Nondestructive testing optimization method based on safety status assessment accuracy feedback of explosion-proof equipment

Abstract

The present invention relates to the technical field of industrial safety and non-destructive testing, and specifically discloses a non-destructive testing optimization method based on accuracy feedback of explosion-proof equipment safety status assessment. By real-time monitoring of the beam steering angle and local temperature gradient of the explosion-proof equipment, key data is obtained using a high-precision sensor network, and advanced data analysis technologies such as clustering algorithms and fast Fourier transforms are applied to calculate abnormal eigenvalues of the beam steering angle and local temperature gradient eigenvalues to evaluate the stability of ultrasonic propagation and the consistency of the internal structure of the equipment. These eigenvalues are combined to construct a eigenvector, which is input into a support vector machine model and outputs a safety status score of the equipment. This score is used to determine whether the current detection process is in an unstable state. Once the system is determined to be unstable, the beam steering angle will be automatically adjusted to cover the abnormal area, and the scanning density of the abnormal area displayed by the local temperature gradient will be increased to ensure more detailed detection of potential problems.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial safety and non-destructive testing, and in particular to a non-destructive testing optimization method based on safety status assessment accuracy feedback of explosion-proof equipment. Background Art

[0002] With the increasing demands for safety and reliability in industrial production, explosion-proof equipment, as a key safety protection facility, plays an indispensable role in industries such as the chemical, oil, and natural gas industries. Over long-term operation, these devices may suffer structural damage or performance degradation due to factors such as material aging, environmental corrosion, or improper operation, thus affecting their normal function and safety. Traditional non-destructive testing methods rely primarily on regular manual inspections and limited sensor monitoring. While this method can detect surface defects in equipment to a certain extent, its ability to identify subtle internal changes and potential hidden dangers is limited. Therefore, how to achieve real-time and accurate monitoring of the status of explosion-proof equipment and take timely preventive measures to avoid major accidents has become a critical issue that needs to be addressed.

[0003] The existing technology has the following deficiencies:

[0004] When the equipment being tested has a complex geometry or internal structure, ensuring comprehensive coverage of all critical areas by nondestructive testing becomes particularly difficult, as traditional testing methods may not be able to effectively address issues such as signal attenuation, scattering, and poor probe contact in complex pathways. For example, deep holes, narrow passages, or uneven thicknesses in equipment can cause abnormal propagation of ultrasonic and other detection signals, creating blind spots for detection. At the same time, interfaces between different internal materials can also interfere with signals, affecting the accuracy of defect identification. This invention aims to significantly improve the accuracy and response speed of equipment monitoring by integrating advanced sensing technology and intelligent data analysis methods, providing a more solid guarantee for industrial safety. Summary of the Invention

[0005] The purpose of the present invention is to provide a non-destructive testing optimization method based on safety status assessment accuracy feedback of explosion-proof equipment to solve the problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The nondestructive testing optimization method based on the safety status assessment accuracy feedback of explosion-proof equipment includes the following steps:

[0008] S1: During the monitoring period of the safety status of explosion-proof equipment, real-time monitoring of the beam steering angle and local temperature gradient;

[0009] The beam steering angle is used to dynamically adjust the direction of the ultrasonic beam to adapt to complex geometric shapes, and the local temperature gradient is measured by infrared thermal imaging technology to reflect the internal material integrity.

[0010] S2: Determine the abnormal characteristic value of the beam steering angle based on the degree of change in the beam steering angle and evaluate the stability of ultrasonic propagation;

[0011] S3: Analyze the variation of local temperature gradient, calculate the characteristic value of local temperature gradient, and evaluate the consistency of the internal structure of explosion-proof equipment;

[0012] S4: Comprehensively analyze the abnormal characteristics of the beam steering angle and the characteristic values of the local temperature gradient to determine whether the current detection process is in an unstable state;

[0013] S5: If it is determined to be in an unstable state, the system automatically adjusts the beam steering angle and increases the scanning density for abnormal areas displayed by local temperature gradients.

