Pipeline upgrading optimization method and system suitable for old building

Abstract

The invention relates to a pipeline upgrading optimization method suitable for an old building, and the method is characterized in that the method comprises the following steps: S1, preprocessing and three-dimensional digital scanning diagnosis; s2, intelligent planning of a spraying path and a strategy; s3, intelligent spraying operation based on real-time feedback; and S4, rapid curing and digital acceptance.

Description

Technical Field

[0001] This application relates to the field of pipeline repair technology, and more specifically, to a method and system for upgrading and optimizing pipelines in old buildings. Background Technology

[0002] Due to their age, drainage pipe systems in older residential communities generally face the problem of aging pipe materials, primarily cast iron or concrete. These materials are prone to chemical corrosion, physical wear, and structural deterioration during long-term service. Internal pipe defects are highly complex, including various types such as corrosion pits, leaks, structural cracks, holes, and pipe misalignments. These defects are also spatially random and densely distributed, often accompanied by environmental constraints such as narrow working spaces and tangled pipelines, greatly increasing the difficulty of repair. Traditional excavation and replacement methods require large-scale demolition of road surfaces and building structures, resulting not only in high direct construction costs but also social costs such as traffic disruptions, disturbance to residents' lives, and secondary environmental pollution. The overall implementation cycle is long and causes significant disturbance to residents. Among existing trenchless repair technologies, integral lining methods such as CIPP (Compound In-Pipe Proofing) require extremely stringent cleanliness standards for the pipeline interior. All deposits, rust layers, and adhering substances must be thoroughly removed; otherwise, the lining layer will fail to bond with the pipe wall. Furthermore, this technology has poor adaptability to pipeline geometry, making it difficult to handle sections with small bending radii or severe deformation. Moreover, its overall wrapping repair strategy cannot provide differentiated treatment for localized defects, leading to excessive material usage and wasted repair resources. Traditional spraying repair methods heavily rely on manual operation. Workers manually control the spraying equipment in confined spaces. Limited visibility and operational stability result in inconsistent spray thickness control, large fluctuations in coating quality, and defects such as missed areas, build-up, or insufficient adhesion. In addition, the lack of real-time status monitoring and dynamic adjustment mechanisms prevents optimization of spraying parameters based on actual pipeline conditions, leading to low repair efficiency, extended construction periods, and an inability to meet the precise repair needs of complex defects. In summary, existing technologies have significant limitations in addressing the combined challenges of various types of defects in drainage pipes of old buildings, confined spaces, and efficient repair. There is an urgent need to develop a trenchless repair solution that can intelligently diagnose defect distribution, adaptively plan repair paths, achieve precise on-demand spraying, and quickly complete curing.

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for upgrading and optimizing pipelines in old buildings, which has the advantages of efficient repair, precise control and reduced construction interference.

[0005] This application provides a method for upgrading and optimizing pipelines in old buildings, and the technical solution is as follows: A pipeline upgrade and optimization method applicable to old buildings, comprising the following steps: S1: Pretreatment and three-dimensional digital scanning diagnosis: Clean the drainage pipeline, and use a detection robot equipped with a vision and laser scanning module to collect internal pipeline data, generating a three-dimensional digital model of the pipeline with defect type, position, and size markings; S2: Intelligent planning of spraying path and strategy: Based on the three-dimensional digital model, through the planning algorithm of the control system, according to the severity level and distribution of defects, automatically plan the optimal traveling path of the spraying robot, and generate a differentiated spraying thickness strategy for different defect areas; S3: Intelligent spraying operation based on real-time feedback: The spraying robot travels according to the planned path, and the nozzle at the end of its robotic arm sprays a high molecular composite material. At the same time, the sensing unit integrated on the nozzle monitors the spraying state parameters in real time and feeds the parameters back to the control system to dynamically adjust the spraying operation parameters and achieve closed-loop control of the coating quality; S4: Rapid curing and digital acceptance: After spraying, the high molecular composite material cures in situ in the pipeline. Subsequently, use the detection robot to perform post-repair scanning to generate a three-dimensional model after repair, and complete digital acceptance through comparative analysis with the model before repair. Further, the present application also proposes that the specific steps for generating a differentiated spraying thickness strategy in step S2 include: Planning continuous spraying with a first standard thickness (T1) for defective areas of corrosion and leakage; For defective areas of structural cracks, holes, or offsets, plan a locally thickened spraying area centered on the defect and with a width greater than the defect range, and the second target thickness (T2) of this area satisfies T2>T1. Further, the present application also proposes that the generation method of the locally thickened spraying area is: After the control system identifies the contour of a serious defect, it automatically generates a "return" shape or spiral thickening path covering the contour, and the calculation of the second target thickness (T2) is positively correlated with the depth, width, and pipeline operating pressure of the defect. Further, the present application also proposes that the spraying state parameters monitored in real time in step S3 include the wet film thickness of the coating and the rheological viscosity of the material; The spraying operation parameters adjusted dynamically include the traveling speed (V) of the spraying robot, the rotational speed (R) of the nozzle, and the pumping flow rate (Q) of the high molecular composite material; Closed-loop control is achieved through the following functional relationship for dynamic adjustment: When the measured thickness (H_actual) is less than the target thickness (H_target), the control system synchronously reduces V and / or increases Q; When the material viscosity exceeds the preset range, adjust R to optimize the atomization effect. Furthermore, this application also proposes that the polymer composite material in step S3 is a two-component on-site mixing material, wherein component A includes a flexible modified epoxy resin matrix, an ultraviolet tracer and toughening fibers, and component B includes a curing agent, nano-silica reinforcing filler and penetrating crystalline active masterbatch; the initial setting time of the mixed material can be adjusted within 15-45 minutes, and the final bond strength between the solidified body and the concrete pipe wall is not less than 2.5 MPa. Furthermore, this application also proposes that the method further includes step S5: generating and storing a digital repair archive, the archive being a structured data package, the contents of which include at least: a three-dimensional comparison model before / after repair, a list of defects, a spraying path planning diagram, time series data of key construction parameters (V, R, Q), and a thickness distribution cloud map of the final coating. Furthermore, this application proposes that the specific method for generating the three-dimensional digital model of the pipeline in step S1 is as follows: by fusing the 360° panoramic high-definition video stream obtained by the inspection robot with the point cloud data obtained by the lidar, a synchronous positioning and map building algorithm is used to reconstruct a three-dimensional real scene model of the pipeline interior with color and texture information in real time. Furthermore, this application also proposes that the body of the spraying robot integrates a six-degree-of-freedom robotic arm, a high-speed centrifugal rotating nozzle, and a non-contact infrared thickness sensor and a miniature rotational viscometer embedded inside the nozzle, which together constitute a sensing unit. Furthermore, this application also proposes that, when performing step S3, if the three-dimensional digital model shows that there is a bend greater than 90° or a sudden change in pipe diameter in the pipeline, the control system will mark the section as a special process section and instruct the painting robot to automatically switch to a low-speed, high-precision painting mode before entering the section, and activate the anti-collision interference detection algorithm of the robotic arm. Furthermore, this application also proposes a system for performing the above-described method, comprising: The inspection robot unit is used to perform scanning diagnosis and acceptance in steps S1 and S4; The painting robot unit is used to perform the painting operation in step S3, and includes a walking module, a robotic arm, an intelligent nozzle and a sensing unit. The central control unit is used to execute the intelligent planning in step S2 and receive feedback data from the sensing unit to implement closed-loop control. It includes a data processor, a planning algorithm module and a human-machine interface. The material supply unit is used to store, mix in proportion, and pump polymer composite materials at a constant temperature to the spraying robot unit. As can be seen from the above, the pipeline upgrade and optimization method and system provided in this application, applicable to old buildings, generates a three-dimensional model through preprocessing scanning, intelligently plans the spraying path, provides real-time feedback to adjust spraying parameters, and enables rapid curing and acceptance, thus achieving efficient and precise pipeline repair. It has the advantages of efficient repair, precise control, and reduced construction interference. Attached Figure Description

[0006] Figure 1 This is a flowchart of the pipeline upgrade and optimization method in the embodiments of this application.

