Informatization management method for dynamic monitoring key links of special equipment container
By constructing a multi-dimensional monitoring system and information management methods, the problems of fragmented data on special equipment containers and lack of real-time monitoring in inspection have been solved, realizing comprehensive monitoring and anomaly early warning, and improving inspection accuracy and safety management efficiency.
Patent Information
- Application Number
- CN202511881910.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-15
- Publication Date
- 2026-04-07
AI Technical Summary
The fragmented data on special equipment containers makes it impossible to form a unified understanding of their health status throughout their entire life cycle. Traditional testing methods cannot achieve real-time, quantitative, and full-field stress distribution assessment, and lack dynamic monitoring and early warning capabilities.
A multi-dimensional monitoring system is constructed by using digital pressure sensors, high-definition cameras, and acoustic emission monitoring equipment. Combined with convolutional neural networks and X-ray detection, it enables the fusion of multi-source information and real-time monitoring of special equipment containers, generating equipment qualification files and managing them in the cloud.
It enables comprehensive monitoring and early warning of anomalies for special equipment containers, improves the detection coverage and accuracy, ensures the authenticity and reliability of data, and enhances the efficiency of safety management and the scientific nature of decision-making.
Smart Images
Figure CN121810310A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of special equipment container monitoring technology, and in particular to an information management method for key aspects of dynamic monitoring of special equipment containers. Background Technology
[0002] Special equipment containers, as pressure-bearing equipment involving life safety and high risks, are directly related to industrial production safety and public safety in terms of safety and reliability. Design data, manufacturing process data, hydrostatic test data, periodic inspection data, and operational status data of special equipment containers are usually managed by different systems, operating independently and lacking effective correlation and integration. This fragmented data situation makes it impossible to form a unified and coherent understanding of the container's health status throughout its entire life cycle, and management decisions lack comprehensive data support. In the critical hydrostatic test stage, current methods mainly rely on pressure gauge readings and manual macroscopic inspection. These traditional methods cannot obtain real-time, quantitative, and full-field stress distribution in the container shell, especially in stress concentration areas such as welds and openings, making it difficult to accurately assess its pressure-bearing limits and potential defects. In the non-destructive testing stage, although conventional methods such as ultrasound and radiography can detect internal defects, they usually require offline shutdown and are periodic "snapshots," failing to achieve dynamic and continuous monitoring of defect initiation and expansion. Traditional dynamic monitoring of special equipment containers has some technical shortcomings, including reliance on manual recording of test data, lack of real-time monitoring of the testing process, and lack of early warning and prediction capabilities for abnormal situations. Therefore, there is an urgent need to develop an information management method for key aspects of dynamic monitoring of special equipment containers. Summary of the Invention
[0003] To overcome the shortcomings of relying on manual recording of test data, lack of real-time monitoring of the testing process, and lack of early warning and prediction capabilities for abnormal situations, this invention provides an information management method for key aspects of dynamic monitoring of special equipment containers.
[0004] The technical solution of this invention: an information management method for key aspects of dynamic monitoring of special equipment containers, comprising the following steps: S1: Acquire multi-source information of special equipment containers, multi-source data of hydrostatic tests, ambient temperature information, and personnel qualification certification; acquire first and second image data of key processes. S2: Obtain the actual water pressure data of the digital pressure sensor, establish a dynamic water pressure curve, calculate the water pressure qualification rate, and establish water pressure test logs for different special equipment containers; S3: Obtain the third image data of the water pressure test, compare the third image data with the first image data, obtain the first authenticity of the water pressure test, and record the water pressure test log. S4: Obtain defect films of special equipment containers using X-rays, identify and locate defects in the special equipment containers, calculate the non-destructive testing qualification, and establish a non-destructive testing log for the special equipment containers; S5: Obtain the fourth image data of the non-destructive testing experiment, compare the fourth image data with the second image data, obtain the second authenticity of the non-destructive testing experiment, and record the non-destructive testing log. S6: Based on the hydrostatic test log, non-destructive testing log, and the type of special equipment container, construct the equipment qualification file for the current special equipment container and generate a unique QR code and serial number; S7: Based on the equipment qualification file, construct a cloud container qualification directory, set a first directory according to the special equipment container type, and manage and issue warnings for the special equipment containers by serial number.
