Intelligent measurement and adaptive matching method for pipe connection size

Abstract

The present application belongs to the field of pipeline connecting piece detection and assembly, in particular to a pipeline connecting piece size intelligent measurement and adaptive matching method, S1, three-dimensional image acquisition, through an intelligent measurement terminal integrating a binocular industrial camera, a laser ranging module, a posture sensor and a multi-spectral light supplement module, S2, high-precision data correction, S3, construction of a labeled hierarchical intelligent iterative database, S4, dynamic weight adaptive intelligent matching, S5, four-level deviation hierarchical adaptive compensation of non-standard pipe fittings, S6, multi-physical field coupling dynamic simulation checking, and S7, labeled machine learning iterative optimization. The present application is rationally designed, through multi-source sensing fusion measurement, dynamic weight matching, four-level deviation compensation, multi-physical field simulation checking and machine learning iterative optimization, realizes full-process high-precision adaptive matching of pipeline connecting pieces, significantly improves assembly quality and efficiency, and effectively reduces leakage and failure risks.

Description

Technical Field

[0001] This invention relates to the field of pipe connector inspection and assembly technology, and in particular to a method for intelligent measurement and adaptive matching of pipe connector dimensions. Background Technology

[0002] In pipeline engineering construction and operation and maintenance scenarios such as oil and gas transportation, municipal water supply and drainage, chemical fluids, and heating networks, the matching and assembly quality of pipelines and fittings directly determines the pressure-bearing capacity, sealing reliability, and long-term service life of the pipeline network. Currently, pipeline fitting assembly still largely relies on manual measurement with calipers and angle gauges, depending on worker experience to compare fitting specifications for selection. This existing technology has the following significant drawbacks:

[0003] Low measurement accuracy and large human error: Manual contact measurement is easily affected by wear on the pipe surface, eccentric ports, and irregular curved surfaces. The acquisition of key parameters such as outer diameter, wall thickness, thread, sealing groove, and docking angle has high dispersion. Thin-walled and irregular pipes are prone to measurement deformation.

[0004] Pipe fitting matching is limited to a single dimension: Traditional matching relies solely on the comparison of the nominal diameter, without considering multiple working conditions such as pressure, temperature, conveying medium, material compatibility, construction cost, and service life. Standard pipe fittings have a low tolerance for mismatch, and there is no systematic matching solution for non-standard pipe fittings.

[0005] The current system lacks a tiered compensation mechanism for deviations: deviations in pipe fitting dimensions, angles, and sealing gaps are uniformly judged as scrap, and minor deviations cannot be compensated for during assembly, resulting in a significant waste of pipe fitting materials. Therefore, we propose an intelligent measurement and adaptive matching method for pipe fitting dimensions to address the above issues. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by proposing an intelligent measurement and adaptive matching method for pipe connector dimensions.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A method for intelligent measurement and adaptive matching of pipe connector dimensions, comprising:

[0009] S1. Stereoscopic image acquisition: Through an intelligent measurement terminal integrating a binocular industrial camera, laser ranging module, attitude sensor and multispectral supplementary lighting module, multi-directional and multi-angle image scanning and dimension acquisition are performed on the pipe port to be assembled and the pipe connector to be selected, to obtain the original dimensional data of pipe outer diameter, inner diameter, wall thickness, port flatness, connector nominal diameter, sealing groove size, thread parameters and fitting angle.

[0010] S2. High-precision data correction: The original acquired data is sequentially subjected to adaptive median filtering for noise reduction, lens and perspective distortion correction, Bezier surface fitting, and abnormal data secondary reconstruction and verification to achieve high-precision restoration of the contours of irregular curved surfaces, eccentric ports, and locally worn pipe components. For scenarios with missing effective data, parameters are reconstructed by interpolation of neighboring points to output standardized dimensions and deformation correction parameters.

