Pipe quality tracing method, quality tracing system, medium and product

Abstract

The invention discloses a pipe quality tracing method, a quality tracing system, a medium and a product, and relates to the field of industrial manufacturing and quality control. By implementing the method, the RFID tag is embedded into the pipe and the micro environment sensor is integrated, so that real-time monitoring and data acquisition of the whole life cycle of the pipe are realized. Under the current circulation node, if the health index of the pipe indicated by the pipe health data is smaller than a preset index threshold value, the quality tracing system automatically extracts the historical circulation record of the pipe from the block chain node and the historical environment data collected by the micro environment sensor for analysis; the responsibility nodes causing the pipe quality problem are accurately positioned, and a responsibility tracing report is generated. According to the method, hysteresis and subjectivity of a traditional manual inspection mode are avoided, and the accuracy and efficiency of quality tracing are improved. And meanwhile, the block chain technology ensures that the data cannot be tampered, and a reliable basis is provided for responsibility investigation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial manufacturing and quality control, and particularly relates to a pipe quality traceability method, a pipe quality traceability system, a medium and a product. BACKGROUND

[0002] With the continuous improvement of industrial manufacturing level, pipe materials as important industrial basic materials have been widely used. The quality management and responsibility traceability of pipe materials in the production, transportation, storage and use links have important significance for ensuring engineering safety and improving management efficiency.

[0003] At present, the pipe quality traceability method mainly adopts the way of printing or pasting bar code on the surface of the pipe for identification. In the pipe circulation process, the management personnel reads the bar code information by hand-held scanning equipment, and records the basic information and test results of the pipe in the enterprise internal management system. At the same time, the management personnel needs to regularly conduct manual inspection on the pipe, assess the surface state of the pipe through visual inspection, and manually input the inspection results into the enterprise internal management system.

[0004] However, in actual application, the pipe may be in poor environment for a long time, and its quality condition will change with time, and the existing pipe quality traceability method is difficult to monitor the environmental parameters of the pipe in real time, and cannot discover potential quality hidden dangers in time. SUMMARY

[0005] The present application provides a pipe quality traceability method, a pipe quality traceability system, a medium and a product, which are used to improve the accuracy of pipe quality traceability.

[0006] Firstly, this application provides a pipe quality traceability method, applied to a quality traceability system. The method includes: sending a printing instruction to an RFID printer to write basic information of the pipe into an RFID tag, the basic information representing the source attribute of the pipe, and the RFID tag being embedded inside the pipe; receiving on-site collected data sent by a terminal device after reading the RFID tag at the current circulation node, the on-site collected data including a timestamp, operator information, GPS coordinates of the current circulation node, and pipe health data; determining a health index of the pipe based on the pipe health data; when the health index is less than a preset index threshold, extracting historical circulation records of the pipe and historical environmental data collected by a micro-environmental sensor from a blockchain node, the micro-environmental sensor being integrated into the RFID tag and uploading the collected environmental data to the blockchain node in real time; dividing the historical environmental data into multiple sets of environmental parameters for historical circulation nodes according to the historical timestamps and historical GPS information in the historical circulation records, calculating the duration and degree of abnormal environmental parameters in each set; determining the responsible node based on the duration and degree of abnormality, and generating a responsibility traceability report based on the operator information of the responsible node.

[0007] By adopting the above technical solution, RFID tags are embedded inside the pipes and integrated with miniature environmental sensors, enabling real-time monitoring and data collection throughout the pipe's entire lifecycle. At the current circulation node, if the pipe's health index, indicated by the health data, is lower than a preset threshold, the quality traceability system automatically extracts the pipe's historical circulation records and historical environmental data collected by the miniature environmental sensors from the blockchain node for analysis. This accurately identifies the responsible node causing the pipe quality problem and generates a responsibility traceability report. This method avoids the lag and subjectivity of traditional manual inspection methods, improving the accuracy and efficiency of quality traceability. Simultaneously, blockchain technology ensures data immutability, providing a reliable basis for accountability.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, a health index of the pipe is determined based on pipe health data, including stress-strain data, surface damage image data, and internal structure scan data. Specifically, this includes: determining characteristic parameters of the pipe based on stress-strain data, surface damage image data, and internal structure scan data; and inputting the characteristic parameters into a pre-trained pipe health assessment model to obtain the pipe health index.

[0009] By employing the aforementioned technical solution, the quality traceability system collects multi-dimensional health data of the pipes, including stress-strain data, surface damage image data, and internal structure scan data, to extract feature parameters. These parameters are then input into a pre-trained pipe health assessment model, thereby objectively and accurately evaluating the pipes' health status. This intelligent assessment method based on multi-source data overcomes the limitations of traditional single-detection methods and can comprehensively reflect the actual condition of the pipes. Simultaneously, through the deep learning capabilities of the pre-trained model, the quality traceability system can identify potential quality hazards, enabling early warning and prediction, and effectively reducing the risk of quality accidents.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, after determining the characteristic parameters of the pipe based on stress-strain data, surface damage image data, and internal structure scan data, the method further includes: constructing a digital twin model of the pipe; inputting the characteristic parameters into the digital twin model to obtain the current state simulation result of the pipe, wherein the current state simulation result includes a list of damage types and the damage severity corresponding to each damage type.