[0014] As a further solution of the present invention, determining the abnormal characteristic value of the beam steering angle according to the degree of change of the beam steering angle and evaluating the stability of ultrasonic propagation specifically includes:

[0015] During the monitoring period of the safety status of the explosion-proof equipment, the beam steering angle data is obtained in a time series, and the abnormal characteristic value of the beam steering angle is calculated according to the degree of change of the beam steering angle. It is determined whether the abnormal characteristic value of the beam steering angle is greater than or equal to a preset threshold. If so, the ultrasonic propagation is stable; if not, the ultrasonic propagation is unstable.

[0016] As a further solution of the present invention: the process of obtaining the abnormal characteristic value of the beam steering angle is:

[0017] Obtain the beam steering angle dataset of explosion-proof equipment during the monitoring period;

[0018] Choose the number of clusters And initialize For each point in the beam steering angle dataset, calculate the Euclidean distance between it and each centroid. And assign it to the nearest cluster, update the centroid of each cluster to make it equal to the average position of all points in the corresponding cluster;

[0019] Repeat the assignment and update steps until the centroid reaches the preset maximum number of iterations;

[0020] Determine whether the Euclidean distance between each point in the data point set and the centroid of the cluster to which it belongs is greater than or equal to a preset threshold. If so, it is determined to be an outlier. Calculate the average Euclidean distance of the outliers in the data point set to obtain the beam steering angle anomaly feature value.

[0021] As a further solution of the present invention: analyzing the variation range of the local temperature gradient, calculating the characteristic value of the local temperature gradient, and evaluating the consistency of the internal structure of the explosion-proof equipment specifically include:

[0022] During the monitoring period of the safety status of the explosion-proof equipment, the temperature data inside the explosion-proof equipment is obtained in a time series, and the local temperature gradient characteristic value is calculated according to the change amplitude of the local temperature gradient. It is judged whether the local temperature gradient characteristic value is greater than or equal to a preset threshold. If so, the internal structure of the explosion-proof equipment is inconsistent, and if not, the internal structure of the explosion-proof equipment is consistent.

[0023] As a further solution of the present invention: the process of obtaining the local temperature gradient characteristic value is:

[0024] Obtain temperature data sets of explosion-proof equipment during the monitoring period ,in, represents the temperature dataset, represents the number of samples in the dataset, Indicates the time series Temperature value at a time point;

[0025] The local temperature gradient is obtained by calculating the temperature difference between adjacent time points and integrating all temperature differences into a difference sequence;

[0026] Fast Fourier transform is applied to the local temperature gradient to obtain the frequency domain representation, and the ratio of the sum of squares of all non-zero frequency components to the standard deviation is calculated to obtain the local temperature gradient eigenvalue.

[0027] As a further solution of the present invention, the comprehensive analysis of the abnormal characteristics of the beam steering angle and the characteristic values of the local temperature gradient specifically includes:

[0028] The abnormal characteristics of the beam steering angle and the characteristic values of the local temperature gradient are obtained, and the abnormal characteristics of the beam steering angle and the characteristic values of the local temperature gradient are constructed into a comprehensive feature vector as the input of the machine learning model. The output of the model is the safety status score of the explosion-proof equipment. The machine learning model is a support vector machine model.

[0029] As a further solution of the present invention: the process of obtaining the security status score is as follows:

[0030] The abnormal characteristics of beam steering angles, local temperature gradient characteristic values and safety status scores of historical explosion-proof equipment during the historical monitoring period are obtained as training data sets, and the support vector machine model is trained with minimizing the error between the predicted safety status score and the actual safety status score as the training goal. The support vector machine model is trained, and the comprehensive feature vector of the current explosion-proof equipment is input into the trained support vector machine model according to the trained support vector machine model, and the model will output an accurate safety status score.

[0031] As a further solution of the present invention: the determining whether the current detection process is in an unstable state specifically includes:

[0032] Determine whether the safety status score of the current detection process of the explosion-proof equipment is greater than or equal to a preset threshold. If so, the current detection process is in an unstable state; if not, the current detection process is in a stable state.

[0033] As a further solution of the present invention, the system automatically adjusts the beam steering angle and increases the scanning density for abnormal areas indicated by local temperature gradients, specifically including:

[0034] If the safety status assessment of the explosion-proof equipment determines that it is unstable, for abnormal beam steering angles, the system determines the angle range that needs to be adjusted based on the abnormal characteristic value of the beam steering angle and changes the direction of the beam by controlling the mechanical structure;

[0035] The system first determines whether the local temperature gradient characteristic value is greater than or equal to the preset threshold. If so, the corresponding area is marked. For the marked area, the system adjusts the scanning strategy to increase the scanning frequency of these areas to the original times, and is an integer greater than 1.