[0007] Figure 2 This is a block diagram of the pipeline upgrade and optimization system in the embodiments of this application. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0009] The following disclosure provides numerous different embodiments or examples for implementing various structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of the invention. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0010] Traditional methods for repairing drainage pipes in old residential areas, such as excavation and replacement, are costly. Existing trenchless technologies, such as integral lining, have stringent requirements for pipe cleanliness and shape and cannot achieve differentiated and precise repairs. Traditional spraying methods rely on manual labor, resulting in uneven quality and low efficiency, making it difficult to adapt to complex working conditions and achieve precise on-demand repairs.

[0011] In this regard, such as Figure 1 As shown, this application proposes a pipeline upgrade and optimization method suitable for old buildings, including the following steps: S1: Preprocessing and 3D digital scanning diagnosis: The drainage pipe is cleaned, and a detection robot equipped with vision and laser scanning modules is used to collect data inside the pipe to generate a 3D digital model of the pipe with defect type, location and size annotations. S2: Intelligent planning of spraying path and strategy: Based on this three-dimensional digital model, the system's planning algorithm automatically plans the optimal travel path of the spraying robot according to the severity level and distribution of defects, and generates differentiated spraying thickness strategies for different defect areas. S3: Intelligent spraying operation based on real-time feedback: The spraying robot moves along the planned path, and the nozzle at the end of its robotic arm sprays polymer composite materials. At the same time, the sensing unit integrated on the nozzle monitors the spraying status parameters in real time and feeds the parameters back to the control system to dynamically adjust the spraying operation parameters and realize closed-loop control of coating quality. S4: Rapid curing and digital acceptance: After spraying, the polymer composite material is cured in situ inside the pipeline. Then, the inspection robot is used to scan after repair to generate a three-dimensional model after repair. By comparing and analyzing the model before repair, digital acceptance is completed.

[0012] For ease of understanding, the following explains some key terms in this embodiment: Inspection robots typically refer to automated devices that can move inside pipelines and perform inspection tasks. They can be equipped with various sensors to acquire various information about the inside of the pipeline.

[0013] The vision and laser scanning module typically refers to a combination of optical sensors integrated on an inspection robot. The vision module is used to acquire images or video information of the inside of the pipe, while the laser scanning module is used to acquire geometric dimensions and point cloud data of the inside of the pipe.

[0014] A 3D digital model of a pipeline is a digital model that is created by processing and reconstructing the internal data of a pipeline. It has three-dimensional spatial information and defect annotations and is used to intuitively display the internal condition of the pipeline.

[0015] A control system typically refers to a central processing unit responsible for receiving data, executing algorithms, and issuing instructions to coordinate the operation of various devices. It may include both hardware and software components.

[0016] Planning algorithms typically refer to computational programs within a control system used to analyze data and generate decision logic, such as algorithms used to calculate robot travel paths or painting strategies.

[0017] A spraying robot is generally an automated device that can move inside pipes and perform spraying tasks. It is usually equipped with a robotic arm and a nozzle to uniformly coat the inner wall of the pipe with repair material.

[0018] A robotic arm, typically referring to a movable part with multiple degrees of freedom on a painting robot, is used to precisely control the position and orientation of the spray nozzle.

[0019] A nozzle, typically referring to the actuator at the end of a robotic arm used to spray polymer composite materials, can employ various spraying principles, such as pneumatic spraying or centrifugal spraying.

[0020] Polymer composite materials typically refer to materials composed of two or more materials with different properties, at least one of which is a polymer material. When used for pipeline repair, they have good mechanical properties and corrosion resistance.

[0021] A sensing unit typically refers to a collection of sensors integrated on a nozzle or spraying robot to monitor various physical or chemical quantities during the spraying process in real time.

[0022] Spraying state parameters typically refer to physical quantities related to coating formation and material properties that are measured in real time during the spraying process, such as coating thickness and material viscosity.

[0023] Spraying operation parameters typically refer to parameters that the control system can dynamically adjust, which directly affect the spraying process and coating quality, such as robot travel speed, nozzle rotation speed, and material pump flow rate.

[0024] Closed-loop control typically refers to a control method that monitors the system output (spraying status parameters) in real time, compares it with the target value, and dynamically adjusts the system input (spraying operation parameters) based on the deviation, thereby keeping the system output within the desired range.

[0025] In-situ curing typically refers to the process by which polymer composite materials undergo chemical reactions or physical changes directly inside a pipe to form a robust coating, without the need to disassemble or move the pipe.

[0026] Digital acceptance testing typically refers to an acceptance method that uses data comparison and analysis of 3D models of pipelines before and after repair to quantitatively evaluate the repair effect and quality, replacing traditional manual visual inspection.

[0027] This embodiment provides a method for upgrading and optimizing pipelines in old buildings. First, in step S1, the drainage pipeline undergoes preprocessing and 3D digital scanning diagnosis. Specifically, a high-pressure water jet device can be used to clean the inside of the pipeline to remove impurities such as silt and oil. Then, an inspection robot equipped with a vision module and a laser scanning module enters the pipeline to collect image or video data and 3D point cloud data. This data is transmitted to a processing unit, where data fusion and reconstruction algorithms generate a 3D digital model of the pipeline with defect type, location, and size annotations. For example, this model can clearly display defects such as corrosion, cracks, leaks, misalignments, or holes inside the pipeline, and provide accurate spatial positioning and dimensional information.

[0028] Further, in step S2, intelligent planning of the spraying path and strategy is performed based on the generated 3D digital model. The planning algorithm in the control system analyzes the defect data in the model and automatically calculates the optimal travel path for the spraying robot according to the severity and distribution of the defects. This path planning can comprehensively consider factors such as spraying efficiency, coverage integrity, and obstacle avoidance. Simultaneously, the control system generates differentiated spraying thickness strategies for different types of defect areas. For example, a basic protective coating thickness can be planned for minor surface defects; while for structural damage or leaks, a thicker coating may be needed to provide enhanced repair effects.

[0029] Subsequently, in step S3, a real-time feedback-based intelligent spraying operation is performed. The spraying robot travels within the pipe according to a path planned by the control system, and the nozzle at the end of its robotic arm evenly sprays the polymer composite material onto the inner wall of the pipe. During this process, a sensing unit integrated into the nozzle monitors spraying status parameters in real time, such as the wet film thickness of the coating or the rheological viscosity of the material. These real-time monitored parameters are fed back to the control system. Based on the feedback data, the control system dynamically adjusts the spraying operation parameters, such as the travel speed of the spraying robot, the rotation speed of the nozzle, or the pumping flow rate of the polymer composite material. Through this closed-loop control mechanism of real-time monitoring, feedback, and adjustment, the stability and consistency of the coating quality during the spraying process are ensured, making it meet the preset quality requirements.

[0030] Finally, in step S4, rapid curing and digital acceptance are performed. After the spraying operation is completed, the polymer composite material is cured in situ inside the pipe to form a robust repair coating. After curing, the inside of the repaired pipe is scanned again using an inspection robot to collect data and generate a 3D model of the repaired pipe. By comparing and analyzing the repaired model with the unrepaired model, key indicators such as coating thickness, surface smoothness, and defect coverage in the repaired area can be quantitatively evaluated, thereby completing digital acceptance and ensuring that the repair effect meets design standards.

[0031] This method utilizes 3D digital scanning and diagnosis of drainage pipes in old buildings, combined with intelligent planning algorithms to customize spraying paths and thickness strategies, and employs a real-time feedback mechanism for closed-loop control of the spraying operation. Finally, digital acceptance ensures repair quality. Therefore, this method effectively solves the problems of high cost, insufficient repair accuracy, low efficiency, and difficulty in guaranteeing quality inherent in traditional repair methods. It achieves precise, on-demand repair of complex defects, significantly improving the efficiency and reliability of upgrading and optimizing drainage pipes in old buildings.

[0032] In some of the above embodiments, although a strategy for generating differentiated coating thickness based on the severity and distribution of defects has been proposed, for different types of defects in pipelines, especially severe defects such as structural cracks, holes or misalignments, a uniform differentiation strategy may not provide sufficient repair strength and durability, resulting in poor repair results or material waste.

[0033] In this regard, this application further proposes that, in the above method, the strategy for generating differentiated spray thickness in step S2 specifically includes: planning a continuous spray of a first standard thickness T1 for defective areas of corrosion and leakage; and planning a locally thickened spray area centered on the defect and with a width greater than the defect range for defective areas of structural cracks, holes or misalignments, wherein the second target thickness T2 of the area satisfies T2>T1.