[0011] S3. Construct a tagged, hierarchical, intelligent, iterative database: Pre-store national standards, industry standards, enterprise standards, and non-standard special-shaped pipe fitting parameters, and archive pipe fitting specifications, materials, tolerance grades, wear degree, suitable working conditions, media type, pressure parameters, service life, and construction and maintenance costs with multi-dimensional tags, establish parameter association models, and support real-time data updates and iterations;

[0012] S4. Dynamic weight adaptive intelligent matching: The working condition clustering algorithm dynamically calculates the matching weight coefficient based on pipeline operating pressure, temperature, conveying medium, pipe fitting material and installation space scenario, to complete the accurate matching of standard pipe fittings and the initial adaptation of non-standard pipe fittings.

[0013] S5. Non-standard pipe fittings four-level deviation classification adaptive compensation, preset quantitative classification thresholds for size, angle and sealing gap deviations, divide assembly deviations into four levels: excellent, good, acceptable and unacceptable, and implement differentiated strategies such as direct adaptation, parameter fine-tuning compensation, special rectification adaptation and adaptation failure elimination respectively.

[0014] S6. Multi-physics field coupled dynamic simulation verification: The assembly structure is reconstructed through three-dimensional modeling, and multi-physics field coupled simulation is carried out in combination with fluid, heat and structural mechanics to dynamically simulate medium impact, temperature deformation, corrosion aging and fatigue stress conditions, and simultaneously complete the verification of assembly space interference and installation margin.

[0015] S7. Labeled machine learning iterative optimization: Labeled measurement, matching, simulation and on-site acceptance data are entered into the database. The matching rules are summarized through clustering machine learning algorithms, and the dynamic weights, deviation thresholds and compensation parameters are continuously optimized to achieve autonomous iterative upgrades of the system.

[0016] Preferably, the standardized acquisition parameters in S1 are limited to: scanning distance 30-80cm, scanning angle 0-360°, and acquisition frame rate 30-60fps; multispectral supplementary lighting adopts adaptive switching of warm and cool color temperature, and the terminal has built-in anti-shake and vibration reduction components to suppress vibration interference.

[0017] Preferably, the S2 intermediate size reconstruction fitting formula is:

[0018]

[0019] in, For Bessel basis functions, The coordinates of the effective sampling control points in the neighborhood. These are the surface parameters.

[0020] Preferably, in S4, the dynamic calculation of matching weight coefficients constructs a nine-dimensional matching evaluation system that includes dimensional tolerance, sealing performance, assembly accuracy, working condition adaptability, pressure bearing capacity, material compatibility, media adaptability, service life, and construction and maintenance costs.

[0021] The preferred formula for calculating dynamic weight normalization is:

[0022]

[0023] in, For the first Final weight of item dimension The sensitivity coefficient for working conditions and scenarios. Based on fixed weights, .

[0024] Preferably, the comprehensive deviation quantification formula in S5 is:

[0025]

[0026] in, For dimensional deviation, For angle deviation, For the deviation of the sealing gap, The deviation weighting coefficient and .

[0027] Preferably, in S5, the four-level deviation quantification thresholds are as follows: dimensional deviation ≤ 0.02 mm and angle deviation ≤ 0.1° is excellent; dimensional deviation 0.02-0.05 mm and angle deviation 0.1-0.3° is good; dimensional deviation 0.05-0.1 mm and angle deviation 0.3-0.5° is acceptable; and dimensional deviation > 0.1 mm or angle deviation > 0.5° is defective.

[0028] The preferred formula for adaptive gap compensation for good grade deviation is:

[0029]

[0030] in, To compensate for the correction factor, To compensate for gaps during final assembly.

[0031] Preferably, the clustering machine learning algorithm in S7 performs clustering training on sample data with different working conditions, materials, and deviation types, autonomously optimizes the nine-dimensional weight ratio and the four-level deviation classification threshold, and the iteration cycle is real-time update after each operation is completed, and global parameter optimization is completed once a month.