[0036] Beneficial effects of the present invention:

[0037] (1) The present invention integrates the technology of real-time monitoring of beam steering angle and local temperature gradient, and uses advanced data analysis methods such as clustering algorithm and fast Fourier transform to achieve precise monitoring and in-depth analysis of the operating status of explosion-proof equipment. Specifically, a high-precision angle sensor is used to continuously record the changes in the beam steering angle, and the abnormal characteristic value of the beam steering angle is calculated by clustering algorithm to evaluate the stability of ultrasonic propagation; at the same time, a temperature sensor network distributed at key positions of the equipment is used to obtain local temperature data, and the local temperature gradient characteristic value is calculated after fast Fourier transform processing to evaluate the consistency of the internal structure of the equipment. This method not only significantly improves the detection sensitivity and accuracy of potential faults, but also can identify subtle changes that may affect the safety of the equipment at an early stage, thereby providing a scientific basis for timely preventive measures. When the system detects an abnormal situation, it can automatically adjust the beam steering angle to cover the relevant area and increase the scanning density to ensure a more detailed inspection of the hidden danger area. This precise and dynamic safety hazard detection mechanism not only greatly improves the safety and reliability of the operation of explosion-proof equipment, but also provides strong support for optimizing maintenance strategies and reducing operating costs, reflecting the outstanding contribution of the present invention to improving the level of industrial safety.

[0038] (2) If the current detection process is determined to be in an unstable state by comprehensively analyzing the abnormal characteristic values of the beam steering angle and the characteristic values of the local temperature gradient, the present invention can intelligently adjust the beam steering angle to accurately cover the abnormal area and dynamically increase the scanning density for the abnormal area displayed by the local temperature gradient. This adaptive adjustment mechanism not only ensures a more detailed and comprehensive detection of potential problem areas, but also significantly improves the response speed and adaptability of the overall monitoring system. Based on real-time monitoring data, the system can dynamically optimize the detection strategy, which not only ensures the focus on key risk areas, but also avoids unnecessary waste of resources, and realizes efficient, flexible and accurate equipment monitoring. In addition, with the accumulation of more historical data and the continuous optimization of machine learning models (such as support vector machines), the present invention supports the self-learning and improvement of the system, thereby continuously improving the prediction accuracy and reliability. This feature not only enhances the safety and operating efficiency of explosion-proof equipment, but also provides a solid foundation for future expansion and upgrading, making the present invention show wide applicability and excellent foresight in practical applications, becoming an important innovative means to ensure industrial safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further described below with reference to the accompanying drawings.

[0040] Figure 1 This is a flowchart of the specific steps of the non-destructive testing optimization method based on the safety status assessment accuracy feedback of explosion-proof equipment of the present invention; DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0042] See also Figure 1 As shown, the present invention is a non-destructive testing optimization method based on the safety status assessment accuracy feedback of explosion-proof equipment, comprising the following steps:

[0043] S1: During the monitoring period of the safety status of explosion-proof equipment, real-time monitoring of the beam steering angle and local temperature gradient;

[0044] The beam steering angle is used to dynamically adjust the direction of the ultrasonic beam to adapt to complex geometric shapes, and the local temperature gradient is measured by infrared thermal imaging technology to reflect the internal material integrity.

[0045] S2: Determine the abnormal characteristic value of the beam steering angle based on the degree of change in the beam steering angle and evaluate the stability of ultrasonic propagation;

[0046] S3: Analyze the variation of local temperature gradient, calculate the characteristic value of local temperature gradient, and evaluate the consistency of the internal structure of explosion-proof equipment;

[0047] S4: Comprehensively analyze the abnormal characteristics of the beam steering angle and the characteristic values of the local temperature gradient to determine whether the current detection process is in an unstable state;

[0048] S5: If it is determined to be in an unstable state, the system automatically adjusts the beam steering angle and increases the scanning density for abnormal areas displayed by local temperature gradients.

