A method of retrofitting a modular fire sprinkler system

Abstract

This application relates to a method for retrofitting a modular fire sprinkler system, comprising the following steps: S1, on-site data acquisition and digital twin construction; S2, dynamic intelligent partitioning and design of modules; S3, factory prefabrication and pre-assembly; S4, on-site assembly and verification; S5, digital delivery and operation and maintenance integration.

Description

Technical Field

[0001] This application relates to the field of building fire protection technology, and more specifically, to a method for modifying a modular fire sprinkler system. Background Technology

[0002] With the continuous improvement of building safety standards and the diversified evolution of building functions, a large number of existing buildings face an urgent need to upgrade their fire sprinkler systems. However, traditional upgrade methods have exposed a series of deep-seated problems during implementation. In the design phase, upgrade plans heavily rely on manual on-site surveys and historical two-dimensional drawings, making it difficult to accurately identify the complex spatial relationships of existing pipelines, such as the intersections and conflicts between ducts, cable trays, and other building structures. This leads to significant deviations between the design and actual site conditions, necessitating extensive temporary adjustments and rework during the installation phase, extending the construction period and increasing safety risks. In the construction phase, a workshop-style operation mode of "on-site measurement-cutting-welding / threaded connection" is commonly adopted, generating high-intensity noise, dust pollution, and hot work hazards, severely disrupting normal building operations, such as interrupting customer flow in shopping malls, deteriorating hospital treatment environments, and hindering the operation of data center equipment. Simultaneously, construction progress is constrained by site conditions, making effective control difficult. Furthermore, there is a fundamental contradiction between the non-standardized characteristics of existing building structures and existing modular technologies. Current technologies are primarily designed for standard floor heights in new buildings, failing to flexibly address the varying dimensions and complex obstacles encountered at renovation sites. This prevents the efficient advantages of modular construction from being realized, necessitating significant manual labor on-site. In terms of operation and maintenance management, post-renovation system information is largely stored in paper form, lacking effective data connectivity with intelligent building operation and maintenance platforms. This results in difficulties in locating faulty equipment during later maintenance, cumbersome maintenance record tracking, and the inability to dynamically simulate the system's hydraulic state, posing potential risks to the long-term safe operation of buildings. Although some existing technologies (such as CN114738731A) propose prefabricated fire protection systems, their design concepts are limited to standard scenarios in new buildings, lacking adaptability mechanisms for complex existing building environments. They cannot achieve closed-loop management of the entire process, from high-precision on-site surveying, intelligent module dynamic division, factory prefabrication to digital delivery and intelligent operation and maintenance. Therefore, an innovative method that can systematically solve the above problems is urgently needed.

[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 modular fire sprinkler system retrofit method, which has the advantages of improving retrofit accuracy, reducing construction interference, and improving operation and maintenance efficiency.

[0005] This application provides a method for retrofitting a modular fire sprinkler system, the technical solution of which is as follows: Includes the following steps: S1. On-site data acquisition and digital twin construction: Through 3D laser scanning and multi-source data fusion, a current benchmark BIM model reflecting the actual conditions of the construction site is constructed. S2. Module Dynamic Intelligent Division and Design: In the existing baseline BIM model, based on the preset rule engine, the fire sprinkler system pipeline to be modified is intelligently divided into standard modules and adaptive non-standard interface modules to adapt to non-standard conditions on site, and the production information and unique code of all modules are generated. S3. Factory prefabrication and pre-assembly: Based on the production information generated in step S2, standard modules and adaptive non-standard interface modules are prefabricated, pre-assembled and pre-tested in the factory. S4. On-site assembly and verification: Transport the prefabricated modules to the construction site, identify and locate them according to their unique codes, and use augmented reality (AR) equipment for installation guidance and verification to complete the modular assembly. S5. Digital Delivery and Operation Integration: Update the as-built information after module assembly to the existing baseline BIM model to form an as-built digital twin model, and integrate it with the intelligent operation and maintenance platform. Furthermore, this application also proposes that, in step S1, multi-source data fusion includes: fusing and reverse modeling point cloud data obtained by 3D laser scanning, existing pipeline status data obtained by infrared thermal imager, and existing building 2D drawing data to generate a current benchmark BIM model with an accuracy within ±5mm. Furthermore, this application also proposes that in step S2, the rule engine includes at least obstacle avoidance rules and pipeline optimization rules; when the algorithm path encounters an obstacle, the obstacle avoidance rules are triggered, and one or more adaptive non-standard interface modules are called or generated and combined with standard modules to avoid obstacles. Furthermore, this application also proposes that, in step S2, a unique code is generated for each module, which runs through the entire lifecycle of the module's design, production, logistics, installation, and operation and maintenance; the production information includes at least detailed processing drawings, bill of materials, installation coordinates and attitude data, and a QR code label associated with the unique code. Furthermore, this application also proposes that the pipeline optimization rules are used to optimize the module partitioning scheme under the premise of satisfying obstacle avoidance. The optimization objectives include maximizing the number of standard modules used, minimizing the total pipeline length, or minimizing the number of connection nodes. Furthermore, this application also proposes that step S2 further includes: establishing a parameterized module library, which pre-stores three-dimensional models and their variable geometric parameters of various standard modules, including water supply riser modules, horizontal main and branch pipe modules, terminal sprinkler assembly modules, and valve group prefabrication modules; intelligent partitioning involves selecting standard modules from the parameterized module library for combination. Furthermore, this application also proposes that, in step S4, the installation guidance and verification using an augmented reality (AR) device specifically involves: scanning the QR code label on the module, and in the AR device's field of view, superimposing and displaying a virtual image of the module's theoretical installation position, connection sequence, and posture in the real space environment, and comparing and verifying it with the actual position. Furthermore, this application also proposes that the comparison and verification includes: capturing the actual image of the module already in place through an AR device, matching the contour or feature points with the superimposed virtual image, and calculating the position deviation; when the position deviation exceeds the preset tolerance, issuing a prompt message. Furthermore, this application also proposes that, in step S3, the pre-commissioning includes conducting functional pressure tests on the prefabricated valve assembly modules containing valves, instruments and fittings in the factory, and delivering the whole assembly after the tests are passed. Furthermore, this application also proposes that, in step S5, each component in the as-built digital twin model is associated with a unique code of its physical module, and at least one of the functions of rapid fault location, digital archiving of maintenance records, and simulation of system hydraulic status is realized in the intelligent operation and maintenance platform. As can be seen from the above, the modular fire sprinkler system retrofit method provided in this application solves the problems of large design deviations, strong construction interference, inflexible modularization, and difficult operation and maintenance management in the prior art by constructing a high-precision digital twin model, dynamically and intelligently dividing modules, factory prefabrication and pre-assembly, on-site assembly verification, and digital delivery integration. It has the advantages of improving retrofit accuracy, reducing construction interference, and improving operation and maintenance efficiency. Attached Figure Description

[0006] Figure 1 This is a flowchart of the modular fire sprinkler system modification method in the embodiments of this application. Detailed Implementation

[0007] 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.