[0005] Preferably, the acquisition of multi-source information of the special equipment container, multi-source data of the hydrostatic test, and ambient temperature information, as well as the acquisition of first and second image data of key links, includes: obtaining the specific equipment type of the current special equipment container, including fixed pressure vessels and mobile storage tanks, through the production report of the special equipment container; obtaining multi-source data of the hydrostatic test through the monitoring report of the current special equipment container, the multi-source data including the critical hydrostatic threshold, the maximum hydrostatic threshold, and the first acoustic emission signal of the acoustic emission detection equipment under normal conditions; acquiring the ambient temperature information of the special equipment container based on the temperature sensor array; recording and certifying the personnel entering the factory by setting up a personnel qualification inspection gate at the factory entrance; constructing a dynamic camera network for hydrostatic testing and non-destructive testing experiments, and acquiring the first and second image data of the special equipment container in real time during the hydrostatic testing and non-destructive testing experiments; the first and second image data are image data of the special equipment container as a whole monitored in the hydrostatic testing and non-destructive testing experiments, respectively.
[0006] Preferably, the step of acquiring the actual water pressure data from the digital pressure sensor, establishing a dynamic water pressure curve, calculating the water pressure compliance rate, and establishing water pressure test logs for different special equipment containers includes: acquiring the actual water pressure data experienced by the current special equipment container during the water pressure test using a digital pressure sensor; dynamically increasing the pressure of the current special equipment container using a digital pressure sensor; simultaneously monitoring the acoustic emission signal of the current special equipment container in real time using an acoustic emission detection device; acquiring the second acoustic emission signal of the water pressure test; comparing it with the first acoustic emission signal to obtain the leakage status of the current special equipment container during the water pressure test; calculating the ratio of the leakage pressure to the critical water pressure threshold as the water pressure compliance rate; and establishing a water pressure test log based on the water pressure compliance rate and type of the special equipment container, wherein the water pressure compliance rate formula is: ; in, This refers to the water pressure compliance of the special equipment container at this time; This refers to the actual water pressure data when the special equipment container leaks. This is the critical water pressure threshold for the water pressure test.
[0007] Preferably, comparing the second acoustic emission signal with the first acoustic emission signal to obtain the leakage status of the special equipment container during the hydrostatic test includes: during the dynamic pressurization process of the hydrostatic test, comparing the waveforms of the second acoustic emission signal and the first acoustic emission signal; if the waveforms of the second acoustic emission signal and the first acoustic emission signal are different, and the actual stress data of the special equipment container is less than the critical hydrostatic pressure threshold, then the special equipment container is unqualified; if the waveforms of the second acoustic emission signal and the first acoustic emission signal are different, and the actual stress data of the special equipment container is greater than the critical hydrostatic pressure threshold and less than the maximum hydrostatic pressure threshold, then the special equipment container is qualified; if the waveforms of the second acoustic emission signal and the first acoustic emission signal are the same, and the actual stress data of the special equipment container is equal to the maximum hydrostatic pressure threshold, then the special equipment container is excellent.
[0008] Preferably, the step of acquiring the third image data of the hydrostatic test, comparing the third image data with the first image data, obtaining the first realism of the hydrostatic test, and recording the hydrostatic test log includes: acquiring third image data of the surface state of the special equipment container during the hydrostatic test using a high-definition camera; extracting key points of the images from the third image data and the first image data using a convolutional neural network; obtaining the first realism of the hydrostatic test based on the trajectory of the key points in the two images using a realism formula; and recording the first realism in the hydrostatic test log, wherein the realism formula is: ; ; in, The key point numbers of the two types of image data; The dynamic minimum distance between two image keypoint sequences; The straight-line distance between the two image keypoint sequences; The realism of two image keypoint sequences; and These are the previous keypoint numbers of the current keypoint numbers in the two types of image data, respectively. The dynamic minimum distance between the previous keypoints in the two image data sets; The dynamic minimum distance between the current key point number of the first image data and the previous key point number of the third image data. It is the dynamic minimum distance between the previous key point number of the first image data and the current key point number of the third image data.
[0009] Preferably, the acquisition of third image data of the surface state of the special equipment container during the hydrostatic test via a high-definition camera includes: the third image data includes facial biometric data of the operator, operation process data, monitoring time, and ambient temperature extracted using facial recognition technology; the third image data is acquired through a dynamic camera and dynamic camera network of a pressure sensor during human operation; in the personnel activity area, feature point detection technology is used to acquire facial contour feature points of the personnel, and facial information of the personnel is identified through feature points; in the equipment status area, edge detection is used to extract the edges of the container surface and personnel surface in the third image data to obtain the third image data.
[0010] Preferably, the step of acquiring defect films of special equipment containers through X-ray, identifying and locating defects in the special equipment containers, calculating the non-destructive testing (NDT) pass rate, and establishing an NDT log for the special equipment containers includes: acquiring vulnerable locations of the special equipment containers through key process monitoring logs, wherein the vulnerable locations are easily damaged parts of the special equipment containers obtained from historical logs; emitting gamma rays at the weld seams of the special equipment containers on one side using X-ray inspection equipment, acquiring surface crack information at the weld seams on the other side of the special equipment containers using AI black film, acquiring internal defect data of the special equipment containers through ultrasonic testing equipment, identifying and locating defects in the special equipment containers based on the surface crack information and defect data, wherein the defect identification includes cracks, delamination, and porosity; calculating the ratio of the remaining non-destructive area to the total area of the special equipment containers based on the defect type and defect location to obtain the NDT pass rate, and establishing an NDT log for the NDT experiment.