[0034] Specifically, for non-structural defects such as corrosion and leakage detected inside the pipeline, this application plans to use continuous spraying with a first standard thickness T1. Corrosion and leakage are common surface deterioration or micropore problems in pipelines, usually not involving damage to the main pipeline structure. Therefore, by using a spraying robot to move uniformly over these defective areas at a constant speed and flow rate, a continuous polymer composite material coating can be formed, effectively restoring the pipeline's corrosion and leakage prevention functions. This first standard thickness T1 is a benchmark thickness determined based on factors such as the corrosion resistance and leakage resistance of the selected material, as well as the expected service life of the pipeline, aiming to provide sufficient repair and protection for general defects.

[0035] However, for more severe pipeline defects such as structural cracks, holes, or misalignments, these defects directly affect the pipeline's load-bearing capacity and structural integrity, requiring stronger reinforcement. Therefore, this application plans to generate a locally thickened sprayed area centered on the defect, with a width greater than the defect's extent. This localized thickening strategy aims to concentrate the repair material on the weakest part of the structure and, by extending the width of the thickened area beyond the defect's extent, ensure that the thickened coating can fully cover the defect and its surrounding affected area, thereby providing broader stress dispersion and structural support, effectively enhancing the mechanical strength and compressive strength of the defective area, and preventing further defect propagation. The localized thickening spraying can be achieved by controlling a spraying robot to perform multi-layer spraying in the defective area, or by significantly reducing the travel speed when passing through the defective area to accumulate more polymer composite material in a specific region.

[0036] In addition, the second target thickness T2 of the local thickening area is set to satisfy T2 > T1. This design principle clarifies the differentiated repair strength requirements for different types of defects, ensuring additional reinforcement for structural defects. The determination of the second target thickness T2 will comprehensively consider engineering factors such as the depth and width of the defect, the operating pressure of the pipeline, and the required structural strength, etc., to provide mechanical support far beyond that required for conventional anti-corrosion and anti-seepage.

[0037] Through the above technical solutions, the present application can implement a more refined and targeted repair strategy according to the actual type and severity of pipeline defects. Specifically, for general defects such as corrosion and leakage, continuous spraying with the first standard thickness T1 can effectively restore the anti-corrosion and anti-seepage functions of the pipeline while avoiding unnecessary material waste. For serious defects such as structural cracks, holes or offsets, a local thickening spraying area centered on the defect and with a width greater than the defect range is planned, and its second target thickness T2 is ensured to be significantly greater than T1, so as to provide additional structural support and mechanical strength at the部位 where reinforcement is most needed. This differentiated thickness strategy enables the repair materials to be utilized efficiently and precisely, significantly improving the repair effect and durability for various defects. Especially when dealing with structural damage, it can provide more reliable reinforcement, effectively extend the service life of old pipelines, and at the same time optimize the material cost and construction efficiency.

[0038] In some of the above embodiments of the present application, a differentiated spraying thickness strategy for pipeline defects is proposed. Especially for serious defects such as structural cracks, holes or offsets, a local thickening spraying area centered on the defect and with a width greater than the defect range is planned. However, in actual operation, how to accurately generate the paths of these local thickening areas and scientifically determine the second target thickness of the thickening area according to the specific situation of the defect to ensure the repair effect while avoiding material waste is a problem that needs to be further solved.

[0039] In response to this, the present application further proposes that the generation method of the local thickening spraying area is: after the control system identifies the contour of the serious defect, it automatically generates a "square" or spiral thickening path covering the contour, and the calculation of the second target thickness T2 is positively correlated with the depth, width of the defect and the operating pressure of the pipeline.

[0040] Specifically, the control system identifying the contour of the serious defect means that after obtaining the three-dimensional digital model inside the pipeline, the data processor of the central control unit uses image processing and defect analysis algorithms to accurately identify the boundaries and geometric contours of serious defects such as structural cracks, holes or offsets marked in the model. This can include using edge detection, region growing or machine learning-based image segmentation techniques to obtain an accurate two-dimensional or three-dimensional geometric representation of the defect area.

[0041] Automatically generating a "hui" - shaped or spiral thickening path that covers the contour means that after the control system accurately identifies the contour of a severe defect, the planning algorithm module will, based on the geometry and size of the defect, automatically generate a spraying path that can completely cover the defect area and extend outward by a certain width. For example, for irregular - shaped defects, the system can generate a spiral path that spreads outward from the center of the defect, or a "hui" - shaped (i.e., reciprocating or zig - zag) path that advances layer by layer outward based on the defect contour. The generation of these paths takes into account the spraying width of the nozzle and the required coating overlap rate to ensure the uniformity and integrity of the thickened area.

[0042] The calculation of the second target thickness T2 being positively correlated with the depth, width of the defect, and the pipeline operating pressure means that while the planning algorithm module of the central control unit determines the spraying path of the local thickening area, it will also dynamically calculate the second target thickness T2 required for this area based on parameters such as the defect depth, defect width, and pipeline operating pressure obtained from the three - dimensional digital model. This means that T2 is not a fixed value but is adjusted according to the severity of the specific defect and the actual load borne by the pipeline. For example, the deeper and wider the defect, or the higher the pipeline operating pressure, the larger the calculated value of T2, to provide stronger structural support and sealing performance. This calculation can be based on preset engineering formulas or empirical models to ensure the pertinence and reliability of the repair.

[0043] By accurately identifying the contour of severe defects through the control system and automatically generating a "hui" - shaped or spiral thickening path that covers the contour, it can ensure that the polymer composite material is comprehensively and evenly covered in the defect area, effectively avoiding problems such as spraying omission or unevenness. At the same time, the calculation of the second target thickness T2 being positively correlated with the depth, width of the defect, and the pipeline operating pressure enables the spraying thickness to be dynamically adjusted according to the actual severity of the defect and the stress environment of the pipeline. This not only ensures that the repair layer can provide sufficient structural strength and sealing performance to effectively cope with the challenges brought by severe defects such as structural cracks, holes, or offsets, but also avoids unnecessary material waste, improving the economy and efficiency of the repair. This refined path planning and thickness control significantly enhance the pertinence and reliability of the repair, ensuring the long - term stability of the repair of old building pipelines.

[0044] In some embodiments described above, a method for intelligent spraying repair of pipelines in old buildings is proposed. During the spraying process, a sensing unit monitors spraying status parameters in real time and feeds them back to the control system to dynamically adjust the spraying parameters, achieving closed-loop control of coating quality. However, without precise monitoring of key spraying status parameters and targeted dynamic adjustment strategies, uneven coating thickness and poor material atomization may occur, thus affecting repair quality and material utilization efficiency.

[0045] In response, this application further proposes that in step S3, the real-time monitored spraying state parameters include the wet film thickness of the coating and the rheological viscosity of the material; the dynamically adjusted spraying operation parameters include the traveling speed V of the spraying robot, the rotation speed R of the nozzle, and the pumping flow rate Q of the polymer composite material; the closed-loop control achieves dynamic adjustment through the following functional relationship: when the measured thickness H_actual is less than the target thickness H_target, the control system synchronously reduces V and / or increases Q; when the material viscosity exceeds the preset range, R is adjusted to optimize the atomization effect.

[0046] Specifically, wet film thickness is a direct indicator of coating volume and uniformity. During the spraying process, a non-contact infrared thickness sensor integrated into the nozzle can continuously acquire wet film thickness data on the polymer composite material in real time. This data directly reflects the immediate effect of the spraying operation and is a key feedback parameter for achieving precise thickness control. Meanwhile, the rheological viscosity of the material is an important physical parameter affecting the atomization effect, leveling properties, and final coating quality of the polymer composite material. During the spraying process, a miniature rotational viscometer integrated inside the nozzle can monitor the viscosity changes of the polymer composite material in real time. Viscosities that are too high or too low will affect the uniformity and adhesion of the spraying; therefore, real-time monitoring is essential to ensure spraying quality.