[0032] The beneficial effects of this invention are:

[0033] By integrating a binocular industrial camera, laser rangefinder, attitude sensor and multispectral supplementary light into an intelligent measurement terminal, combined with adaptive median filtering, distortion correction and Bezier surface fitting, it can achieve sub-millimeter-level high-precision three-dimensional contour reconstruction of irregular curved surfaces, eccentric ports and locally worn pipe parts, significantly improving the accuracy of the original data and providing a reliable foundation for subsequent matching;

[0034] By constructing a dynamic weight matching system that includes nine evaluation indicators and introducing a working condition clustering algorithm to adjust the weight coefficients in real time according to the actual operating scenario, the matching results are more in line with the specific working condition requirements, avoiding the over-matching or under-matching problems caused by traditional single tolerance matching, and are especially suitable for special environments such as high temperature, high pressure, and corrosive media.

[0035] By employing a four-level deviation classification adaptive compensation strategy, decisions such as direct adaptation, parameter fine-tuning, special rectification, or elimination are made for different deviation levels. The gap compensation amount for good-grade deviations is accurately calculated using a quantitative compensation formula. This maximizes the utilization of repairable pipe fittings while ensuring safety, significantly reducing material waste and maintenance costs. Detailed Implementation

[0036] This application discloses an intelligent measurement and adaptive matching method for pipe connector dimensions.

[0037] Example 1

[0038] A method for intelligent measurement and adaptive matching of pipe connector dimensions, comprising:

[0039] S1. Stereoscopic image acquisition: Through an intelligent measurement terminal integrating a binocular industrial camera, laser ranging module, attitude sensor and multispectral supplementary lighting module, multi-directional and multi-angle image scanning and dimension acquisition are performed on the pipe port to be assembled and the pipe connector to be selected, to obtain the original dimensional data of pipe outer diameter, inner diameter, wall thickness, port flatness, connector nominal diameter, sealing groove size, thread parameters and fitting angle.

[0040] S2, High-precision data correction: The original acquired data is sequentially subjected to adaptive median filtering for noise reduction, lens and perspective distortion correction, Bézier surface fitting, and abnormal data secondary reconstruction and verification to achieve high-precision restoration of the contours of irregular curved surfaces, eccentric ports, and locally worn pipe components. For scenarios with missing effective data, parameters are reconstructed by interpolation of neighboring points to output standardized dimensions and deformation correction parameters.

[0041] S3. Construct a tagged, hierarchical, intelligent, iterative database: Pre-store national standards, industry standards, enterprise standards, and non-standard special-shaped pipe fitting parameters, and archive pipe fitting specifications, materials, tolerance grades, wear degree, suitable working conditions, media type, pressure parameters, service life, and construction and maintenance costs with multi-dimensional tags, establish parameter association models, and support real-time data updates and iterations;

[0042] S4. Dynamic weight adaptive intelligent matching: The working condition clustering algorithm dynamically calculates the matching weight coefficient based on pipeline operating pressure, temperature, conveying medium, pipe fitting material and installation space scenario, to complete the accurate matching of standard pipe fittings and the initial adaptation of non-standard pipe fittings.

[0043] S5. Non-standard pipe fittings four-level deviation classification adaptive compensation, preset quantitative classification thresholds for size, angle and sealing gap deviations, divide assembly deviations into four levels: excellent, good, acceptable and unacceptable, and implement differentiated strategies such as direct adaptation, parameter fine-tuning compensation, special rectification adaptation and adaptation failure elimination respectively.

[0044] S6. Multi-physics field coupled dynamic simulation verification: The assembly structure is reconstructed through three-dimensional modeling, and multi-physics field coupled simulation is carried out in combination with fluid, heat and structural mechanics to dynamically simulate medium impact, temperature deformation, corrosion aging and fatigue stress conditions, and simultaneously complete the verification of assembly space interference and installation margin.

[0045] S7. Labeled machine learning iterative optimization: Labeled measurement, matching, simulation and on-site acceptance data are entered into the database. The matching rules are summarized through clustering machine learning algorithms, and the dynamic weights, deviation thresholds and compensation parameters are continuously optimized to achieve autonomous iterative upgrades of the system.

