Method and system for predicting tightness state of steel pipe bolt based on ultrasonic identification

Abstract

The invention relates to the technical field of steel pipe bolt tightness state prediction, in particular to a steel pipe bolt tightness state prediction method and system based on ultrasonic recognition. The method comprises the following steps: acquiring a multi-channel ultrasonic reflection signal, projecting the multi-channel ultrasonic reflection signal to a three-dimensional coordinate space, and constructing an ultrasonic energy attenuation distribution function; based on an ultrasonic energy attenuation distribution function, mapping all the multi-channel ultrasonic reflection signals to a four-dimensional space-time disturbance tensor field, and performing decoupling processing to obtain a disturbance energy migration rate; uniformly representing the disturbance energy offset rate as a state vector, and constructing a nonlinear time sequence recursion model to obtain a prediction state vector; and based on the prediction state vector, introducing a double-branch evaluation mechanism, and carrying out state classification and pre-tightening force regression prediction. The problems that a traditional steel pipe bolt tightness state prediction technology is inaccurate in ultrasonic signal processing and analysis, high-dimensional space disturbance characteristics caused by bolt looseness cannot be obtained, recognition accuracy is low, and stability is poor are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steel pipe bolt tightness prediction, and particularly relates to a steel pipe bolt tightness prediction method and system based on ultrasonic identification. BACKGROUND

[0002] At present, in steel structure engineering, as a connecting key component, the pre-tightening force state of a bolt directly relates to the stress performance and operation safety of the overall structure. In particular, in a steel pipe structure, once the bolt is loose, the pre-tightening force is insufficient or the connection fails, it is easy to cause serious problems such as structural deformation, node cracking and even overall instability. Traditional bolt loosening detection methods mainly include artificial torque re-inspection, mechanical knocking and listening, dye penetration, magnetic powder detection and strain gauge installation and the like. However, these methods generally have problems such as dependence on manual operation, complicated operation, low detection efficiency, strong subjectivity of data and the like, and are difficult to cover high-altitude, closed or densely arranged areas, and cannot meet the needs of real-time, accuracy and automation level of complex structure systems. In recent years, with the development of non-destructive testing technology, ultrasonic detection has been gradually applied to the state identification of bolt connection parts due to its strong penetration, high sensitivity and non-contact advantages. However, the existing ultrasonic detection is mostly focused on the identification of defects such as weld cracks and thickness corrosion, and the identification of bolt connection loosening state mostly only stays at the level of simple features such as energy change or echo time difference, and a complete physical model between ultrasonic propagation and connection state has not been established, which is difficult to cope with complex ultrasonic signal interference under different structure forms and different working environments. In addition, the existing ultrasonic signal processing methods mostly use linear or low-dimensional means such as traditional filtering, Fourier transform and wavelet analysis, which cannot extract high-dimensional spatial disturbance features formed by bolt loosening state under multi-angle, multi-direction and multi-channel interference, limiting the recognition accuracy and system stability. At the same time, most of the existing state identification methods rely on static threshold judgment or shallow neural network classification, lack dynamic modeling ability of bolt connection state in time series, cannot realize the prediction of state evolution trend, and have weak generalization ability, which is difficult to adapt to complex scene deployment.

[0003] In summary, the traditional steel pipe bolt tightness prediction technology still has the technical problems of inaccurate ultrasonic signal processing and analysis, inability to obtain high-dimensional spatial disturbance features caused by bolt loosening, low recognition accuracy and poor stability. SUMMARY

[0004] The present application provides a steel pipe bolt tightness prediction method and system based on ultrasonic identification to solve the technical problems of inaccurate ultrasonic signal processing and analysis, inability to obtain high-dimensional spatial disturbance features caused by bolt loosening, low recognition accuracy and poor stability of the traditional steel pipe bolt tightness prediction technology.

