value, proceeding to step S5, otherwise directly calling the preload loss in the existing finite element database;

[0098] S401, comparing the size information recorded in the database according to the geometric features of the target bolt connection obtained in step S3, and obtaining the comparison similarity;

[0099] S402, setting a threshold value of target comparison similarity, and judging the similarity between the model and the real-time bolt connection according to the comparison similarity;

[0100] S403: If it is lower than the threshold, proceed to step S5; otherwise, directly call the preload loss in the existing finite element database as the prediction result.

[0101] S5. Use the trained machine learning model to output the actual bolt connection preload loss in real time.

[0102] S501, normalize and regularize the geometric features and related physical information of the target bolt and input them into the machine learning model to predict the preload force. If the preload force loss exceeds the set value, a signal is sent to notify the maintenance personnel to repair the target bolt. Otherwise, the normal data is detected in real time for archiving and backup;

[0103] S502. For the obtained preload force result, a finite element model is established in combination with corresponding geometric features and physical information, and a finite element database is added.

[0104] In another embodiment of the present invention, a system for intelligent self-optimization prediction of preload force of corroded high-strength bolt connections is provided. The system can be used to implement the above-mentioned method for intelligent self-optimization prediction of preload force of corroded high-strength bolt connections. Specifically, the system for intelligent self-optimization prediction of preload force of corroded high-strength bolt connections includes a data module, a judgment module, a monitoring module, a comparison module and a prediction module.

[0105] Among them, the data module establishes a finite element database of bolt connection preload loss and trains a machine learning prediction model based on a neural network;

[0106] A judgment module determines whether the recognition accuracy of the machine learning prediction model reaches a preset value;

[0107] The monitoring module monitors the preload loss of the target bolt in real time and obtains the real-time geometric feature information of the target bolt connection when the recognition accuracy of the machine learning prediction model reaches the preset value;

[0108] A comparison module compares the real-time geometric feature information of the target bolt connection with the similarity of the finite element model in the finite element database. When the similarity is greater than or equal to a preset value, the preload loss in the existing finite element database is called;

[0109] The prediction module, when the similarity is lower than the preset value, uses the trained machine learning prediction model to output the actual bolt connection preload loss in real time.

[0110] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the intelligent self-optimization prediction method for the preload force of the corroded high-strength bolt connection, including:

[0111] A finite element database of bolt connection preload loss is established, and a neural network-based machine learning prediction model is trained; whether the recognition accuracy of the machine learning prediction model reaches the preset value is determined; when the recognition accuracy of the machine learning prediction model reaches the preset value, the preload loss of the target bolt is monitored in real time to obtain the real-time geometric feature information of the target bolt connection; the real-time geometric feature information of the target bolt connection is compared with the similarity of the finite element model in the finite element database, and when the similarity is greater than or equal to the preset value, the preload loss in the existing finite element database is called; when the similarity is lower than the preset value, the trained machine learning prediction model is used to output the actual bolt connection preload loss in real time.

[0112] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-Volatile Memory), such as at least one disk memory.

[0113] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the intelligent self-optimization prediction method for the preload force of a corroded high-strength bolt connection in the above embodiment; the processor may load and execute the following steps:

[0114] A finite element database of bolt connection preload loss is established, and a neural network-based machine learning prediction model is trained; whether the recognition accuracy of the machine learning prediction model reaches the preset value is determined; when the recognition accuracy of the machine learning prediction model reaches the preset value, the preload loss of the target bolt is monitored in real time to obtain the real-time geometric feature information of the target bolt connection; the real-time geometric feature information of the target bolt connection is compared with the similarity of the finite element model in the finite element database, and when the similarity is greater than or equal to the preset value, the preload loss in the existing finite element database is called; when the similarity is lower than the preset value, the trained machine learning prediction model is used to output the actual bolt connection preload loss in real time.

[0115] See also Figure 2, the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the composition of fluid in the reservoir transformation wellbore in the embodiment is realized. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the calculation system for the composition of fluid in the reservoir transformation wellbore in the embodiment are realized. To avoid repetition, it is not described one by one here.

[0116] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 2 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0117] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0118] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0119] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0120] Any reference to memory, database or other media used in the embodiments provided in the present application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0121] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0122] See also Figure 3 , the terminal device is a chip, and the chip 600 of this embodiment includes a processor 622, which may be one or more, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 622 may be configured to execute the computer program to execute the above-mentioned generalizable monocular absolute depth map estimation method.

[0123] In addition, the chip 600 may further include a power supply component 626 and a communication component 650, wherein the power supply component 626 may be configured to perform power management of the chip 600, and the communication component 650 may be configured to implement communication, for example, wired or wireless communication, of the chip 600. In addition, the chip 600 may further include an input / output interface 658. The chip 600 may operate based on an operating system stored in the memory 632.