[0011] By adopting the above technical solution, the quality traceability system constructs a digital twin model of the pipe and inputs the pipe's characteristic parameters into the digital twin model for simulation analysis to obtain detailed simulation results including damage type and severity. This digital twin technology can accurately reflect the current physical state changes of the pipe in a virtual environment, which helps to deeply analyze the causes of damage and thus promptly identify problems with the pipe.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the duration and degree of abnormality of abnormal environmental parameters in each set of environmental parameters are calculated, specifically including: determining the environmental safety tolerance threshold range of the pipe material based on the basic information of the pipe material and a preset material knowledge base; determining the abnormal environmental parameters and the degree of abnormality of the abnormal environmental parameters in the set of environmental parameters based on the environmental safety tolerance threshold range; and determining the duration of abnormal environmental parameters based on historical timestamps and the sampling frequency of the micro environmental sensor.

[0013] By adopting the above technical solution, firstly, the quality traceability system queries a pre-set material knowledge base based on the basic information of the pipe material to determine the environmental safety tolerance threshold range of the pipe material, ensuring the scientific nature and relevance of the assessment standards. Secondly, the quality traceability system compares the historical environmental data of the pipe material with the environmental safety tolerance threshold range, which not only identifies abnormal environmental parameters but also quantifies the degree of abnormality. Then, by combining the sampling frequency of the micro environmental sensor and historical timestamps, the quality traceability system can accurately calculate the duration of abnormal environmental parameters. This multi-dimensional abnormal parameter analysis method provides detailed data support for subsequent responsibility attribution, improving the accuracy and reliability of quality traceability.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the responsibility node is determined based on the duration and degree of abnormality, specifically including: calculating the environmental impact score of historical flow nodes according to the duration and degree of abnormality; identifying historical flow nodes whose environmental impact score exceeds a preset score as potential responsibility nodes; extracting the target damage type with the highest damage severity from the damage type list; calculating the damage contribution of each potential responsibility node to the target damage type; and identifying the potential responsibility node with the highest damage contribution as the responsibility node.

[0015] By adopting the above technical solution, firstly, the quality traceability system calculates the environmental impact score of historical flow nodes based on the duration and severity of abnormal environmental parameters, and filters out potential responsible nodes by setting a reasonable score threshold (preset score). Subsequently, the quality traceability system combines a damage type list obtained from digital twin model simulation to focus on analyzing the impact of each potential responsible node on the target damage type with the highest damage severity, and determines the main responsible node by calculating the damage contribution of each potential responsible node. This responsibility node analysis method considers both the impact of environmental factors and the actual damage situation, making the determination of responsibility more objective and fair, and providing strong support for quality management and accountability.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after receiving the on-site collected data sent by the receiving terminal device after reading the RFID tag at the current transfer node, the method further includes: processing the on-site collected data through a preset hash function to obtain a hash digest value; uploading the hash digest value to a blockchain node to trigger a smart contract for data verification; and storing the on-site collected data to the blockchain node after the smart contract completes the data verification.

[0017] By adopting the above technical solutions, the quality traceability system incorporates blockchain and smart contract technologies to construct a secure and reliable data storage and verification mechanism. The system hashes the data collected on-site, generating a unique hash digest value which is then uploaded to the blockchain node. The data verification process is automatically executed through smart contracts. This data processing method ensures the authenticity and immutability of the data. Simultaneously, the automated verification mechanism of smart contracts reduces human intervention, improves data processing efficiency, provides a trustworthy data foundation for the quality traceability system, and effectively prevents data forgery and tampering.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after sending a printing instruction to the RFID printer to write the basic information of the pipe into the RFID tag, the method further includes: performing a reliability test on the RFID tag, the reliability test including temperature cycling test, vibration test and electromagnetic interference test; generating an RFID identification code for the RFID tag based on a preset encryption algorithm, binding the RFID identification code with the basic information of the pipe, and uploading it to a blockchain node.

[0019] By employing the aforementioned technical solutions, the performance of RFID tags under various harsh environments was comprehensively verified through temperature cycling tests, vibration tests, and electromagnetic interference tests. Simultaneously, the quality traceability system uses a preset encryption algorithm to generate unique RFID identification codes for each tag, binding these codes to the basic information of the pipe material and storing them in a blockchain node to establish a secure and reliable identity authentication mechanism. These rigorous quality control and security measures ensure the reliable operation of the quality traceability system in practical applications, providing a solid technical guarantee for pipe quality management.

