BIM (Building Information Modeling)-based fire-fighting pipeline prefabrication full-process optimization method and system

Abstract

The invention provides a BIM (Building Information Modeling)-based firefighting pipeline prefabrication full-process optimization method and system, which are applied to the crossing field of process control and intelligent construction in the building industry, and the method and device comprise the following steps: integrating BIM design change data through a real-time synchronization mechanism, and generating a unified change set, a decision tree algorithm is utilized to analyze the change amplitude so as to dynamically adjust processing parameters to form an optimized instruction set, linear programming is combined to dynamically distribute material inventory, real-time logistics data is fused, and an efficient distribution plan is generated through a path optimization algorithm. According to the scheme, real-time linkage of design change and production execution can be achieved, machining rework and material waste caused by information lag are reduced, the cooperative efficiency of material scheduling and logistics distribution is improved, and the resource recovery efficiency is improved. And the adaptive capability of the system is enhanced through closed-loop feedback.

Description

Technical Field

[0001] This invention relates to the intersection of process control and intelligent construction in the building industry, and in particular to a BIM-based method and system for optimizing the entire process of prefabricated fire protection pipelines. Background Technology

[0002] In the field of building construction, the prefabrication and installation of fire protection piping systems is a crucial step in ensuring building fire safety. With the promotion of industrialized construction models, Building Information Modeling (BIM) technology is widely used in the design and construction phases to achieve 3D visualization and clash detection. However, the application of existing BIM technology is limited to static modeling and phased simulation, failing to effectively support dynamic collaboration throughout the entire process from design to manufacturing, logistics, and on-site installation. When design changes occur, relevant information is usually transmitted manually, leading to information delays, distortions, or omissions. This can result in prefabricated components being produced according to incorrect drawings, causing material waste and project delays. Material management relies on initial material lists and lacks a linkage mechanism with real-time processing needs and inventory status, making it difficult to achieve dynamic optimization of resource allocation. Logistics and distribution planning does not fully consider real-time traffic conditions and the actual receiving capacity of the construction site, easily leading to problems such as waiting for transport vehicles or difficulties in unloading. Furthermore, there is a lack of accurate data comparison methods between on-site installation results and the design model, making it impossible to systematically identify deviations and trace their technical roots. The aforementioned problems collectively lead to severe information silos between various business processes, and the overall process lacks closed-loop feedback and adaptive adjustment capabilities, which seriously affects the quality stability, resource utilization efficiency, and predictability of project delivery in fire protection pipeline prefabrication projects.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a BIM-based method and system for optimizing the entire process of prefabricated fire protection piping. This aims to solve the problems of physical processing rework and material waste caused by delayed information transmission of design changes and data silos from multiple sources, as well as resource mismatch and process disruption caused by the lack of dynamic coordination between materials, logistics, and on-site construction. By constructing a unified data foundation integrating BIM models, IoT data, and enterprise business systems, and combining machine learning-driven risk prediction, dynamic material allocation supported by linear programming, intelligent logistics scheduling guided by path optimization algorithms, and a closed-loop feedback mechanism based on virtual-physical comparison, the invention achieves real-time response, global resource optimization, and system adaptive evolution throughout the entire process from design changes to on-site installation. This effectively reduces rework rates and project costs, and improves the overall collaborative efficiency and delivery certainty of prefabricated fire protection piping projects.

[0005] This invention provides a BIM-based method for optimizing the entire process of prefabrication of fire protection piping, including: Updated data is obtained from BIM design change records using a computer, and updated data from different sources are processed through a real-time synchronization mechanism to obtain a unified change dataset. Based on a unified change dataset, the decision tree algorithm is used to analyze design changes. When the magnitude of the design change exceeds a first preset threshold, the processing parameters of the processing equipment are adjusted to obtain an optimized processing instruction set. The material requirements list is parsed from the optimized processing instruction set. Based on the material requirements list, the material inventory is allocated using a linear programming algorithm to obtain a dynamic material allocation scheme. Based on the dynamic material allocation scheme, real-time logistics data is obtained, and transportation routes are calculated or recalculated using path optimization algorithms based on the real-time logistics data to obtain an efficient logistics and distribution plan. Obtain feedback data from on-site installation, and use data comparison methods to compare the feedback data with the dynamic material allocation plan. When the feedback data from on-site installation does not match the dynamic material allocation plan, recalculate the material waste rate to obtain optimized resource recycling instructions.

[0006] In some alternative embodiments, updated data from different sources is processed via a real-time synchronization mechanism, including: Updated data from different sources is transmitted to a unified message channel through a distributed message queue; When a conflict is detected between updated data, the updated data with the latest timestamp is selected and synchronized by comparing the timestamps of the updated data.

