A visualization method for mechanical and electrical pipeline concealed engineering based on two-dimensional code technology

Abstract

The application relates to the field of electromechanical pipeline engineering and discloses a kind of visualization method of electromechanical pipeline concealed engineering based on two-dimensional code technology, comprising the following steps: S1. quantum dot and pipe integrated preparation;S2. multidimensional data embedding and encryption;S3. edge computing node deployment: install MEMS computing buckle with vibration and temperature sensing function at pipeline key node;S4. multimodal data fusion;S5. man-machine interaction and decision-making;S6. self-repairing and verification.Through the integrated preparation process of quantum dots and pipes, the pipeline has real-time transmission capabilities of parameters such as strain and temperature, combined with edge computing nodes and hyperspectral cameras, static pipe information and dynamic sensing data are encrypted and written into two-dimensional codes, processed by extended Kalman filtering algorithm, precise mapping of local data to global BIM model is realized, a full-process digital twin system from production to operation is constructed, and the problems of easy loss and update lag of traditional paper records are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electromechanical pipeline engineering, and in particular to a visualization method for concealed electromechanical pipeline engineering based on QR code technology. Background Art

[0002] In the field of concealed electromechanical pipeline engineering, the full lifecycle management and operation and maintenance of pipeline systems have always faced core challenges such as information gaps, delayed monitoring, and inefficient maintenance. In traditional management models, pipeline information is often recorded in paper documents or simple spreadsheets. During the construction process, this information is easily lost, erroneous, or updated in a timely manner due to human intervention or environmental factors. For example, key data such as the model specifications and installation path of galvanized steel pipes are often missing after completion, resulting in the inability to accurately trace the original design parameters during the operation and maintenance phase. In addition, traditional methods for monitoring the status of operating pipelines lack real-time performance and accuracy. For example, data obtained only through manual inspections or single-point temperature sensors cannot capture subtle changes in dynamic parameters such as pipeline strain and vibration, and cannot detect potential faults caused by mechanical stress, temperature anomalies, etc. at an early stage. For example, strain accumulation at elbows in air conditioning water pipe networks can cause sudden ruptures without warning, resulting in serious safety accidents.

[0003] During the later repair and maintenance phase, due to the lack of efficient information exchange and positioning methods, operations and maintenance personnel struggle to quickly obtain the pipeline's real-time status and historical maintenance records, resulting in lengthy fault location times and cumbersome inspection processes. For example, when a concealed pipeline leaks, traditional methods require wall excavation and comparison with blueprints. This is not only time-consuming and labor-intensive, but may also cause secondary damage to surrounding facilities and result in high repair costs. While existing pipeline monitoring solutions exist, such as RFID-based information management or BIM model visualization, these generally suffer from insufficient technical integration. RFID cannot carry dynamic sensor data, BIM models lack real-time mapping with physical pipelines, and data processing capabilities are limited, making it difficult to cope with the multi-source data fusion requirements under complex working conditions. Furthermore, the visualization effects of existing solutions are mostly static model displays, unable to implement dynamic interactive functions such as damage warnings and maintenance guidance. This makes it difficult to meet the management requirements of modern electromechanical engineering for concealed pipelines, requiring "high-precision monitoring and intelligent operation and maintenance." Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention aims to provide a visualization method for concealed electromechanical pipeline projects based on QR code technology. By combining quantum dot sensing technology, QR code encoding technology, edge computing, multimodal data fusion and augmented reality technology, it can realize information visualization management of the entire process of electromechanical pipelines from production, construction to operation and maintenance, improve the accuracy, real-time performance and operation and maintenance efficiency of pipeline monitoring, and reduce operation and maintenance costs and safety risks.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A visualization method for concealed electromechanical pipeline engineering based on QR code technology, comprising the following steps:

[0006] S1. Integrated preparation of quantum dots and tubes: The tube surface is plasma-etched to a roughness of Ra2-5μm, and a two-dimensional code matrix is ​​generated on the surface using a laser-induced coding process. A quantum dot composite slurry is prepared and filled into the two-dimensional code grooves, forming a quantum dot coding layer with mechanical strain response and self-healing capabilities.

[0007] S2. Multidimensional data embedding and encryption: Write multidimensional data into the QR code and build a data protection system by combining AES-256 encryption and Hyperledger Fabric consortium chain;

[0008] S3. Edge computing node deployment: MEMS computing bolts with vibration and temperature sensing capabilities are installed at key pipeline nodes to locate damage by fusing vibration and temperature data.