[0020] In a second aspect, embodiments of this application provide a quality traceability system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the quality traceability system to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a quality traceability system, cause the quality traceability system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a quality traceability system, cause the quality traceability system to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the quality traceability system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By adopting the above technical solution, RFID tags are embedded inside the pipes and integrated with miniature environmental sensors, enabling real-time monitoring and data collection throughout the pipe's entire lifecycle. At the current circulation node, if the pipe's health index, indicated by the health data, is lower than a preset threshold, the quality traceability system automatically extracts the pipe's historical circulation records and historical environmental data collected by the miniature environmental sensors from the blockchain node for analysis. This accurately identifies the responsible node causing the pipe quality problem and generates a responsibility traceability report. This method avoids the lag and subjectivity of traditional manual inspection methods, improving the accuracy and efficiency of quality traceability. Simultaneously, blockchain technology ensures data immutability, providing a reliable basis for accountability.

[0025] 2. By adopting the above technical solution, firstly, the quality traceability system calculates the environmental impact score of historical flow nodes based on the duration and severity of abnormal environmental parameters, and filters out potential responsible nodes by setting a reasonable score threshold (preset score). Subsequently, the quality traceability system combines the damage type list obtained from digital twin model simulation to focus on analyzing the impact of each potential responsible node on the target damage type with the highest damage severity, and determines the main responsible node by calculating the damage contribution of each potential responsible node. This responsibility node analysis method considers both the impact of environmental factors and the actual damage situation, making the determination of responsibility more objective and fair, and providing strong support for quality management and accountability.

[0026] 3. By adopting the above technical solutions, the quality traceability system incorporates blockchain and smart contract technologies to construct a secure and reliable data storage and verification mechanism. The quality traceability system hashes the data collected on-site, generating a unique hash digest value which is then uploaded to the blockchain node. The data verification process is automatically executed through smart contracts. This data processing method ensures the authenticity and immutability of the data. Simultaneously, the automated verification mechanism of smart contracts reduces human intervention, improves data processing efficiency, provides a reliable data foundation for the quality traceability system, and effectively prevents data forgery and tampering. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a pipe quality traceability method in an embodiment of this application; Figure 2 This is another flowchart illustrating the pipe quality traceability method in this application embodiment; Figure 3 This is a schematic diagram of the physical device structure of a quality traceability system in the embodiments of this application. Detailed Implementation

[0028] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0030] The following describes the process of the method provided in this implementation. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a pipe quality traceability method in an embodiment of this application.

[0031] S101. Send a printing command to the RFID printer so that the RFID printer writes the basic information of the pipe into the RFID tag. The basic information indicates the source attribute of the pipe. The RFID tag is used to embed inside the pipe. Among them, RFID printer refers to a special printing device that can write information into RFID tags, including standard RFID printers and industrial RFID printers; printing instruction refers to the command data issued by the quality traceability system to control the RFID printer to perform the writing operation; basic information refers to the source-related attribute data of the pipe, such as production batch, manufacturer, production date, material specifications, etc.; RFID tag refers to an electronic tag used to store and transmit pipe information, which consists of a chip, antenna and shell; pipe interior refers to the tag embedded in the cavity reserved in the pipe wall.

[0032] Specifically, the quality traceability system obtains basic information about the pipes from the production management system. This information includes the production batch number, manufacturer code, production date and time, material type, and dimensions. The system then encapsulates this basic information into a printing instruction according to a predefined data structure and sends it to the RFID printer via a network or serial interface. Upon receiving the printing instruction, the RFID printer activates its writing module to write the basic information into the storage chip of the RFID tag to be embedded in the pipe. After writing is complete, the worker embeds the RFID tag containing the basic information into a pre-reserved cavity in the pipe wall, completing the tag implantation.

[0033] S102. Receive the field-collected data sent by the terminal equipment after reading the RFID tag at the current circulation node. The field-collected data includes timestamp, operator information, GPS coordinates of the current circulation node, and pipe health data. Among them, terminal equipment refers to handheld or fixed reading devices used to read RFID tags; current circulation node refers to the circulation node where the terminal equipment is located at the current moment; circulation node refers to the various points where the pipe material stops during transportation, storage, and use; timestamp indicates the precise time of data collection; operator information includes the operator's identity ID, name, and other basic information; GPS coordinates are used to identify the latitude and longitude information of the current location; and pipe health data refers to the detection data reflecting the current health status of the pipe material.

[0034] Specifically, when the pipes are transported to a new distribution node, the operator uses a terminal device on-site to read the RFID tags embedded in the pipes. The terminal device has a built-in real-time clock module that automatically records a timestamp when reading the RFID tag; the timestamp format is "YYYY-MM-DD HH:MM:SS.mmm". The operator logs in to the terminal device via fingerprint recognition or an employee card for authentication, and the terminal device automatically retrieves the operator's information. The terminal device integrates a high-precision GPS positioning module to automatically obtain the GPS coordinates of the current distribution node. The operator can collect pipe health data in the following ways, without limitation: non-destructive testing of the pipes using ultrasonic testing and electromagnetic emission technology to obtain stress distribution data; or automatically capturing images of the pipe surface using a high-definition camera and analyzing them in real time using image recognition algorithms to detect scratches, dents, corrosion, and other damage; for internal structural inspection of the pipes, an integrated portable ultrasonic scanner can be used to scan the cross-section of the pipe, and the ultrasonic reflection signals can be processed to generate pipe wall thickness and internal defect distribution maps, etc. The operator sends the data collected on-site (timestamp, operator information, GPS coordinates of the current circulation node, and pipe health data) to the quality traceability system through the terminal device.