[0005] The present invention provides a method and system for predicting the tightness of steel pipe bolts based on ultrasonic identification, specifically including the following technical solutions: A method for predicting the tightness of steel pipe bolts based on ultrasonic identification includes the following steps: S1. Acquire multi-channel ultrasonic reflection signals and project them into a three-dimensional coordinate space. Quantify the energy diffusion of ultrasonic pulses as they propagate in space and construct an ultrasonic energy attenuation distribution function. Based on the ultrasonic energy attenuation distribution function, map all multi-channel ultrasonic reflection signals to a four-dimensional spatiotemporal perturbation tensor field to obtain the ultrasonic perturbation energy amplitude. Then, perform decoupling processing to obtain the perturbation energy offset rate. S2. The disturbance energy offset rate is uniformly represented as a state vector, and a nonlinear time-series recursive model is constructed to obtain the predicted state vector. Based on the predicted state vector, a dual-branch evaluation mechanism is introduced to perform state classification and preload regression prediction, thereby realizing the prediction of the tightness state of steel pipe bolts.

[0006] Preferably, S1 specifically includes: The phased array ultrasonic sensor group transmits and receives signals to the target bolt to obtain multi-channel ultrasonic reflection signals; the phased array ultrasonic sensor group is composed of ultrasonic sensing units.

[0007] Preferably, S1 specifically includes: Based on the actual deployment coordinates of the ultrasonic sensing unit and the energy diffusion coefficient, an ultrasonic energy attenuation distribution function is constructed. Based on the ultrasonic energy attenuation distribution function, all multi-channel ultrasonic reflection signals are superimposed and mapped to a four-dimensional spatiotemporal perturbation tensor field to obtain the ultrasonic perturbation energy amplitude.

[0008] Preferably, S1 specifically includes: Based on the amplitude of ultrasonic disturbance energy, a steady-state energy reference state tensor is obtained. A direction modulation factor is introduced, and combined with the signal period modulation term, the degree of deviation of the echo energy from the steady state in the main propagation direction is quantified to obtain the disturbance energy offset rate.

[0009] Preferably, S2 specifically includes: Based on the state vector, a two-level nonlinear transformation is performed, and a nonlinear time-series recursive model is constructed by combining the nonlinear energy term of the state perturbation. The predicted state vector is obtained by mapping through nonlinear recursive rules.

[0010] Preferably, S2 specifically includes: The double-branch evaluation mechanism includes a classification branch and a regression branch; in the classification branch, based on the predicted state vector, state label determination is performed through probability distribution matching; in the regression branch, a pretightening force prediction function is constructed through a nonlinear weighted residual fitting term and a gradient normalization term of a state evolution tensor residual, combined with the state vector and the predicted state vector, to obtain a predicted pretightening force value of the bolt.

[0011] Preferably, S2 specifically includes: In the construction process of the pretightening force prediction function, a residual term is generated based on the state vector and the predicted state vector, a nonlinear weighted residual fitting term is constructed combined with a nonlinear exponential compression term, and a change rate of the state vector is combined to predict the pretightening force value of the bolt.

[0012] A steel pipe bolt tightness state prediction system based on ultrasonic recognition, comprising the following parts: A signal sensing module, a signal projection mapping module, an intelligent decoupling module, a state vector construction module, a state vector prediction module, and a state classification prediction evaluation module; The signal sensing module emits and receives signals to the target bolt through the phased array ultrasonic sensor group, obtains multi-channel ultrasonic reflection signals, and sends the multi-channel ultrasonic reflection signals to the signal projection mapping module; The signal projection mapping module projects the multi-channel ultrasonic reflection signals to a three-dimensional coordinate space, defines an ultrasonic energy attenuation distribution function in the three-dimensional coordinate space, and based on the ultrasonic energy attenuation distribution function, superimposes and maps all multi-channel ultrasonic reflection signals to a four-dimensional space-time disturbance tensor field to obtain an ultrasonic disturbance energy amplitude; the ultrasonic disturbance energy amplitude is sent to the intelligent decoupling module; The intelligent decoupling module obtains a stationary energy benchmark state tensor based on the ultrasonic disturbance energy amplitude, introduces a direction modulation factor, combines a signal period modulation term, quantifies the degree of echo energy deviation from the stable state in the main propagation direction, and obtains a disturbance energy deviation rate; the disturbance energy deviation rate is sent to the state vector construction module; The state vector construction module unifies the disturbance energy deviation rates in different directions as a state vector, takes the state vector as the basis for judging whether the current connection state is abnormal, and sends it to the state vector prediction module and the state classification prediction evaluation module; The state vector prediction module designs a nonlinear time series recursive model based on the state vector to map the dynamic evolution of the state vector and obtain a predicted state vector; the predicted state vector is sent to the state classification prediction evaluation module; The state classification prediction evaluation module introduces a double-branch evaluation mechanism based on the state vector and the predicted state vector to perform state classification and pretightening force regression prediction, and realizes steel pipe bolt tightness state prediction.