[0009] S4. Multimodal data fusion: A hyperspectral camera scans the pipeline surface, reads the raw QR code data, and simultaneously captures the quantum dot emission wavelength to invert the strain value. This data is then integrated with the edge computing node inertial data and the QR code location information to perform a time-space coordinate system conversion.

[0010] S5. Human-computer interaction and decision-making: Use the AR device to scan the QR code, display the pipeline status in real time based on the AR device, and trigger tactile feedback based on the fused data from step S4;

[0011] S6. Self-healing repair and verification: When surface cracks are detected that cause the quantum dot coding layer to break, the damaged area is heated to above 60°C and the cracks are filled by the flow of shape memory polymer.

[0012] Preferably, the preparation of the quantum dot composite slurry in S1 includes: mixing 15%-20% of cadmium telluride quantum dots, 40%-45% of shape memory polymer, 10%-15% of silicon carbide nanowires and 35%-40% of ultraviolet curing resin in a mass ratio, and forming a slurry after ball milling and dispersion; wherein the shape memory polymer triggers flow repair at above 60°C, and the silicon carbide nanowires bridge to enhance crack resistance.

[0013] Preferably, the laser induced coding process in S1 uses a femtosecond laser to scan the surface of the tube to generate a two-dimensional code with a single code element size of 0.8mm×0.8mm and a groove depth of 80-100μm, and realizes molecular-level fusion of quantum dot slurry and the tube through vacuum filling and ultraviolet curing.

[0014] Preferably, the multi-dimensional data in S2 includes static data, including pipe model, production batch number and construction responsibility person, which is written into the two-dimensional code after AES-256 encryption, and dynamic data including a reserved blank storage area for subsequent writing of real-time sensing data of stress and temperature, and 20% of the two-dimensional code matrix is demarcated as an erasable area.

[0015] Preferably, the MEMS computing buckle in S3 is built-in six-axis inertial sensor, temperature sensor and Bluetooth 5.2 module, which is used for monitoring global vibration and temperature, positioning the damage position after fusing the two data, and maintaining only Bluetooth low power consumption broadcast when the buckle is in sleep state.

[0016] Preferably, the erasable area adopts a chalcogenide glass material, and data erasing is realized by laser modulation.

[0017] Preferably, the trigger mechanism of the MEMS computing buckle includes that when the vibration amplitude is greater than 0.1g or the temperature exceeds the threshold value, the sampling rate of the six-axis inertial sensor and the temperature sensor is switched from 1Hz to 100Hz, and the data is encrypted and transmitted through Bluetooth with a communication radius of 200m-250m.

[0018] Preferably, the space-time coordinate system conversion in S4 needs to fuse the two-dimensional code positioning data, quantum dot strain inversion data and buckle inertial data, the two-dimensional code positioning data is generated through geometric coding and dynamically bound with the three-dimensional coordinate of the pipeline, so as to realize the mapping from local measurement space to global BIM model.

[0019] Preferably, the AR device in S5 updates the model state in combination with real-time detection data, including quantum dot strain value and MEMS vibration data, realizes dynamic maintenance path planning, and triggers the early warning logic according to the original design parameters, including material strength and allowable strain threshold, and the comparison between the strain value detected in real time.

[0020] Preferably, the self-healing repair in S6 includes wrapping a flexible resistance heating film and heating to above 60℃ at a rate of 5℃ / min, maintaining for 10-15min to make the shape memory polymer flow to fill the crack, and the conductivity is restored to ≥85% after cooling.

[0021] The application provides a visual method for mechanical and electrical pipeline concealed engineering based on two-dimensional code technology.

[0022] 1. This invention uses an integrated preparation process of quantum dots and pipes to enable pipelines to have real-time sensing capabilities for parameters such as strain and temperature. By combining edge computing nodes and hyperspectral cameras, static pipe information (such as model and batch number) and dynamic sensor data are encrypted and written into a QR code. After processing through the extended Kalman filter algorithm, accurate mapping of local data to the global BIM model is achieved, building a full-process digital twin system from production to operation and maintenance, solving the problem of easy loss and delayed update of traditional paper records.

[0023] 2. The present invention uses an AR interactive system to superimpose early warning information and maintenance instructions in real time, and links the PLM system to achieve dynamic planning of maintenance routes, which greatly improves efficiency compared to traditional methods. When a crack is detected, the shape memory polymer self-repairs through thermal stimulation to restore conductivity, and cooperates with blockchain evidence storage to form a closed-loop management, which not only reduces manual inspection costs, but also ensures the reliability of operation and maintenance audits through an unalterable data chain.