[0011] Preferably, the step of acquiring the fourth image data of the nondestructive testing experiment, comparing the fourth image data with the second image data, obtaining the second authenticity of the nondestructive testing experiment, and recording the nondestructive testing log includes: acquiring the fourth image data of the surface state of the special equipment container in the nondestructive testing experiment through a high-definition camera; performing image comparison and recognition on the fourth image data and the second image data through a convolutional neural network; extracting key points from the two images; obtaining the second authenticity of the nondestructive testing experiment based on the running trajectory of the key points in the two images using an authenticity formula; and recording the second authenticity in the nondestructive testing log; wherein the fourth image data is close-up data of the container surface state and personnel operation image data.
[0012] Preferably, the step of constructing an equipment qualification file for the current special equipment container based on the hydrostatic test log, non-destructive testing log, and the type of the special equipment container, and generating a unique QR code and serial number, includes: performing standardized preprocessing on the hydrostatic test log and non-destructive testing log of the current special equipment container to obtain multi-source data of the hydrostatic test log and non-destructive testing log; performing data fusion based on the current special equipment container and multi-source data to construct an equipment qualification file, wherein the equipment qualification file includes the hydrostatic qualification degree, non-destructive qualification degree, first authenticity degree, second authenticity degree of the special equipment container, and the retrieval personnel information of the special equipment container in key stages; constructing a qualification queue for the current special equipment container through weighted calculation of the two qualification degrees and the two authenticity degrees to obtain the serial number of the special equipment container; and generating a unique QR code for any special equipment container based on the serial number.
[0013] Preferably, the step of constructing a cloud-based qualified container directory based on the equipment qualification file, setting a first directory according to the special equipment container type, and managing and issuing early warnings for the special equipment containers using serial numbers includes: constructing a qualified directory of special equipment containers on a cloud server based on the qualified queue and serial numbers, using the special equipment container type as the first directory, and using water pressure qualification and non-destructive testing qualification as the second and third directories respectively, and managing the special equipment containers using their serial numbers; simultaneously monitoring the operation process of the special equipment containers in key stages in real time, and issuing early warnings for special equipment containers in case of abnormalities, including human interference, water leakage during water pressure testing, errors in radiographic non-destructive testing, and personal safety issues.
[0014] The present invention has the following advantages: 1. This invention integrates a digital pressure sensor, a high-definition camera, and an acoustic emission monitoring device to construct a multi-dimensional monitoring system. It can simultaneously capture macroscopic pressure changes, microscopic material acoustic signals, and visual image data, achieving comprehensive monitoring of key aspects and significantly improving the coverage and accuracy of detection. 2. This invention uses dynamic camera network technology to achieve tamper-proof evidence storage of test data. Combined with the functions of recording the qualifications of test personnel and tracking the calibration status of equipment, it constructs a complete quality traceability system, ensuring the authenticity and reliability of the data, providing a credible basis for accident investigation and liability determination, and also facilitating the long-term health status tracking of equipment. 3. This invention identifies abnormal test conditions and quality risks in real time. This proactive safety management model can detect potential faults early and prevent accidents from happening. At the same time, through intelligent recommendation of maintenance strategies and automatic generation of regulatory reports, it significantly improves the efficiency of safety management and the scientific nature of decision-making. Attached Figure Description
[0015] Figure 1 This is a flowchart of the information management method for key aspects of dynamic monitoring of special equipment containers according to the present invention; Figure 2 This is a schematic diagram of the information management method for key aspects of dynamic monitoring of special equipment containers according to the present invention. Detailed Implementation
[0016] The following description is only a preferred embodiment of the present invention and does not limit the scope of protection of the present invention.