[0035] S103. Determine the health index of the pipe based on the pipe health data; Among them, the health index is a comprehensive score used to quantitatively characterize the overall health status of the pipe, and the value range is usually 0-100.

[0036] Specifically, firstly, the quality traceability system preprocesses the pipe health data, including data cleaning, outlier handling, and data standardization. Then, the system inputs the preprocessed pipe health data into a pre-trained health assessment model, outputting a comprehensive health index between 0 and 100. A higher health index indicates a better overall health status for the pipe, while a lower index suggests potential quality issues.

[0037] The steps to build a deep learning-based health assessment model are as follows: First, the quality traceability system acquires pipe health data and corresponding health indices for a preset time period (e.g., 6 months). The health indices can be obtained through expert evaluation and are not limited here. The quality traceability system stores the pipe health data and corresponding health indices in dataset D, with each data entry formatted as (pipe health data, health index). Here, the pipe health data serves as the input feature for model training, and the health index is the output feature for model training.

[0038] Then, the quality traceability system constructs an LSTM-based recurrent neural network, which includes an input layer, two LSTM hidden layers, a fully connected layer, and an output layer. The input layer takes in the pipe health data, the hidden layer has 64 nodes, the fully connected layer has 32 nodes, and the output layer outputs the health index.

[0039] Next, the quality traceability system uses the Adam optimizer with a learning rate of 0.001 and a training batch size of 32. These settings can be adjusted based on actual conditions and are not limited here. 80% of the sample data is divided into a training dataset and 20% into a validation dataset. Training is performed for 100 epochs, and the model with the highest accuracy on the validation dataset is saved. These settings can also be adjusted based on actual conditions and are not limited here. An epoch is the process of the training dataset passing completely through the neural network once. In machine learning and deep learning, an epoch is a unit used to measure the number of times the entire training dataset has been repeatedly learned. Specifically, an epoch is completed when the neural network completes one forward computation and one backward propagation, meaning all data has been processed by the network once. The quality traceability system uses binary cross-entropy as the loss function and employs Early Stopping to prevent overfitting. When the value of the loss function exceeds a preset threshold, the model training is considered complete, and a health assessment model is obtained.

[0040] Finally, the quality traceability system inputs the input features from the validation dataset into the health assessment model, then obtains the model's predicted output. This predicted output is compared with the actual output features from the validation dataset, and performance metrics such as accuracy, precision, recall, F1 score, and mean squared error (MSE) are used to evaluate the model's performance. Based on the model's performance on the validation dataset, its parameters are adjusted, including adjusting the learning rate, changing model complexity (e.g., increasing or decreasing the number of layers or nodes in the neural network), and modifying the regularization strength. This process may require multiple iterations, each based on the previous learning results, to optimize the health assessment model.

[0041] Optionally, in general, the health index of the pipe is determined based on the pipe health data, including stress-strain data, surface damage image data, and internal structure scan data. This can be achieved in the following ways, without limitation: Based on the stress-strain data, surface damage image data, and internal structure scan data, determine the characteristic parameters of the pipe; input the characteristic parameters into a pre-trained pipe health assessment model to obtain the pipe health index.

[0042] Among them, stress-strain data represents the quantitative measurement values ​​of stress and deformation in various parts of the pipe; surface damage image data refers to high-definition image information that records the characteristics of surface defects in the pipe; internal structure scan data refers to ultrasonic test data that reflects the internal integrity of the pipe; feature parameters refer to standardized feature indicators extracted from stress-strain data, surface damage image data, and internal structure scan data; and the pre-trained pipe health assessment model refers to a machine learning model trained on historical data.

[0043] Specifically, firstly, the quality traceability system preprocesses stress-strain data, surface damage image data, and internal structure scan data: For stress-strain data, signal filtering and peak extraction are performed to calculate the stress concentration factor and strain distribution uniformity; for surface damage image data, enhancement and segmentation are performed to extract features such as defect type, size, depth, and distribution density; for internal structure scan data, echo analysis and defect imaging are performed to obtain pipe wall thickness variations and internal defect distribution maps. Then, the quality traceability system converts the above data into feature parameters according to a standardized format, including a set of stress feature parameters, a set of surface feature parameters, and a set of structural feature parameters. Finally, the quality traceability system inputs the complete feature parameters into a pre-trained deep learning model, which calculates a health index between 0 and 100 through a multi-layer neural network. This health index comprehensively reflects the overall health condition of the pipe; the closer to 100, the better the overall health condition, and the closer to 0, the worse the overall health condition.

[0044] Optionally, after determining the characteristic parameters of the pipe based on stress-strain data, surface damage image data, and internal structure scan data, the following steps may be performed, or may not be performed, and are not limited here: construct a digital twin model of the pipe; input the characteristic parameters into the digital twin model to obtain the simulation results of the current state of the pipe, which include a list of damage types and the severity of damage corresponding to each damage type.