[0024] 3. This invention uses femtosecond laser etching and vacuum filling technology to achieve molecular-level bonding between the QR code layer and the pipe. The information density reaches 100 bytes / cm², and it has IP68 protection capability, which is suitable for long-term operation in concealed projects. At the same time, the edge computing node adopts a "sleep-wake-up" mode, and adjacent nodes are automatically networked if the distance is ≤5m. The battery life of a single node is more than 3 years. Combined with dynamic key encryption and blockchain evidence storage, it takes into account low power consumption and data security, solving the problems of low integration and weak data processing capabilities of traditional solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] Example:

[0028] Please see the attached Figure 1 The embodiment of the present invention provides a visualization method for concealed electromechanical pipeline engineering based on QR code technology, comprising the following steps:

[0029] S1. Quantum dot-tube integrated preparation;

[0030] S101. Substrate pretreatment: Based on the target pipeline material, such as galvanized steel pipes and PVC pipes, a plasma etching process is used to form a micron-level roughness (Ra2-5μm) on the surface to enhance the adhesion of quantum dots. This roughness can significantly enhance the subsequent adhesion of quantum dots to the pipe surface. For metal materials such as stainless steel pipes, in addition to the plasma etching treatment, an additional anodizing treatment is performed to generate a 50-100nm oxide layer on its surface, further enhancing the adhesion of quantum dots through chemical bonding.

[0031] S102. Quantum dot composite slurry preparation: Mix the following components by mass: Cadmium telluride quantum dots (CdTe), with a particle size of 3-5nm and a luminescence wavelength of 540nm, accounting for 15%; Shape Memory Polymer (SMP), with a glass transition temperature (Tg) of 60°C, accounting for 40%. When the temperature exceeds Tg (60°C), it flows to fill the pores of the slurry, increasing density. Simultaneously, during crack repair, it self-heals through thermal stimulation, restoring 88% conductivity. 60°C is close to the maximum operating temperature of conventional electromechanical pipelines, such as heating pipes, which are approximately 50-70°C. This ensures material stability at room temperature while triggering self-healing when the pipeline overheats. For high-temperature applications, such as industrial pipelines, a higher Tg value, such as 80-100°C, is required. Silicon carbide nanowires, used to enhance mechanical strength, account for 10%; and UV-curable resin, account for 35%. Disperse the slurry by ball milling at 300 rpm for 2 hours to form a homogenous quantum dot slurry. Among them, cadmium telluride quantum dots serve as the core sensing unit, and their specific luminescence wavelength is in the sensitive range of subsequent hyperspectral cameras, and the quantum confinement effect makes them respond linearly to mechanical strain; in addition to having self-repair functions, shape memory polymers can also fill the microscopic pores in the slurry when cured at 60°C, thereby improving the density of the composite material; silicon carbide nanowires enhance the composite material's resistance to crack propagation through the bridging effect. Bridging refers to the formation of a "bridge"-like connection between silicon carbide nanowires at the crack tip or both sides of the crack inside the material. "Bridging" is a key concept in the field of materials science that describes the interaction between reinforcement and matrix. The core mechanism is to disperse the stress concentration at the crack tip through the mechanical properties of the nanowires, and to significantly improve the overall toughness and crack resistance of the material by physically connecting the cracks and dispersing the stress.

[0032] The obtained quantum dot homogeneous slurry was subjected to correlation test. The specific experimental process and experimental structure include:

[0033] 1. Quantum dot adhesion test

[0034] Method: Cross-hatch method (ASTM D3359 standard);

[0035] Test tool: 100-grid knife, spacing 1mm;

[0036] Tape: 3M610 test tape;

[0037] Results: The tape was peeled off without falling off after cross-cutting, and the adhesion grade was 5B, indicating that the plasma etching process effectively improved the interfacial bonding strength.

[0038] 2. Quantum dot luminescence characteristics test

[0039] Methods: Hyperspectral camera scanning;

[0040] Excitation light source: 405nm laser, power 10mW;

[0041] Test parameters:

[0042] Initial emission wavelength λ0 = 540 nm;

[0043] Strain response test: 0.1% strain is applied to the pipe, and the wavelength shift is Δλ=1.3nm;

[0044] Calculate the strain value:

[0045] Formula: ε=Δλ / (K•λ0), K=1.2 (calibration coefficient);

[0046] ε is the strain value;

[0047] K is the calibration coefficient;

[0048] λ0 is the initial emission wavelength;

[0049] Results: ε=1.3 / (1.2×540)=0.002=0.2%, which is less than 5% of the actual applied strain error, indicating that quantum dots have a linear response to mechanical strain.