[0007] In some alternative embodiments, analyzing design changes also includes: From a unified change dataset, a set of technical features associated with design changes are extracted. This set of technical features includes geometric features, material features, and three-dimensional topological connection features. A set of technical features is input into a pre-trained machine learning model to output a risk value that represents the probability of failure in physical processing.

[0008] In some optional embodiments, the step of adjusting the processing parameters of the processing equipment includes: When the risk value exceeds the second preset threshold, a composite control instruction is generated as an optimized processing instruction set. The composite control instruction includes enhanced physical quality inspection instructions for controlling the machine operation parameters of the processing equipment and for transmitting them to the quality inspection station.

[0009] In some alternative embodiments, the input features of the decision tree algorithm include: the magnitude of the design change, the material type of the component, and the equipment parameters of the processing equipment.

[0010] In some alternative embodiments, a linear programming algorithm is used to allocate material inventory, including: Construct a linear programming model with the objective function of minimizing inventory shortage costs and the constraint of material inventory levels; Solve the linear programming model to obtain the allocation results.

[0011] In some optional embodiments, the method further includes: Based on an efficient logistics and distribution plan, process data is integrated, and time series forecasting models are used to assess the overall project progress in order to obtain quantitative indicators of potential delays. When the quantitative indicators of potential delays exceed the preset warning threshold, an automatic notification mechanism is triggered to update the process optimization strategy and obtain a revised full-process timeline.

[0012] In some optional embodiments, the time series prediction model is an ARIMA model.

[0013] In some optional embodiments, feedback data from the field installation is obtained, including: The physical point cloud data of the completed project is obtained by deploying 3D scanning equipment at the construction site, and the physical point cloud data is used as feedback data for on-site installation.

[0014] In some optional embodiments, the comparison is performed using a data comparison method, including: The physical point cloud data and the design model derived from the BIM design change record are registered in three-dimensional space under a unified coordinate system. By calculating the spatial distance between the physical point cloud data and the model surface of the design model, physical components that are not installed or are misaligned can be identified to quantify the material waste rate.

[0015] In some optional embodiments, after obtaining the optimized resource reclamation instruction, the method further includes: Based on the material waste rate, trace back the entire lifecycle data chain of components associated with the material waste rate, and use a causal rule base to locate the technical root cause of waste.

[0016] In some optional embodiments, the method further includes: Based on the technological origins, the internal technical parameter model is automatically and quantitatively corrected.

[0017] In some alternative embodiments, the intrinsic technical parameter model is modified, including: When the technical root cause is identified as a design margin issue, the standard loss factor in the bill of materials generation model should be reduced. Alternatively, when the technical root cause is identified as a processing accuracy issue, tighten the risk assessment threshold used in the decision tree algorithm.

[0018] In some alternative embodiments, the path optimization algorithm is Dijkstra's algorithm, A* algorithm, or ant colony algorithm.

[0019] In some optional embodiments, the method further includes: Based on the optimized resource recycling instructions, integrate the real-time data from each stage and determine whether further synchronization of the real-time data from each stage is needed to obtain a closed-loop dataset for the process.

[0020] This invention provides a BIM-based optimization system for the entire prefabrication process of fire protection piping, comprising: The data processing unit is used to obtain updated data from BIM design change records via computer and process updated data from different sources through a real-time synchronization mechanism to obtain a unified change dataset. The processing instruction generation unit is used to analyze design changes using a decision tree algorithm based on a unified change dataset. When the magnitude of the design change exceeds a first preset threshold, the processing parameters of the processing equipment are adjusted to obtain an optimized processing instruction set. The material scheduling unit is used to parse the material requirement list from the optimized processing instruction set, and allocate the material inventory based on the material requirement list using a linear programming algorithm to obtain a dynamic material allocation scheme. The logistics planning unit is used to acquire real-time logistics data based on dynamic material allocation schemes, and to calculate or recalculate transportation routes based on real-time logistics data and using path optimization algorithms to obtain an efficient logistics distribution plan. The feedback analysis unit is used to acquire feedback data from on-site installation. It uses data comparison methods to compare the feedback data from on-site installation with the dynamic material allocation plan. When the feedback data from on-site installation does not match the dynamic material allocation plan, the material waste rate is recalculated to obtain optimized resource recovery instructions.