[0046] In this embodiment, the standardized acquisition parameters in S1 are limited to: scanning distance 30-80cm, scanning angle 0-360°, and acquisition frame rate 30-60fps; multispectral supplementary lighting adopts adaptive switching of warm and cool color temperature, and the terminal has a built-in anti-shake and vibration reduction component to suppress vibration interference.

[0047] In this embodiment, the fitting formula for the S2 intermediate size reconstruction is:

[0048]

[0049] in, For Bessel basis functions, The coordinates of the effective sampling control points in the neighborhood. These are the surface parameters.

[0050] In this embodiment, the matching weight coefficients dynamically calculated in S4 construct a nine-dimensional matching evaluation system that includes dimensional tolerance, sealing performance, assembly accuracy, working condition adaptability, pressure bearing capacity, material compatibility, media adaptability, service life, and construction and maintenance costs.

[0051] In this embodiment, the formula for calculating dynamic weight normalization is:

[0052]

[0053] in, For the first Final weight of item dimension The sensitivity coefficient for working conditions and scenarios. Based on fixed weights, .

[0054] In this embodiment, the comprehensive deviation quantification formula in S5 is:

[0055]

[0056] in, For dimensional deviation, For angle deviation, For the deviation of the sealing gap, The deviation weighting coefficient and .

[0057] In this embodiment, in S5, the four-level deviation quantification thresholds are as follows: size deviation ≤ 0.02mm and angle deviation ≤ 0.1° is excellent; size deviation 0.02-0.05mm and angle deviation 0.1-0.3° is good; size deviation 0.05-0.1mm and angle deviation 0.3-0.5° is acceptable; size deviation > 0.1mm or angle deviation > 0.5° is defective.

[0058] In this embodiment, the adaptive gap compensation formula for good grade deviation is:

[0059]

[0060] in, To compensate for the correction factor, To compensate for gaps during final assembly.

[0061] Preferably, the clustering machine learning algorithm in S7 performs clustering training on sample data with different working conditions, materials, and deviation types, autonomously optimizes the nine-dimensional weight ratio and the four-level deviation classification threshold, and the iteration cycle is real-time update after each operation is completed, and global parameter optimization is completed once a month.

[0062] Example 2

[0063] A method for intelligent measurement and adaptive matching of pipe connector dimensions, applied to the assembly of low-pressure, ambient-temperature plastic pipes for municipal water supply and drainage, includes the following steps:

[0064] S1. Stereoscopic Image Acquisition: Activate the intelligent measurement terminal, place the PE pipe port and plastic flange connector on the pipe support fixture, control the scanning rotating pan-tilt head to achieve 0-360° omnidirectional scanning, set the scanning distance to 50cm, and the acquisition frame rate to 45fps. The multispectral supplementary lighting module switches to cold light mode to eliminate reflections from the plastic pipe fittings, and the anti-shake and shock-absorbing base suppresses the rotation vibration of the pan-tilt head. The binocular industrial camera and laser ranging module simultaneously acquire raw data of the pipe's outer diameter, inner diameter, wall thickness, flange sealing groove dimensions, thread specifications, and mating angle.

[0065] This step utilizes an integrated 3D scanning terminal to achieve non-contact, non-destructive measurement, avoiding deformation of plastic pipes under pressure, and using adaptive warm and cold lighting to eliminate image failure caused by reflections.

[0066] S2. High-precision data correction: Adaptive median filtering is performed on the acquired raw data to remove ambient stray light noise, corrects lens radial distortion and long-distance perspective distortion, uses Bezier surface fitting to repair data loss caused by slight bumps on the flange edge, reconstructs the wear contour through neighborhood point interpolation, and outputs standardized dimensions and deformation correction parameters.

[0067] This step accurately reconstructs the three-dimensional contours of damaged and worn pipe fittings, eliminating systematic errors caused by optical acquisition, and achieving a measurement accuracy of 0.01mm.

[0068] S3. Construct a tagged, hierarchical, intelligent iterative database: retrieve the national standard parameters of water supply and drainage PE pipes in the database, bind multi-dimensional tags for pipe fitting specifications DN50, PE100 material, normal temperature and low pressure conditions, clean water medium, pressure bearing 0.8MPa, design life 50 years, and low-cost construction, establish a parameter association model, and store the measurement data in the database in real time to complete iterative updates.