[0008] 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.

[0009] Traditional fire sprinkler system retrofits in existing buildings suffer from problems such as low design precision, significant construction site interference, difficulty in balancing standardization and customization, and disconnection of operation and maintenance data after retrofit. They are difficult to adapt to complex and ever-changing site environments, resulting in low retrofit efficiency and high costs.

[0010] In this regard, such as Figure 1 As shown, this application proposes a method for retrofitting a modular fire sprinkler system, including the following steps: S1. On-site data acquisition and digital twin construction: Through 3D laser scanning and multi-source data fusion, a current benchmark BIM model reflecting the actual conditions of the construction site is constructed. S2. Module Dynamic Intelligent Division and Design: In the existing baseline BIM model, based on the preset rule engine, the fire sprinkler system pipeline to be modified is intelligently divided into standard modules and adaptive non-standard interface modules to adapt to non-standard conditions on site, and the production information and unique code of all modules are generated. S3. Factory prefabrication and pre-assembly: Based on the production information generated in step S2, the standard module and the adaptive non-standard interface module are prefabricated, pre-assembled and pre-tested in the factory. S4. On-site assembly and verification: Transport the prefabricated modules to the construction site, identify and locate them according to their unique codes, and use augmented reality (AR) equipment for installation guidance and verification to complete the modular assembly. S5. Digital Delivery and Operation Integration: Update the as-built information after module assembly to the current baseline BIM model to form an as-built digital twin model, and integrate it with the intelligent operation and maintenance platform.

[0011] For ease of understanding, the following explains some key terms in this embodiment: Digital twin: A digital twin is a virtual model of a physical entity or process, which enables bidirectional mapping and interaction between the physical entity and the virtual model through real-time data connection. In this method, the digital twin is used to accurately reflect the state of the fire sprinkler system before and after the renovation, and supports the entire process of design, construction and operation and maintenance.

[0012] BIM Model: BIM model is short for Building Information Modeling. It is a multi-dimensional digital model that includes building geometry, component attributes, spatial relationships, and other information. In this approach, the BIM model serves as the benchmark platform for carrying site data and design information.

[0013] Rule Engine: A rule engine is a software component used to execute predefined business rules. In this approach, the rule engine is used to automatically partition the fire sprinkler system piping into modules to meet specific design and construction requirements.

[0014] Standard modules: Standard modules refer to modular components whose size, shape, and connection methods all conform to preset specifications, and are usually mass-produced.

[0015] Adaptive non-standard interface module: Adaptive non-standard interface module refers to a customized module designed or generated to adapt to complex and non-standard conditions on site. Its size or shape can be adjusted according to actual needs to achieve seamless connection with standard modules.

[0016] Unique Code: A unique code is a unique identifier assigned to each module, used for tracking, identification, and management throughout the module's lifecycle.

[0017] Augmented Reality (AR) Devices: Augmented Reality (AR) devices are technological devices that can overlay virtual information onto a real-world view, such as AR glasses or tablets. In this method, they are used to provide on-site installation guidance and verification.

[0018] Intelligent Operation and Maintenance Platform: The intelligent operation and maintenance platform is a comprehensive management system integrating functions such as equipment monitoring, fault diagnosis, maintenance management, and data analysis. In this method, it is used to realize the intelligent operation and maintenance of the upgraded fire sprinkler system.

[0019] This application provides a method for retrofitting a modular fire sprinkler system. The method first acquires precise three-dimensional information of the construction site through on-site data acquisition and digital twin construction. Specifically, this can be achieved by combining manual measurement with existing drawings, or by acquiring point cloud data through photogrammetry, and then constructing a baseline BIM model reflecting the actual conditions of the construction site based on this data. This model provides a precise digital foundation for subsequent design and construction.

[0020] Furthermore, within the aforementioned baseline BIM model, dynamic intelligent module partitioning and design are performed. This process, based on a pre-set rule engine, intelligently divides the fire sprinkler system piping to be modified into standard modules and adaptive non-standard interface modules to accommodate on-site non-standard conditions. For example, initial partitioning can be performed based on simple geometric features such as pipe length and the number of bends, defining straight pipe sections as standard modules and bends or diameter changes as non-standard interface modules. Subsequently, production information and unique codes are generated for all partitioned modules. Production information may include basic parameters such as module dimensions and material type, while the unique code can be a simple sequence of numbers or a combination of letters to identify each module.

[0021] Subsequently, based on the production information generated in step S2, the standard module and the adaptive non-standard interface module are prefabricated, pre-assembled, and pre-tested in the factory. For example, pipe sections are cut and welded in the factory according to the production information, and simple connection tests are performed to ensure the physical integrity of the modules. Pre-assembly may involve connecting several adjacent modules in the factory to form a larger prefabricated unit.

[0022] After prefabrication in the factory, the prefabricated modules are transported to the construction site for on-site assembly and verification. On-site, workers can manually identify and locate the modules based on their unique codes, such as by checking labels or markings. During installation, augmented reality (AR) devices can be used for guidance and verification. For example, the AR device can display a simple diagram of the module's theoretical installation location, allowing workers to place the module in the approximate position based on the diagram and then visually compare the result.

[0023] Finally, digital delivery and operation and maintenance integration are completed. After module assembly, the as-built information is updated to the aforementioned baseline BIM model, forming an as-built digital twin model. This as-built digital twin model can include information such as the actual installation location and connection relationships of the modules. Subsequently, this as-built digital twin model is integrated with the intelligent operation and maintenance platform, for example, by importing the model data into the platform to facilitate subsequent equipment management and maintenance.

[0024] This application utilizes digital twin technology to achieve precise design and optimization of the renovation plan. Combined with modular prefabrication and AR-assisted assembly, it significantly improves construction efficiency and accuracy, reduces on-site operational interference, and effectively resolves the contradiction between standardization and customization in traditional renovations. Simultaneously, digital delivery and integrated operation and maintenance provide data support for the full lifecycle management of the fire sprinkler system, enhancing the level of intelligent operation and maintenance.