[0017] Example: An information management method for key aspects of dynamic monitoring of special equipment containers, such as... Figure 1 and Figure 2 As shown, it includes the following steps: S1: Acquire multi-source information of special equipment containers, multi-source data of hydrostatic tests, ambient temperature information, and personnel qualification certification; acquire first and second image data of key processes. S2: Obtain the actual water pressure data of the digital pressure sensor, establish a dynamic water pressure curve, calculate the water pressure qualification rate, and establish water pressure test logs for different special equipment containers; S3: Obtain the third image data of the water pressure test, compare the third image data with the first image data, obtain the first authenticity of the water pressure test, and record the water pressure test log. S4: Obtain defect films of special equipment containers using X-rays, identify and locate defects in the special equipment containers, calculate the non-destructive testing qualification, and establish a non-destructive testing log for the special equipment containers; S5: Obtain the fourth image data of the non-destructive testing experiment, compare the fourth image data with the second image data, obtain the second authenticity of the non-destructive testing experiment, and record the non-destructive testing log. S6: Based on the hydrostatic test log, non-destructive testing log, and the type of special equipment container, construct the equipment qualification file for the current special equipment container and generate a unique QR code and serial number; S7: Based on the equipment qualification file, construct a cloud container qualification directory, set a first directory according to the special equipment container type, and manage and issue warnings for the special equipment containers by serial number.
[0018] The production report of the special equipment container is used to obtain the specific equipment type of the current special equipment container, including stationary pressure vessels and mobile storage tanks; the monitoring report of the current special equipment container is used to obtain multi-source data of the hydrostatic test, including the critical hydrostatic threshold, the maximum hydrostatic threshold, and the first acoustic emission signal of the acoustic emission detection equipment under normal conditions; the ambient temperature information of the special equipment container is obtained based on the temperature sensor array; personnel qualification inspection gates are set up at the factory entrance to record and certify the qualifications of personnel entering the factory; a dynamic camera network for hydrostatic testing and non-destructive testing is constructed to acquire first and second image data of the special equipment container in real time during hydrostatic testing and non-destructive testing; the first and second image data are image data of the special equipment container as a whole monitored in hydrostatic testing and non-destructive testing, respectively.
[0019] It should be noted that the dynamic camera network covering the hydrostatic test and non-destructive testing areas consists of multiple high-definition camera groups, using a deployment method that combines fixed camera positions with mobile pan-tilt units. During the hydrostatic test, the camera network collects first image data of the overall state of the container; during the non-destructive testing, the system collects second image data of the container's testing area; both types of image data contain time synchronization information and spatial positioning data, forming a complete visual monitoring record.
[0020] The actual water pressure data of the special equipment container during the hydrostatic test is obtained through a digital pressure sensor. The container is dynamically pressurized using the digital pressure sensor, while the acoustic emission signal of the container is monitored in real time using an acoustic emission detection device. A second acoustic emission signal from the hydrostatic test is obtained and compared with the first acoustic emission signal to determine the leakage status of the container. The ratio of the leakage pressure to the critical water pressure threshold is calculated as the hydrostatic pass rate. A hydrostatic test log is established based on the hydrostatic pass rate and type of the special equipment container. The formula for the hydrostatic pass rate is: ; in, This refers to the water pressure compliance of the special equipment container at this time; This refers to the actual water pressure data when the special equipment container leaks. This is the critical water pressure threshold for the water pressure test.
[0021] It should be noted that the actual water pressure data borne by the container is collected in real time by a digital pressure sensor array deployed on the pressurization pipeline. Based on the actual water pressure data, the special equipment container is slowly pressurized. According to the pressurization process, every standard atmosphere is used as the critical water pressure threshold of the special equipment container. The critical water pressure threshold is obtained by the special equipment container's instruction manual and the historical log of the water pressure test. For example, the critical water pressure threshold is set to 1 standard atmosphere, 2 standard atmospheres, and 3 standard atmospheres respectively. The maximum water pressure threshold is obtained by the special equipment container's instruction manual as the maximum water pressure that the special equipment container can withstand. For example, the maximum water pressure threshold is set to 5 standard atmospheres. When a leakage characteristic signal is detected, the digital pressure sensor records the corresponding actual water pressure data as the leakage pressure value. At the same time, the relationship between the pressure value and the critical water pressure threshold and the maximum water pressure threshold is judged to calculate the water pressure qualification of the special equipment container.
[0022] During the dynamic pressurization process of the hydrostatic test, the waveforms of the second acoustic emission signal and the first acoustic emission signal are compared. If the waveforms of the second acoustic emission signal and the first acoustic emission signal are different, and the actual stress data of the special equipment container is less than the critical hydrostatic threshold, then the special equipment container is unqualified. If the waveforms of the second acoustic emission signal and the first acoustic emission signal are different, and the actual stress data of the special equipment container is greater than the critical hydrostatic threshold but less than the maximum hydrostatic threshold, then the special equipment container is qualified. If the waveforms of the second acoustic emission signal and the first acoustic emission signal are the same, and the actual stress data of the special equipment container is equal to the maximum hydrostatic threshold, then the special equipment container is excellent.