[0047] To achieve dynamic adjustment, this application utilizes the travel speed V of the spraying robot, the rotation speed R of the nozzle, and the pumping flow rate Q of the polymer composite material as adjustable parameters. The travel speed V of the spraying robot directly determines the distance the sprayed area moves per unit time. By adjusting the travel speed V, the deposition amount of polymer composite material per unit area can be changed, thereby achieving coarse or fine adjustment of the coating thickness. The rotation speed R of the nozzle mainly affects the atomization effect of the polymer composite material and the uniformity of the spray pattern. A high-speed rotating nozzle can uniformly atomize and disperse the material onto the inner wall of the pipe, forming a smooth coating. By dynamically adjusting the rotation speed R of the nozzle, changes in material viscosity can be adapted, optimizing the atomization effect and ensuring a uniform coating without dripping. The pumping flow rate Q of the polymer composite material directly controls the total amount of material sprayed from the nozzle per unit time, and is one of the most direct and effective means of achieving precise control of coating thickness.

[0048] Based on this, the closed-loop control of this application achieves dynamic adjustment through a specific functional relationship. When the real-time monitored wet film thickness H_actual is lower than the preset target thickness H_target, it indicates that the current spraying amount is insufficient. At this time, the control system will immediately execute an adjustment strategy, by simultaneously reducing the travel speed V of the spraying robot, thereby reducing the area covered by the nozzle per unit time and increasing the amount of material deposited per unit area; or by increasing the pump flow rate Q of the polymer composite material, directly increasing the total amount of material sprayed per unit time. These two methods can be used individually or in combination to quickly and effectively increase the measured thickness H_actual to the target thickness H_target, ensuring that the coating thickness meets the standard. In addition, when the real-time monitored rheological viscosity of the polymer composite material exceeds the preset ideal working range, the control system will immediately adjust the rotation speed R of the nozzle. By optimizing the rotation speed R of the nozzle, the influence of material viscosity changes on the atomization effect can be compensated. For example, when the viscosity is high, the rotation speed R can be appropriately increased to enhance the atomization ability, thereby ensuring that the polymer composite material can be uniformly sprayed on the inner wall of the pipe in the best condition, ensuring the smoothness and adhesion of the coating.

[0049] Through the above technical solution, this application enables refined and adaptive control of pipeline repair spraying operations. Real-time monitoring of the wet film thickness and material rheological viscosity provides crucial and immediate data feedback to the control system, allowing it to accurately determine the current spraying status. Based on this feedback, the control system can dynamically adjust the traveling speed V of the spraying robot, the rotation speed R of the nozzle, and the pumping flow rate Q of the polymer composite material. Specifically, when the coating thickness is insufficient, reducing the traveling speed V or increasing the pumping flow rate Q can quickly replenish the material, ensuring the coating reaches the preset thickness standard; when the material viscosity changes, adjusting the nozzle rotation speed R can optimize the atomization effect, ensuring uniform material spraying and avoiding coating defects caused by viscosity fluctuations. This closed-loop control mechanism significantly improves the uniformity, density, and adhesion of the sprayed coating, effectively avoiding errors and instabilities caused by human experience judgment, thereby greatly improving repair quality and efficiency, reducing material waste, and ensuring the long-term reliability of pipeline repair in old buildings.

[0050] To address this issue, this application proposes a pipeline upgrade and optimization method suitable for old buildings. In the intelligent spraying operation step based on real-time feedback, a spraying robot moves along a planned path, and the nozzle at the end of its robotic arm sprays a polymer composite material. Simultaneously, a sensing unit integrated into the nozzle monitors the spraying status parameters in real time and feeds these parameters back to the control system to dynamically adjust the spraying operation parameters, achieving closed-loop control of coating quality. However, in practical applications, traditional single-component or performance-limited polymer composite materials may struggle to simultaneously meet the requirements of rapid curing, excellent bond strength, and comprehensive repair of different types of defects (such as corrosion, cracks, and leaks), especially in the complex internal environment of pipelines, where material performance stability and construction efficiency face challenges.

[0051] Therefore, this application further proposes that the above-mentioned polymer composite material is a two-component on-site mixing material, wherein component A includes a flexible modified epoxy resin matrix, an ultraviolet tracer and toughening fibers, and component B includes a curing agent, nano-silica reinforcing filler and penetrating crystalline active masterbatch; the initial setting time of the mixed material can be adjusted within 15-45 minutes, and the final bond strength between the solidified body and the concrete pipe wall is not less than 2.5 MPa.

[0052] Specifically, the two-component on-site mixing material refers to a material composed of two or more components mixed in a specific ratio before use and then chemically reacted and cured on-site. This material system ensures that each component remains stable before spraying, preventing premature curing, while allowing for rapid reaction after mixing, enabling quick on-site construction and curing. Typically, components A and B are delivered to the mixing head separately via a precise metering pump, where they are dynamically mixed inside the nozzle or at the moment of spraying. The flexible modified epoxy resin matrix in component A is modified by introducing flexible segments or toughening agents into traditional epoxy resin, giving it a certain degree of elasticity and toughness after curing, rather than brittleness. This improves the coating's crack resistance, impact resistance, and deformation resistance, adapting to minor deformations and thermal expansion and contraction of the pipeline, preventing the repair layer from cracking due to pipeline movement. The ultraviolet tracer is a substance that fluoresces under ultraviolet light. Its function is to facilitate quality inspection after repair. By irradiating with an ultraviolet lamp, the uniformity and continuity of the coating, as well as the presence of missed areas or weak points, can be quickly and intuitively checked, aiding in digital acceptance. The toughening fibers are short fibers added to the matrix material to improve its toughness, tensile strength, and crack resistance. They form a three-dimensional network structure within the coating, effectively preventing crack propagation, improving the overall mechanical properties of the coating, especially its impact and fatigue resistance, and enhancing its repair effect on structural defects. The curing agent in component B reacts chemically with the epoxy resin, causing it to transform from a liquid to a solid state, initiating and controlling the curing process of the polymer composite material, and determining the curing speed and the properties of the final cured product. The nano-silica reinforcing filler consists of silica particles with a particle size in the nanometer range. Added as a filler to the polymer material, it significantly improves the coating's hardness, wear resistance, compressive strength, and impermeability, while also improving the material's rheological properties, making it easier to spray and form, and reducing curing shrinkage. The described penetrating crystalline active masterbatch is a masterbatch containing active chemical substances. In the presence of water, it reacts with free calcium ions in concrete to form water-insoluble crystals, filling the pores and micro-cracks within the concrete. Through this penetrating crystallization process, it not only enhances the physical adhesion between the coating and the concrete pipe wall but also penetrates deep into the concrete to form a dense structure, improving the concrete's impermeability and durability, thus achieving deep repair and protection of the pipe substrate. Furthermore, the initial setting time of the mixed material can be adjusted within 15-45 minutes, providing a sufficient window for construction, ensuring the continuity and uniformity of the spraying operation, while avoiding excessive waiting time that could affect construction efficiency. Its adjustability allows for flexible adjustments based on ambient temperature, pipe length, and the complexity of defects. The final solidified body achieves a bond strength of no less than 2.5 MPa with the concrete pipe wall, ensuring that the repair coating firmly adheres to the inner wall of the pipe, resisting water erosion, internal pressure, and external loads, preventing coating peeling, and guaranteeing the long-term stability of the repair effect.

[0053] By employing a two-component, on-site mixed polymer composite material, this application effectively addresses the problems of traditional materials in pipeline repair, such as limited performance, low construction efficiency, and unsustainable repair effects. Component A, with its flexible modified epoxy resin matrix, imparts excellent toughness and crack resistance to the coating, enabling it to adapt to minor pipeline deformations and preventing cracking of the repair layer. The ultraviolet tracer provides a convenient and intuitive means for post-repair quality inspection, ensuring traceability of repair quality. Toughening fibers form a reinforcing network within the coating, significantly improving its impact resistance and fatigue resistance. Component B, with its nano-silica reinforcing filler, enhances the coating's hardness, wear resistance, and impermeability, while the penetrating crystalline active masterbatch penetrates deep into the concrete pipe wall to form crystals, not only strengthening the bond between the coating and the substrate but also providing deep repair and reinforcement to the pipeline substrate itself. Furthermore, the adjustable initial setting time makes the construction process more flexible and efficient, allowing for optimization of the work window based on site conditions. The final solidified body's bond strength of no less than 2.5 MPa with the concrete pipe wall ensures that the repair coating can adhere stably to the inner wall of the pipe for a long time, effectively resisting water erosion and internal pressure. This significantly improves the overall performance and durability of pipeline repair in old buildings, achieving efficient, reliable, and verifiable repair results.