[0045] Among them, the digital twin model refers to a digital model constructed in virtual space that has the same geometric features and physical properties as the physical pipe; the feature parameters represent a set of standardized feature indicators extracted from the pipe health data; the current state simulation result refers to the simulated data of the current state of the pipe calculated by the digital twin model based on the input feature parameters; the damage type list refers to a list of various possible damage forms identified by the digital twin model, such as fatigue cracks, corrosion thinning, mechanical wear, etc.; the damage severity refers to the quantitative assessment value of the degree of harm of each damage type, usually divided into 1-5 levels.

[0046] Specifically, firstly, the quality traceability system, based on the pipe's basic information (including material, specifications, structure, etc.), calls a pre-set digital twin model template to construct a digital twin model that conforms to the current pipe's characteristics. This digital twin model includes multiple sub-models such as a finite element mesh model, a material constitutive relation model, and a damage evolution model. Then, the quality traceability system converts the extracted feature parameters into input parameters for the digital twin model, including stress distribution parameters, surface morphology parameters, and internal structural parameters. After receiving the digital twin model, a state simulation is performed through a computational engine, taking into account the combined effects of material properties, environmental factors, and service conditions. After the simulation is completed, the quality traceability system analyzes the simulation results to identify various potential types of damage, such as cracks caused by stress concentration, corrosion caused by environmental factors, and deformation caused by mechanical action. For each identified damage type, the quality traceability system assesses the severity of the damage based on its development stage, scope of impact, and potential hazards.

[0047] S104. When the health index is less than the preset index threshold, extract the historical circulation records of the pipe and the historical environmental data collected by the micro environmental sensor from the blockchain node. The micro environmental sensor is integrated into the RFID tag and uploads the collected environmental data to the blockchain node in real time. Among them, the preset index threshold represents the health index warning value pre-set by the quality traceability system, which is usually determined based on the pipe type and application scenario; the blockchain node refers to the node server that stores transaction data in the distributed network; the historical circulation record represents the detailed information of all circulation links of the pipe from the factory to the current moment; the miniature environmental sensor refers to the miniaturized sensor component integrated into the RFID tag, including temperature sensor, humidity sensor, vibration sensor, etc.; and the historical environmental data represents the time series data of environmental parameters continuously collected by the miniature environmental sensor.

[0048] Specifically, firstly, the quality traceability system compares the calculated health index with a preset threshold. When the health index is lower than the preset threshold, the system calls the blockchain interface to extract the historical circulation records of the pipes from the blockchain nodes, including information such as the time, location, and operator of each circulation node. Simultaneously, the system queries and extracts historical environmental data stored in the blockchain nodes. This historical environmental data (parameters such as temperature, humidity, and vibration of the environment in which the pipes are located, as well as the timestamps of data collection) is automatically collected and periodically uploaded by miniature environmental sensors in RFID tags.

[0049] S105. Based on the historical timestamps and historical GPS information in the historical transfer records, the historical environmental data is divided into multiple sets of environmental parameters for historical transfer nodes, and the duration and degree of abnormality of the abnormal environmental parameters in each set of environmental parameters are calculated. Among them, historical timestamps refer to the various time points recorded in the historical circulation records; historical GPS information represents the geographical coordinates of the pipe at each circulation node; environmental parameter set represents all environmental parameter data collected during a certain circulation node; abnormal environmental parameters refer to environmental parameters that exceed the safe range; duration indicates the length of time the pipe is in abnormal environmental parameters; and degree of abnormality refers to the degree to which abnormal environmental parameters deviate from the safe range.

[0050] Specifically, firstly, the quality traceability system determines the start and end times of the pipes at each circulation node based on historical timestamps and GPS information from historical circulation records. Then, the system segments the historical environmental data according to these time intervals, forming multiple sets of environmental parameters corresponding to different circulation nodes. For each set of environmental parameters, the system sets a safe range for the environmental parameters based on the pipe's material characteristics and usage standards. By comparing the actual environmental parameter values ​​in the set with the safe environmental parameter values ​​within the safe range, the system identifies abnormal environmental parameters. For each abnormal environmental parameter, the system calculates its duration (based on the start and end times of the abnormal environmental parameter) and its degree of abnormality (calculating the deviation between the actual environmental parameter value and the safe environmental parameter value).

[0051] Optionally, under normal circumstances, the duration and degree of abnormality of abnormal environmental parameters in each set of environmental parameters can be calculated in the following ways, without limitation: determine the environmental safety tolerance threshold range of the pipe based on the basic information of the pipe and the preset material knowledge base; determine the abnormal environmental parameters and the degree of abnormality of the abnormal environmental parameters in the set of environmental parameters based on the environmental safety tolerance threshold range; determine the duration of abnormal environmental parameters based on historical timestamps and the sampling frequency of the micro environmental sensor.

[0052] S106. Based on the duration and degree of abnormality, determine the responsible node and generate a responsibility tracing report based on the operator information of the responsible node.