[0013] The beneficial effects of the technical solutions of the present application are: 1. By constructing a four-dimensional space-time disturbance tensor field and introducing a stationary energy benchmark state tensor, a disturbance energy offset rate is generated to quantify the offset influence of the bolt loosening state on the ultrasonic energy propagation path in a specific direction, thereby enhancing the sensitivity to abnormal propagation patterns at the data feature level, realizing the ability to automatically extract directional disturbance changes from continuous ultrasonic echo fields, and enhancing the robustness of state perception.

[0014] 2. The disturbance energy offset rate is constructed as a multi-dimensional time state vector, and a nonlinear time sequence recursive model with time dynamics and state memory capability is constructed by a double-layer nonlinear transformation and a nonlinear energy item of the state disturbance, which can capture the continuous evolution trend of the bolt connection state at multiple time points and spatial directions, breaking through the limitation of traditional static recognition models that can only make point-in-time judgments, and having the ability to provide early warning for structural state changes.

[0015] 3. A double-branch evaluation mechanism integrating static residual and dynamic gradient is constructed, the classification branch is matched with the conditional probability of the probability density function of each state category pre-trained, and the state category with the maximum posterior probability is selected as the current bolt state label according to the Bayesian decision criterion, which has good interpretability and stability; the regression branch adopts a pretightening force prediction function constructed based on the disturbance prediction residual and the state change rate double variables, which couples the physical residual characteristics and the prediction trend into a mathematical model, not only improving the prediction accuracy, but also quantifying the bolt connection loosening degree, directly serving the structural safety evaluation and maintenance decision. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A steel pipe bolt loosening state prediction system structure diagram based on ultrasonic identification according to the present application; Figure 2 A steel pipe bolt loosening state prediction method flow chart based on ultrasonic identification according to the present application. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0019] The application provides a steel pipe bolt tightness state prediction method and system based on ultrasonic recognition.

[0020] Referring to the drawings Figure 1 It shows a steel pipe bolt tightness state prediction system structure diagram provided by an embodiment of the application, and the system comprises the following parts: The signal sensing module, the signal projection mapping module, the intelligent decoupling module, the state vector construction module, the state vector prediction module and the state classification prediction evaluation module; The signal sensing module transmits and receives signals to the target bolt through the phased array ultrasonic sensor group, obtains multi-channel ultrasonic reflection signals, and sends the multi-channel ultrasonic reflection signals to the signal projection mapping module; The signal projection mapping module projects the multi-channel ultrasonic reflection signals to a three-dimensional coordinate space, defines an ultrasonic energy attenuation distribution function in the three-dimensional coordinate space, and superimposes and maps all the multi-channel ultrasonic reflection signals to a four-dimensional space-time disturbance tensor field based on the ultrasonic energy attenuation distribution function to obtain an ultrasonic disturbance energy amplitude; the ultrasonic disturbance energy amplitude is sent to the intelligent decoupling module; The intelligent decoupling module obtains a stationary energy benchmark state tensor based on the ultrasonic disturbance energy amplitude, introduces a direction modulation factor, combines a signal period modulation term, quantifies the degree of echo energy deviation from the stable state in the main propagation direction, and obtains a disturbance energy deviation rate; the disturbance energy deviation rate is sent to the state vector construction module; The state vector construction module uniformly represents the disturbance energy deviation rates in different directions as a state vector, takes the state vector as the basis for judging whether the current connection state is abnormal, and sends the state vector to the state vector prediction module and the state classification prediction evaluation module; The state vector prediction module designs a nonlinear time series recursive model based on the state vector to map the dynamic evolution of the state vector, obtains a next-time state vector, i.e., a predicted state vector, through a nonlinear recursive rule mapping, and sends the predicted state vector to the state classification prediction evaluation module; The state classification prediction evaluation module introduces a double-branch evaluation mechanism based on the state vector and the predicted state vector, performs state classification and pretightness regression prediction, and realizes steel pipe bolt tightness state prediction.