[0050] 3. Mechanical properties test of composite materials

[0051] Method: Three-point bending test;

[0052] Sample: Cut a 5mm×5mm×20mm specimen from the pipe coding layer;

[0053] Test parameters:

[0054] Span: 10mm;

[0055] Loading speed: 1mm / min;

[0056] result:

[0057] Bending strength: 85MPa, pure PVC pipe is 50MPa;

[0058] Elongation at break: 12%;

[0059] Analysis: The "bridging effect" of silicon carbide nanowires significantly enhances the composite material's resistance to crack propagation.

[0060] 4. Self-healing performance verification

[0061] Methods: Thermal stimulation repair test;

[0062] Damage simulation: Use a blade to create a 0.5mm wide crack in the coding layer;

[0063] Repair process:

[0064] Heating temperature: 60℃;

[0065] Holding time: 10min;

[0066] Cooling method: Natural cooling to room temperature;

[0067] Test results:

[0068] Conductivity recovery rate: 88%, measured by four-probe method;

[0069] Appearance observation: The cracks are not visible to the naked eye, only slight traces remain;

[0070] Conclusion: Shape memory polymers flow above Tg to fill cracks and achieve self-healing function.

[0071] Ultimately, through a plasma etching-slurry filling-laser curing process, quantum dots were successfully integrated onto galvanized steel pipes, achieving 5B adhesion, meeting engineering application requirements. The quantum dot emission wavelength exhibited a sensitivity of 1.3nm / 0.1% strain, with a linearity of R²=0.98, enabling real-time monitoring of pipe stress. Silicon carbide nanowires (10% by weight) increased the composite's flexural strength by 70%, and shape memory polymers achieved over 85% performance recovery after crack repair. Future research is underway to optimize the laser scanning speed, such as attempting to achieve 300mm / s for improved efficiency, and explore suitable process parameters for non-metallic pipes such as PVC.

[0072]

[0073] S103. Laser-induced coding: A femtosecond laser with a wavelength of 1030 nm and a pulse width of 350 fs is scanned across the tube surface to generate a two-dimensional code matrix according to the following parameters: single code element size 0.8 mm × 0.8 mm, groove depth 80 μm, and scanning speed 200 mm / s. Quantum dot slurry is vacuum-filled into the grooves and UV-cured with a wavelength of 365 nm, an intensity of 20 mW / cm², and an exposure time of 60 seconds to form a permanent coding layer. The ultrashort pulse characteristics of the femtosecond laser enable cold processing, avoiding thermal effects on the tube material properties. This code element size maintains an information density of approximately 100 bytes / cm² while also ensuring the recognition accuracy of the scanning equipment. The vacuum filling and UV-curing processes achieve molecular-level fusion of the coding layer with the tube, resulting in excellent stability and durability.

[0074] S2. Multidimensional data embedding and encryption, where multidimensional data includes static data, including text information such as pipe model (ASTM standard), production batch number, and construction responsible person, which are written into the QR code after being encrypted with AES-256. Dynamic data includes a reserved blank storage area for subsequent writing of real-time sensor data such as stress and temperature. At the same time, 20% of the area in the QR code matrix is ​​designated as an erasable area, using chalcogenide glass material and the principle of laser phase change to achieve local data rewriting. The single-point erasable life is greater than 10 4 When the MEMS computing buckle detects a vibration amplitude greater than 0.1g, it triggers high-speed sampling (100Hz), encrypts stress, temperature and other data via low-power Bluetooth, and writes them into the erasable area, forming a closed loop of "real-time sensing-data encryption-dynamic storage";

[0075] Specifically, a fiber laser with a wavelength of 1064nm is used, which is matched with an acousto-optic modulator to achieve precise control of power and pulse width. The erasable area is aligned with the machine vision system through the QR code positioning mark. When writing, a 5mW / 10ns pulse causes local amorphization and increases resistance. When erasing, a 3mW / 50ns pulse induces recrystallization and reduces resistance.

[0076] By combining AES-256 encryption with the Hyperledger Fabric consortium chain, an end-to-end data protection system is built. The encryption key is dynamically generated based on the pipeline topology and updated every 72 hours. The maintenance data hash value adopts a multi-signature mechanism when writing it to the consortium chain to ensure that the data cannot be tampered with.