[0069] This step involves storing pipe fitting parameters using multi-label categorization, which allows for quick filtering of pipe fittings suitable for the same working conditions, facilitating subsequent machine learning extraction of matching patterns.

[0070] S4. Dynamic weight adaptive intelligent matching: The working condition clustering algorithm identifies the current normal temperature clean water low pressure working condition, dynamically calculates the weight of each dimension of the nine-dimensional evaluation system, increases the weight of media adaptability, construction cost and sealing performance, and decreases the weight of pressure bearing capacity. The comprehensive matching score is calculated by the normalized weight formula to complete the accurate matching of standard PE flanges.

[0071] This step relies on dynamic adjustment and evaluation based on operating conditions, prioritizing the matching of low-cost, water-resistant, and sealing pipe fittings for low-pressure water supply and drainage scenarios, with the matching results closely matching the on-site usage requirements.

[0072] S5. Non-standard pipe fittings four-level deviation classification adaptive compensation: The measured size deviation of the matching flange is 0.03mm and the angle deviation is 0.2°, which is judged as good deviation; the assembly compensation gap is calculated by substituting into the good gap compensation formula, and the parameter compensation is completed by using thickened rubber sealing gaskets to finely adjust the sealing gap, without the need to scrap pipe fittings.

[0073] This step enables adaptive compensation for pipe fittings with slight deviations, reducing the scrapping of plastic flanges and minimizing the loss of pipe materials during water supply and drainage construction.

[0074] S6. Multi-physics field coupled dynamic simulation verification: Based on the modified three-dimensional pipe fitting model, an assembly simulation model is constructed to simulate the impact of ambient temperature clean water medium and small deformation of ambient temperature. The verification shows that the flange assembly has no spatial interference, the stress on the sealing surface is uniform, and there is no risk of long-term leakage.

[0075] This step simulates the long-term operating conditions of the pipeline network in advance, avoiding the risk of chronic leakage that cannot be predicted by manual trial installation.

[0076] S7. Labeled machine learning iterative optimization: The measurement data of the PE pipe for water supply and drainage, the matching weights, the good grade compensation parameters, the simulation acceptance results are bound to the working conditions and material labels and entered into the database; after the completion of a single operation, the local weight parameters are updated in real time, and the global sample clustering training is completed in the monthly cycle, and the deviation classification threshold under the low pressure water supply and drainage working conditions is automatically fine-tuned.

[0077] The operational data in this embodiment continuously feeds back into the system, automatically optimizing the matching accuracy and compensation effect of similar water supply and drainage pipe fittings in the future.

[0078] Example 3

[0079] A method for intelligent measurement and adaptive matching of pipe connector dimensions, applied to the assembly of high-temperature and high-pressure metal pipes in chemical industries, includes the following steps:

[0080] S1. Stereoscopic image acquisition: Fix stainless steel chemical pipes and forged steel flange connectors to the tooling, set the scanning distance to 60cm, the acquisition frame rate to 60fps, switch the multispectral supplementary light to warm light mode to eliminate strong metal light reflection, and rotate the pan-tilt unit 360° at a constant speed to acquire the original three-dimensional data of pipe wall thickness, thread, sealing trapezoidal groove, and docking tilt angle.

[0081] This step involves high-frame-rate acquisition adapted to highly reflective metal pipe fittings, fully capturing the minute dimensional features of precision threads and sealing grooves in high-temperature and high-pressure pipe fittings.

[0082] S2. High-precision data correction: Filtering removes metal reflection noise, corrects perspective distortion of telephoto lenses, and uses Bezier surface interpolation to reconstruct the complete outline of local defects caused by long-term corrosion of pipelines, outputting standardized dimensional parameters after high-temperature deformation correction.

[0083] This step restores the true outline of corroded and worn metal pipe fittings, ensuring the accuracy of measurements of critical sealing dimensions of high-pressure pipe fittings.