[0049] In S1, during the monitoring period of the explosion-proof equipment safety status, the beam steering angle and local temperature gradient are monitored in real time, including:

[0050] During the safety status monitoring cycle of explosion-proof equipment, real-time monitoring of beam steering angles and local temperature gradients is a critical step in ensuring safe operation. First, to collect beam steering angle data, a high-precision angle sensor installed on the explosion-proof equipment continuously records the current beam steering angle at a fixed interval (e.g., once per second). The angle sensor provides precise angle measurements, ensuring that even the slightest angle changes are captured. This data is then transmitted in real time to a central monitoring system for storage and subsequent analysis. To ensure data accuracy and reliability, the system also performs preliminary quality checks on the collected data to eliminate abnormal readings.

[0051] Meanwhile, local temperature gradients are monitored using a network of temperature sensors distributed at key locations on explosion-proof equipment. These sensors collect ambient temperature information at preset intervals (e.g., once per minute) and calculate the temperature difference between adjacent time points, thereby reflecting the temperature trend within the local area. To accurately capture rapidly changing temperature patterns, certain key areas may be equipped with a higher density of sensors or adopt a more frequent data collection strategy. All collected temperature data is also transmitted to a central monitoring system, where it is processed using a specially designed algorithm to calculate the characteristic values of local temperature gradients, enabling the timely identification of potential safety hazards. This process not only helps understand the operating status of the equipment but also provides an important basis for subsequent risk assessments.

[0052] In S2, based on the degree of change in the beam steering angle, the abnormal characteristic value of the beam steering angle is determined to evaluate the stability of ultrasonic propagation, including:

[0053] During the monitoring period of the safety status of the explosion-proof equipment, the beam steering angle data is obtained in a time series, and the abnormal characteristic value of the beam steering angle is calculated according to the degree of change of the beam steering angle. It is determined whether the abnormal characteristic value of the beam steering angle is greater than or equal to a preset threshold. If so, the ultrasonic propagation is stable; if not, the ultrasonic propagation is unstable.

[0054] The process of obtaining the abnormal characteristic value of the beam steering angle is as follows:

[0055] Obtain the beam steering angle dataset of explosion-proof equipment during the monitoring period ,in, represents the beam steering angle dataset, represents the number of samples in the dataset, Indicates the time series The number of beam steering angles at each time point;

[0056] Choose the number of clusters And initialize centroid ,in, represents the desired number of clusters, Indicates the The initial centroids of the clusters;

[0057] For each point in the beam steering angle dataset, calculate the Euclidean distance between it and each centroid And assign it to the closest cluster, update the centroid of each cluster so that it is equal to the average position of all points in the corresponding cluster, and the update formula is: ;

[0058] Where, Indicates the A set of data points in a cluster, Represents a cluster The number of data points in , Indicates the beam steering angles, The number of beam steering angles;

[0059] Repeat the assignment and update steps until the centroid reaches the preset maximum number of iterations;

[0060] Determine whether the Euclidean distance between each point in the data point set and the centroid of the cluster to which it belongs is greater than or equal to a preset threshold. If so, it is determined to be an outlier. Calculate the average Euclidean distance of the outliers in the data point set to obtain the beam steering angle anomaly feature value.

[0061] It should be noted that: by real-time monitoring of the beam steering angle data of explosion-proof equipment during the monitoring period, and applying clustering algorithms to analyze these data to calculate the abnormal characteristic value of the beam steering angle, the stability of ultrasonic propagation is evaluated. Specifically, by quantitatively analyzing the degree of change in the beam steering angle, abnormal points that deviate from the normal range are identified, and a comprehensive abnormal characteristic value is calculated based on these abnormal points. If the abnormal characteristic value of the beam steering angle is greater than or equal to the preset threshold, it indicates that there may be an instability problem in ultrasonic propagation; otherwise, the ultrasonic propagation is considered stable. The technical effect of this method is to provide an effective means to dynamically monitor and evaluate the safety and reliability of the operation of explosion-proof equipment. Its innovation lies in the combination of time series data analysis and clustering technology, which can accurately capture subtle anomalies in the changes in the beam steering angle, providing strong support for the timely discovery of potential safety hazards.