[0021] Referring to the drawings Figure 2 It shows a steel pipe bolt tightness state prediction method flow chart provided by an embodiment of the application, and the method comprises the following steps: S1. Acquire multi-channel ultrasonic reflection signals and project them onto a three-dimensional coordinate space. Quantify the energy diffusion of ultrasonic pulses as they propagate in space and construct an ultrasonic energy attenuation distribution function. Based on the ultrasonic energy attenuation distribution function, map all multi-channel ultrasonic reflection signals onto a four-dimensional spatiotemporal perturbation tensor field and perform decoupling processing to obtain the perturbation energy offset rate. The target bolt is subjected to signal transmission and reception using a phased array ultrasonic sensor array, resulting in multi-channel ultrasonic reflection signals, denoted as... , indicating the first In the ultrasonic sensing unit, the first For the transmit-receive channel combination at time The acquired ultrasonic reflection signal; the phased array ultrasonic sensor group is composed of Composed of several ultrasonic sensing units, deployed by professional technicians near key connection nodes of the steel pipe; furthermore, to give the subsequent analysis physical spatial meaning, the multi-channel ultrasonic reflection signals are projected into a three-dimensional coordinate space to form continuous propagation. Specifically, an ultrasonic energy attenuation distribution function is defined in the three-dimensional coordinate space to simulate the energy diffusion caused by factors such as medium loss, contact interface reflection, and structural geometric transformations when the ultrasonic pulse propagates in space, as defined below: , in, It is the ultrasonic energy attenuation distribution function, which is a Gaussian distribution function in three-dimensional space. It is used to describe how the influence of ultrasonic energy on each point decreases with distance when ultrasonic waves propagate in space. At spatial coordinate points Above, by the first In the ultrasonic sensing unit, the first The influence factor of ultrasonic energy formed by the combination of transmitting and receiving channels; Indicates the first The actual layout coordinates of each ultrasonic sensing unit; Indicates the first The energy diffusion coefficients in each channel direction are used to control the diffusion radius of the Gaussian distribution function. They reflect the scattering characteristics of directional ultrasonic energy during spatial propagation and are determined based on the actual application scenario and beam broadening model. The reference value range is [value range missing]. ; Based on the aforementioned ultrasonic energy attenuation distribution function, all multi-channel ultrasonic reflection signals are superimposed and mapped onto a four-dimensional spatiotemporal perturbation tensor field. It is represented as: , in, Indicates a point in space and time The amplitude of ultrasonic disturbance energy at the location is used as the raw input for subsequent energy shift feature extraction; Indicates the number of transmit-receive channel combinations; After constructing the four-dimensional spatiotemporal perturbation tensor field, to reveal the spatial path offset of the perturbation energy caused by bolt loosening, it is necessary to decouple the perturbation offset caused by the bolt loosening state on the propagation path from the four-dimensional spatiotemporal perturbation tensor field. By introducing the perturbation energy offset rate, the degree to which the echo energy deviates from the steady state in a certain main transmission direction can be quantified, reflecting the influence of the bolt joint interface state on the ultrasonic propagation characteristics in that direction. The specific implementation formula is as follows: , in, At the main propagation direction angle Previous moment The rate of deviation of the ultrasonic energy field relative to the steady-state energy field of the structure, i.e., the perturbation energy deviation rate; It is a stationary energy reference state tensor, obtained by applying the state at a point in space. Ultrasonic disturbance energy amplitude at all times The average of the integral over time is obtained; It is the main propagation direction angle, representing the configuration angle of the transmission channel, and is the incident center angle of the phased array ultrasonic sensor group; This is the direction modulation factor, used to adjust the projection weights of different main propagation directions in the integral, reflecting direction selectivity. It is obtained through ultrasonic simulation, experimental fitting, etc., and the reference value range is [value range missing]. ; It is one complete cycle of ultrasound, and is the reciprocal of the frequency of the emitted signal; It is a signal periodic modulation term used to extract response features of periodic disturbances; It is a very small positive number, used to prevent division by zero, and can take values ​​of... ; It is the difference between the current four-dimensional spacetime perturbation tensor field and the stationary energy reference state tensor, i.e., the perturbation term; By adjusting the directional weights for different main propagation directions, the integral is made to focus more on the perturbation distribution along the specified direction. Used to control the accuracy of direction selection; By integrating in three-dimensional space, the total distribution of perturbation energy in a certain direction in space is obtained; the denominator represents the total amount of the stationary energy reference state tensor, which serves as a normalization factor to make the perturbation energy offset rate comparable, thereby eliminating the absolute difference in energy offset between different structural scales or measurement points.