[0084] S3. Construct a tagged, hierarchical, intelligent iterative database: retrieve industry standard parameters for high-pressure stainless steel pipe fittings in the chemical industry, bind tags for 316L stainless steel material, 200℃ high temperature, corrosive chemical media, 16MPa pressure, long service life, and high construction and maintenance costs, and update the database parameter association model.

[0085] This step distinguishes between pipe fittings operating under special conditions such as high-temperature corrosion and chemical processes, and classifies them separately from those operating under normal temperature water supply and drainage conditions to avoid mismatching caused by confusion of operating conditions.

[0086] S4. Dynamic weighted adaptive intelligent matching: Working condition clustering identifies high temperature and high pressure corrosive media working conditions, dynamically improves the weight of pressure bearing capacity, material compatibility, high temperature resistance sealing performance, and corrosion resistance life, and reduces the weight of construction cost. The comprehensive matching score is calculated through the normalized weight formula to screen suitable forged steel high pressure flanges.

[0087] This step prioritizes ensuring pressure resistance, corrosion resistance, and high-temperature resistance for high-risk chemical operations, preventing safety accidents caused by mismatched low-pressure-resistant pipe fittings.

[0088] S5. Four-level deviation classification and adaptive compensation for non-standard pipe fittings: If the size deviation of the pipe fitting is 0.08mm and the angle deviation is 0.4°, it is judged as acceptable deviation and a special rectification and adaptation is carried out. Deviation compensation is completed through high-precision thread grinding and customized thickened metal sealing rings; if the size deviation is 0.12mm, it is judged as scrap grade and directly eliminated, and it is prohibited from being put into high-pressure assembly.

[0089] This step strictly controls the deviation of high-pressure pipe fittings in a graded manner. Pipe fittings that exceed the standard are scrapped directly, while pipe fittings with minor deviations are rectified in a special manner, taking into account both safety and material utilization.

[0090] S6. Multiphysics field coupled dynamic simulation verification: Construct a high-temperature and high-pressure fluid-thermal-structure coupled simulation model to simulate the thermal expansion of high-temperature medium, high-pressure fluid impact, long-term corrosion fatigue stress, verify the installation margin of assembly space, simulate stress concentration-free and stable sealing gap, and meet the requirements of long-term chemical operation.

[0091] This step uses multiple simulations to predict leakage risks caused by high-temperature deformation and high-pressure fatigue, ensuring the safe operation of chemical pipeline networks.

[0092] S7. Labeled machine learning iterative optimization: Input the complete set of labeled data of high temperature and high pressure chemical working conditions into the database, update the nine-dimensional matching weight of high pressure working conditions in real time, conduct monthly global clustering training, optimize the fourth-level deviation threshold of high pressure pipe fittings, and improve the compensation accuracy of high pressure non-standard pipe fittings.

[0093] This embodiment accumulates samples of high-risk chemical working conditions, continuously optimizes the matching and compensation algorithm for high-pressure pipe fittings, and improves the safety of chemical pipeline assembly.

[0094] Example 4

[0095] A method for intelligent measurement and adaptive matching of pipe fitting dimensions, applied to emergency repair scenarios of non-standard and irregular cast iron pipe fittings in heating pipe networks, includes the following steps:

[0096] S1. Stereoscopic image acquisition: Under on-site emergency repair conditions, the scanning distance is 35cm, the acquisition frame rate is 30fps, and the adaptive switching between warm and cold light is adapted to complex outdoor lighting. The pan-tilt unit scans old cast iron eccentric pipes and irregular elbow connectors from all directions, and acquires raw data on wear wall thickness, eccentric ports, and irregular sealing grooves.

[0097] This step is suitable for outdoor emergency repairs in confined spaces and under varying lighting conditions, and can quickly complete the 3D acquisition of old, eccentric, and irregularly shaped pipe fittings.

[0098] S2. High-precision data correction: For rust and local defects on cast iron surfaces, Bezier surface fitting and neighborhood interpolation are used to reconstruct the complete outline of the eccentric port, correct the perspective distortion caused by outdoor shooting, and output the rust deformation correction dimensions.