[0025] In some of the embodiments described above in this application, a baseline BIM model is proposed to be formed by on-site data collection and digital twin construction. However, in actual construction sites, the existing building structures are complex and the pipeline layout is hidden. It is difficult to obtain all the necessary information comprehensively and accurately by relying on a single data source or conventional modeling methods. This may lead to deviations between the BIM model and the actual situation, which in turn affects the accuracy of subsequent module division and prefabrication design.

[0026] In this regard, this application further proposes that in step S1, the multi-source data fusion includes: fusing and reverse modeling the point cloud data obtained by three-dimensional laser scanning, the existing pipeline status data obtained by infrared thermal imager, and the existing building two-dimensional drawing data to generate the existing benchmark BIM model with an accuracy within ±5mm.

[0027] Specifically, multi-source data fusion refers to integrating and processing information acquired from different sensors or data acquisition methods to overcome the limitations of a single data source and improve data integrity, accuracy, and reliability. In this embodiment, its purpose is to comprehensively and accurately reflect the actual situation at the construction site, providing high-quality input for subsequent digital twin model construction.

[0028] The point cloud data acquired by the 3D laser scanning is obtained by emitting a laser beam and receiving reflected signals through 3D laser scanning technology, which quickly and non-contactly acquires massive amounts of 3D coordinate point data on the surface of an object, forming a point cloud. This point cloud data accurately records the geometric shape and spatial location information of objects such as building structures, equipment, and pipelines on site, and is the foundation for constructing high-precision 3D models.

[0029] The existing pipeline status data acquired by the infrared thermal imager is obtained by detecting the infrared radiation emitted from the surface of an object and converting it into a visual image, thereby reflecting the temperature distribution of the object. In the renovation of fire sprinkler systems, infrared thermal imagers can detect abnormal fluid temperatures, blockages, leaks, or insulation damage within existing pipelines. Especially for buried or hidden pipelines, it can help determine their operational status and location, compensating for the limitations of purely geometric data acquisition.

[0030] The existing two-dimensional building drawings typically include floor plans, elevations, sections, and equipment and pipeline layouts. These drawings record the building's original design information and historical modifications. Combining them with on-site collected three-dimensional data can verify the accuracy of the on-site survey data, supplement any missing semantic information in the three-dimensional data, and provide important reference for reverse modeling.

[0031] Reverse modeling refers to the process of reconstructing a precise three-dimensional geometric model of a target object based on existing information such as point cloud data, image data, or two-dimensional drawings, using specialized software and techniques. In the renovation of fire sprinkler systems, reverse modeling is the process of converting various data collected on-site into an editable and analyzable BIM model. It involves steps such as point cloud registration, feature extraction, model construction, and parametric processing, aiming to generate a digital model that is highly consistent with the actual physical object.

[0032] The aforementioned existing baseline BIM model with an accuracy within ±5mm means that the deviation between the generated existing baseline BIM model and the actual construction site in terms of physical dimensions and spatial location does not exceed ±5mm. This high precision requirement is crucial for subsequent modular design, factory prefabrication, and on-site assembly, ensuring precise matching between prefabricated modules and on-site interfaces, and avoiding rework and waste caused by model errors.

[0033] By fusing precise geometric point cloud data acquired through 3D laser scanning, existing pipeline status data obtained through infrared thermal imaging, and existing building 2D drawings with high-precision reverse modeling, the limitations of a single data source can be overcome. This allows for the comprehensive and accurate acquisition of complex information from the construction site, including the location and operational status of concealed pipelines. The resulting baseline BIM model, with an accuracy within ±5mm, accurately and precisely reflects the actual site conditions, providing a highly reliable digital foundation for subsequent dynamic intelligent modular division and design (step S2). This significantly reduces module design deviations and on-site installation errors caused by inaccurate models, thereby effectively improving the overall feasibility and implementation efficiency of the renovation plan.

[0034] In some of the embodiments described above in this application, a pre-set rule engine is proposed to intelligently divide the pipelines of the fire sprinkler system to be modified into standard modules and adaptive non-standard interface modules to adapt to non-standard on-site conditions within the existing baseline BIM model, and to generate production information and unique codes for all modules. However, in actual fire sprinkler system renovation projects, construction sites often have complex existing structures, equipment, or pipelines that may conflict with the planned pipeline routes. If the rule engine fails to effectively identify and handle these obstacles, the designed modular solution may not be able to be installed smoothly on-site, requiring a large amount of rework or on-site modifications, thereby reducing the efficiency and economy of modular prefabrication.

[0035] In this regard, this application further proposes that in the intelligent partitioning and design process, the rule engine includes at least obstacle avoidance rules and pipeline optimization rules; when the algorithm path encounters an obstacle, the obstacle avoidance rules are triggered, and one or more of the adaptive non-standard interface modules are called or generated and combined with the standard modules to avoid the obstacle.

[0036] Specifically, the rule engine is the core intelligent unit for realizing dynamic intelligent partitioning and design of modules. By integrating a series of preset logical judgments and processing mechanisms, it can analyze the geometric information, attribute information, and user-defined constraints in the BIM model, and automatically generate or adjust the module partitioning scheme accordingly. This rule engine can be built based on expert systems, constraint solvers, or machine learning algorithms to ensure the automation and intelligence of the design process.

[0037] The obstacle avoidance rules are a key component of the rule engine, guiding the system to adjust pipeline paths and module configurations to avoid conflicts when potential obstacles are detected. These rules can include a series of predefined strategies. For example, when a pipeline path collides with or is too close to an obstacle, the system should prioritize shifting upwards, downwards, to the left, or to the right by a preset distance, or bypassing the obstacle by adding bends, reducers, or other methods. The priority and specific parameters of these strategies can be configured based on engineering experience and design requirements.

[0038] The pipeline optimization rules, while meeting obstacle avoidance requirements, further enhance the overall performance of the modular partitioning scheme. These rules can comprehensively consider multiple optimization objectives. For example, while ensuring pipelines avoid obstacles, they can minimize the total pipeline length to reduce material consumption; reduce the number of connection nodes to lower installation complexity and leakage risk; or maximize the use of standard modules to improve prefabrication efficiency and reduce costs. These optimization objectives can be weighted and prioritized according to the specific needs of the project.

[0039] When the algorithm path encounters an obstacle, it means that during pipeline route planning or module layout in the BIM model, the system has detected a spatial conflict or violation of the preset safety distance between the proposed pipeline and an existing obstacle. This detection can be achieved through the collision detection function built into the BIM software or a custom spatial analysis algorithm. Once a conflict is detected, the obstacle avoidance rule is triggered, and the system will automatically initiate the corresponding processing mechanism.