[0021] In some optional embodiments, the processing instruction generation unit is further configured to: A set of technical information is parsed from the unified change dataset, which includes geometric information, material information, and three-dimensional topological connection information. A set of technical information is input into a pre-trained machine learning model to generate a risk assessment result that characterizes the probability of physical processing failure. Based on the risk assessment result, a decision is made on whether to interrupt or adjust the original processing instructions.

[0022] In some alternative embodiments, the system further includes: The project progress assessment unit is used to integrate process data based on an efficient logistics and distribution plan, and use a time series forecasting model to generate quantitative indicators of potential delays in the overall project progress. When the quantitative indicators of potential delays exceed the preset warning line, an automatic notification is triggered to generate a revised full-process timetable.

[0023] In some optional embodiments, the feedback analysis unit is also used for: It receives physical point cloud data representing the completed state of the site, collected by 3D scanning equipment, as feedback data for on-site installation; The physical point cloud data is registered in three dimensions with the design model derived from the BIM design change record. The installation differences of physical components are identified by calculating the spatial deviation between the physical point cloud data and the design model. By tracing back the lifecycle data chain of physical components based on installation differences, the technical reasons leading to waste can be located, and the internal technical parameter model of the system can be adaptively updated based on the technical reasons.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention.

[0025] The BIM-based optimization method and system for the entire process of prefabrication of fire protection pipelines of the present invention has the following beneficial effects: This invention achieves real-time response and early warning of risks for design changes during the prefabrication of fire protection pipelines by constructing a closed-loop data system encompassing design, processing, materials, logistics, and on-site installation. Utilizing multi-source data fusion and intelligent algorithms, it dynamically optimizes material allocation and logistics routes, significantly reducing resource waste and operating costs. Through virtual-to-physical comparison and feedback mechanisms, it accurately identifies installation deviations and generates optimized resource recovery instructions, improving material utilization and construction quality, and enhancing the controllability and overall efficiency of project execution. Attached Figure Description

[0026] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0027] Figure 1 This is a flowchart of a BIM-based optimization method for the entire process of prefabrication of fire protection pipelines according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a BIM-based prefabrication process optimization system for fire protection pipelines according to an embodiment of the present invention. Detailed Implementation

[0028] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0029] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0030] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined. Therefore, the actual execution order may change depending on the specific circumstances.

[0031] In the full lifecycle management of engineering projects, the integrity and real-time nature of information flow are fundamental to ensuring consistency and collaboration across all stages. When design changes occur, if relevant information cannot be seamlessly transferred and dynamically synchronized between design, production, logistics, and construction stages, it will inevitably lead to a disconnect between physical execution and the planned scheme, resulting in rework, material waste, and project delays. To address this issue, a data linkage mechanism covering the entire process needs to be constructed, enabling downstream systems to instantly perceive and respond to upstream design changes. This mechanism relies on the integration of multi-source data and conflict resolution techniques, ensuring that all participants make decisions based on the same factual state through unified timestamp verification and data version control. Based on this, mathematical optimization models are used to solve resource allocation, achieving globally optimal material usage configuration with cost and efficiency as objective functions while satisfying process constraints. Simultaneously, path planning algorithms, combined with real-time traffic and on-site operational capacity data, transform the transportation process from static scheduling to dynamic adjustment, effectively reducing delivery delays caused by external uncertainties. At the project's end, digital feedback on the completed status is introduced. By acquiring actual installation data through 3D scanning of the physical entity and spatially matching it with the design intent, unexpected conditions such as missing components and positional deviations can be accurately identified, thereby quantifying resource consumption. This reverse data flow from the physical world to the digital system forms a complete data loop, enabling the system not only to monitor results but also to trace the technical causes of deviations based on historical data chains and rule-based reasoning models. When specific error patterns are repeatedly identified, the system can solidify experience into prediction and decision-making models through parameter tuning, making risk assessments more accurate in subsequent similar scenarios and forming continuously evolving intelligent control capabilities. This overall architecture, through the combination of data-driven positive execution and reverse feedback, achieves a technological leap from passive response to proactive prevention and from isolated decision-making to collaborative optimization.

[0032] like Figure 1 As shown in the figure, this embodiment of the invention provides a BIM-based optimization method for the entire process of prefabrication of fire protection pipelines. The method includes the following steps: Step S100: Acquire and process multi-source update data to build a unified change dataset.

[0033] The computer system automatically extracts updated component parameter data, including changes in geometric dimensions, material properties, and relationships, from design change records on the Building Information Modeling (BIM) design platform. Simultaneously, the system collects relevant business data from Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), and other engineering management systems through a real-time synchronization mechanism. This data from various sources is aggregated and integrated in the cloud, undergoing data cleaning, format standardization, and conflict resolution to form a structured, time-aligned unified change dataset, serving as the foundational input for subsequent decision-making. In other optional implementations, the real-time synchronization mechanism can employ message middleware or an event-driven architecture to facilitate data flow between heterogeneous systems.