[0054] In some of the embodiments described above in this application, a pipeline upgrade and optimization method is proposed, which achieves effective repair of pipelines in old buildings through digital scanning, intelligent planning, real-time spraying, and digital acceptance. However, in practical applications, how to systematically record, trace, and manage key data throughout the repair process to facilitate subsequent quality assessment, maintenance decisions, and compliance with potential regulatory requirements remains a problem that needs to be solved.

[0055] In this regard, this application further proposes that the method also includes step S5: generating and storing a digital repair archive, wherein the archive is a structured data package, the contents of which include at least: a three-dimensional comparison model before / after repair, a list of defects, a spraying path planning diagram, time series data of key construction parameters (V, R, Q), and a thickness distribution cloud map of the final coating.

[0056] Specifically, step S5 aims to systematically and structurally record and save key data throughout the pipeline repair process, forming a complete digital archive. Its purpose is to provide a comprehensive view of the repair process, facilitating subsequent querying, analysis, evaluation, and traceability, ensuring the verifiability and manageability of repair quality. This step can be implemented by integrating an archive management module into the central control unit. Upon completion of the repair work, this module automatically collects and integrates data from different steps, such as the pre-repair 3D model in step S1, planning data in step S2, real-time spraying parameters in step S3, and the post-repair 3D model and comparative analysis results in step S4. This data can be organized in specific file formats (such as XML, JSON, or proprietary database formats) and stored on a local server or cloud storage system, ensuring data security and accessibility.

[0057] The archives are designed as structured data packets, meaning the data is not randomly piled up but organized according to predefined formats and logical relationships, facilitating machine parsing and human understanding. A data packet refers to a logical unit formed by grouping related data together. Its purpose is to improve data readability, searchability, and analyzability, laying the foundation for automated processing and data mining. For example, a data packet can adopt a hierarchical structure: the top layer contains basic project information (such as pipe ID, repair date, and operator); the next layer contains pre-repair data (such as 3D model file path and defect list); the next layer contains repair process data (such as time series data of spraying parameters); and the final layer contains post-repair data (such as post-repair model and thickness distribution cloud map). Each data item should have clearly defined field names and data types to ensure data consistency.

[0058] The archive contains at least a 3D comparison model before and after repair. This refers to the comparison and analysis of the 3D model of the pipe's interior obtained by the inspection robot before repair and the 3D model of the pipe generated by scanning again after repair. Its purpose is to visually demonstrate the repair effect, verify whether the defects have been effectively covered and repaired, and quantify the geometric changes before and after repair. The pre-repair model is generated in step S1, and the post-repair model is generated in step S4. The comparative analysis can be performed in the data processor of the central control unit, generating a visualization report through point cloud registration and difference analysis algorithms, highlighting the geometric changes and coating thickness of the repaired area. These model files (such as STL, OBJ, or PLY formats) and the comparison report can be part of the archive.

[0059] The archive also includes a defect annotation list. This is a detailed description and location marker of all identified defects (such as corrosion, cracks, leaks, misalignments, etc.) inside the pipeline before repair. Its purpose is to clarify the repair objectives, record the original state of the defects, and serve as a benchmark for evaluating the repair effect. The defect annotation list can be generated by the inspection robot or operator based on the three-dimensional digital model in step S1. The list can contain information such as defect type, precise coordinates, size, and severity level, and can be associated with specific areas in the three-dimensional model to form interactive annotations.

[0060] In addition, the file contains a spraying path planning diagram. This is a visual representation of the spraying robot's travel path and spraying strategy generated by the control system in step S2. Its purpose is to record the planning process, facilitate verification of the planning's rationality, and serve as a navigation basis for the robot in actual operation. The planning diagram can be a two-dimensional or three-dimensional graphic file, showing the expected trajectory of the spraying robot within the pipeline, the division of spraying areas, and the spraying thickness strategy for different areas. It can include information such as path points, speed curves, and nozzle attitude, and be overlaid on a three-dimensional digital model.

[0061] The archive also records time-series data of key construction parameters (V, R, Q). This refers to the records of the changes in the spraying robot's travel speed V, the nozzle rotation speed R, and the polymer composite material pump flow rate Q over time throughout the entire spraying operation, as recorded in step S3. Its purpose is to provide real-time execution details of the spraying operation, enabling analysis of the relationship between spraying quality and these parameters, and serving as a crucial basis for fault diagnosis and quality traceability. The sensing unit monitors these parameters in real time and transmits the data to the central control unit at high frequency. The control system stores this data, along with timestamps, as a time-series database or log file. This data can be used to generate parameter graphs, visually displaying parameter fluctuations during the spraying process.

[0062] Finally, the archive contains a thickness distribution cloud map of the final coating. This is a spatial distribution map of the coating thickness on the inner surface of the pipe, obtained through post-repair scanning (step S4) or real-time monitoring during spraying (step S3). Its purpose is to visually and quantitatively assess whether the coating is uniform, whether the target thickness has been achieved, and to identify potential weak or excessively thick areas. The cloud map can be color-coded, with different colors representing different coating thickness ranges for easy and rapid identification.

[0063] Through the aforementioned technical solution, this application enables the systematic and structured integration and storage of key data from each stage of the pipeline upgrade and optimization method. The before / after 3D comparison model visually demonstrates the repair effect and verifies effective defect coverage; the defect labeling list clarifies the repair objectives and the original defect state; the spraying path planning diagram records the details of intelligent planning; the time-series data of key construction parameters provides real-time execution basis for spraying operations; and the final coating thickness distribution cloud map quantitatively assesses coating quality. These structured data packages collectively constitute a comprehensive digital repair archive, greatly enhancing the traceability, verifiability, and manageability of the repair process. This allows users to easily review the entire repair process, accurately assess repair quality, provide reliable data support for future maintenance and decision-making, and meet stringent quality control and regulatory requirements, thereby significantly improving the overall quality and efficiency of pipeline repair projects.

[0064] In some embodiments described above, a method is proposed to clean drainage pipes and use an inspection robot equipped with vision and laser scanning modules to collect internal pipe data, generating a 3D digital model of the pipe with defect type, location, and size annotations. However, in practical applications, relying solely on single sensor data or simple model building methods may result in insufficient detail, geometric accuracy, or realism in the generated 3D model, thereby affecting the accuracy of subsequent defect identification and the precision of spraying planning.

[0065] In this regard, this application further proposes a specific method for generating the three-dimensional digital model of the pipeline in step S1: by fusing the 360° panoramic high-definition video stream obtained by the detection robot with the point cloud data obtained by the lidar, a synchronous positioning and map building algorithm is used to reconstruct a three-dimensional real-scene model of the pipeline interior with color and texture information in real time.

[0066] Specifically, fusing the 360° panoramic high-definition video stream acquired by the inspection robot with the point cloud data acquired by LiDAR refers to aligning and integrating data from different sensors in time and space. The 360° panoramic high-definition video stream, captured by a wide-angle or multi-camera system mounted on the inspection robot, provides a direct representation of the pipe's inner wall color, texture, surface condition, and various visible defects, offering a realistic visual appearance and texture details for the 3D model. The point cloud data acquired by LiDAR uses LiDAR sensors to precisely measure the 3D coordinates of numerous points on the pipe's inner wall, forming the pipe's internal geometric framework. This accurately reflects the pipe's geometric shape, size, deformation, and structural features such as the depth and width of defects, providing precise geometric structure and dimensional information for the 3D model. Through sensor calibration, coordinate system transformation, and data registration techniques, the visual information in the video is accurately mapped onto the geometric structure of the point cloud data, resulting in comprehensive data that combines precise geometry with realistic visual representation.

[0067] Building upon this foundation, a Simultaneous Localization and Mapping (SLAM) algorithm is employed. This algorithm utilizes continuously acquired 360° panoramic high-definition video streams and LiDAR point cloud data as input from the inspection robot. By analyzing visual features in the video stream and geometric features in the point cloud data, the SLAM algorithm can estimate the precise pose (position and orientation) of the inspection robot inside the pipe in real time, while simultaneously incrementally constructing a 3D geometric map of the pipe's interior. The advantage of this algorithm is that it can achieve high-precision localization and mapping even when GPS signals are unavailable or in complex environments (such as inside a pipe), providing a solid foundation for subsequent 3D model reconstruction.