[0053] Among them, the responsibility node indicates the main point of responsibility for the pipe quality problem; the operator information refers to the information of the relevant operator at the responsibility node; and the responsibility traceability report is an analysis report describing the cause of the quality problem, the degree of impact, and the determination of responsibility.

[0054] Specifically, firstly, the quality traceability system calculates an environmental impact score for each circulation node, considering two dimensions: the duration and severity of the abnormal environmental parameters. For duration, the system assigns different weighting coefficients based on the length of time; for severity, it sets different impact factors based on the degree of deviation from safe limits. The system multiplies the scores from both dimensions to obtain the final environmental impact score for that circulation node. The system then selects the circulation node with the highest environmental impact score as the responsible node and extracts the operator information for that node from the blockchain nodes. Finally, the system generates a responsibility traceability report, which details the discovery process of the quality problem, the results of the environmental anomaly analysis, the basis for responsibility determination, and information on the relevant responsible persons.

[0055] Optionally, under normal circumstances, the determination of responsible nodes based on duration and degree of abnormality can be achieved in the following ways, without limitation: calculate the environmental impact score of historical flow nodes based on duration and degree of abnormality; identify historical flow nodes with environmental impact scores exceeding a preset score as potential responsible nodes; extract the target damage type with the highest damage severity from the damage type list; calculate the damage contribution of each potential responsible node to the target damage type; and identify the potential responsible node with the highest damage contribution as the responsible node.

[0056] Among them, the environmental impact score refers to the quantitative score reflecting the comprehensive impact of environmental anomalies at the transfer node; the preset score represents the environmental impact score warning value pre-set by the quality traceability system; the potential responsibility node refers to the transfer node whose environmental impact score exceeds the preset score and may cause damage to the pipe; the target damage type represents the most serious form of damage to the current pipe; the damage contribution refers to the degree of influence of environmental anomalies at a specific transfer node on the formation of the target damage type; and the responsibility node refers to the transfer node that is finally determined to cause the main damage to the pipe.

[0057] Specifically, firstly, the quality traceability system calculates an environmental impact score based on the duration and severity of abnormal environmental parameters. The calculation uses a weighted scoring method, where the weighting coefficient for duration is set according to the time span, and the weighting coefficient for severity is determined based on the degree to which the parameter deviates from the safe range. The system multiplies the weighted scores of the two dimensions to obtain the environmental impact score for each historical flow node. Then, the system compares the environmental impact scores of each historical flow node with preset scores, selecting those exceeding the preset scores as potential responsible nodes. Next, based on the damage type list generated by the digital twin model, the system selects the damage type with the highest severity as the target damage type. For each potential responsible node, the system analyzes the correlation between its abnormal environmental parameters and the target damage type, combining materials science principles and damage mechanism models to calculate the potential responsible node's contribution to the formation of the target damage type. Finally, the system compares the damage contribution of all potential responsible nodes and determines the potential responsible node with the highest damage contribution as the final responsible node. This multi-dimensional method of liability determination considers both the direct impact of environmental anomalies and the specific type of damage, making the liability determination results more accurate and reliable.

[0058] By adopting the above technical solution, RFID tags are embedded inside the pipes and integrated with miniature environmental sensors, enabling real-time monitoring and data collection throughout the pipe's entire lifecycle. At the current circulation node, if the pipe's health index, indicated by the health data, is lower than a preset threshold, the quality traceability system automatically extracts the pipe's historical circulation records and historical environmental data collected by the miniature environmental sensors from the blockchain node for analysis. This accurately identifies the responsible node causing the pipe quality problem and generates a responsibility traceability report. This method avoids the lag and subjectivity of traditional manual inspection methods, improving the accuracy and efficiency of quality traceability. Simultaneously, blockchain technology ensures data immutability, providing a reliable basis for accountability.

[0059] The following provides a more detailed description of the process of the method provided in this implementation. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the pipe quality traceability method in this application embodiment.

[0060] S201. Send a printing command to the RFID printer so that the RFID printer writes the basic information of the pipe into the RFID tag. The basic information indicates the origin attribute of the pipe. The RFID tag is used to embed inside the pipe.

[0061] For details, please refer to step S101, which will not be repeated here.

[0062] S202. Conduct reliability testing on RFID tags, including temperature cycling test, vibration test and electromagnetic interference test.

[0063] Among them, reliability testing refers to the test method to verify the normal operation capability of RFID tags under various extreme environments; temperature cycling test refers to the performance test of RFID tags repeatedly cyclically under different temperature environments; vibration test refers to the reliability verification of RFID tags by simulating mechanical vibration during transportation and use; electromagnetic interference test refers to the test method to evaluate the signal stability of RFID tags under various electromagnetic environments.