[0034] Step S200: Analyze the design changes and generate an optimized set of machining instructions.

[0035] Based on a unified change dataset, a decision tree algorithm is used to assess the technical impact of design changes. This algorithm, based on preset rules or trained judgment logic, comprehensively considers multiple technical dimensions involved in the change and outputs a judgment result indicating whether it constitutes a significant process risk. When the overall impact of the design change exceeds a first preset threshold, the system no longer follows the standard processing flow but dynamically adjusts the operating parameters of the processing equipment to generate an optimized processing instruction set containing control parameters such as specific cutting speed, welding current, and gas type, and pushes it to the control system of the prefabrication line. In other optional implementations, the decision tree algorithm can be replaced with other highly interpretable machine learning models, such as random forests or gradient boosting trees, to improve the accuracy of risk identification.

[0036] Step S300: Analyze material requirements and formulate a dynamic material allocation plan.

[0037] Based on the generated optimized processing instruction set, the system automatically parses out a precise material requirements list, including the specifications, length, and quantity of the required pipes. Subsequently, a linear programming algorithm is invoked, taking into account factors such as current available inventory, procurement cycle, and cost weights, to solve for the optimal material allocation strategy that meets production demands. This strategy encompasses the specific location and quantity of materials to be transferred from existing inventory, as well as the types and scale of materials to be procured, ultimately forming an executable dynamic material allocation plan. In other optional implementations, the optimization algorithm can also incorporate integer programming or multi-objective optimization frameworks to consider additional objectives such as surplus material utilization and transportation convenience.

[0038] Step S400: Plan an efficient logistics and distribution route.

[0039] After determining the set of materials to be delivered based on the dynamic material allocation scheme, the system acquires external logistics data such as vehicle resource status, real-time road traffic conditions, and the receiving capacity of the construction site. Based on this, a route optimization algorithm is applied to calculate the transportation route from the prefabrication plant to the construction site, ensuring delivery is completed in the shortest possible time and avoiding on-site waiting or congestion. If unforeseen circumstances occur during transportation that reduce the efficiency of the original route, the system can recalculate alternative routes and update scheduling instructions in real time. In other optional implementations, route replanning can be automatically initiated under predetermined triggering conditions, such as detecting that the estimated arrival time deviates from the plan by more than a set tolerance.

[0040] Step S500: Obtain on-site feedback and generate optimized resource recycling instructions.

[0041] After the components are installed on-site, the system acquires on-site installation feedback data reflecting the actual installation status. This feedback data is then compared with the bill of materials expected to be used in the aforementioned dynamic material allocation scheme using a data comparison method. If instances of non-installation, misaligned installation, or material shortages are detected, the system recalculates the actual material consumption and waste ratio, and then generates optimized resource recovery instructions for the reuse of remaining materials, waste sorting and disposal, or re-procurement and replenishment. In other optional implementations, the data comparison process can be enhanced with spatial matching algorithms to support finer-grained deviation identification.

[0042] The aforementioned technical features form a close-loop collaborative relationship: a unified change dataset serves as the starting point, driving sequential responses in processing, materials, and logistics; while on-site feedback data acts as terminal information feedback, in turn influencing the correction of resource allocation logic. This end-to-end data connectivity mechanism breaks down the information barriers between design, production, and construction in the traditional model, enabling every change to be tracked and responded to across the entire chain, and every resource consumption to be accurately predicted and verified. As a result, the system achieves a shift from passive response to proactive control, effectively alleviating rework, mismatches, and waste caused by information lag or silos, thereby systematically improving the overall execution efficiency and delivery reliability of prefabricated fire protection piping projects.

[0043] Through the above solution, this embodiment can achieve seamless flow and consistent maintenance of design change information across systems, ensuring that downstream links receive accurate input in a timely manner; it can predict high-risk operations before physical processing and intervene in advance to reduce the probability of quality accidents; it can dynamically optimize material usage based on real-time supply and demand status to improve inventory turnover; it can flexibly adjust logistics strategies in combination with changes in the external environment to reduce transportation delays; and through reverse verification of the actual on-site status, it can continuously calibrate the resource allocation model, ultimately achieving a comprehensive improvement in the efficiency of resource utilization and project certainty throughout the entire process.