[0040] After the obstacle avoidance rule is triggered, the system will resolve the obstacle problem by calling or generating one or more of the aforementioned adaptive non-standard interface modules. "Calling" refers to selecting a suitable non-standard module from a pre-established module library, such as a pre-defined irregular elbow, eccentric joint, or transition section for a specific obstacle type (e.g., beam, column). "Generating" means that the system can dynamically create customized non-standard interface modules through parametric design based on the specific geometry and location of the obstacle, as well as the required detour path; for example, generating pipe segments with specific bending angles and offsets. These adaptive non-standard interface modules are designed to seamlessly connect with standard modules, thereby maintaining the continuity and functionality of the overall pipeline while avoiding obstacles.

[0041] Through the aforementioned technical solution, this application effectively addresses the challenges posed by on-site obstacles to the implementation of modular prefabrication schemes during the intelligent partitioning and design phase of fire sprinkler system retrofitting. The obstacle avoidance rules and pipeline optimization rules introduced in the rule engine enable the system to automatically identify and intelligently handle complex on-site environments. When a pipeline path encounters an obstacle, the obstacle avoidance rule triggering mechanism responds rapidly and achieves flexible pipeline avoidance by calling or generating customized adaptive non-standard interface modules combined with standard modules. This not only significantly reduces manual intervention and design rework caused by obstacles in traditional design, improving design efficiency and accuracy, but also ensures that the designed modular scheme can better adapt to actual on-site conditions, reducing the complexity of on-site construction and potential modification costs, thereby guaranteeing the smooth progress of modular prefabrication and assembly, and improving the efficiency and quality of the entire retrofitting project.

[0042] In some of the embodiments described above in this application, a method for upgrading fire sprinkler systems through digital twins and dynamic modular partitioning is proposed, involving intelligent partitioning of modules, factory prefabrication, and on-site assembly. However, ensuring the accurate transmission, tracking, and application of information for each module throughout its entire lifecycle, from design to final operation and maintenance, to avoid information silos and improve construction efficiency, remains a problem that needs to be solved.

[0043] To this end, this application further proposes generating a unique code for each module, which is used throughout the entire lifecycle of the module's design, production, logistics, installation, and operation and maintenance. Simultaneously, the production information includes at least detailed processing drawings, a bill of materials, installation coordinates and attitude data, and a QR code label associated with the unique code.

[0044] The unique code refers to an identifier assigned to each independently defined module, possessing uniqueness across the entire project. This code, acting as a module's "digital ID card," is created during the module's design phase and stored as a core attribute in the digital twin model. Throughout subsequent production, logistics, installation, commissioning, and ultimately operation, maintenance, and disposal, all information related to the module, including design parameters, production batches, quality inspection results, logistics status, installation location, and maintenance records, can be linked to and traced using this unique code. For example, the code can employ a combination of numbers, letters, and symbols, or adhere to industry standards such as UUIDs to ensure its global or project-wide uniqueness.

[0045] The production information comprises a complete set of detailed data required to guide module manufacturing, transportation, and installation. Specifically, the detailed manufacturing drawings provide detailed manufacturing process information, such as module geometry, connection methods, welding requirements, and surface treatments, typically presented as CAD drawings or 3D model views, guiding precise manufacturing in the factory. The bill of materials details all components constituting the module, including material specifications, quantities, and suppliers, for procurement management and quality control. The installation coordinates and orientation data, derived from the BIM model, show the module's precise 3D position and spatial orientation on the construction site, ensuring accurate positioning and orientation for precise on-site installation guidance. The uniquely coded QR code label is a two-dimensional barcode used to store the module's unique code and other key information. It can be printed on weather-resistant materials and securely affixed to a prominent location on the module for quick on-site scanning and information retrieval.

[0046] By assigning a unique code to each module and ensuring its consistency throughout its design, production, logistics, installation, and operation and maintenance lifecycle, precise tracking and management of the entire process is achieved. Detailed production information, including detailed processing drawings, material lists, installation coordinates and orientation data, and QR code labels linked to the unique codes, ensures the accuracy and efficiency of factory prefabrication and on-site assembly. This significantly reduces errors and omissions in information transmission, improves construction efficiency and quality, and provides a solid data foundation for subsequent digital operation and maintenance. It also makes fault location and maintenance record retrieval more convenient, thereby enhancing the overall intelligence level and management efficiency of the renovation project.

[0047] In some embodiments described above in this application, a method is proposed to intelligently divide the pipeline of the fire sprinkler system to be modified into standard modules and adaptive non-standard interface modules to adapt to non-standard on-site conditions during the fire sprinkler system renovation process, and to utilize obstacle avoidance rules to avoid obstacles on the construction site. However, merely meeting the obstacle avoidance requirements may lead to shortcomings in the overall efficiency and economy of the generated module division scheme. For example, it may generate too many adaptive non-standard interface modules, increase the total pipeline length, or introduce unnecessary connection nodes, thereby increasing production and installation costs and potentially affecting the long-term reliability of the system.

[0048] To address this, this application further proposes the aforementioned pipeline optimization rule, which is used to optimize the module partitioning scheme while satisfying obstacle avoidance requirements. The pipeline optimization rule seeks a better modular design scheme to improve the overall economy, construction efficiency, and system performance of the renovation project, based on ensuring that the pipeline can successfully avoid all obstacles. This rule is typically implemented through algorithms, such as heuristic algorithms, genetic algorithms, or simulated annealing algorithms, to evaluate and compare various possible module partitioning schemes.

[0049] The optimization objectives include at least one of maximizing the use of standard modules, minimizing the total pipeline length, or minimizing the number of connection nodes. Maximizing the use of standard modules aims to prioritize the use of prefabricated modules with standardized specifications and manufacturing processes. By maximizing the use of standard modules, the cost and production cycle of non-standard customization can be significantly reduced, the efficiency and quality of factory prefabrication can be improved, and the on-site installation process can be simplified, reducing on-site cutting and welding operations, thereby reducing overall project costs and shortening the construction period. This can be achieved by setting a weight in the optimization algorithm to give higher scores to the use of standard modules. Minimizing the total pipeline length aims to reduce the total material usage of the fire sprinkler system pipeline, thereby directly reducing material costs. In addition, a shorter total pipeline length also helps to reduce hydraulic losses, improve the hydraulic performance of the system, and may save installation space. During the optimization process, the algorithm evaluates the total pipeline length under different partitioning schemes and tends to select the scheme with the shorter total length. This can be achieved by calculating the pipe segment length in the BIM model and incorporating it into the optimization function. Minimizing the number of connection nodes aims to reduce the number of connection points in the pipeline system, such as flange connections, threaded connections, or welded points. Connection nodes are potential leakage points; reducing their number can improve system reliability and sealing, and lower subsequent maintenance costs. Simultaneously, reducing connection nodes also means reducing the amount of connectors used and the time spent on-site connection work, thereby lowering material and installation costs. The optimization algorithm counts the number of connection nodes in different partitioning schemes and selects the scheme with the fewest nodes.