[0064] Specifically, firstly, staff conducted temperature cycling tests on the RFID tags in a temperature test chamber, cyclically placing the tags within a temperature range of -40℃ to 85℃, with each cycle lasting 4 hours, for a total of 100 cycles, to test the RFID tags' read / write performance and data retention capabilities under extreme temperatures. Next, vibration tests were performed, fixing the RFID tags to a vibration table and subjecting them to random vibration at preset frequencies (10-500Hz) and accelerations (5g) for 24 hours to verify the mechanical strength and signal stability of the RFID tags. Finally, electromagnetic interference tests were conducted, simulating various electromagnetic interference environments (including industrial electromagnetic noise and wireless communication interference) in an electromagnetic shielding room to test the RFID tags' anti-interference capabilities and data transmission accuracy. The data was then sent to the quality traceability system, which recorded all test data and evaluated it according to preset reliability indicators.

[0065] S203. Generate RFID identification codes for RFID tags based on preset encryption algorithms, bind the RFID identification codes with the basic information of the pipes, and upload them to the blockchain node.

[0066] Among them, the preset encryption algorithm refers to the standard encryption method adopted by the quality traceability system to generate a secure and reliable identification code; the RFID identification code represents the unique identity of each RFID tag; binding means establishing a correspondence between the RFID identification code and the basic information of the pipe.

[0067] Specifically, firstly, the quality traceability system uses secure hash algorithms such as SHA-256, combined with the pipe's unique serial number and production time, to generate an initial key. Then, based on this initial key, it uses the AES-256 encryption algorithm to generate a unique RFID tag. The generated RFID tag contains information fields such as manufacturer code, product type code, and serial number, and also has anti-counterfeiting and anti-tampering characteristics. The quality traceability system writes this RFID tag into the RFID tag's storage chip and establishes a mapping relationship between the RFID tag and the pipe's basic information in the database. Finally, the quality traceability system packages the bound complete data record, uses the data format specified by the blockchain protocol, and uploads it to the blockchain network through a secure channel to achieve tamper-proof data storage and distributed management.

[0068] S204. The receiving terminal device reads the RFID tag at the current circulation node and sends the field collection data, which includes timestamp, operator information, GPS coordinates of the current circulation node, and pipe health data.

[0069] For details, please refer to step S102, which will not be repeated here.

[0070] S205. Process the collected data on site using a preset hash function to obtain a hash digest value.

[0071] The preset hash function refers to the cryptographic algorithm pre-set by the quality traceability system for calculating data digests; the hash digest value refers to a fixed-length data fingerprint calculated by the preset hash function, used to verify data integrity and consistency.

[0072] Specifically, firstly, the quality traceability system standardizes the received on-site collected data, organizing timestamps, operator information, GPS coordinates, and pipe health data according to a predefined format. Then, the system uses the SHA-256 hash algorithm (a preset hash function) to calculate a 256-bit hash digest value from the standardized on-site collected data. This hash digest value is unidirectional and unique, uniquely identifying the original dataset; even minor changes to the data will result in a completely different hash value, thus ensuring data integrity.

[0073] S206. Upload the hash digest value to the blockchain node to trigger the smart contract for data verification.

[0074] In this context, a blockchain node refers to a server in a distributed network responsible for storing and maintaining data; a smart contract refers to automatically executed program code deployed on the blockchain; and data verification refers to the process of verifying the legality and validity of data through smart contracts.

[0075] Specifically, firstly, the quality traceability system encapsulates the hash digest value into a blockchain transaction request and sends it to the blockchain network through a secure channel. When a blockchain node receives the transaction request, it automatically triggers the execution of a pre-deployed smart contract. The smart contract includes multiple verification modules, such as data format verification, operation permission verification, and business rule verification, to ensure that the uploaded data meets the requirements.

[0076] S207. After the smart contract completes data verification, the data collected on-site will be stored in the blockchain node.

[0077] Specifically, firstly, the quality traceability system encapsulates the complete on-site collected data according to the data structure defined by the blockchain protocol, including data content, timestamps, signatures, and other information. Then, through the blockchain network's consensus mechanism, the system broadcasts the on-site collected data to all network nodes. After verifying the validity of the transactions, the system packages the on-site collected data into a block and links it to previous blocks through a chain structure. Once data is stored on the blockchain, it possesses the characteristics of immutability and traceability, ensuring data security and trustworthiness.

[0078] S208. Determine the health index of the pipe based on the pipe health data.

[0079] For details, please refer to step S103, which will not be repeated here.

[0080] S209. When the health index is less than the preset index threshold, extract the historical circulation records of the pipe and the historical environmental data collected by the micro environmental sensor from the blockchain node. The micro environmental sensor is integrated into the RFID tag and uploads the collected environmental data to the blockchain node in real time.

[0081] For details, please refer to step S104, which will not be repeated here.

[0082] S210. Based on the historical timestamps and historical GPS information in the historical transfer records, the historical environmental data is divided into multiple sets of environmental parameters for historical transfer nodes, and the duration and degree of abnormality of the abnormal environmental parameters in each set of environmental parameters are calculated.

[0083] For details, please refer to step S105, which will not be repeated here.

[0084] S211. Based on the duration and degree of abnormality, determine the responsible node and generate a responsibility tracing report based on the operator information of the responsible node.