[0044] In one specific implementation, the real-time fusion process of multi-source heterogeneous data achieves unified access and orderly processing of updated data from different systems by introducing a distributed message queue mechanism. First, the data acquisition plugins of the BIM collaboration platform used by the design unit, the MES system of the prefabrication plant, the enterprise's ERP system, and the logistics management system act as message producers, encapsulating their respective updated data into structured message bodies and publishing them to a distributed message middleware cluster built on Kafka. Specifically, the updated data from each system is mapped to independent topic partitions, such as "bim_change", "mes_status", and "erp_inventory", and a multi-replica mechanism is configured to ensure high availability and persistent storage of the messages.

[0045] Then, the cloud platform's data processing unit, acting as a message receiver, subscribes to these topics and pulls data streams sequentially from the message queue. During the receiving process, when the system detects multiple updates to the same component or material arriving in quick succession, it triggers conflict detection logic. Specifically, the system extracts the timestamp field from each update record. This timestamp is generated by the data source and uses the UTC standard time format, with millisecond-level precision. By comparing the timestamps of update records with the same primary key (such as component ID or material code), the system automatically selects the record with the latest timestamp as valid data for subsequent processing. The remaining duplicate or expired data is marked as "redundant" and placed in the archive queue, not participating in the construction of the current change dataset.

[0046] In other alternative implementations, the distributed message queue can be replaced by message middleware with similar publish-subscribe capabilities, such as Apache Pulsar or RabbitMQ; the timestamp synchronization mechanism can also be combined with the NTP protocol or GPS time service to further improve cross-system time consistency; in high-concurrency scenarios, a vector clock-based logical clock algorithm can also be introduced to help determine the causal order of events, thereby enhancing the accuracy of conflict resolution.

[0047] Through the above solution, this embodiment can achieve efficient aggregation and consistent processing of data change events across heterogeneous systems, effectively avoid data overwrite errors caused by network latency or concurrent operations, and ensure that the change dataset on which the entire process depends always reflects the latest and uniquely correct engineering status.

[0048] In one specific implementation, analyzing design changes further includes: firstly, extracting a set of technical features related to the design change from a unified change dataset. Specifically, this set of technical features includes geometric features, material features, and three-dimensional topological connection features. Geometric features cover changes in the dimensional parameters of the changed components, such as pipe diameter, wall thickness, and length; material features characterize changes in material type and their physicochemical properties, such as changes in weldability and coefficient of thermal expansion between carbon steel and stainless steel; and three-dimensional topological connection features quantify changes in assembly complexity caused by the design change by analyzing the spatial adjacency relationships and connection logic between components in the BIM model, such as the stress concentration risk introduced by changing from a straight connection to a multi-angle bend splice.

[0049] Then, the extracted multi-dimensional technical features are combined into a structured feature vector and input into a pre-trained machine learning model for inference computation. This machine learning model employs a Gradient Boosting Decision Tree (GBDT) architecture, and during the training phase, it has learned the non-linear mapping relationship between design change features and corresponding physical processing results (such as rework and scrap records) from a large number of historical projects. The model output is a value between 0 and 1, representing the probability that the design change will lead to subsequent physical processing failure, i.e., the processing risk value. This risk value serves as the basis for subsequent control decisions, determining whether to adjust the original processing flow.

[0050] In some other alternative implementations, the machine learning model can be replaced with an algorithm model with similar classification or regression capabilities, such as a support vector machine (SVM), random forest, or deep neural network; the extraction of three-dimensional topological connection relationship features can also be based on the embedding representation learning of the component connection graph in the BIM model by graph neural network (GNN), thereby achieving a higher level of relationship abstraction.

[0051] Through the above-described scheme, this embodiment extends the impact of design changes from the explicit geometric and material levels to the implicit process and assembly logic levels. By systematically modeling and intelligently analyzing multi-dimensional technical characteristics, it achieves a quantitative assessment of processing risks. Compared to existing technologies, this embodiment not only relies on human experience to judge the impact of changes but also constructs a calculable and reproducible risk prediction mechanism, significantly improving the accuracy and response speed in identifying potential processing problems and providing a reliable data foundation for downstream adaptive control.

[0052] In one specific implementation, the step of adjusting the processing parameters of the processing equipment includes: when the system determines that the risk value associated with the design change exceeds a preset second threshold, generating a composite control instruction as an optimized processing instruction set. Specifically, the composite control instruction consists of two functional sub-instructions: one is a machine operation parameter instruction for directly controlling the operating parameters of the physical processing equipment, and the other is an enhanced physical quality inspection instruction for strengthening the inspection intensity of subsequent quality verification stages.