[0050] By introducing pipeline optimization rules and optimizing the module partitioning scheme while satisfying obstacle avoidance requirements, this application effectively solves the suboptimal problem caused by only considering obstacle avoidance. Specifically, by setting the optimization objective to maximize the number of standard modules used, minimize the total pipeline length, or minimize the number of connection nodes, a more economical and efficient modular design scheme can be generated while avoiding obstacles. For example, maximizing the use of standard modules can significantly reduce the cost and production cycle of non-standard customization; shortening the total pipeline length directly reduces material consumption and hydraulic losses; and reducing the number of connection nodes improves system reliability and reduces installation and maintenance costs. This optimization mechanism enables the retrofitting of fire sprinkler systems to not only adapt to complex site environments but also achieve the best balance between cost, efficiency, and performance, thereby significantly improving the overall benefits of the retrofitting project.

[0051] In the aforementioned renovation method, step S2 involves intelligently dividing the fire sprinkler system piping into standard modules and adaptive non-standard interface modules based on a preset rule engine within the existing baseline BIM model. However, how to efficiently, accurately, and systematically identify, design, and combine a large number of standard modules during intelligent partitioning to ensure the quality and prefabrication of modular design is a problem that needs to be solved.

[0052] To this end, this application further proposes that step S2 also includes establishing a parametric module library. This parametric module library is a structured database used to store and manage digital information of commonly used standard modules in fire sprinkler systems. The establishment of this library aims to provide predefined, reusable module resources for the intelligent partitioning process, thereby improving design efficiency and standardization. During the establishment process, various standard modules need to be classified, coded, and their geometric, connection, and material attributes defined. The parametric module library pre-stores three-dimensional models of various standard modules and their variable geometric parameters, including, for example, water supply riser modules, horizontal branch and main pipe modules, terminal sprinkler assembly modules, and valve group prefabrication modules. These models are not fixed entities but parametric models with variable geometric parameters. For example, the water supply riser module can be adjusted according to parameters such as floor height and pipe diameter; the horizontal branch and main pipe module can be varied according to parameters such as length, number of branches, and pipe diameter; the terminal sprinkler assembly module can be configured according to parameters such as sprinkler spacing, sprinkler type, and branch pipe length; and the valve group prefabrication module can be flexibly combined according to parameters such as valve type, quantity, and connection method. These variable geometric parameters allow for adaptation to the specific needs of different projects and site conditions while maintaining the standardized characteristics of the modules, avoiding the tedious work of remodeling for every minor change. Based on this, the intelligent partitioning involves selecting and combining standard modules from the parametric module library. This means that during dynamic intelligent partitioning and design of modules, the system no longer generates standard modules from scratch, but instead intelligently retrieves, selects, and combines standard modules that meet design requirements and site conditions from the parametric module library through a rule engine. When a specific type of standard module is needed, the system will call the most matching parametric model from the library based on its function, size, connection method, and other attributes, adjust its variable geometric parameters according to actual needs, and then combine it with other modules to form a complete piping system.

[0053] By establishing a parametric module library and enabling the intelligent partitioning process to select and combine standard modules based on this library, the problems of low design efficiency, insufficient standardization, and difficulty in quickly adapting to diverse needs in fire sprinkler system retrofitting are effectively solved. Specifically, the parametric module library pre-stores 3D models and variable geometric parameters of various standard modules, eliminating the need for intelligent partitioning to design each standard component from scratch. Instead, it utilizes existing, validated modules in the library, significantly shortening the design cycle. Furthermore, since all standard modules originate from a unified parametric module library, module standardization and quality consistency are ensured, reducing design error rates and providing a reliable foundation for subsequent factory prefabrication and on-site assembly. This library-based intelligent selection and combination method not only improves the automation level of modular design but also enhances the system's adaptability to different site conditions, thereby improving the efficiency and reliability of the entire retrofitting method.

[0054] In some of the embodiments described above in this application, a modular assembly method is proposed by generating a unique code and production information for each module and identifying and locating it on-site based on the unique code. However, in actual construction sites, relying solely on the installation coordinates and attitude data in the production information for precise module installation and verification may face problems such as operators' misunderstanding of complex three-dimensional spatial information, insufficient positioning accuracy, and low installation efficiency. Especially in confined spaces or areas with limited visibility, it is difficult to intuitively and accurately guide the modules into place, thereby affecting the overall installation quality and progress.

[0055] In response, this application further proposes to use augmented reality (AR) devices for installation guidance and verification. Specifically, this process includes scanning the QR code label on the module, and then overlaying a virtual image of the module's theoretical installation location, connection sequence, and orientation onto the real-world environment within the AR device's field of view, comparing and verifying this image with the actual location.

[0056] The augmented reality (AR) device is an interactive device capable of overlaying virtual information onto a real-world view. It is typically equipped with a camera, display screen, sensors (such as an inertial measurement unit (IMU) and depth sensor), and a processor. It can recognize the real environment in real time and render and overlay virtual images, serving as a human-computer interaction interface to provide intuitive visual guidance and information feedback. The QR code label on the scanning module refers to the image recognition and decoding of a pre-attached QR code label on the module by the AR device's built-in camera or an externally connected scanning module. After decoding, the system can retrieve the module's theoretical installation location, connection sequence, and attitude data from a database based on the unique code, thus establishing a bridge between the physical module and digital information. The virtual image of the module's theoretical installation location, connection sequence, and attitude, overlaid and displayed in the real-world environment within the AR device's field of view, refers to the AR device capturing images of the real environment through its camera and combining this with its internal positioning and attitude sensors to calculate the device's position and orientation in space in real time. Then, based on the theoretical installation data of the module obtained from the database, a 3D virtual model of the module is accurately rendered and overlaid on the real-world image, including its expected installation location, connection interfaces with other modules, and correct spatial orientation. The connection sequence can be marked on the virtual image using arrows, color coding, or text prompts, transforming complex installation instructions into intuitive visual cues and reducing the difficulty of understanding. Comparison and verification with the actual position refers to the augmented reality (AR) device capturing an actual image of the module already in place or in the process of being placed using its camera, and comparing it in real time with the overlaid virtual image. This comparison can be based on image processing algorithms, such as feature point matching, contour matching, or depth information comparison, to calculate the positional and angular deviations between the actual module and the theoretical virtual module, thereby providing immediate feedback to assist installers in making precise adjustments and for quality control.