[0085] For details, please refer to step S106, which will not be repeated here.

[0086] The quality traceability system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3This is a schematic diagram of the physical device structure of a quality traceability system in an embodiment of this application.

[0087] It should be noted that, Figure 3 The structure of the quality traceability system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0088] like Figure 3 As shown, the quality traceability system includes a CPU 301, which can perform various appropriate actions and processes based on a program stored in the read-only memory ROM 302 or a program loaded from the storage section 308 into the random access memory RAM 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O interface 305 is also connected to the bus 304.

[0089] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0090] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by CPU 301, it performs the various functions defined in the present invention.

[0091] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0093] Specifically, the quality traceability system of this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the pipe quality traceability method provided in the above embodiment.

[0094] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the quality traceability system described in the above embodiments; or it may exist independently and not assembled into the quality traceability system. The storage medium carries one or more computer programs that, when executed by a processor of the quality traceability system, cause the quality traceability system to implement the pipe quality traceability method provided in the above embodiments.

[0095] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0096] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0097] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for tracing the quality of pipe materials, characterized in that, The method, applied to a quality traceability system, includes: A printing instruction is sent to an RFID printer so that the RFID printer writes basic information about the pipe into an RFID tag. The basic information indicates the origin attribute of the pipe. The RFID tag is used to embed inside the pipe. The receiving terminal device reads the RFID tag at the current circulation node and sends the field collection data, which includes timestamp, operator information, GPS coordinates of the current circulation node, and pipe health data; Based on the pipe health data, the health index of the pipe is determined; When the health index is less than a preset index threshold, the historical circulation records of the pipe and the historical environmental data collected by the micro environmental sensor are extracted from the blockchain node. The micro environmental sensor is integrated into the RFID tag and uploads the collected environmental data to the blockchain node in real time. Based on the historical timestamps and historical GPS information in the historical flow records, the historical environmental data is divided into multiple sets of environmental parameters for historical flow nodes, and the duration and degree of abnormality of abnormal environmental parameters in each set of environmental parameters are calculated. Based on the duration and the degree of abnormality, the responsible node is determined, and a responsibility tracing report is generated based on the operator information of the responsible node.

2. The method according to claim 1, characterized in that, The health index of the pipe is determined based on the pipe health data, which includes stress-strain data, surface damage image data, and internal structure scan data, specifically including: Based on the stress-strain data, the surface damage image data, and the internal structure scan data, the characteristic parameters of the pipe are determined. The feature parameters are input into a pre-trained pipe health assessment model to obtain the health index of the pipe.

3. The method according to claim 2, characterized in that, After the step of determining the characteristic parameters of the pipe based on the stress-strain data, the surface damage image data, and the internal structure scan data, the method further includes: Construct a digital twin model of the pipe; The feature parameters are input into the digital twin model to obtain the current state simulation result of the pipe. The current state simulation result includes a list of damage types and the damage severity corresponding to each damage type.

4. The method according to claim 1, characterized in that, The calculation of the duration and degree of abnormality of the abnormal environmental parameters in each set of environmental parameters specifically includes: Based on the basic information of the pipe and a preset material knowledge base, the environmental safety tolerance threshold range of the pipe is determined; Based on the environmental safety tolerance threshold range, determine the abnormal environmental parameters in the set of environmental parameters and the degree of abnormality of the abnormal environmental parameters; The duration of the abnormal environmental parameters is determined based on the historical timestamp and the sampling frequency of the micro environmental sensor.

5. The method according to claim 3, characterized in that, The determination of the responsible node based on the duration and the degree of abnormality specifically includes: Calculate the environmental impact score of the historical flow node based on the duration and the degree of anomaly. Historically significant nodes whose environmental impact scores exceed a preset score are identified as potential liability nodes. Extract the target damage type with the highest damage severity from the damage type list; Calculate the damage contribution of each potential responsible node to the target damage type; The potential responsible node with the highest contribution to the damage is identified as the responsible node.

6. The method according to claim 1, characterized in that, After the step of receiving the on-site collected data sent by the receiving terminal device after reading the RFID tag at the current transfer node, the method further includes: The collected data is processed using a preset hash function to obtain a hash digest value; The hash digest value is uploaded to the blockchain node to trigger a smart contract for data verification; After the smart contract completes data verification, the data collected on-site is stored in the blockchain node.

7. The method according to claim 1, characterized in that, After the step of sending a printing instruction to the RFID printer to cause the RFID printer to write the basic information of the pipe into the RFID tag, the method further includes: The RFID tag is subjected to reliability testing, which includes temperature cycling testing, vibration testing, and electromagnetic interference testing. The RFID identification code of the RFID tag is generated based on a preset encryption algorithm, the RFID identification code is bound to the basic information of the pipe, and uploaded to the blockchain node.

8. A quality traceability system, characterized in that, The quality traceability system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the quality traceability system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the quality traceability system, it causes the quality traceability system to perform the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the quality traceability system, the quality traceability system performs the method as described in any one of claims 1-7.

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