[0022] S2. The disturbance energy offset rate is uniformly represented as a state vector, and a nonlinear time-series recursive model is constructed to obtain the predicted state vector. Based on the predicted state vector, a dual-branch evaluation mechanism is introduced to perform state classification and preload regression prediction, thereby realizing the prediction of the tightness state of steel pipe bolts.

[0023] The perturbation energy shift rate in different directions is uniformly represented as a state vector. ,Right now: , in, Indicates the angle of the main propagation direction. Previous moment The perturbation energy shift rate; The number of main propagation directions selected is determined based on specific application requirements, such as... Then you can select once every 45 degrees; This indicates transpose; the state vector reflects the multi-directional energy perturbation distribution at a certain moment and is the basis for determining whether there is an anomaly in the current connection state; Furthermore, to predict the evolution trajectory of the aforementioned state vector over time, a nonlinear temporal recursive model is constructed to map the dynamic evolution of the state vector. The nonlinear temporal recursive model obtains the state vector at the next time step through a nonlinear recursive rule, thus yielding the predicted state vector. The specific implementation formula is as follows: , in, The next moment The state vector, i.e. the predicted state vector; It is the first-level linear transformation weight matrix of the perturbation state mapping, used to transform the current state vector Mapping to an intermediate feature space, the coupling relationships between perturbation features are extracted and learned through backpropagation. The first-layer linear transformation weight matrix is ​​then used. The reference value range for each element in the table is: ; It is a perturbation offset vector (bias term), which reflects the basic perturbation offset in different directions. It is obtained through backpropagation learning. The reference value range for each element in the table is: ; It is the second-layer perturbation response synthesis weight matrix, obtained through backpropagation learning. The reference value range for each element is: ; It is a perturbation energy convergence adjustment factor, used to adjust the total perturbation energy. The nonlinear feedback strength, analogous to the damping factor, has a reference range of values. ; It is the main term for predicting the state response in the direction of the disturbance; It is the nonlinear energy term of the state perturbation. By taking the logarithm of the square of the L2 norm of the state vector, the amplification of large energy perturbations is suppressed to enhance robustness. The above backpropagation learning method is a well-known technique in the art and will not be described in detail here. Furthermore, to achieve final identification and quantification of bolt states, a dual-branch evaluation mechanism is introduced for state classification and preload regression prediction. This mechanism includes a classification branch and a regression branch. In the classification branch, existing probability distribution matching is used to determine the state label. Specifically, the predicted state vector is input into the probability density function of each trained state category to calculate its likelihood value (conditional probability) under each category. This is then combined with the prior probability value of the corresponding category, and the posterior probability is calculated using Bayes' theorem. Finally, the state category with the highest posterior probability is selected as the current bolt state label based on the Bayesian decision criterion, thus achieving multi-class classification based on probability matching. The methods used in the classification branch are all well-known techniques to those skilled in the art and will not be elaborated upon here. In the regression branch, the following preload prediction function is constructed using the physical residual model: , in, It is the predicted preload force of the bolt at the current moment; ,Right now It is in the 1st One main propagation direction angle The perturbation energy offset rate on; It is in the One main propagation direction angle Forecast time The perturbation energy shift rate; These are the main term fitting coefficients, calibrated using the finite element method, with