[0068] Ultimately, a real-time 3D model of the pipe's interior, incorporating color and texture information, is reconstructed. Real-time reconstruction means that the 3D model is built continuously and instantaneously as the inspection robot moves. By precisely mapping the color and texture information from the 360° panoramic high-definition video stream onto the geometric model constructed from the LiDAR point cloud, the resulting 3D model not only possesses accurate geometry but also displays the true color, surface details, and texture of the pipe's inner wall. This "real-world" model significantly enhances the model's realism and readability, making defect identification, classification, and labeling more intuitive and accurate.

[0069] By employing the aforementioned technical solution, the 360° panoramic high-definition video stream acquired by the inspection robot is deeply fused with the point cloud data acquired by LiDAR. Using simultaneous localization and mapping (SLAM) algorithms, a real-time 3D realistic model of the pipe's interior, complete with color and texture information, can be reconstructed. This model, integrating visual details and precise geometric information, overcomes the limitations of single-sensor data in terms of realism or geometric accuracy, allowing defects inside the pipe (such as cracks, corrosion, leaks, and misalignments) to be presented in a more intuitive and accurate manner. Based on this high-fidelity 3D realistic model, the control system, when intelligently planning the spraying path and strategy, can more accurately identify defect types, precisely locate defect positions, and quantify defect sizes. This generates more refined and differentiated spraying strategies, significantly improving the accuracy and effectiveness of repair operations and providing a solid data foundation for subsequent intelligent spraying operations and digital acceptance.

[0070] In some of the embodiments described above in this application, although it is proposed to use a spraying robot for pipeline repair and to achieve closed-loop control by monitoring the spraying status parameters in real time through a sensing unit, in actual operation, the internal environment of the pipeline is complex and variable. Traditional spraying equipment may be difficult to adapt flexibly to various pipeline structures and defect types, and its ability to perceive spraying quality in real time and accurately is limited, thus affecting the coating uniformity and repair effect.

[0071] In this regard, this application further proposes that the body of the spraying robot integrates a six-degree-of-freedom robotic arm, the spray head is a high-speed centrifugal rotating spray head, and the spray head is embedded with a non-contact infrared thickness sensor and a micro rotational viscometer, which together constitute the sensing unit.

[0072] Specifically, the painting robot integrates a six-degree-of-freedom (DOF) robotic arm. A six-DOF robotic arm refers to a robotic arm with six independent motion joints, enabling it to perform arbitrary combinations of translational (X, Y, Z axes) and rotational (pitch, yaw, roll) movements in three-dimensional space. This high degree of freedom design gives the robotic arm extremely high flexibility and precision, allowing it to penetrate deep into pipes and perform delicate operations in narrow, curved, or structurally complex areas. For example, in painting operations, the robotic arm can precisely adjust the attitude and angle of the nozzle to adapt to the curvature of the pipe's inner wall, ensuring uniform spraying, and can flexibly avoid obstacles or perform localized thickening spraying on specific defect areas. This is typically achieved by a series of servo-motor driven joints that work collaboratively through a control system.

[0073] The nozzle is a high-speed centrifugal rotary nozzle. A high-speed centrifugal rotary nozzle uses centrifugal force to atomize and disperse the coating material into fine particles via a high-speed rotating spray disc or cup. This type of nozzle produces a uniform and fine atomization effect, ensuring a smooth, drip-free coating on the inner wall of the nozzle. High-speed rotation helps to evenly disperse the material, reducing spray streaks and uneven thickness. It is typically implemented by a high-speed rotating component driven by a motor, with the coating material entering through a central orifice and being ejected.

[0074] The nozzle is embedded with a non-contact infrared thickness sensor. A non-contact infrared thickness sensor is a sensor that measures coating thickness without contacting the surface of the object being measured, utilizing the principle of infrared reflection or transmission. This sensor can measure the thickness of the wet film coating in real time and accurately during the spraying process, providing crucial feedback data for closed-loop control. Non-contact measurement avoids damage or contamination of the wet coating, making it particularly suitable for spraying polymer composite materials. Its implementation typically includes an infrared emitter and a receiver, calculating the thickness by analyzing the intensity or phase difference of the reflected or transmitted infrared signal.

[0075] The nozzle incorporates a miniature rotational viscometer. A miniature rotational viscometer is a device integrated within the nozzle that monitors material viscosity in real time by measuring the shear force or torque generated by the fluid under the action of rotating components. Real-time monitoring of the rheological viscosity of the sprayed material is crucial for ensuring spraying quality. Material viscosity affects atomization, leveling, and the final coating thickness. When the viscosity exceeds a preset range, the control system can promptly adjust spraying parameters (such as nozzle rotation speed and pump flow rate) to maintain optimal spraying conditions. This is typically achieved by using a miniature rotating probe that determines viscosity by measuring the resistance it encounters as it rotates within the material.

[0076] The aforementioned non-contact infrared thickness sensor and miniature rotational viscometer together constitute the sensing unit. Integrating these sensors inside the spray head forms a compact and efficient sensing unit. This integrated design allows the sensor to be closest to the spraying point, obtaining the most direct and accurate spraying state parameters, thereby providing high-precision real-time feedback to the control system and achieving more refined closed-loop control.

[0077] Through the aforementioned technical solutions, the six-degree-of-freedom robotic arm integrated into the spraying robot significantly enhances the flexibility and adaptability of spraying operations, enabling it to precisely reach any position inside the pipeline and spray in the optimal posture, effectively addressing the repair challenges of pipeline bends, diameter changes, or complex defect areas. The high-speed centrifugal rotating nozzle ensures uniform atomization and fine coating of polymer composite materials, avoiding uneven coating or sagging. More importantly, the non-contact infrared thickness sensor and miniature rotational viscometer embedded inside the nozzle can acquire key parameters such as coating wet film thickness and material rheological viscosity in real time and without damage, providing high-precision, low-latency feedback data for the control system. This allows the control system to dynamically adjust spraying parameters more promptly and accurately, achieving closed-loop control of coating quality, thereby significantly improving the uniformity, density, and adhesion strength of the repair coating to the pipe wall, ensuring the long-term reliability and durability of pipeline repair in old buildings.

[0078] In some embodiments described above, this application proposes a method for upgrading and optimizing pipelines in old buildings. This method involves scanning and diagnosing with a detection robot, intelligent planning with a control system, intelligent spraying with a spraying robot, and finally, rapid curing and digital acceptance. However, in actual intelligent spraying operations, drainage pipes in old buildings often have complex geometric structures, such as sharp bends or abrupt changes in pipe diameter. These complex areas pose significant challenges to the movement and spraying operation of the spraying robot, potentially leading to uneven spraying, reduced coating quality, and even the risk of collision between the robotic arm and the inner wall of the pipe, thus affecting the overall repair effect and operational safety.

[0079] In this regard, this application further proposes that, when performing step S3, if the three-dimensional digital model shows that the pipeline has a bend greater than 90° or a sudden change in pipe diameter, the control system will mark the section as a special process section and instruct the painting robot to automatically switch to a low-speed, high-precision painting mode before entering the section, and activate the anti-collision interference detection algorithm of the robotic arm.

[0080] Specifically, before executing the painting operation, the control system uses the 3D digital model of the pipeline generated in step S1 to conduct a detailed analysis of the pipeline's internal structure. This model includes information such as the pipeline's geometry, dimensions, and defect distribution. The control system identifies geometric features in the model data, for example, by calculating the rate of change of curvature of the pipeline's centerline or the gradient change of its cross-sectional area, to accurately identify and locate areas in the pipeline where the radius of curvature is less than a specific threshold (corresponding to bends greater than 90°) or the rate of change of cross-sectional dimensions exceeds a preset threshold (corresponding to abrupt changes in pipe diameter). These areas are typically the most challenging and problematic parts of the painting operation. Once these complex geometric areas are identified, the control system highlights them in the 3D digital model or marks them with specific labels, thus forming a logical area of ​​a "special process section." This marking not only visually alerts the operator but, more importantly, serves as a trigger condition for subsequent adjustments to the painting strategy, ensuring that the painting robot can adopt different operating modes for these special areas.