[0077] S3. Edge computing node deployment: MEMS computing bolts are installed at key pipeline nodes, including elbows and valves. The bolts have built-in: a six-axis inertial sensor (sampling rate 100Hz), a temperature sensor with a range of -40~150℃ and an accuracy of ±0.5℃, and a low-power Bluetooth 5.2 module to monitor global vibration and temperature (vibration amplitude 0.1g trigger mechanism). After fusing the two data, the damage location (such as abnormal strain + sudden vibration change in a certain section of pipeline) can be located. When the bolt is dormant, it only maintains Bluetooth low-power broadcast (1Hz sampling). Only when high-speed sampling (100Hz) is triggered will real-time data be written to the QR code area, reducing the number of encryption operations. The number meets the low-frequency requirement of "dynamic key update every 72 hours" in S2. At the same time, the buckle shell adopts IP68 protection design, and is mechanically locked to the pipeline through clamps. It automatically forms a network when the distance between adjacent buckles is ≤5m, and uses TDMA time division multiple access protocol to synchronize data. The power consumption is ≤1mW / node. In addition, the MEMS computing buckle adopts "sleep-wake" working mode. Normally, the sensor runs at a sampling rate of 1Hz. When the vibration amplitude is detected to be greater than 0.1g, high-speed sampling (100Hz) is triggered. Combined with the long-distance mode and dynamic power regulation of Bluetooth 5.2, the battery life of a single node can reach more than 3 years, and AA lithium batteries can be used.

[0078] S4. Multimodal data fusion: A handheld terminal equipped with a 405nm laser and a hyperspectral camera (spectral resolution 5nm) scans the pipeline surface:

[0079] Read the raw QR code data; synchronously capture the quantum dot emission wavelength (accuracy ±2nm), invert the local strain value formula: ε=Δλ / (K·λ0), with K=1.2 as the calibration coefficient, and establish a spatiotemporal coordinate system conversion model: map the buckle inertial data (local coordinate system) to the global BIM model through the extended Kalman filter; match the dynamic QR code position information with the point cloud model, with an error compensation of ≤3mm;

[0080] Specifically, the space-time coordinate system conversion needs to integrate three types of information: QR code positioning data, quantum dot wavelength strain data, and bolt inertial data. The QR code positioning data is generated through geometric coding and dynamically bound to the pipeline's three-dimensional coordinates. Multi-algorithm collaboration is used to achieve mapping from the local measurement space to the global BIM model, including coordinate system definition and state modeling. The local coordinate system (bolt coordinate system) takes the MEMS bolt installation point as the origin, with the X-axis along the pipeline axis, the Y-axis perpendicular to the pipeline direction, and the Z-axis perpendicular to the pipe cross-section. The global coordinate system (BIM coordinate system) adopts the engineering standard Cartesian coordinate system (X-east, Y-north, Z-elevation), which is consistent with the BIM model benchmark. The state vector design: Include Position (3D), Speed ​​(3D), Angular velocity (3D, describing posture);

[0081] Extended Kalman filter process, state prediction:

[0082] Among them, the state transfer function Through inertial sensor data (Accelerometer, gyroscope) integral calculation, is process noise (random interference such as pipeline vibration), For a priori state estimation, the "estimated global coordinates of the pipeline" are predicted using only inertial bolt data; is the optimal state at time k-1.

[0083] Measurement Update:

[0084] BIM constraint input: Use pipeline 3D direction constraints to predict positions (e.g., force points to fall on pipelines);

[0085] Quantum dot strain correction: deformation value through strain inversion Correcting position prediction errors:

[0086] in is the strain-position mapping function, To measure noise (wavelength accuracy The error introduced), is the measured strain value, is the predicted strain value.

[0087] Kalman gain iteration:

[0088]

[0089] The final output global coordinates ( , , );

[0090] Refers to the Kalman gain, which weighs the "credibility of the predicted value" and "credibility of the observed value"; if the inertial latch prediction is accurate ( Small), large observation noise ( Large), small gain, more reliable prediction; if the QR code / quantum dot observation is accurate ( Small), large gain, more reliable observation.

[0091] is the prior error covariance matrix, reflecting the uncertainty of the state predicted using only inertial data (such as the coordinate error range caused by inertial bolt drift).

[0092] It is the observation matrix, which describes the mapping relationship between “observation values, such as QR code position and quantum dot strain” and “state quantity pipeline coordinates”.

[0093] It is the observation noise covariance matrix, which quantifies the “unreliability of observation data”, such as QR code scanning error and quantum dot wavelength measurement noise.

[0094] is the observation value at time k, such as the position of the QR code scan and the coordinate information of the strain conversion of the quantum dot inversion;

[0095] Observe the model prediction value, use the "prior state "Theoretical observation value derived from the observation model (such as the QR code position mapping relationship) is different from the actual observation value." Compare and find residuals;

[0096] It is the posterior state estimation (optimal estimation). After fusing the "inertial prediction" and "observation data", the output "optimal value of pipeline global coordinates" is used for BIM model mapping.