[0053] First, the generation of machine operation parameter instructions is based on the material type and geometric features involved in the current change, and the matching processing parameter configuration is retrieved from a pre-set process knowledge base. For example, when the pipe material is changed from carbon steel to stainless steel, the system automatically calls the plasma cutting process parameters corresponding to stainless steel, generates an instruction data packet containing fields such as cutting speed, gas type, and current intensity, and sends it to the designated CNC cutting equipment controller through an industrial communication protocol (such as OPC UA) to ensure that the processing adapts to the physical properties of the new material.

[0054] Then, the generation of enhanced physical quality inspection instructions is based on the structural complexity or potential failure modes introduced by the change. For example, if the change requires the addition of multiple welded joints or the formation of spatial irregular connections in a pipe section, the system will trigger a higher coverage non-destructive testing requirement. Specifically, the system generates a structured instruction that includes the inspection method (such as X-ray inspection), the inspection range (all welds), the inspection standard number, and the result feedback path, and pushes it to the operation terminal of the quality inspection station through the task scheduling interface of the MES system, replacing the original sampling inspection plan.

[0055] In other alternative implementations, machine operation parameter instructions may also include thermal input control parameters for the automatic welding equipment, fixture positioning coordinates, or cooling rate adjustment instructions; enhanced physical quality inspection instructions may also employ different inspection methods such as ultrasonic testing, magnetic particle testing, or eddy current testing, the specific choice depending on the component material, geometry, and the capabilities of the on-site inspection equipment. Furthermore, the data format of composite control instructions may be encapsulated in JSON or XML to support cross-system parsing and execution.

[0056] Through the above-described solution, this embodiment enables a multi-dimensional collaborative response to high-risk design changes. It not only adjusts the execution parameters of the processing to reduce the probability of manufacturing defects but also strengthens the monitoring of downstream quality inspection stages, thereby forming a closed-loop quality assurance mechanism without relying on manual intervention. Compared with existing technologies, this embodiment improves the granularity and response efficiency of the system to complex process risks by simultaneously outputting two types of instructions: equipment control and quality inspection. This effectively reduces processing failures or missed potential problems caused by insufficient single control methods, enhancing the overall stability and controllability of the prefabrication process.

[0057] In one specific implementation, the input feature set used by the decision tree algorithm includes the magnitude of the design change, the material type of the component, and the equipment parameters of the processing equipment. Specifically, when performing processing risk analysis, the system first extracts the geometric parameter changes of the current design change from a unified change dataset and calculates the relative magnitude of the change compared to the original design value. For example, when the diameter of a section of fire-fighting pipeline changes from DN100 to DN150, the system automatically calculates the diameter change magnitude as 50% and inputs this value as the "magnitude of design change" feature into the decision tree model.

[0058] Next, the system parses the material type code used for the changed component from the BIM model attribute fields and maps it to a classification variable that can be recognized by the model. For example, carbon steel Q235-B corresponds to code "0", and stainless steel 304L corresponds to code "1". Different materials exhibit different process stability during processing due to differences in physical properties such as welding performance and thermal deformation tendency. This classification information is used as the "material type of component" feature in subsequent judgments.

[0059] Then, the system obtains a snapshot of the equipment parameters of the CNC cutting machine or automatic welding machine currently planned to process the component through a real-time communication interface with the MES system. This includes operating status parameters such as equipment model, current spindle speed, cutting current setting, and gas flow rate. After normalization, these parameters form a "processing equipment parameter" vector to characterize the actual operating conditions of the current production equipment. For example, a plasma cutting machine that has been running continuously for more than 8 hours may have nozzle wear issues, resulting in an actual cutting accuracy lower than the nominal value. This potential impact is incorporated into the risk assessment system through dynamic feedback of equipment parameters.

[0060] The three types of features mentioned above—the magnitude of the design change, the material type of the component, and the equipment parameters of the processing equipment—together form a structured feature vector, which serves as the input to the Gradient Boosting Decision Tree (GBDT) model, driving the model to quantify the likelihood of the current change operation causing physical processing failure.

[0061] In other alternative implementations, the magnitude of design changes can be further subdivided into independent geometric dimension change indicators, such as length change rate, diameter change rate, and wall thickness change rate; material type characteristics can be expanded to include continuous physical parameters such as material yield strength, thermal conductivity, and weldability grade; the equipment parameters of the processing equipment can also be collected in real time by adding IoT sensors to collect multimodal signals such as vibration, temperature, and power consumption, and after feature extraction, they can be transformed into high-order statistical features (such as root mean square and kurtosis) as supplementary inputs to enhance the model's ability to perceive the hidden equipment degradation state.