[0124] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0125] Example

[0126] The following is an example of the prediction process of the preload loss of a certain type of corroded bolt;

[0127] This embodiment provides a method for intelligent self-optimization prediction of the preload force of a corroded high-strength bolt connection, which specifically includes the following steps:

[0128] According to the bolt connection model used in the current engineering structure, the ANSYS finite element model is established, and the preload loss simulation is carried out to establish the bolt connection preload loss database. The particle swarm algorithm is used to optimize the BP neural network for training using the existing database.

[0129] After there are 1,000 finite element models in the database, determine whether the prediction accuracy of the trained neural network reaches the preset value, and perform real-time monitoring of the target bolt connection after reaching the preset value. If it is not enough, continue to optimize the database;

[0130] Use a CCD industrial camera to take pictures on site, obtain bolt connection pictures during the time period of good light every day at the target site, and transmit the pictures to a remote server. Finally, use computer vision technology including but not limited to Gaussian filtering noise reduction, binarization operation, Harris corner point detection and other technologies to obtain the geometric feature information of the bolt connection in the target component;

[0131] The obtained geometric feature information is used to compare the finite element model size in the existing database, and the target comparison similarity threshold is set to 95%. The similarity between the model and the real-time bolt connection is determined based on the comparison similarity. If the similarity is greater than or equal to 95%, the preload loss in the database is directly called as the result of the target bolt connection prediction;

[0132] At the same time, for bolt connections below the threshold, the preload loss is output as the prediction result using the trained neural network model based on the real-time geometric dimension characteristics of the bolt connections. A finite element model is established based on the geometric dimension characteristics to simulate the preload loss and finally added to the existing database.

[0133] See also Figure 4 , the figure shows the process of particle swarm optimization of BP neural network, which includes initializing parameters such as particle speed, position, inertia factor, number of iterations, and then iteratively calculating the fitness value, individual extreme value, and group extreme value of the particles, and finally finding the global optimal solution. In this process, the particle swarm optimization (PSO) algorithm can help the BP neural network to conduct a global search in the weight space, thereby avoiding falling into the local optimal solution. This means that the PSO-BP neural network is more likely to find the global optimal solution or a solution close to the global optimal solution during the training process, which improves the generalization ability and prediction accuracy of the model.

[0134] The adjustment of the weight and threshold of the BP neural network in the process can effectively improve the convergence speed and accuracy of the BP neural network, thereby improving the accuracy of the prediction of the preload force of the corroded high-strength bolt connection.

[0135] For the data structure in the overall data set, the PSO optimization algorithm has stronger learning ability. By simulating swarm intelligence, the PSO algorithm can guide the efficient optimization of network weights, which enables the BP neural network to better learn the complex patterns in the input data during the training process, especially those nonlinear mapping relationships, such as the relationship between corrosion characteristics and preload loss.

[0136] pass Figure 4 It can be seen that the advantage of the present invention is that the training process of the BP neural network is optimized by using the improved particle swarm algorithm to optimize the BP neural network, which significantly improves the training efficiency and prediction accuracy of the model, especially when dealing with nonlinear problems. This optimization method is particularly suitable for application scenarios that require high-precision predictions, such as intelligent prediction of the preload of rusted high-strength bolt connections.

[0137] See also Figure 5 , the figure shows a flow chart of obtaining the size characteristics of the target bolt connection and predicting the preload force in an embodiment of the present invention. In the figure, an industrial camera is used to take pictures, and the target bolt connection size is obtained in real time through computer vision technology.

[0138] Then, a comparison is made in the existing finite element database based on the obtained dimensions. If there is a model with a similarity greater than a preset value, the preload loss of the model is directly called as the prediction result.

[0139] Otherwise, the trained PSO-BP neural network model is used to predict the preload value, and the acquired feature information is used for finite element modeling, preload loss simulation is performed, and it is added to the database to achieve self-optimization.

[0140] In summary, the present invention provides an intelligent self-optimization prediction method and system for the preload force of corroded high-strength bolt connections. By constructing an intelligent self-optimization prediction system for the preload force of corroded high-strength bolt connections based on finite element analysis and particle swarm algorithm to optimize BP neural network, the whole process from model simulation, real-time monitoring to intelligent prediction is realized. In addition, no damage or destruction is caused to existing steel structure nodes and bolt connections during the detection process. The method is low in cost and highly practical, and can also provide a theoretical basis for the performance monitoring of existing bolt connections. The embodiment shows that by establishing a database through an accurate model, optimizing the neural network to a predetermined accuracy, combining image processing technology to collect data on-site, and establishing a high threshold comparison strategy for rapid classification and processing, it not only ensures the immediate and accurate prediction of the preload force loss of similar bolt connections, but also effectively responds to complex cases using the neural network model. At the same time, the database is continuously upgraded through self-iteration of newly added simulation data, and the accuracy and generalization ability of the prediction system are continuously improved.