[0023] It should be noted that the hydrostatic test of special equipment containers is divided into three stages: unqualified, qualified, and excellent. For specific special equipment containers, different hydrostatic test conditions need to be met. The waveforms of the second and first acoustic emission signals are compared. If the second acoustic emission signal shows a sudden peak and the peak value differs from the first acoustic emission signal, then the special equipment container is leaking. The sudden peak is defined as a rapid increase or decrease in the peak value of the waveform. If the special equipment container leaks at this time and the actual stress data equals the critical hydrostatic threshold, then the special equipment container is unqualified. If the special equipment container leaks at this time and the actual stress data equals the maximum hydrostatic threshold, then the special equipment container is qualified.
[0024] A third image of the surface condition of the special equipment container during the hydrostatic test is acquired using a high-definition camera. Key points of the third and first image data are extracted using a convolutional neural network. Based on the trajectory of the key points in both images, the first accuracy of the hydrostatic test is obtained using an accuracy formula, and this first accuracy is recorded in the hydrostatic test log. The accuracy formula is as follows: ; ; in, The key point numbers of the two types of image data; The dynamic minimum distance between two image keypoint sequences; The straight-line distance between the two image keypoint sequences; The realism of two image keypoint sequences; and These are the previous keypoint numbers of the current keypoint numbers in the two types of image data, respectively. The dynamic minimum distance between the previous keypoints in the two image data sets; The dynamic minimum distance between the current key point number of the first image data and the previous key point number of the third image data. It is the dynamic minimum distance between the previous key point number of the first image data and the current key point number of the third image data.
[0025] It should be noted that a convolutional neural network model is used to extract features from the two types of image data. The network structure includes convolutional layers, pooling layers, and fully connected layers to identify and locate key features on the container surface, including weld areas, structural abrupt changes, and potential defect locations. The extracted key point sequences generate corresponding motion trajectory descriptors to characterize the deformation process of the container under pressure load. The formula for the straight-line distance between key point indices is: ; in, The straight-line distance between the current keypoint indices of the two image data sets; Keypoint extraction for both types of image data originates from the surface condition of containers and human intervention processes within the images; the boundary conditions for the realism formula are: ; ; ; As a recursive formula, the authenticity of two types of image key points needs to be obtained recursively. The authenticity index obtains the authenticity of the current image by quantifying the degree of matching between the key point trajectories of the two sets of image data. The value range is between 0 and 1. The log data is stored in a structured format, including fields such as authenticity value, key point coordinate sequence, image timestamp, and stress stage identifier.
[0026] The third image data includes facial biometric data of the operator, operation process data, monitoring time, and ambient temperature extracted through facial recognition technology; the third image data is acquired through dynamic cameras and dynamic camera networks of pressure sensors during human operation; in the personnel activity area, feature point detection technology is used to acquire facial contour feature points of the personnel, and facial information of the personnel is identified through feature points; in the equipment status area, edge detection is used to extract the edges of the container surface and personnel surface in the third image data to obtain the third image data.
[0027] The vulnerable locations of special equipment containers are obtained through key process monitoring logs. These vulnerable locations are the parts of the special equipment container that are prone to damage, as identified from historical logs. On one side of the special equipment container, gamma rays are emitted from the weld seam using a radiographic testing device. On the other side, surface crack information of the weld seam is obtained using an AI black film. Internal defect data of the special equipment container is obtained using an ultrasonic testing device. Based on the surface crack information and defect data, defects in the special equipment container are identified and located. Defect identification includes cracks, delamination, and porosity. Based on the defect type and location, the ratio of the remaining undamaged area to the total area of the special equipment container is calculated to obtain the non-destructive testing qualification rate, and a non-destructive testing log is established for the non-destructive testing experiments.
[0028] It should be noted that the vulnerable locations of the special equipment container are obtained based on the number of times different parts of the special equipment container were damaged in the historical log. The vulnerable locations are obtained by sorting the parts in descending order according to the number of damages. A gamma-ray emitting device is deployed on one side of the container to irradiate the designated weld area, while an AI black film imaging system is set up on the corresponding other side to receive the penetration signal. The AI black film uses intelligent photosensitive material. Due to the different densities of defects and base materials, the film is unevenly photosensitive, forming an image and automatically recording the surface crack distribution characteristics of the weld area. The distribution characteristics include crack size and crack type. A multi-channel ultrasonic testing device is used to scan the same area to obtain the echo signal data of defects inside the container. The non-destructive qualification index is obtained by calculating the ratio of the remaining undamaged area of the container to the total surface area. The specific calculation process is as follows: first, the projected area of the defect area is extracted from the three-dimensional model; then, the cumulative defect area is subtracted from the total area; finally, the proportion of undamaged area is calculated. A fourth image of the surface condition of a special equipment container in a non-destructive testing experiment is acquired using a high-definition camera. The fourth image and the second image are compared and identified using a convolutional neural network. Key points are extracted from the two images. Based on the trajectory of the key points in the two images, the second authenticity of the non-destructive testing experiment is obtained using an authenticity formula, and the second authenticity is recorded in the non-destructive testing log. The fourth image is close-up data of the container surface condition and personnel operation image data.