[0081] The "low-speed, high-precision spraying mode" refers to a significant reduction in the travel speed (V) of the spraying robot, while the nozzle rotation speed (R) and the pumping flow rate (Q) of the polymer composite material are finely adjusted to ensure more uniform and denser coating coverage on complex curved surfaces or in areas with varying diameters. For example, the travel speed can be reduced to less than 50% of the conventional mode, and the nozzle rotation speed and flow rate are finely adjusted according to the local geometry of the pipe and the target thickness to compensate for potential material buildup due to the reduced speed and to ensure sufficient atomization and uniform adhesion of the material. The anti-collision interference detection algorithm is a real-time path planning and monitoring algorithm based on a 3D digital model and a robot kinematics model. This algorithm is activated before the spraying robot enters a special process section. It continuously monitors the real-time position and orientation of the robotic arm and its end-effector nozzle inside the pipe and compares it with the 3D geometric model of the pipe. Once a potential collision risk is predicted between any part of the robotic arm and the inner wall of the pipe (e.g., the distance is less than a safety threshold), the algorithm immediately issues a warning and automatically adjusts the robotic arm's trajectory or suspends operation to avoid physical collisions and ensure the safety of the equipment and the pipe.

[0082] Through the aforementioned technical solution, during intelligent spraying operations, the control system can pre-identify and mark complex geometric areas inside the pipeline, such as bends greater than 90° or abrupt changes in pipe diameter, defining them as special process sections. Before the spraying robot enters these special process sections, the system automatically switches to a low-speed, high-precision spraying mode and activates the robotic arm's anti-collision interference detection algorithm. This predictive and adaptive adjustment mechanism effectively solves problems such as the difficulty in robot motion control, the inability to guarantee spraying quality, and potential collision risks when performing spraying operations in complex pipeline environments. Specifically, the low-speed, high-precision spraying mode ensures uniform coverage and precise thickness control of polymer composite materials at bends or diameter changes, avoiding defects such as missed spraying, accumulation, or uneven coating, thereby significantly improving repair quality. Simultaneously, the activation of the anti-collision interference detection algorithm monitors the robotic arm's movement trajectory in real time, effectively avoiding the risk of collision between the robotic arm and the inner wall of the pipeline, ensuring the safety of the spraying operation and the integrity of the equipment. Therefore, this application can significantly improve the intelligence level, operational efficiency, and repair quality of pipeline repair in old buildings, while reducing operational risks.

[0083] In some of the embodiments described above in this application, a method for upgrading and optimizing pipelines in old buildings is proposed. This method aims to achieve precise pipeline repair through steps such as preprocessing, 3D scanning diagnosis, intelligent planning, real-time feedback spraying, and digital acceptance. However, in its implementation, without a highly integrated and collaborative hardware platform to support these complex steps requiring precise control and real-time response, it is difficult to guarantee seamless connection between each link, efficient data flow, and accurate execution of operations, which may affect the overall efficiency and final quality of the repair.

[0084] In response, this application proposes a system for performing the above method, the system comprising a detection robot unit 100, a spraying robot unit 200, a central control unit 300, and a material supply unit 400.

[0085] The inspection robot unit 100 is used to perform scanning, diagnosis, and acceptance testing of the pipeline's interior after repair. It typically includes a self-propelled or remotely controlled mobile body equipped with high-resolution cameras, LiDAR, and other sensors, enabling comprehensive, high-precision image and point cloud data acquisition of the pipeline's interior. This data is used to generate a 3D digital model of the pipeline and identify and label pipeline defects. After repair, the unit re-enters the pipeline to scan and evaluate the repair effectiveness, ensuring that the repair quality meets requirements.

[0086] The spraying robot unit 200 is specifically designed for spraying polymer composite materials inside pipes. It typically consists of a walking module, a robotic arm, a smart nozzle, and a sensing unit. The walking module is responsible for the robot's precise movement and positioning within the pipe; the robotic arm provides the nozzle with multi-degree-of-freedom motion capabilities, allowing it to flexibly cover various areas of the pipe's inner wall; the smart nozzle is responsible for uniformly and precisely spraying the polymer composite material onto defect areas; and the sensing unit monitors key parameters during the spraying process in real time, providing data support for closed-loop control.

[0087] The central control unit 300 is the "brain" of the entire system, responsible for coordinating and managing the operation of all units. It includes a data processor for processing large amounts of data from the sensor units of the inspection robot unit and the painting robot unit; a planning algorithm module for intelligently planning painting paths and strategies based on a 3D digital model and dynamically adjusting operational parameters according to real-time feedback; and a human-machine interface for operators to monitor system status, input commands, and view repair reports. This unit achieves closed-loop control of the painting operation by receiving feedback data from the sensor units, ensuring painting quality.

[0088] The material supply unit 400 is responsible for the storage, precise mixing, and temperature-controlled pumping of polymer composite materials. It typically includes multiple storage tanks for storing two-component or multi-component polymer composite materials; a precise metering and mixing system to ensure that the components are mixed uniformly according to a preset ratio; and a temperature control and pumping system to deliver the mixed material to the intelligent nozzle of the spraying robot unit at a stable flow rate and temperature to ensure material performance and spraying effect.

[0089] This system organically integrates pipeline inspection, planning, spraying, and acceptance into a highly automated and intelligent overall solution. The inspection robot unit accurately acquires defect information within the pipeline, providing a precise data foundation for subsequent repairs. The central control unit uses this data for intelligent planning, ensuring the spraying robot unit operates with optimal paths and strategies, especially when handling complex defects, achieving differentiated spray thicknesses. During operation, the integrated sensing unit of the spraying robot unit feeds back spraying status parameters to the central control unit in real time, enabling the system to dynamically adjust spraying parameters and achieve closed-loop control of coating quality. This significantly improves the accuracy and reliability of the repair, avoiding errors and inconsistencies that may occur in traditional manual operations. Furthermore, the material supply unit ensures the precise proportioning and stable delivery of polymer composite materials, providing material support for high-quality spraying. This integrated system design not only improves construction efficiency and reduces labor costs, but more importantly, through refined and intelligent control, it ensures the uniformity, adhesion, and durability of the repair coating, thereby extending the service life of aging building pipelines and improving the overall quality and economic benefits of the repair project.

[0090] The following example will provide a more detailed explanation of the above technical solution: In a renovation project of the underground drainage network in an old residential area, a DN300 cast iron main drainage pipe suffered from severe internal corrosion, multiple leaks, several obvious structural cracks, and a misaligned pipe joint due to long-term use. The pipe was located in a narrow underground space, and traditional excavation and repair methods were costly and time-consuming.

[0091] First, the project team adopted a pipeline upgrade and optimization method. In step S1, the preprocessing and 3D digital scanning diagnostic phase, workers thoroughly cleaned the target drainage pipe to remove internal silt and deposits. Subsequently, an inspection robot equipped with vision and laser scanning modules was deployed into the pipeline. By fusing its acquired 360° panoramic high-definition video stream with point cloud data obtained from LiDAR, and employing simultaneous localization and mapping algorithms, the robot reconstructed a real-time 3D realistic model of the pipe's interior with color and texture information. This model accurately marked the location, type, and size of corrosion areas, leaks, structural cracks, holes, and misalignments within the pipe.

[0092] Next, enter the intelligent planning stage of the spraying path and strategy in step S2. Based on the three-dimensional digital model of the pipeline generated in stage S1, the data processor of the central control unit, through its planning algorithm module, automatically plans the optimal travel path of the spraying robot according to the severity level and distribution of the defects identified in the model. At the same time, the control system generates a differentiated spraying thickness strategy for different defect areas: for the defect areas of corrosion and leakage, the system plans continuous spraying with the first standard thickness T1; for serious defect areas such as structural cracks, holes or offsets, after the control system identifies their contours, it automatically generates a "return" shaped local thickening path covering the contour and plans a local thickening spraying area centered on the defect and with a width greater than the defect range, and the second target thickness T2 of this area satisfies T2 > T1. The calculation of the second target thickness T2 is positively correlated with the depth, width of the defect and the pipeline operating pressure, ensuring effective reinforcement of structural defects.