[0057] By introducing augmented reality (AR) devices for installation guidance and verification, this application effectively solves the problems of inaccurate module installation positioning, low efficiency, and error-prone manual verification in traditional construction. Specifically, by scanning the QR code label on the module, the AR device instantly overlays a virtual image of the module's theoretical installation position, connection sequence, and orientation onto the real construction environment, transforming abstract digital information into intuitive 3D visual guidance. This greatly reduces the difficulty for installers to understand complex 3D drawings, making the module placement process more intuitive and accurate. Simultaneously, the AR device can compare the module's actual position with its theoretical position in real time, providing immediate feedback to assist installers in making precise adjustments, thereby significantly improving installation accuracy and efficiency, reducing rework, and ensuring the quality of modular assembly.

[0058] In some embodiments described above in this application, augmented reality (AR) devices are proposed for installation guidance and verification. This involves overlaying virtual images of the theoretical installation position, connection sequence, and orientation of the modules onto the AR device's field of view and comparing them with the actual positions. However, in actual construction sites, relying solely on visual comparison or simple positional judgment may not be sufficient to accurately quantify the actual installation deviation of the modules, nor can it promptly and effectively detect and correct installation errors exceeding permissible limits, thus affecting the accuracy and efficiency of modular assembly.

[0059] To address this, this application further proposes that the comparison and verification include: capturing an actual image of the module already in place using an AR device, matching its contours or feature points with an overlaid virtual image, and calculating the positional deviation; when the positional deviation exceeds a preset tolerance, a prompt message is issued. Specifically, after the module is placed in the predetermined position, construction personnel use the built-in camera of the augmented reality (AR) device or an externally connected image sensor to acquire real-time visual data of the module and its surrounding environment, i.e., capturing an actual image of the module already in place. This image data forms the basis for subsequent accurate comparison, ensuring the real-time and on-site nature of the comparison. Subsequently, the captured actual image is compared with a virtual 3D model image pre-loaded in the AR device, representing the theoretical installation position and orientation of the module. This comparison is not a simple visual observation, but rather uses image processing algorithms to identify and align the geometric contours of the module in the actual image or specific feature points on its surface, such as corners, edges, and texture features, with the corresponding contours or feature points in the virtual image. Contour matching focuses on the overall shape fit, while feature point matching focuses more on the precise alignment of local details. After completing the contour or feature point matching, the system quantifies the difference in position and orientation between the actual module and the theoretical virtual module in three-dimensional space based on the matching results, thereby calculating the positional deviation. This includes, but is not limited to, translational deviations on the X, Y, and Z axes, as well as rotational deviations around each axis. The calculated deviation values ​​provide objective data on the module installation accuracy. To ensure installation quality, the system presets a maximum allowable positional deviation range, i.e., the preset tolerance. Once the calculated positional deviation value exceeds this preset tolerance, the AR device will immediately issue a clear prompt to the construction personnel through visual (such as warning signs or color changes on the screen), auditory (such as alarm sounds), or tactile (such as device vibration) means, indicating that there is a problem with the installation and adjustments are needed.

[0060] Through the above technical solution, this application provides a more accurate and automated installation verification mechanism. By capturing actual images with AR devices and matching them with virtual images in terms of contours or feature points, the deviation between the actual installation position and the theoretical position of the module can be objectively and quantitatively calculated, overcoming the subjectivity and inaccuracy of traditional manual visual comparison. When a deviation exceeds the preset tolerance, the system can promptly issue a prompt, effectively avoiding rework or potential system failures caused by inaccurate installation. This significantly improves the accuracy, efficiency, and reliability of modular assembly, ensuring the quality of fire sprinkler system retrofitting.

[0061] In some of the embodiments described above in this application, factory prefabrication and pre-assembly steps are proposed. However, in the process of implementation, if key functional components, especially valve assembly prefabrication modules containing valves, instruments and pipe fittings, are not fully functionally verified before leaving the factory, functional failures may occur after on-site installation, thereby affecting the construction progress and system reliability.

[0062] In this regard, this application further proposes that in step S3, the pre-commissioning includes performing functional pressure tests on the prefabricated valve assembly modules containing valves, instruments and fittings in the factory, and delivering the whole assembly after the tests are passed.

[0063] Specifically, pre-commissioning refers to the process of verifying the functionality and performance of a module before it is officially put into use. Its purpose is to identify and resolve potential problems as early as possible in a controlled environment, preventing defects from being brought to the construction site. Pre-commissioning is conducted in the factory, meaning that the factory's professional testing equipment, standardized testing procedures, and experienced technicians can be utilized to ensure the accuracy and reliability of the tests. This typically provides a more stable and controlled testing environment compared to on-site conditions. Prefabricated valve assembly modules are a key component of fire sprinkler systems, typically integrating various valves (such as control valves and check valves), pressure gauges, flow meters, and connecting pipes. These components work together to control water flow, pressure, and monitor system status; their functionality directly affects the safety and effectiveness of the entire fire protection system. Functional pressure testing is a crucial verification for prefabricated valve assembly modules. This test typically involves pressurizing the module internally to a specific value higher than its design operating pressure (e.g., 1.5 times the operating pressure) and maintaining this pressure for a period of time to check for leaks, weld defects, or loose component connections. It also verifies the valve's opening and closing function and the accuracy of instrument readings. Only after passing rigorous functional pressure tests and confirming that all indicators meet design requirements will the prefabricated valve assembly module be deemed a qualified product and delivered to the construction site as a complete unit. This means that the module already possesses reliable functionality upon leaving the factory, eliminating the need for additional on-site commissioning or rework.

[0064] By employing the aforementioned technical solutions, functional pressure testing is conducted on prefabricated valve assembly modules, including valves, instruments, and fittings, during the factory prefabrication and pre-assembly stages. This effectively identifies and eliminates potential defects in the modules during manufacturing and assembly, such as leaks, valve malfunctions, or instrument inaccuracies. This proactive quality control ensures that the modules delivered to the construction site are fully functional and reliable, thereby avoiding time-consuming and costly on-site troubleshooting and rework. This not only significantly improves on-site installation efficiency and first-time success rate, shortening project duration, but also guarantees the operational reliability and safety of key components of the fire sprinkler system from the outset, laying a solid foundation for subsequent digital delivery and integrated operation and maintenance.