[0029] It should be noted that in the non-destructive testing experiment, a dynamic camera was placed on the gamma-ray emitting device to capture images of the AI black film on the other side of the gamma-ray emitting device and the special equipment container, thereby acquiring fourth image data including the tool's state.
[0030] Standardize and preprocess the current hydrostatic test logs and non-destructive testing logs of special equipment containers to obtain multi-source data of the hydrostatic test logs and non-destructive testing logs; perform data fusion based on the current special equipment containers and multi-source data to construct a qualified queue and obtain the qualified file. The qualified file includes the hydrostatic qualification degree, non-destructive qualification degree, first authenticity degree, second authenticity degree of the special equipment container, and the retrieval personnel information of the special equipment container in key stages; construct a qualified queue for the current special equipment containers by weighted calculation of the two qualification degrees and the two authenticity degrees to obtain the serial number of the special equipment containers; generate a unique QR code for any special equipment container based on the serial number.
[0031] It should be noted that an equipment qualification file is constructed, which includes water pressure qualification, non-destructive testing qualification, first authenticity, second authenticity, and key link personnel information. The personnel information is linked to the personnel qualification database through identity identification codes. A qualification queue is constructed using a multi-dimensional evaluation index ranking algorithm. The multi-dimensional evaluation index ranking algorithm uses water pressure qualification and non-destructive testing qualification as the main ranking indicators. Referring to relevant special equipment standards, the algorithm uses first weight and second weight as auxiliary weight parameters for water pressure qualification and non-destructive testing qualification. A comprehensive quality score is obtained through weighted calculation, and the equipment is generated in descending order of the score value. For example, the first weight corresponding to the water pressure pass rate is set to 0.7, and the second weight corresponding to the non-destructive testing pass rate is set to 0.3; the special fusion is to extract the water pressure pass rate, non-destructive testing pass rate, first authenticity, second authenticity, third image data, and fourth image data from the water pressure test log and the non-destructive testing log and transmit them to the equipment qualification file; the exclusive QR code includes Reed-Solomon error correction coding, containing the equipment serial number, testing agency identification, and digital signature information; at the same time, a mapping relationship table between the serial number and the QR code is established to ensure that each serial number corresponds to a unique QR code identifier.
[0032] Based on the qualified queue and serial number, a qualified directory of special equipment containers is constructed on the cloud server. The type of special equipment container is used as the first directory, and the water pressure qualification and non-destructive testing qualification are used as the second and third directories, respectively. The special equipment containers are managed by their serial numbers. At the same time, the operation process of special equipment containers in key stages is monitored in real time, and warnings are issued for special equipment containers in case of abnormalities. Abnormalities include human interference, water leakage in water pressure testing, errors in radiographic non-destructive testing, and personal safety issues.
[0033] It should be noted that the monitoring methods for the special equipment containers include hydrostatic testing and non-destructive testing (NDT). For example, the retrieval of special equipment containers such as low-pressure air storage tanks requires that the hydrostatic test pass rate is qualified and the NDT pass rate is 0.8. For example, special equipment containers such as large spherical tanks require excellent performance in hydrostatic testing and a NDT pass rate of 1. A hierarchical directory architecture is established on the cloud server based on the qualified queue and the corresponding serial number. The hierarchical directory system adopts a three-level index structure: the first-level directory is classified and coded according to the type of special equipment container; the second-level directory is divided into intervals according to the hydrostatic test pass rate; and the third-level directory is subdivided according to the NDT pass rate. A mapping relationship is established between the serial number and the directory node to achieve structured storage and rapid retrieval of all inspected containers. The early warning system generates hierarchical alarm information according to the anomaly type and severity, and automatically associates the corresponding container's serial number and directory position. All early warning events are recorded in the anomaly event database and associated with the qualified files of the relevant containers.
[0034] Although the invention has been described with reference to exemplary embodiments, it should be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the claims should be given the broadest interpretation to cover all variations and equivalent structures and functions.