[0097] The matching mechanism between dynamic QR codes and point cloud models includes point cloud model preprocessing, generating pipeline point clouds (density ≥ 100 points / cm²) through lidar scanning, extracting local point cloud subsets of the QR code area, using the SIFT algorithm to identify QR code element corners and edge inflection points, calculating point cloud normals and curvature, and generating feature descriptors (such as PFH features). It also includes a two-step matching algorithm, coarse alignment (RANSAC): setting the search threshold based on the QR code element size (0.8mm×0.8mm), randomly sampling point cloud feature points to match the QR code corner points, and calculating the initial transformation matrix. , filter matching pairs that meet geometric constraints (code element side length, verticality).

[0098] Fine registration (ICP iteration): Using the coarse registration result as the initial value, iteratively optimize the objective function: in is the feature point of the point cloud, is the feature point of the QR code, R is the rotation matrix, and t is the translation vector. The optimal transformation matrix is ​​solved by SVD decomposition. , and error compensation, including geometric constraint correction: using the rectangular characteristics of the QR code (adjacent sides are vertical and the side length is fixed), the registration results are regularized:

[0099] , R corrected is the rotation matrix after geometric constraint correction, To constrain the "orthogonality" of the rotation matrix (the pipeline QR code is rectangular and the adjacent sides must remain vertical after rotation), tcorrected =t fine The translation vector t is used to maintain the precise registration result and only the rotation error is corrected.

[0100] Apply sliding average filtering (window size 5 frames) to the continuous frame matching results to eliminate handheld scanning jitter errors:

[0101]

[0102] is the final registration transformation matrix at time t, which is used for BIM model mapping and is more stable after eliminating jitter; is the fine registration result at time i.

[0103] Spatiotemporal coordinate fusion and model building, including multi-source data time synchronization and establishment of timestamp mapping table:

[0104]

[0105] Use linear interpolation to align data of different frequencies, such as interpolating wavelength data to 100 Hz;

[0106] Coordinate transformation mathematical model:

[0107] Local-global conversion formula:

[0108] in:

[0109] Global coordinates in the model;

[0110] The rotation matrix and translation vector output by EKF;

[0111] is the local coordinate of the buckle;

[0112] is the coordinate offset caused by strain, calculated as:

[0113] in is the three-dimensional strain component, which is inverted by the wavelength shift of the quantum dots.

[0114] Dynamic error correction model:

[0115] Establish the error prediction function: ;

[0116] in:

[0117] E(t) is the prediction error at time t;

[0118] is the strain gradient (reflecting the severity of deformation);

[0119] is the scanning speed (calculated from inertial data);

[0120] is the empirical coefficient (determined by calibration experiment, such as strain, ;

[0121] Final coordinate correction: ;

[0122] Through the above algorithms and processes, the spatiotemporal fusion of the buckle inertial data, QR code position information and quantum dot strain data can be achieved, and the local measurement data can be accurately mapped to the global BIM model, meeting the error requirement of ≤3mm in the visualization of hidden projects.

[0123] S5. Human-machine interaction and decision-making. Operations and maintenance personnel wear AR glasses to scan pipelines, and the system displays in real time: Danger warning: When the temperature of a certain pipeline section exceeds the threshold or the strain exceeds 0.15%, a red pulse halo is superimposed; Maintenance guidance: Arrows dynamically indicate the valves that need to be operated and display recommended torque values. Linked with the PLM system, the PLM stores the pipeline's three-dimensional BIM design model, providing a geometric reference for the AR device's operation guidance arrows. Combined with real-time detection data such as quantum dot strain values ​​and MEMS vibration data, the model status is updated to achieve dynamic maintenance path planning. The original design parameters of the pipe, such as material strength and allowable strain threshold, are called from the PLM and compared with the real-time detected strain value (ε), triggering the warning logic. When the operator approaches the high-risk area, the built-in bone conduction module of the AR glasses emits a specific frequency vibration (40-80Hz), and the micro-motor in the glove applies reverse resistance (adjustable from 0-10N).

[0124] S6. Self-healing repair and verification: When surface cracks are detected that cause the quantum dot layer to break, the damaged area is heated to above Tg (60°C), allowing the shape memory polymer to flow and fill the crack. After cooling, the conductivity of the original structure is restored to more than 85%. Each maintenance operation generates a hash value containing the following data and writes it to the Hyperledger Fabric consortium chain: a comparison of the quantum dot spectra before and after repair, the operator's biometric information, a timestamp, and geo-fence data.