[0065] In some of the embodiments described above in this application, an as-built digital twin model is formed by updating the as-built information after module assembly to the existing baseline BIM model, and then integrating it with the intelligent operation and maintenance platform. However, in actual operation and maintenance management, if the correlation between components and physical modules in the as-built digital twin model is unclear, and the intelligent operation and maintenance platform fails to fully utilize this correlation information, it may lead to inefficiencies in equipment fault diagnosis, maintenance history tracing, and system performance evaluation, making it difficult to fully leverage the advantages of digital twin technology in full lifecycle management.

[0066] In this regard, this application further proposes that each component in the as-built digital twin model is associated with a unique code of its physical module, and that at least one of the following functions is realized in the intelligent operation and maintenance platform: rapid location of faulty equipment, digital archiving of maintenance records, and simulation of system hydraulic status.

[0067] Specifically, the as-built digital twin model refers to the final, accurate digital model reflecting the actual state of the system, formed after the completion of a fire sprinkler system renovation project. This model is created by updating the information of the actually installed modules (including their precise locations, connections, and actual orientations) to the initial baseline BIM model. This model is a precise mapping of the physical system in digital space, containing the geometric information, attribute information, and topological relationships between all modules. In the as-built digital twin model, each component represents an independent part of the fire sprinkler system, such as a standard module, an adaptive non-standard interface module, a valve, or a sprinkler head. These components have independent identifiers and attributes in the digital model. The unique code of the physical module refers to a unique identifier generated for each physical module during the dynamic intelligent partitioning and design phase, spanning its entire lifecycle of design, production, logistics, installation, and operation and maintenance. This code can be in the form of alphanumeric combinations, barcodes, or QR codes, used to accurately identify and track each module in the physical world. Linkage means that in a completed digital twin model, each digital component is explicitly linked to its corresponding physical module through its attributes or metadata. This linking can be achieved by storing unique codes in the attribute fields of the digital model components or through mapping tables in a database, ensuring that any component in the digital model can be traced back to its corresponding physical entity, and vice versa.

[0068] The intelligent operation and maintenance platform is a comprehensive software system integrating data collection, analysis, visualization, early warning, and management functions. It is used for daily operation monitoring, fault diagnosis, maintenance plan formulation, and execution of fire sprinkler systems. Rapid fault location refers to the ability of maintenance personnel to quickly identify the corresponding digital component in the digital model when a device in the fire sprinkler system malfunctions. This is achieved by leveraging the unique coding relationship between components and physical modules in the as-built digital twin model, thus obtaining its precise physical location information (e.g., floor, area, coordinates). This guides on-site personnel to quickly reach the fault point for inspection and repair. For example, when a sprinkler head triggers a leak alarm, the platform can immediately highlight the sprinkler head on the digital twin model and provide its specific installation location. Digital archiving of maintenance records refers to storing the maintenance history, inspection results, replaced parts, maintenance personnel, and maintenance time of each physical module in the fire sprinkler system in digital form within the intelligent operation and maintenance platform, and associating it with the unique coding of the physical module and the corresponding component in the as-built digital twin model. In this way, maintenance personnel can access all historical maintenance records of a module at any time by querying its unique component or input module code in the digital twin model, achieving transparency and traceability of maintenance information and avoiding the loss and inconvenience of paper records. System hydraulic state simulation refers to using data such as pipeline geometry, valve characteristics, and sprinkler parameters contained in the as-built digital twin model, combined with fluid mechanics calculation models, to simulate and analyze the hydraulic performance of the fire sprinkler system within the intelligent operation and maintenance platform. This includes simulating parameters such as water pressure, flow distribution, and flow velocity under different operating conditions, evaluating the system's fire extinguishing effectiveness during a fire, and identifying potential hydraulic imbalance points or weak links. Through simulation, system behavior can be predicted, operating strategies optimized, or solutions validated before upgrades or modifications can be performed without actually operating the physical system.

[0069] By employing the aforementioned technical solutions, each component in the as-built digital twin model is associated with a unique code of its physical module and integrated into the intelligent operation and maintenance platform. This significantly enhances the intelligence and efficiency of the fire sprinkler system during operation and maintenance. When a system failure occurs, maintenance personnel can quickly locate the specific faulty equipment based on the digital twin model, eliminating the need for extensive on-site troubleshooting and thus shortening response time and reducing maintenance costs. Simultaneously, all maintenance records are digitally linked to and archived with their corresponding physical modules, ensuring the integrity and traceability of the maintenance history and providing reliable data support for subsequent maintenance decisions and system optimization. Furthermore, by simulating the system's hydraulic state within the intelligent operation and maintenance platform, system performance can be predicted and evaluated in a virtual environment. This helps identify potential problems, optimize system operating parameters, and ensure the fire sprinkler system operates efficiently and reliably at critical moments, thereby significantly improving the system's full lifecycle management capabilities and safety.

[0070] The following example will provide a more detailed explanation of the above technical solution: In an existing commercial complex, its fire sprinkler system is undergoing a complete overhaul due to changes in building function and upgraded safety standards. The complex needs to remain operational during the renovation; therefore, minimizing on-site construction disruptions and maximizing renovation efficiency and accuracy are crucial.

[0071] First, in the initial phase of the renovation project, on-site data collection and digital twin construction were carried out. The construction team used 3D laser scanning equipment to conduct a comprehensive scan of the area to be renovated in the commercial complex, obtaining high-precision point cloud data. Simultaneously, infrared thermal imagers were used to detect the operational status of existing pipelines, and 2D building drawings were collected. This multi-source data was fused and, through reverse modeling technology, generated a baseline BIM model with an accuracy within ±5mm. This model accurately reflects the actual conditions of the construction site, including existing structures, equipment, and pipelines, avoiding errors and conflicts caused by inaccurate on-site surveys in traditional renovation designs.