Claims
1. An information management method for key aspects of dynamic monitoring of special equipment containers, characterized in that, Includes the following steps: S1: Acquire multi-source information of special equipment containers, multi-source data of hydrostatic tests, ambient temperature information, and personnel qualification certification; acquire first and second image data of key processes. S2: Obtain the actual water pressure data of the digital pressure sensor, establish a dynamic water pressure curve, calculate the water pressure qualification rate, and establish water pressure test logs for different special equipment containers; S3: Obtain the third image data of the water pressure test, compare the third image data with the first image data, obtain the first authenticity of the water pressure test, and record the water pressure test log. S4: Obtain defect films of special equipment containers using X-rays, identify and locate defects in the special equipment containers, calculate the non-destructive testing qualification, and establish a non-destructive testing log for the special equipment containers; S5: Obtain the fourth image data of the non-destructive testing experiment, compare the fourth image data with the second image data, obtain the second authenticity of the non-destructive testing experiment, and record the non-destructive testing log. S6: Based on the hydrostatic test log, non-destructive testing log, and the type of special equipment container, construct the equipment qualification file for the current special equipment container and generate a unique QR code and serial number; S7: Based on the equipment qualification file, construct a cloud container qualification directory, set a first directory according to the special equipment container type, and manage and issue warnings for the special equipment containers by serial number.
2. The information management method for key aspects of dynamic monitoring of special equipment containers according to claim 1, characterized in that, The acquisition of multi-source information of special equipment containers, multi-source data of hydrostatic tests, and ambient temperature information, as well as the acquisition of first and second image data of key processes, includes: obtaining the specific equipment type of the current special equipment container, including fixed pressure vessels and mobile storage tanks, from the production report of the special equipment container; obtaining multi-source data of hydrostatic tests from the monitoring report of the current special equipment container, including the critical hydrostatic threshold, the maximum hydrostatic threshold, and the first acoustic emission signal of the acoustic emission detection equipment under normal conditions; acquiring the ambient temperature information of the special equipment container based on the temperature sensor array; recording and certifying the personnel entering the factory by setting up a personnel qualification inspection gate at the factory entrance; constructing a dynamic camera network for hydrostatic testing and non-destructive testing experiments to acquire first and second image data of the special equipment container in real time during hydrostatic testing and non-destructive testing experiments; the first and second image data are image data of the special equipment container as a whole monitored in hydrostatic testing and non-destructive testing experiments, respectively.
3. The information management method for key aspects of dynamic monitoring of special equipment containers according to claim 1, characterized in that, The process of acquiring the actual water pressure data from the digital pressure sensor, establishing a dynamic water pressure curve, calculating the water pressure compliance rate, and establishing water pressure test logs for different special equipment containers includes: acquiring the actual water pressure data experienced by the special equipment container during the water pressure test using the digital pressure sensor; dynamically increasing the pressure of the special equipment container using the digital pressure sensor; simultaneously monitoring the acoustic emission signal of the special equipment container in real time using an acoustic emission detection device; acquiring the second acoustic emission signal of the water pressure test; comparing it with the first acoustic emission signal to obtain the leakage status of the special equipment container during the water pressure test; calculating the ratio of the leakage pressure to the critical water pressure threshold as the water pressure compliance rate; and establishing a water pressure test log based on the water pressure compliance rate and type of the special equipment container. The formula for the water pressure compliance rate is: ； in, This refers to the water pressure compliance of the special equipment container at this time; This refers to the actual water pressure data when the special equipment container leaks. This is the critical water pressure threshold for the water pressure test.
4. The information management method for key aspects of dynamic monitoring of special equipment containers according to claim 3, characterized in that, The leakage status of the special equipment container during the hydrostatic test is obtained by comparing the second acoustic emission signal with the first acoustic emission signal. This includes: during the dynamic pressurization process of the hydrostatic test, comparing the waveforms of the second acoustic emission signal and the first acoustic emission signal; if the waveforms of the second acoustic emission signal and the first acoustic emission signal are different, and the actual stress data of the special equipment container is less than the critical hydrostatic threshold, then the special equipment container is unqualified; if the waveforms of the second acoustic emission signal and the first acoustic emission signal are different, and the actual stress data of the special equipment container is greater than the critical hydrostatic threshold and less than the maximum hydrostatic threshold, then the special equipment container is qualified; if the waveforms of the second acoustic emission signal and the first acoustic emission signal are the same, and the actual stress data of the special equipment container is equal to the maximum hydrostatic threshold, then the special equipment container is excellent.
5. The information management method for key aspects of dynamic monitoring of special equipment containers according to claim 1, characterized in that, The process of acquiring third image data of the hydrostatic test, comparing the third image data with the first image data, obtaining the first accuracy of the hydrostatic test, and recording the hydrostatic test log includes: acquiring third image data of the surface state of the special equipment container during the hydrostatic test using a high-definition camera; extracting key points from the third image data and the first image data using a convolutional neural network; obtaining the first accuracy of the hydrostatic test based on the trajectory of the key points in the two images using an accuracy formula; and recording the first accuracy in the hydrostatic test log. The accuracy formula is: ； ； in, The key point numbers of the two types of image data; The dynamic minimum distance between two image keypoint sequences; The straight-line distance between the two image keypoint sequences; The realism of two image keypoint sequences; and These are the previous keypoint numbers of the current keypoint numbers in the two types of image data, respectively. The dynamic minimum distance between the previous keypoints in the two image data sets; The dynamic minimum distance between the current key point number of the first image data and the previous key point number of the third image data. It is the dynamic minimum distance between the previous key point number of the first image data and the current key point number of the third image data.