[0125] The crack detection and self-repairing physical process specifically includes damage identification and positioning, hyperspectral camera scanning quantum dot light wavelength, and if the wavelength offset Δλ exceeds 5 nm, corresponding to a strain of about 0.8%, the quantum dot layer is determined to be broken. At the same time, the MEMS buckle analyzes the vibration mode through vibration data, and combines the pipeline topology structure to locate the crack position to within 50 centimeters. For example, when the strain anomaly of a certain section of the pipeline is superimposed with a sudden change in vibration, the system can lock the specific damage section, and then wrap the flexible resistance heating film around the damage area, and heat it to 65°C at a rate of 5°C / min, which exceeds the glass transition temperature Tg=60°C of the SMP, and maintains it for 10 minutes to make the SMP molecular chain flow and fill the crack pores. After cooling to room temperature, the SMP re-solidifies, and the four-probe method measurement shows that the electrical conductivity is restored to more than 85% of the initial value. For example, after repairing a 0.5 mm wide crack, only a 0.05 mm fine trace can be seen under an optical microscope, and the mechanical strength is restored to 80% of the original structure;

[0126] The collection and hash generation of maintenance data include multidimensional data integration, specifically including:

[0127] Quantum dot spectrum: wavelength data is collected before and after maintenance using a hyperspectral camera to generate a 1024x1024 pixel grayscale image, recording the wavelength shift distribution.

[0128] Operator biometric features: information is collected through fingerprint and face recognition dual-mode sensors, encrypted by AES-256, and the key is dynamically generated by the pipeline topology.

[0129] Time and geographic data: GPS clock provides time stamps accurate to milliseconds, and Beidou+GPS differential positioning obtains geographic fence coordinates with an accuracy of 1 meter;

[0130] Also included: hash value aggregation calculation, first calculate the SHA-256 hash of the spectrum by block, generate the Merkle root; after biometric encryption, perform SHA-512 hash; time and geographic data are combined with the consortium chain node private key to generate an authentication hash through the HMAC-SHA3-256 algorithm, and finally merge the three types of hash values to generate a unique maintenance hash value through the SHA3-512 algorithm, ensuring data integrity, and any data tampering will cause the hash value to change significantly;

[0131] Hyperledger Fabric consortium chain evidence storage mechanism, including consortium chain architecture and permission control. The chain network is composed of nodes of operation and maintenance units, supervision units, and owner units, and adopts the PBFT consensus mechanism. The smart contract stipulates that only operation and maintenance personnel registered with the MSP can initiate transactions, and they need to be endorsed by the supervision node and confirmed by the owner node to form a multi-signature verification. For example, when the operation and maintenance personnel submit the maintenance hash, the system automatically verifies their identity certificate to prevent unauthorized operations; it also includes data chain and traceability logic. The smart contract generates a composite primary key based on the pipeline ID and random UUID, and writes the maintenance hash, spectral summary (not the original image, only the hash), timestamp and other information into the block. Each record is linked to the previous block through the hash chain structure of the blockchain to form an audit trail that cannot be tampered with. For example, by entering the transaction ID through the blockchain browser, the full process data of the maintenance can be traced, including the spectral comparison before and after the maintenance, the operation time and geographic location;

[0132] The hyperspectral camera detects the crack → the MEMS buckle locates the damage → the heating film starts repairing → the conductivity is retested after cooling → the repair data is collected → the hash is calculated locally → the smart contract is called and uploaded to the blockchain → the transaction confirmation number is returned. The entire process is completed within 30 minutes, of which the blockchain confirmation time does not exceed 5 seconds. The temperature control system maintains a heating accuracy of ±1°C through the PID algorithm and automatically powers off when the temperature exceeds the limit. Data transmission uses TLS1.3 encryption to prevent man-in-the-middle attacks. The blockchain nodes regularly back up the ledger to ensure data redundancy. For example, in the case of repairing the air conditioning water pipe network at a certain airport, the hash values ​​of the three repair operations were audited by a third party, verifying that the data cannot be tampered with.

[0133] Through the deep integration of physical self-repair and blockchain technology, this solution realizes a closed loop of the entire process of concealed electromechanical pipeline projects, from damage response to data storage. It not only ensures the reliability of structural repair, but also provides a credible basis for operation and maintenance audits through the tamper-proof nature of blockchain.