[0072] Secondly, dynamic intelligent partitioning and design of modules are performed within the aforementioned existing baseline BIM model. The piping of the fire sprinkler system to be modified is input into an intelligent partitioning algorithm based on a rule engine. This rule engine includes at least obstacle avoidance rules and piping optimization rules. For example, when the algorithm path encounters obstacles such as existing ducts or cable trays, obstacle avoidance rules are triggered. The system avoids obstacles by calling or generating one or more adaptive non-standard interface modules and combining them with pre-stored standard modules in the parametric module library (such as water supply riser modules, horizontal main and branch pipe modules, terminal sprinkler assembly modules, and valve group prefabricated modules). The piping optimization rules optimize the module partitioning scheme while satisfying obstacle avoidance requirements. For example, the optimization objective is to maximize the number of standard modules used while minimizing the total pipe length or the number of connection nodes. Each partitioned module generates a unique code, such as "SP-MAIN-001-A," along with detailed production information, including processing details, a bill of materials, installation coordinates and attitude data, and is associated with a QR code label. This intelligent partitioning method resolves the contradiction between standardization and customization in traditional renovations and avoids the problem of design schemes not matching the site conditions.

[0073] Next, based on the production information generated in step S2, factory prefabrication and pre-assembly are carried out. All standard modules and adaptive non-standard interface modules are prefabricated in the factory. For example, valve assembly prefabricated modules, including valves, instruments, and fittings, undergo functional pressure testing in the factory, and are delivered as a whole after passing the test. These modules are pre-assembled and pre-tested in the factory, forming components that can be directly transported to the site. This factory prefabrication, pre-assembly, and pre-testing model significantly reduces traditional workshop-style operations such as on-site cutting and welding, thereby reducing noise, dust, and hot work, minimizing interference with the normal operation of the commercial complex, and improving construction quality and schedule controllability.

[0074] Subsequently, the prefabricated modules are transported to the construction site for on-site assembly and verification. On-site construction personnel use augmented reality (AR) devices to scan the QR code labels on the modules. The AR device overlays a virtual image of the module's theoretical installation position, connection sequence, and orientation onto the real-world environment within the construction personnel's field of vision. The personnel then install the module according to the AR guidance. After installation, the AR device captures the actual image of the positioned module and matches it with the overlaid virtual image to calculate positional deviations. When the positional deviation exceeds a preset tolerance, the AR device issues a prompt to guide the construction personnel in making adjustments. This AR-assisted installation and verification method improves the accuracy and efficiency of on-site assembly and reduces human error.

[0075] Finally, after module assembly, digital delivery and operation and maintenance integration are carried out. All as-built information, including actual installation locations and connection statuses, is updated to the existing baseline BIM model, forming a completed digital twin model. Each component in the completed digital twin model is uniquely associated with its physical module. This completed digital twin model is integrated with a smart operation and maintenance platform. The smart operation and maintenance platform enables rapid location of faulty equipment, digital archiving of maintenance records, and simulation of the system's hydraulic status. This digital delivery and operation and maintenance integration solves the problem of traditional post-renovation system information relying primarily on paper drawings and being disconnected from the building operation and maintenance management platform, providing comprehensive digital support for later maintenance and management.

[0076] 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 retrofitting a modular fire sprinkler system, characterized in that, Includes the following steps: S1. On-site data acquisition and digital twin construction: Through 3D laser scanning and multi-source data fusion, a current benchmark BIM model reflecting the actual conditions of the construction site is constructed. S2. Module Dynamic Intelligent Division and Design: In the existing baseline BIM model, based on the preset rule engine, the fire sprinkler system pipeline to be modified is intelligently divided into standard modules and adaptive non-standard interface modules to adapt to non-standard conditions on site, and the production information and unique code of all modules are generated. S3. Factory prefabrication and pre-assembly: Based on the production information generated in step S2, the standard module and the adaptive non-standard interface module are prefabricated, pre-assembled and pre-tested in the factory. S4. On-site assembly and verification: Transport the prefabricated modules to the construction site, identify and locate them according to their unique codes, and use augmented reality (AR) equipment for installation guidance and verification to complete the modular assembly. S5. Digital Delivery and Operation Integration: Update the as-built information after module assembly to the existing baseline BIM model to form an as-built digital twin model, and integrate it with the intelligent operation and maintenance platform.

2. The modification method according to claim 1, characterized in that, In step S1, the multi-source data fusion includes: fusing and reverse modeling the point cloud data obtained by 3D laser scanning, the existing pipeline status data obtained by infrared thermal imager, and the existing building 2D drawing data to generate the existing benchmark BIM model with an accuracy within ±5mm.

3. The modification method according to claim 1, characterized in that, In step S2, the rule engine includes at least obstacle avoidance rules and pipeline optimization rules; when the algorithm path encounters an obstacle, the obstacle avoidance rules are triggered, and one or more of the adaptive non-standard interface modules are called or generated and combined with the standard modules to avoid the obstacle.

4. The modification method according to claim 1, characterized in that, In step S2, the unique code generated for each module is used throughout the entire lifecycle of the module's design, production, logistics, installation, and operation and maintenance. The production information includes at least detailed processing drawings, a bill of materials, installation coordinates and attitude data, and a QR code label associated with the unique code.

5. The modification method according to claim 3, characterized in that, The pipeline optimization rules are used to optimize the module partitioning scheme under the premise of obstacle avoidance. The optimization objectives include maximizing the number of standard modules used, minimizing the total pipeline length, or minimizing the number of connection nodes.

6. The modification method according to claim 1, characterized in that, Step S2 also includes: establishing a parametric module library, which pre-stores three-dimensional models and their variable geometric parameters of various standard modules, including water supply riser modules, horizontal main and branch pipe modules, terminal sprinkler assembly modules, and valve group prefabrication modules; the intelligent partitioning is to select standard modules from the parametric module library for combination.

7. The modification method according to claim 4, characterized in that, In step S4, the installation guidance and verification using the augmented reality (AR) device specifically involves scanning the QR code label on the module, and then overlaying a virtual image of the module's theoretical installation position, connection sequence, and posture onto the real space environment within the AR device's field of view, and comparing and verifying it with the actual position.

8. The modification method according to claim 7, characterized in that, The comparison and verification includes: capturing the actual image of the module in place using an AR device, matching its contour or feature points with the superimposed virtual image, and calculating the positional deviation; when the positional deviation exceeds a preset tolerance, issuing a prompt message.

9. The modification method according to claim 1, characterized in that, In step S3, the pre-commissioning includes conducting functional pressure tests on the prefabricated valve assembly modules containing valves, instruments and fittings in the factory, and delivering the whole assembly after passing the tests.

10. The modification method according to claim 1, characterized in that, In step S5, each component in the as-built digital twin model is associated with a unique code of its physical module, and at least one of the following functions is realized in the intelligent operation and maintenance platform: rapid location of faulty equipment, digital archiving of maintenance records, and simulation of system hydraulic status.

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