[0062] Through the above-described scheme, this embodiment combines the technical impact of design changes with the specific processing resource status upon which they depend. This allows the risk prediction process to not only be based on static design information but also incorporate dynamic production environment variables, thereby improving the accuracy and situational adaptability of processing risk assessment. Compared with existing technologies, this embodiment avoids misjudgments or omissions caused by coarse-grained judgments based solely on changes in design drawings. It achieves refined, multi-dimensional, and collaborative assessment of processing risks, providing a reliable basis for subsequently generating precise composite control commands.

[0063] In one specific implementation, for the dynamic allocation process of material inventory, the system constructs and solves a linear programming model to optimize resource allocation. First, the system defines the objective function as minimizing the comprehensive cost caused by inventory shortages, which includes emergency procurement premiums, expedited transportation costs, and indirect losses due to work stoppages and material shortages. The mathematical expression is as follows: in This represents the total shortage cost. For the first The cost of shortage per unit quantity of a certain material This refers to the quantity of corresponding materials that need to be replenished through unplanned channels. Specifically, The value of ci is preset based on the material type, market supply cycle and historical purchase price fluctuation range, and stored in the system parameter library. For example, for standard parts, the ci value is lower, while for non-standard parts that require customization or long-term ordering, the ci value is set higher, so as to reflect the higher priority of avoiding shortages in the optimization solution.

[0064] The system then establishes a set of constraints to ensure the feasibility of the allocation plan. These constraints include a demand satisfaction constraint, which specifies the quantity to be allocated from existing inventory. External supplement quantity The sum must not be less than the total demand required for the current processing task. , represented as Inventory capacity constraints, i.e. It must not exceed the current available actual inventory level. ,Right now Meanwhile, all variables satisfy the nonnegativity constraint. .

[0065] Next, the system calls the embedded linear programming solver engine (such as a solver based on open-source GLPK or commercial Gurobi) to numerically solve the above model and obtain the optimal solution. and Allocation Results. After the solution is completed, the system automatically generates the corresponding sequence of operation instructions: for the portion that can be met by inventory, a warehouse outbound order is generated and pushed to the WMS system; for the portion that requires external replenishment, the electronic purchase order process is triggered, and the order is automatically sent to the preset supplier interface.

[0066] In some other alternative implementations, the objective function can be extended to a multi-objective weighted form, introducing additional dimensions such as maximizing the utilization of surplus materials or minimizing carbon emissions. Accordingly, the weighted summation method or ε-constraint method can be used to transform it into a single-objective problem for solution. The solution algorithm can also be replaced with a simplex method variant or interior point method suitable for large-scale sparse problems, adapted according to the actual computational load and response time requirements.

[0067] Through the above solution, this embodiment can transform material allocation decisions from a static model that relies on human experience to a dynamic optimization process based on precise mathematical modeling. This significantly reduces additional costs caused by inventory shortages while ensuring production continuity, and improves the scientific and economical nature of resource allocation.

[0068] In one specific implementation, the method further includes: starting from the efficient logistics and distribution plan output by the logistics planning unit, the project progress assessment unit begins to integrate real-time process data from various stages such as design, processing, materials, and logistics to form a dynamically updated snapshot of the project execution status. Specifically, this status snapshot includes, but is not limited to, multi-dimensional time node data such as the completion time of BIM design of components, the actual time of material release from the warehouse, the actual start and stop time of prefabrication, and the estimated arrival time of transportation tasks.

[0069] The system then inputs this historical and real-time data, organized by time series, into a pre-trained time series forecasting model. This model is based on patterns of schedule deviations accumulated in historical projects, enabling it to identify non-linear relationships between local delay events and assess their combined impact on the overall project duration. After running, the model outputs a quantified potential delay metric, such as the probability of on-time project completion or the projected total duration deviation.

[0070] Next, the system compares the quantitative indicator with a preset schedule risk warning line. When the indicator is detected to exceed the warning line, a notification mechanism is automatically triggered, pushing a warning message containing risk details to the project manager and relevant responsible persons via the enterprise instant messaging platform or mobile terminal. At the same time, the system calls the process optimization strategy engine to dynamically adjust the scheduling priority or resource allocation plan of subsequent processes based on the current stage of the project and resource constraints, and generates a revised full-process timetable. This timetable is synchronized to the project's collaborative management platform via API for all relevant parties to view and execute.