a reference range of values. Finite element physical calibration is a well-known technique in the art and will not be elaborated here. This is the predicted state weight adjustment factor, used to control the relative influence between the current state and the predicted state. It is determined through cross-validation tuning, and the reference value range is [value range missing]. The cross-validation optimization method is a well-known technique in the art and will not be described in detail here. It is the exponential compression modulation factor, used to control the exponential suppression strength of the predicted state. It is determined by the gain response fitting method, and the reference value range is [value missing]. Gain response fitting is a well-known technique in the art and will not be elaborated here. This is the secondary fitting coefficient, used to control the contribution of the disturbance gradient to the preload. It is determined based on expert experience, and the reference range is [range missing]. ; It is the first-order difference of a vector (discrete gradient), used to measure the rate of change of the state vector in the directional dimension; It is the Euclidean norm, used to calculate the total magnitude of the change in the state vector; It is the residual term, representing the difference between the current time and the next time (prediction) at the nth time step. The squared residuals between the perturbation energy offset rates in each main propagation direction; It is a non-linear exponential compression term, used to adjust the weight of the above residual term when the predicted value is large; It is a nonlinear weighted residual fitting term that can reflect the "positional deviation" between the current state of the structure and its perturbation trend, focusing on static deviation and energy amplitude; It is the gradient normalization term of the residual of the state evolution tensor, which can reflect the rate of change in spatial perturbations and focuses on "dynamic instability".

[0024] In summary, a method and system for predicting the tightness of steel pipe bolts based on ultrasonic recognition has been developed.

[0025] The order of the embodiments is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0026] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0027] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for predicting the tightness of steel pipe bolts based on ultrasonic recognition, characterized in that, Includes the following steps: S1. Acquire multi-channel ultrasonic reflection signals and project them into a three-dimensional coordinate space. Quantify the energy diffusion of ultrasonic pulses as they propagate in space and construct an ultrasonic energy attenuation distribution function. Based on the ultrasonic energy attenuation distribution function, map all multi-channel ultrasonic reflection signals to a four-dimensional spatiotemporal perturbation tensor field to obtain the ultrasonic perturbation energy amplitude. Then, perform decoupling processing to obtain the perturbation energy offset rate. S2. The disturbance energy offset rate is uniformly represented as a state vector, and a nonlinear time-series recursive model is constructed to obtain the predicted state vector. Based on the predicted state vector, a dual-branch evaluation mechanism is introduced to perform state classification and preload regression prediction, thereby realizing the prediction of the tightness state of steel pipe bolts.

2. The method for predicting the tightness of steel pipe bolts based on ultrasonic recognition according to claim 1, characterized in that, S1 specifically includes: The phased array ultrasonic sensor group transmits and receives signals to the target bolt to obtain multi-channel ultrasonic reflection signals; the phased array ultrasonic sensor group is composed of ultrasonic sensing units.

3. The method for predicting the tightness of steel pipe bolts based on ultrasonic recognition according to claim 2, characterized in that, S1 specifically includes: Based on the actual deployment coordinates of the ultrasonic sensing unit and the energy diffusion coefficient, an ultrasonic energy attenuation distribution function is constructed. Based on the ultrasonic energy attenuation distribution function, all multi-channel ultrasonic reflection signals are superimposed and mapped to a four-dimensional spatiotemporal perturbation tensor field to obtain the ultrasonic perturbation energy amplitude.