[0099] This step addresses the issue of incomplete measurement contours for old, eccentric, and corroded pipe fittings, meeting the measurement needs for emergency repairs where no spare standard pipe fittings are available.

[0100] S3. Construct a labeled, hierarchical, intelligent, iterative database: retrieve non-standard parameters from non-standard cast iron pipe fitting enterprises, bind cast iron material, 130℃ hot water medium, medium pressure bearing, worn and old pipe fittings, and low-cost emergency repair labels, and expand the database samples of non-standard and irregular pipe fittings.

[0101] This step improves the parameter library for non-standard old pipe fittings, adapting to special scenarios where there are no standard accessories for pipeline emergency repairs.

[0102] S4. Dynamic weight adaptive intelligent matching: Under the emergency repair conditions of medium-temperature hot water, the weights of the adaptation angle, sealing performance and emergency repair construction cost are dynamically increased to complete the initial adaptation of non-standard irregular elbows.

[0103] This step prioritizes matching easy-to-install, low-cost compatible connectors for emergency repair scenarios, thereby shortening pipeline downtime for maintenance.

[0104] S5. Non-standard pipe fittings four-level deviation classification adaptive compensation: the size deviation of the irregular pipe fitting is 0.04mm and the angle deviation is 0.25°, and it is judged to be good. The sealing gap is calculated by substituting into the compensation formula, and the gap compensation is completed by using a flexible graphite sealing gasket. It can be directly assembled on site.

[0105] In this emergency repair scenario, minor deviations in pipe fittings do not require return to the factory for processing; on-site compensation and assembly are sufficient, significantly shortening the repair period.

[0106] S6. Multi-physics field coupled dynamic simulation verification: Simulate the temperature cycle deformation of hot water and water flow impact conditions, and verify that there is no interference in the assembly of irregular elbows, and that the gap of the sealing gasket is stable after temperature deformation, with no risk of leakage.

[0107] This step involves rapid simulation verification of assembly reliability before on-site emergency repairs to avoid rework.

[0108] S7. Tag-based machine learning iterative optimization: Data on emergency repairs of old, non-standard thermal pipe fittings are entered into the database with tags, and the matching weights of emergency repair conditions are updated in real time. The monthly global optimization of the deviation classification threshold for irregular pipe fittings improves the adaptation efficiency of non-standard pipe fittings in old pipe networks.

[0109] This embodiment accumulates old, non-standard emergency repair samples and continuously optimizes the on-site rapid compensation algorithm to reduce the downtime for maintenance of heating pipeline networks.

[0110] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for intelligent measurement and adaptive matching of pipe connector dimensions, characterized in that, include; S1. Stereoscopic image acquisition: Through an intelligent measurement terminal integrating a binocular industrial camera, laser ranging module, attitude sensor and multispectral supplementary lighting module, multi-directional and multi-angle image scanning and dimension acquisition are performed on the pipe port to be assembled and the pipe connector to be selected, to obtain the original dimensional data of pipe outer diameter, inner diameter, wall thickness, port flatness, connector nominal diameter, sealing groove size, thread parameters and fitting angle. S2. High-precision data correction: The original acquired data is sequentially subjected to adaptive median filtering for noise reduction, lens and perspective distortion correction, Bezier surface fitting, and abnormal data secondary reconstruction and verification to achieve high-precision restoration of the contours of irregular curved surfaces, eccentric ports, and locally worn pipe components. For scenarios with missing effective data, parameters are reconstructed by interpolation of neighboring points to output standardized dimensions and deformation correction parameters. S3. Construct a tagged, hierarchical, intelligent, iterative database: Pre-store national standards, industry standards, enterprise standards, and non-standard special-shaped pipe fitting parameters, and archive pipe fitting specifications, materials, tolerance grades, wear degree, suitable working conditions, media type, pressure parameters, service life, and construction and maintenance costs with multi-dimensional tags, establish parameter association models, and support real-time data updates and iterations; S4. Dynamic weighted adaptive intelligent matching: The working condition clustering algorithm is used to dynamically calculate the matching weight coefficient based on the pipeline operating pressure, temperature, conveying medium, pipe fitting material and installation space scenario, to complete the accurate matching of standard pipe fittings and the initial adaptation of non-standard pipe fittings. S5. Non-standard pipe fittings four-level deviation classification adaptive compensation: preset quantitative classification thresholds for size, angle and sealing gap deviations, divide assembly deviations into four levels: excellent, good, acceptable and unacceptable, and implement differentiated strategies such as direct adaptation, parameter fine-tuning compensation, special rectification adaptation and adaptation failure elimination respectively. S6. Multi-physics field coupled dynamic simulation verification: The assembly structure is reconstructed through three-dimensional modeling, and multi-physics field coupled simulation is carried out in combination with fluid, heat and structural mechanics to dynamically simulate medium impact, temperature deformation, corrosion aging and fatigue stress conditions, and simultaneously complete the verification of assembly space interference and installation margin. S7. Labeled Machine Learning Iterative Optimization: Label the measurement, matching, simulation and on-site acceptance data of each time and enter them into the database. Summarize the matching rules through clustering machine learning algorithms, continuously optimize the dynamic weights, deviation thresholds and compensation parameters, and realize the system's autonomous iterative upgrade.