6. The information management method for key aspects of dynamic monitoring of special equipment containers according to claim 5, characterized in that, The method of acquiring third image data of the surface state of special equipment containers during hydrostatic testing using a high-definition camera includes: the third image data includes facial biometric data of operators extracted using facial recognition technology, operation process data, monitoring time, and ambient temperature; the third image data is acquired through a dynamic camera and dynamic camera network of pressure sensors during human operation; in the personnel activity area, feature point detection technology is used to acquire facial contour feature points of personnel, and facial information of personnel is identified through feature points; in the equipment status area, edge detection is used to extract the edges of the container surface and personnel surface in the third image data to obtain the third image data.
7. The information management method for key aspects of dynamic monitoring of special equipment containers according to claim 1, characterized in that, The process of acquiring defect films of special equipment containers using X-rays, identifying and locating defects in the special equipment containers, calculating the non-destructive testing (NDT) pass rate, and establishing an NDT log for the special equipment containers includes: acquiring vulnerable locations of the special equipment containers through key process monitoring logs, where vulnerable locations are areas of the special equipment containers prone to damage based on historical logs; emitting gamma rays at the weld seams of the special equipment containers on one side using X-ray inspection equipment, acquiring surface crack information at the weld seams on the other side of the special equipment containers using AI black film, acquiring internal defect data of the special equipment containers using ultrasonic testing equipment, identifying and locating defects in the special equipment containers based on the surface crack information and defect data, where defect identification includes cracks, delamination, and porosity; calculating the ratio of the remaining non-destructive area to the total area of the special equipment containers based on the defect type and defect location to obtain the NDT pass rate, and establishing an NDT log for the NDT experiments.
8. The information management method for key aspects of dynamic monitoring of special equipment containers according to claim 1, characterized in that, The process of acquiring the fourth image data of the nondestructive testing experiment, comparing the fourth image data with the second image data to obtain the second authenticity of the nondestructive testing experiment, and recording the nondestructive testing log includes: acquiring the fourth image data of the surface state of the special equipment container and personnel operation in the nondestructive testing experiment at close range using a high-definition camera; performing image comparison and recognition on the fourth image data and the second image data through a convolutional neural network; extracting key points from the two types of images; obtaining the second authenticity of the nondestructive testing experiment based on the running trajectory of the key points in the two types of images using an authenticity formula; and recording the second authenticity in the nondestructive testing log; wherein the fourth image data is close-up data of the container surface state and personnel operation image data.
9. An information management method for key aspects of dynamic monitoring of special equipment containers according to claim 1, characterized in that, The process of constructing an equipment qualification file for the current special equipment container based on the hydrostatic test log, non-destructive testing log, and the type of the special equipment container, and generating a unique QR code and serial number, includes: performing standardized preprocessing on the hydrostatic test log and non-destructive testing log of the current special equipment container to obtain multi-source data of the hydrostatic test log and non-destructive testing log; performing data fusion based on the current special equipment container and multi-source data to construct an equipment qualification file, the equipment qualification file including the hydrostatic qualification degree, non-destructive qualification degree, first authenticity degree, second authenticity degree of the special equipment container, and the retrieval personnel information of the special equipment container in key stages; constructing a qualification queue for the current special equipment container through weighted calculation of the two qualification degrees and the two authenticity degrees to obtain the serial number of the special equipment container; and generating a unique QR code for any special equipment container based on the serial number.
10. An information management method for key aspects of dynamic monitoring of special equipment containers according to claim 1, characterized in that, The process of constructing a cloud-based qualified container directory based on the equipment qualification records, setting a first directory based on the special equipment container type, and managing and issuing early warnings for the special equipment containers using serial numbers includes: constructing a qualified directory for special equipment containers on a cloud server based on the qualified queue and serial numbers, using the special equipment container type as the first directory, and water pressure qualification and non-destructive testing qualification as the second and third directories respectively, and managing the special equipment containers using their serial numbers; simultaneously monitoring the operation process of the special equipment containers in key stages in real time, and issuing early warnings for special equipment containers in case of abnormalities, including human interference, water leakage during water pressure testing, errors in radiographic non-destructive testing, and personal safety issues.