[0062] In S3, the variation range of the local temperature gradient is analyzed, the characteristic value of the local temperature gradient is calculated, and the consistency of the internal structure of the explosion-proof equipment is evaluated, including:

[0063] During the monitoring period of the safety status of the explosion-proof equipment, the temperature data inside the explosion-proof equipment is obtained in a time series, and the local temperature gradient characteristic value is calculated according to the change amplitude of the local temperature gradient. It is judged whether the local temperature gradient characteristic value is greater than or equal to a preset threshold. If so, the internal structure of the explosion-proof equipment is inconsistent, and if not, the internal structure of the explosion-proof equipment is consistent.

[0064] The process of obtaining the local temperature gradient characteristic value is as follows:

[0065] Obtain temperature data sets of explosion-proof equipment during the monitoring period ,in, represents the temperature dataset, represents the number of samples in the dataset, Indicates the time series Temperature value at a time point;

[0066] The local temperature gradient is obtained by calculating the temperature difference between adjacent time points and integrating all temperature differences into a difference sequence;

[0067] Fast Fourier transform is applied to the local temperature gradient to obtain the frequency domain representation, and the ratio of the sum of squares of all non-zero frequency components to the standard deviation is calculated to obtain the local temperature gradient eigenvalue.

[0068] It should be noted that through in-depth analysis of temperature variation patterns, potential structural inconsistencies or abnormal hot spots can be discovered in a timely manner, which is crucial for preventing failures and ensuring safe equipment operation. In addition, the characteristics of frequency domain analysis make this method sensitive to different types of temperature changes, helping to identify small but important changes that may affect equipment performance. The equipment status is assessed by the amplitude of the change in the local temperature gradient and its spectral characteristics. This method goes beyond traditional analysis methods that rely solely on a single dimension. Instead, it comprehensively considers the overall intensity and fluctuations of temperature changes, providing a more comprehensive and accurate description of the equipment's health status.

[0069] In S4, a comprehensive analysis is performed on the abnormal characteristics of the beam steering angle and the characteristic values of the local temperature gradient to determine whether the current detection process is in an unstable state. Specifically, the following are performed:

[0070] The abnormal characteristics of the beam steering angle and the characteristic values of the local temperature gradient are obtained, and the abnormal characteristics of the beam steering angle and the characteristic values of the local temperature gradient are constructed into a comprehensive feature vector as the input of the machine learning model. The output of the model is the safety status score of the explosion-proof equipment. The machine learning model is a support vector machine model.

[0071] The process of obtaining the security status score is as follows:

[0072] The abnormal characteristics of beam steering angles, local temperature gradient characteristic values and safety status scores of historical explosion-proof equipment during the historical monitoring period are obtained as training data sets, and the support vector machine model is trained with minimizing the error between the predicted safety status score and the actual safety status score as the training goal. The support vector machine model is trained, and the comprehensive feature vector of the current explosion-proof equipment is input into the trained support vector machine model according to the trained support vector machine model, and the model will output an accurate safety status score.

[0073] It should be noted that during the training process, cross-validation techniques were used to optimize model parameters, including the penalty coefficient and kernel function parameters, to achieve optimal generalization performance. After training, the resulting support vector machine model can accept new comprehensive feature vectors as input and output a corresponding safety status score for explosion-proof equipment. This score is used to assess the current safety status of the equipment and guide subsequent maintenance decisions.

[0074] In S5, if an unstable state is determined, the system automatically adjusts the beam steering angle and increases the scanning density for abnormal areas indicated by local temperature gradients. Specifically, the system:

[0075] If the safety status assessment of the explosion-proof equipment determines that it is unstable, the system will automatically adjust the beam steering angle to cover the abnormal area through a built-in algorithm for abnormal beam steering angles. Specifically, the system determines the angle range that needs to be adjusted based on the abnormal characteristic value of the beam steering angle and changes the direction of the beam by controlling the mechanical structure. The adjustment calculation expression is: ;

[0076] in, represents the new beam steering angle, represents the original beam steering angle, where , Represents the adjustment coefficient, which is used to adjust the impact of abnormal characteristic values on the angle adjustment amount. represents the abnormal characteristic value of the beam steering angle, Indicates the basic adjustment amount, which is used to compensate for system errors or ensure minimum adjustment requirements. Indicates the angle adjustment amount;

[0077] For abnormal areas with local temperature gradient, the system will increase the scanning density of the area. The system first determines whether the local temperature gradient characteristic value is greater than or equal to the preset threshold. If so, the corresponding area will be marked. For the marked area, the system adjusts the scanning strategy to increase the scanning frequency of these areas to the original times, and is an integer greater than 1.