2. The intelligent measurement and adaptive matching method for pipe connector dimensions according to claim 1, characterized in that, In S1, the standardized acquisition parameters are limited to: scanning distance 30-80cm, scanning angle 0-360°, and acquisition frame rate 30-60fps; multispectral supplementary lighting adopts adaptive switching of warm and cool color temperature, and the terminal has a built-in anti-shake and vibration reduction component to suppress vibration interference.

3. The intelligent measurement and adaptive matching method for pipe connector dimensions according to claim 1, characterized in that, The S2 mid-efficiency size reconstruction fitting formula is: in, For Bessel basis functions, The coordinates of the effective sampling control points in the neighborhood. These are the surface parameters.

4. The intelligent measurement and adaptive matching method for pipe connector dimensions according to claim 1, characterized in that, The S4 section describes the construction of a nine-dimensional matching evaluation system that dynamically calculates matching weight coefficients, encompassing dimensional tolerances, sealing performance, assembly accuracy, working condition adaptability, pressure bearing capacity, material compatibility, media adaptability, service life, and construction and maintenance costs.

5. The intelligent measurement and adaptive matching method for pipe connector dimensions according to claim 4, characterized in that, The formula for calculating dynamic weight normalization is: in, For the first Final weight of item dimension The sensitivity coefficient for working conditions and scenarios. Based on fixed weights, .

6. The intelligent measurement and adaptive matching method for pipe connector dimensions according to claim 1, characterized in that, The formula for quantifying the overall deviation in S5 is: in, For dimensional deviation, For angle deviation, For the deviation of the sealing gap, The deviation weighting coefficient and .

7. The intelligent measurement and adaptive matching method for pipe connector dimensions according to claim 1, characterized in that, In S5, the four-level deviation quantification thresholds are as follows: excellent grade is ≤0.02mm in size and ≤0.1° in angle; good grade is 0.02-0.05mm in size and 0.1-0.3° in angle; acceptable grade is 0.05-0.1mm in size and 0.3-0.5° in angle; and defective grade is >0.1mm in size or >0.5° in angle.

8. The intelligent measurement and adaptive matching method for pipe connector dimensions according to claim 7, characterized in that, The adaptive gap compensation formula for good grade deviation is: in, To compensate for the correction factor, To compensate for gaps during final assembly.

9. The intelligent measurement and adaptive matching method for pipe connector dimensions according to claim 1, characterized in that, In S7, the clustering machine learning algorithm performs clustering training on sample data with different working conditions, materials, and deviation types, and autonomously optimizes the nine-dimensional weight ratio and the four-level deviation classification threshold. The iteration cycle is real-time update after each operation and global parameter optimization is completed once a month.

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