4. The method for predicting the tightness of steel pipe bolts based on ultrasonic recognition according to claim 3, characterized in that, S1 specifically includes: Based on the amplitude of ultrasonic disturbance energy, a steady-state energy reference state tensor is obtained. A direction modulation factor is introduced, and combined with the signal period modulation term, the degree of deviation of the echo energy from the steady state in the main propagation direction is quantified to obtain the disturbance energy offset rate.

5. The method for predicting the tightness of steel pipe bolts based on ultrasonic recognition according to claim 1, characterized in that, S2 specifically includes: Based on the state vector, a two-level nonlinear transformation is performed, and a nonlinear time-series recursive model is constructed by combining the nonlinear energy term of the state perturbation. The predicted state vector is obtained by mapping through nonlinear recursive rules.

6. The method for predicting the tightness of steel pipe bolts based on ultrasonic recognition according to claim 5, characterized in that, S2 specifically includes: The dual-branch evaluation mechanism includes a classification branch and a regression branch. In the classification branch, the state label is determined based on the predicted state vector through probability distribution matching. In the regression branch, the preload prediction function is constructed by combining the state vector and the predicted state vector through the nonlinear weighted residual fitting term and the gradient normalization term of the state evolution tensor residual, and the predicted preload value of the bolt is obtained.

7. The method for predicting the tightness of steel pipe bolts based on ultrasonic recognition according to claim 6, characterized in that, S2 specifically includes: In the process of constructing the preload prediction function, a residual term is generated based on the state vector and the predicted state vector. A nonlinear weighted residual fitting term is constructed by combining the nonlinear exponential compression term and the rate of change of the state vector. The preload value of the bolt is then predicted.

8. A steel pipe bolt tightness state prediction system based on ultrasonic recognition, applied to the steel pipe bolt tightness state prediction method based on ultrasonic recognition as described in claim 1, characterized in that, Includes the following parts: The system comprises a signal sensing module, a signal projection mapping module, an intelligent decoupling module, a state vector construction module, a state vector prediction module, and a state classification, prediction, and evaluation module. The signal sensing module transmits and receives signals from the target bolt through a phased array ultrasonic sensor group, obtains multi-channel ultrasonic reflection signals, and sends the multi-channel ultrasonic reflection signals to the signal projection mapping module. The signal projection mapping module projects multi-channel ultrasonic reflection signals onto a three-dimensional coordinate space, defines an ultrasonic energy attenuation distribution function in the three-dimensional coordinate space, and maps all multi-channel ultrasonic reflection signals onto a four-dimensional spatiotemporal perturbation tensor field based on the ultrasonic energy attenuation distribution function to obtain the ultrasonic perturbation energy amplitude; the ultrasonic perturbation energy amplitude is then sent to the intelligent decoupling module. The intelligent decoupling module obtains the steady-state energy reference tensor based on the amplitude of ultrasonic disturbance energy, and introduces a direction modulation factor. Combined with the signal periodic modulation term, it quantifies the degree to which the echo energy in the main propagation direction deviates from the steady state, and obtains the disturbance energy offset rate. The disturbance energy offset rate is then sent to the state vector construction module. The state vector construction module uniformly represents the perturbation energy offset rate in different directions as a state vector, uses the state vector as the basis for judging whether there is an anomaly in the current connection state, and sends it to the state vector prediction module and the state classification prediction and evaluation module. The state vector prediction module designs a nonlinear time-series recursive model based on the state vector to map the dynamic evolution of the state vector and obtain the predicted state vector; the predicted state vector is then sent to the state classification prediction and evaluation module. The state classification prediction and evaluation module, based on the state vector and the predicted state vector, introduces a dual-branch evaluation mechanism to perform state classification and preload regression prediction, thereby realizing the prediction of the tightness state of steel pipe bolts.

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