[0134] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A visualization method for concealed electromechanical pipeline engineering based on QR code technology, characterized in that: The following steps are involved: S1. Integrated preparation of quantum dots and tubes: The tube surface is plasma-etched to a roughness of Ra2-5μm, and a two-dimensional code matrix is ​​generated on the surface using a laser-induced coding process. A quantum dot composite slurry is prepared and filled into the two-dimensional code grooves, forming a quantum dot coding layer with mechanical strain response and self-healing capabilities. S2. Multidimensional data embedding and encryption: Write multidimensional data into the QR code and build a data protection system by combining AES-256 encryption and Hyperledger Fabric consortium chain; S3. Edge computing node deployment: MEMS computing bolts with vibration and temperature sensing capabilities are installed at key pipeline nodes to locate damage by fusing vibration and temperature data. S4. Multimodal data fusion: A hyperspectral camera scans the pipeline surface, reads the raw QR code data, and simultaneously captures the quantum dot emission wavelength to invert the strain value. This data is then integrated with the edge computing node inertial data and the QR code location information to perform a time-space coordinate system conversion. S5. Human-computer interaction and decision-making: Use the AR device to scan the QR code, display the pipeline status in real time based on the AR device, and trigger tactile feedback based on the fused data from step S4; S6. Self-healing repair and verification: When surface cracks are detected that cause the quantum dot coding layer to break, the damaged area is heated to above 60°C and the cracks are filled by the flow of shape memory polymer.

2. The visualization method of electromechanical pipeline concealment engineering based on QR code technology according to claim 1 is characterized in that: The preparation of the quantum dot composite slurry in S1 includes: mixing 15%-20% of cadmium telluride quantum dots, 40%-45% of shape memory polymer, 10%-15% of silicon carbide nanowires and 35%-40% of ultraviolet curing resin in a mass ratio, and forming a slurry after ball milling and dispersion; wherein the shape memory polymer triggers flow repair at above 60°C, and the silicon carbide nanowires bridge to enhance crack resistance.

3. The visualization method of concealed electromechanical pipeline engineering based on QR code technology according to claim 1 is characterized in that: The laser-induced coding process in S1 uses a femtosecond laser to scan the surface of the tube to generate a two-dimensional code with a single code element size of 0.8mm×0.8mm and a groove depth of 80-100μm, and realizes the molecular-level fusion of quantum dot slurry and the tube through vacuum filling and ultraviolet curing.

4. The visualization method of electromechanical pipeline concealment engineering based on QR code technology according to claim 1 is characterized in that: The multidimensional data in S2 includes static data, including pipe model, production batch number and construction responsible person, which are written into the QR code after AES-256 encryption. The dynamic data includes a reserved blank storage area for subsequent writing of real-time sensor data of stress and temperature. At the same time, 20% of the area in the QR code matrix is ​​designated as an erasable area.

5. The visualization method of concealed electromechanical pipeline engineering based on QR code technology according to claim 1 is characterized in that: The MEMS computing buckle in the S3 has a built-in six-axis inertial sensor, temperature sensor and Bluetooth 5.2 module to monitor global vibration and temperature, and locate the damage position by fusing the data of the two. When the buckle is in sleep mode, it only maintains Bluetooth low-power broadcasting.

6. The visualization method of concealed electromechanical pipeline engineering based on QR code technology according to claim 4 is characterized in that: The erasable area uses chalcogenide glass material, and data erasure is achieved through laser modulation.

7. The visualization method of electromechanical pipeline concealment engineering based on QR code technology according to claim 1 or 5, characterized in that: The trigger mechanism of the MEMS computing buckle includes: when the vibration amplitude is greater than 0.1g or the temperature exceeds the threshold, the sampling rate of the six-axis inertial sensor and temperature sensor is switched from 1Hz to 100Hz, and data is encrypted and transmitted via Bluetooth with a communication radius of 200m-250m.

8. The visualization method of concealed electromechanical pipeline engineering based on QR code technology according to claim 1 is characterized in that: The space-time coordinate system conversion in S4 requires the integration of QR code positioning data, quantum dot strain inversion data and bolt inertia data. The QR code positioning data is generated through geometric coding and dynamically bound to the three-dimensional coordinates of the pipeline to achieve mapping from the local measurement space to the global BIM model.

9. The visualization method of concealed electromechanical pipeline engineering based on QR code technology according to claim 1 is characterized in that: The AR device in the S5 combines real-time detection data, including quantum dot strain values ​​and MEMS vibration data, to update the model status and realize dynamic maintenance path planning. It compares the original design parameters, including material strength and allowable strain threshold, with the real-time detected strain value to trigger the early warning logic.

10. The visualization method of concealed electromechanical pipeline engineering based on QR code technology according to claim 1, characterized in that: The self-healing repair in S6 includes: using a wrapped flexible resistive heating film and heating it to above 60°C at a rate of 5°C / minute, maintaining it for 10-15 minutes to allow the shape memory polymer to flow and fill the cracks, and after cooling, the conductivity is restored to ≥85%.

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