[0141] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0142] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0143] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0144] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0145] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0147] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0151] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. An intelligent self-optimization prediction method for preload force of corroded high-strength bolt connections, characterized in that: The following steps are involved: Establish a finite element database of bolt connection preload loss and train a neural network-based machine learning prediction model; Determine whether the recognition accuracy of the obtained machine learning prediction model reaches a preset value; When the recognition accuracy of the machine learning prediction model reaches the preset value, the preload loss of the target bolt is monitored in real time to obtain the real-time geometric feature information of the target bolt connection; Compare the similarity between the obtained real-time geometric feature information of the target bolt connection and the finite element model in the finite element database. When the similarity is greater than or equal to a preset value, call the preload loss in the existing finite element database. When the similarity is lower than the preset value, the trained machine learning prediction model is used to output the actual bolt connection preload loss in real time.

2. The intelligent self-optimization prediction method for preload force of corroded high-strength bolt connection according to claim 1 is characterized in that: The establishment of bolt connection preload loss database is as follows: Establish a three-dimensional solid geometry model of the uncorroded bolt connection; Performing unit division and applying boundary conditions to the three-dimensional solid geometric model of the non-corroded bolt connection to obtain a first analysis model; Apply the first step load to the first analysis model and calculate the preload force of the first analysis model; Randomly define components as corrosion units based on the size of uncorroded bolt connections and the actual bolt corrosion principle; Randomly set the physical properties related to bolt connection according to the actual bolt corrosion principle; The modified unit is killed by using the birth and death unit method to obtain the second analysis model; Apply the second step load to the second analysis, calculate the preload of the second analysis model, and calculate the preload loss value of the corroded bolt; Record the relevant corroded bolt size information, physical information and preload loss value; Repeat the above steps, organize the data obtained each time, establish a bolt connection preload loss database, and train a neural network-based machine learning prediction model.

3. The intelligent self-optimization prediction method for preload force of corroded high-strength bolt connection according to claim 2 is characterized in that: The prediction model based on neural network uses particle swarm algorithm to optimize BP neural network.

4. The intelligent self-optimization prediction method for preload force of corroded high-strength bolt connection according to claim 1 is characterized in that: When the recognition accuracy of the prediction model does not reach the preset value, the finite element database is increased and the training of the neural network-based machine learning prediction model is returned.

5. The intelligent self-optimization prediction method for preload force of corroded high-strength bolt connection according to claim 1 is characterized in that: To obtain the real-time geometric feature information of bolt connections, please refer to: Performing grayscale processing on the obtained bolt connection image to obtain a grayscale image; The grayscale image is denoised by Gaussian filtering to obtain a denoised image; The minimum error method is used to determine the binarization threshold, and the denoised image is binarized to obtain a binary image. Using Harris corner point detection, identify and mark the center of the bolt head; Use edge detection technology to accurately determine the overall profile of the bolt, ensuring that the bolt boundary can still be accurately defined under the influence of corrosion; Arrange and export the geometric dimensions of each bolt connection, such as diameter, length, corrosion degree, etc.

6. The intelligent self-optimization prediction method for preload force of corroded high-strength bolt connection according to claim 1 is characterized in that: The real-time geometric feature information of the target bolt connection is compared with the similarity of the finite element model in the finite element database. When the similarity is greater than or equal to the preset value, the preload loss of the existing finite element database is called as follows: Compare the size information recorded in the database according to the geometric features of the target bolt connection, and obtain the comparison similarity; Set the target comparison similarity threshold, and judge the similarity between the model and the real-time bolt connection based on the comparison similarity; If it is higher than the threshold, the preload loss in the existing finite element database is directly called as the prediction result.

7. The intelligent self-optimization prediction method for preload force of corroded high-strength bolt connection according to claim 1 is characterized in that: The trained machine learning prediction model is used to output the actual bolt connection preload loss in real time: The geometric features and related physical information of the target bolt are normalized and regularized and input into the machine learning model for preload prediction. If the preload loss exceeds the set value, a signal is sent to notify the maintenance personnel to repair the target bolt. Otherwise, normal data will be detected in real time for archiving and backup. For the obtained preload force results, a finite element model is established in combination with the corresponding geometric features and physical information, and a finite element database is added.

8. An intelligent self-optimization prediction system for preload force of corroded high-strength bolt connections, characterized in that: include: Data module, to establish a finite element database of bolt connection preload loss and train a machine learning prediction model based on a neural network; A judgment module determines whether the recognition accuracy of the machine learning prediction model reaches a preset value; The monitoring module monitors the preload loss of the target bolt in real time and obtains the real-time geometric feature information of the target bolt connection when the recognition accuracy of the machine learning prediction model reaches the preset value; A comparison module compares the real-time geometric feature information of the target bolt connection with the similarity of the finite element model in the finite element database. When the similarity is greater than or equal to a preset value, the preload loss in the existing finite element database is called; The prediction module, when the similarity is lower than the preset value, uses the trained machine learning prediction model to output the actual bolt connection preload loss in real time.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 7.

10. A computing device, characterized in that: include: One or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for executing the method according to any one of claims 1 to 7.

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