[0078] The present invention operates by collecting data on the explosion-proof equipment's beam steering angle and local temperature gradient in real time during a monitoring cycle. The beam steering angle is recorded at fixed intervals by a high-precision angle sensor, ensuring that even the slightest angular changes are captured. The local temperature gradient is measured by a network of temperature sensors distributed at key locations on the equipment, reflecting the integrity of the internal materials. Based on the degree of change in the beam steering angle, a clustering algorithm is used to calculate the abnormal beam steering angle characteristic value and assess the stability of ultrasonic propagation. If the abnormal characteristic value exceeds a preset threshold, instability is considered. The amplitude of the local temperature gradient change is analyzed, and the local temperature gradient characteristic value is calculated using a fast Fourier transform to assess the consistency of the equipment's internal structure. If the local temperature gradient characteristic value exceeds the standard, it indicates possible structural inconsistency. The abnormal beam steering angle characteristics and local temperature gradient characteristic values are constructed into a comprehensive feature vector, which is input into a support vector machine model. The resulting output is a safety status score for the equipment, which is used to determine whether the current detection process is unstable. If the system determines an unstable state, the beam steering angle is automatically adjusted to cover the abnormal area and the scanning density is increased for areas with abnormal local temperature gradients, ensuring more detailed detection of potential problem areas. This method can not only accurately identify safety hazards in equipment operation, but also improve overall monitoring efficiency by dynamically adjusting detection strategies, thereby effectively ensuring the safe operation of explosion-proof equipment. It has important practical value and innovative significance.

[0079] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0080] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0081] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0082] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0083] The above is a detailed description of an embodiment of the present invention. However, the content is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A non-destructive testing optimization method based on the accuracy feedback of explosion-proof equipment safety status assessment is characterized by: The following steps are involved: S1: During the monitoring period of the safety status of explosion-proof equipment, real-time monitoring of the beam steering angle and local temperature gradient; The beam steering angle is used to dynamically adjust the direction of the ultrasonic beam to adapt to complex geometric shapes, and the local temperature gradient is measured by infrared thermal imaging technology to reflect the internal material integrity. S2: Determine the abnormal characteristic value of the beam steering angle based on the degree of change in the beam steering angle and evaluate the stability of ultrasonic propagation; The process of obtaining the abnormal characteristic value of the beam steering angle is as follows: Obtain the beam steering angle dataset of explosion-proof equipment during the monitoring period; Choose the number of clusters And initialize centroid; For each point in the beam steering angle dataset, calculate the Euclidean distance between it and each centroid And assign it to the closest cluster, update the centroid of each cluster so that it is equal to the average position of all points in the corresponding cluster, and the update formula is: ; Where, Indicates the A set of data points in a cluster, Represents a cluster The number of data points in , Indicates the beam steering angles, represents the number of beam steering angles, represents the number of clusters, represents the desired number of clusters; Repeat the assignment and update steps until the centroid reaches the preset maximum number of iterations; Determine whether the Euclidean distance between each point in the data point set and the centroid of the cluster to which it belongs is greater than or equal to a preset threshold. If so, it is determined to be an outlier. Calculate the average Euclidean distance of the outliers in the data point set to obtain the beam steering angle anomaly feature value. S3: Analyze the variation of local temperature gradient, calculate the characteristic value of local temperature gradient, and evaluate the consistency of the internal structure of explosion-proof equipment; The process of obtaining the local temperature gradient characteristic value is as follows: Obtain temperature data sets of explosion-proof equipment during the monitoring period ,in, represents the temperature dataset, represents the number of samples in the temperature dataset, Indicates the time series Temperature value at a time point; The local temperature gradient is obtained by calculating the temperature difference between adjacent time points and integrating all temperature differences into a difference sequence; Apply fast Fourier transform to the local temperature gradient to obtain the frequency domain representation, calculate the ratio of the square sum of all non-zero frequency components to the standard deviation value, and obtain the local temperature gradient eigenvalue; S4: Comprehensively analyze the abnormal characteristics of the beam steering angle and the characteristic values of the local temperature gradient to determine whether the current detection process is in an unstable state; S5: If it is determined to be in an unstable state, the system automatically adjusts the beam steering angle and increases the scanning density for abnormal areas displayed by local temperature gradients.