[0093] Subsequently, perform the intelligent spraying operation based on real-time feedback in step S3. A spraying robot unit, whose body is integrated with a six-degree-of-freedom robotic arm, and a high-speed centrifugal rotary spray head is equipped at the end. A non-contact infrared thickness sensor and a micro rotary viscometer are embedded inside the spray head, jointly constituting a sensing unit. The spraying robot travels inside the pipeline according to the path planned in stage S2, and the spray head at the end of its robotic arm sprays the polymer composite material. The polymer composite material is a two-component on-site mixing type material, which is mixed by the material supply unit in proportion and pumped to the spraying robot at a constant temperature. Component A contains a flexible modified epoxy resin matrix, a UV tracer and toughening fibers, and component B contains a curing agent, a nano-silica reinforcing filler and a permeation crystallization type active masterbatch. During the spraying process, the sensing unit integrated on the spray head monitors the spraying state parameters such as the wet film thickness and the rheological viscosity of the material in real time, and feeds these parameters back to the central control unit. The central control unit realizes dynamic adjustment through the closed-loop control function relationship: when the measured thickness H_actual is less than the target thickness H_target, the control system synchronously reduces the travel speed V of the spraying robot and / or increases the pumping flow rate Q of the polymer composite material; when the material viscosity exceeds the preset range, the system adjusts the rotation speed R of the spray head to optimize the atomization effect. In addition, if the three-dimensional digital model shows that there is an elbow with an angle greater than 90° or a sudden change in pipe diameter in the pipeline, the control system marks this section as a special process section and instructs the spraying robot to automatically switch to the low-speed high-precision spraying mode before entering this section, and at the same time starts the anti-collision interference detection algorithm of the robotic arm to ensure the spraying quality and equipment safety in complex areas. This real-time monitoring and dynamic adjustment mechanism significantly improves the uniformity of the coating quality and the accuracy of repair, avoiding the problems of uneven quality and low efficiency in traditional manual spraying.

[0094] After the spraying operation is completed, the process proceeds to step S4, rapid curing and digital acceptance. The polymer composite material inside the pipeline cures rapidly in situ. The initial setting time of the mixed material can be adjusted within 15-45 minutes, and the final bond strength between the solidified body and the concrete pipe wall is no less than 2.5 MPa. After curing, a post-repair scan is performed again using an inspection robot to generate a 3D model of the repaired area. The central control unit compares and analyzes the post-repair model with the pre-repair model to visually demonstrate the repair effect, completing digital acceptance and ensuring that all defects are effectively repaired and the coating thickness meets design requirements.

[0095] Finally, the system also performed step S5 to generate and store a digital repair archive. This archive is a structured data package containing at least a 3D comparison model before and after repair, a list of defects, a spraying path planning diagram, time-series data of key construction parameters (V, R, Q), and a thickness distribution cloud map of the final coating. This archive provides a valuable digital asset for the long-term operation and maintenance of the pipeline.

[0096] Using the above method, the complex defects such as corrosion, leakage, cracks, and misalignment of the old cast iron drainage pipe were precisely and efficiently repaired. The entire process required no excavation, significantly shortening the construction period and reducing social costs. Unlike the traditional integral lining method, which has stringent requirements on pipe cleanliness and shape, this method can adapt to complex working conditions, achieving differentiated and precise repairs, and significantly improving repair quality and efficiency.

[0097] The above description describes specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for upgrading and optimizing pipelines in old buildings, characterized in that, It includes the following steps: S1: Pretreatment and three-dimensional digital scanning diagnosis: Clean the drainage pipeline, and use a detection robot equipped with a vision and laser scanning module to collect internal pipeline data, generating a three-dimensional digital model of the pipeline with defect types, positions, and size markings; S2: Intelligent planning of spraying path and strategy: Based on the three-dimensional digital model, through the planning algorithm of the control system, automatically plan the optimal traveling path of the spraying robot according to the severity level and distribution of defects, and generate a differentiated spraying thickness strategy for different defect areas; S3: Intelligent spraying operation based on real-time feedback: The spraying robot travels according to the planned path, and the nozzle at the end of its robotic arm sprays a polymer composite material. At the same time, the sensing unit integrated on the nozzle monitors the spraying state parameters in real time and feeds back the parameters to the control system to dynamically adjust the spraying operation parameters and achieve closed-loop control of the coating quality; S4: Rapid curing and digital acceptance: After spraying, the polymer composite material cures in situ in the pipeline. Subsequently, use the detection robot to perform post-repair scanning, generate a post-repair three-dimensional model, and complete digital acceptance through comparative analysis with the pre-repair model.

2. The method according to claim 1, characterized in that, The specific generation of the differentiated spraying thickness strategy in step S2 includes: Planning continuous spraying with a first standard thickness T1 for defect areas of corrosion and leakage; For defect areas of structural cracks, holes, or offsets, plan a locally thickened spraying area centered on the defect and with a width greater than the defect range. The second target thickness T2 of this area satisfies T2 > T1.

3. The method according to claim 2, characterized in that, The generation method of the locally thickened spraying area is: After the control system identifies the contour of the severe defect, it automatically generates a "return" shape or spiral thickening path covering the contour. The calculation of the second target thickness T2 is positively correlated with the depth, width, and pipeline operating pressure of the defect.

4. The method according to claim 1, characterized in that, The spraying state parameters monitored in real time in step S3 include the wet film thickness of the coating and the rheological viscosity of the material; The spraying operation parameters adjusted dynamically include the traveling speed V of the spraying robot, the rotation speed R of the nozzle, and the pumping flow rate Q of the polymer composite material; The closed-loop control achieves dynamic adjustment through the following functional relationship: When the measured thickness H_actual is less than the target thickness H_target, the control system synchronously reduces V and / or increases Q; When the material viscosity exceeds the preset range, adjust R to optimize the atomization effect.

5. The method according to claim 1, characterized in that, The polymer composite material in step S3 is a two-component on-site mixing type material. Its component A contains a flexible modified epoxy resin matrix, an ultraviolet tracer, and toughening fibers, and component B contains a curing agent, a nano-silica reinforcing filler, and a penetrating crystalline active masterbatch; The initial setting time of the mixed material can be adjusted within 15 - 45 minutes, and the bonding strength of the final consolidated body to the concrete pipe wall is not less than 2.5 MPa.

6. The method according to claim 1, characterized in that, The method further includes step S5: generating and storing a digital repair archive, which is a structured data package, the contents of which include at least: a three-dimensional comparison model before / after repair, a list of defects, a spraying path planning diagram, time series data of key construction parameters (V, R, Q), and a thickness distribution cloud map of the final coating.

7. The method according to claim 1, characterized in that, The specific method for generating the three-dimensional digital model of the pipeline in step S1 is as follows: by fusing the 360° panoramic high-definition video stream obtained by the detection robot with the point cloud data obtained by the lidar, a three-dimensional real-scene model of the pipeline interior with color and texture information is reconstructed in real time using a synchronous positioning and map building algorithm.

8. The method according to claim 1, characterized in that, The spraying robot integrates a six-degree-of-freedom robotic arm, and the spray head is a high-speed centrifugal rotating spray head. The spray head is also embedded with a non-contact infrared thickness sensor and a miniature rotational viscometer, which together constitute the sensing unit.

9. The method according to claim 1, characterized in that, When performing step S3, if the three-dimensional digital model shows that there is a bend greater than 90° or a sudden change in pipe diameter, the control system marks the section as a special process section and instructs the painting robot to automatically switch to low-speed, high-precision painting mode before entering the section, and to start the anti-collision interference detection algorithm of the robotic arm.

10. A system for performing the method according to any one of claims 1-9, characterized in that, include: The inspection robot unit is used to perform scanning diagnosis and acceptance in steps S1 and S4; The painting robot unit is used to perform the painting operation in step S3, and includes a walking module, a robotic arm, an intelligent nozzle and a sensing unit. The central control unit is used to execute the intelligent planning in step S2 and receive feedback data from the sensing unit to implement closed-loop control. It includes a data processor, a planning algorithm module and a human-machine interface. A material supply unit is used to store, mix in proportion, and pump the polymer composite material to the spraying robot unit at a constant temperature.

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