2. The nondestructive testing optimization method based on explosion-proof equipment safety status assessment accuracy feedback according to claim 1 is characterized in that: The method of determining the abnormal characteristic value of the beam steering angle according to the degree of change of the beam steering angle and evaluating the stability of ultrasonic propagation specifically includes: During the monitoring period of the safety status of the explosion-proof equipment, the beam steering angle data is obtained in a time series, and the abnormal characteristic value of the beam steering angle is calculated according to the degree of change of the beam steering angle. It is determined whether the abnormal characteristic value of the beam steering angle is greater than or equal to a preset threshold. If so, the ultrasonic propagation is stable; if not, the ultrasonic propagation is unstable.

3. The nondestructive testing optimization method based on explosion-proof equipment safety status assessment accuracy feedback according to claim 1 is characterized in that: The analysis of the variation range of the local temperature gradient, calculation of the characteristic value of the local temperature gradient, and evaluation of the consistency of the internal structure of the explosion-proof equipment specifically include: During the monitoring period of the safety status of the explosion-proof equipment, the temperature data inside the explosion-proof equipment is obtained in a time series, and the local temperature gradient characteristic value is calculated according to the change amplitude of the local temperature gradient. It is determined whether the local temperature gradient characteristic value is greater than or equal to a preset threshold. If so, the internal structure of the explosion-proof equipment is inconsistent; if not, the internal structure of the explosion-proof equipment is consistent.

4. The nondestructive testing optimization method based on explosion-proof equipment safety status assessment accuracy feedback according to claim 1 is characterized in that: The comprehensive analysis of the abnormal characteristics of the beam steering angle and the characteristic values of the local temperature gradient specifically includes: The abnormal characteristics of the beam steering angle and the local temperature gradient characteristic values are obtained, and the abnormal characteristics of the beam steering angle and the local temperature gradient characteristic values are constructed into a comprehensive feature vector as the input of the machine learning model. The output of the model is the safety status score of the explosion-proof equipment. The machine learning model is a support vector machine model.

5. The nondestructive testing optimization method based on explosion-proof equipment safety status assessment accuracy feedback according to claim 4 is characterized in that: The process of obtaining the security status score is as follows: The abnormal characteristics of beam steering angles, local temperature gradient eigenvalues and safety status scores within the historical monitoring period of the safety status of explosion-proof equipment are obtained as training data sets, and the support vector machine model is trained with minimizing the error between the predicted safety status score and the actual safety status score as the training goal. The support vector machine model is trained, and the comprehensive feature vector of the current explosion-proof equipment is input into the trained support vector machine model according to the trained support vector machine model, and the model will output an accurate safety status score.

6. The nondestructive testing optimization method based on explosion-proof equipment safety status assessment accuracy feedback according to claim 1 is characterized in that: The determining whether the current detection process is in an unstable state specifically includes: Determine whether the safety status score of the current detection process of the explosion-proof equipment is greater than or equal to a preset threshold. If so, the current detection process is in an unstable state; if not, the current detection process is in a stable state.

7. The nondestructive testing optimization method based on explosion-proof equipment safety status assessment accuracy feedback according to claim 1 is characterized in that: The system automatically adjusts the beam steering angle and increases the scan density in areas where abnormalities are indicated by local temperature gradients, including: If the safety status assessment of the explosion-proof equipment determines that it is unstable, for abnormal beam steering angles, the system determines the angle range that needs to be adjusted based on the abnormal characteristic value of the beam steering angle and changes the direction of the beam by controlling the mechanical structure; The system first determines whether the local temperature gradient characteristic value is greater than or equal to the preset threshold. If so, the corresponding area is marked. For the marked area, the system adjusts the scanning strategy to increase the scanning frequency of these areas to the original times, and is an integer greater than 1.

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