[0071] In other alternative implementations, the time series forecasting model can be implemented using different algorithms such as Long Short-Term Memory (LSTM), exponential smoothing, or structured time series models; the automatic notification mechanism can be configured as a tiered alarm mode, triggering different levels of response processes based on the severity of the delay; the revised end-to-end timeline can also be further visualized using the Path Method (CPM) to assist management decision-making.

[0072] Through the above solution, this embodiment can achieve dynamic perception and proactive intervention of the overall project schedule risk, which not only transforms the traditional passive response schedule management into proactive predictive management, but also ensures the continuous effectiveness of the project plan through an automated strategy update mechanism, thereby significantly improving the delivery certainty and management efficiency of complex engineering projects.

[0073] In one specific implementation, based on the above embodiments, the time series forecasting model employs an Autoregressive Integral Moving Average (ARIMA) model to quantitatively assess the overall project progress. Specifically, the ARIMA model first receives a multi-dimensional time series data stream integrated by the project progress assessment unit, including timestamp sequences of nodes such as component processing completion time, material arrival time, and on-site installation progress. Next, the original time series is tested for stationarity. If the series is non-stationary, it is transformed into a stationary series through differencing, and the differencing order is determined. Then, based on the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots of the processed sequences, the order of the autoregressive term is identified. and the order of the moving average term Thus, a specific combination of parameters can be constructed. The ARIMA model structure is described. This model is trained using similar delay propagation patterns accumulated from historical projects, enabling it to dynamically learn the impact of local disturbances on the overall project duration. In actual operation, whenever the system detects a new schedule deviation event (such as equipment failure or logistical delay), the ARIMA model immediately updates the input sequence and recalculates the completion probability distribution of future paths, outputting a continuous potential delay quantification index for subsequent automatic early warning and plan adjustments.

[0074] In other alternative implementations, the time series prediction model can be replaced with a SARIMA model with similar time series modeling capabilities to adapt to construction cycle scenarios with significant seasonal effects, or a state-space Kalman filter can be used to recursively estimate real-time progress data with high noise. In addition, with sufficient training samples, deep learning architectures such as Long Short-Term Memory (LSTM) networks can be used to replace traditional statistical models to capture more complex nonlinear progress evolution characteristics.

[0075] Through the above-described scheme, this embodiment can dynamically and quantitatively predict project schedule risks based on rigorous statistical principles, improving the accuracy and robustness of schedule delay assessment. It is particularly suitable for complex engineering environments with frequent disturbances and uncertainties, thereby providing more forward-looking decision support for project management.

[0076] In one specific implementation, feedback data from on-site installation is acquired using a 3D scanning device deployed at the construction site. Specifically, this 3D scanning device is configured to perform non-contact spatial data acquisition on the area of ​​fire-fighting pipelines where installation has been completed, generating a physical point cloud dataset representing the actual completed state. This dataset is then uploaded to a cloud-based data processing platform as feedback data from the on-site installation. The device can be fixedly mounted on a mobile inspection robot or operated handheld by construction personnel, supporting flexible deployment in complex construction environments. The system uses timestamps and component ID tags to associate the point cloud data with the corresponding components in the BIM model, ensuring the traceability of the feedback information.

[0077] In other alternative implementations, the 3D scanning device may employ different types of sensors based on laser ranging, structured light, or photogrammetry principles; the acquisition frequency of point cloud data may be set to be triggered by process nodes or automatically executed at set intervals according to the construction schedule; data transmission may be synchronized to the central server in real time or near real time via wireless communication methods such as Wi-Fi, 4G / 5G.

[0078] Through the above scheme, this embodiment can achieve high-precision digital restoration of the actual installation status on site, providing an objective and quantifiable data basis for subsequent quality deviation analysis and resource waste assessment, thereby effectively supporting the closed-loop feedback mechanism of the system from the physical world to the digital system.

[0079] In one specific implementation, the 3D spatial comparison between physical point cloud data and the design model derived from BIM design change records is achieved as follows: First, the system imports the physical point cloud data obtained from the construction site into a unified data processing environment and loads the latest version of the BIM design model corresponding to the construction area. This model contains updated component geometry and topology information due to design changes. Specifically, to ensure the accuracy of the comparison, the system performs a 3D spatial registration operation: using the Iterative Closest Point (ICP) algorithm, combined with several pre-set known coordinate control points on site as initial alignment references, the physical point cloud data and the BIM design model are rigidly transformed and aligned in a unified world coordinate system, achieving sub-centimeter-level matching accuracy in spatial position.

[0080] Next, the system iterates through the registered point cloud dataset, calculating the shortest Euclidean distance between the theoretical surface and the actual point cloud for each pipe component in the design model. For any component, if the point cloud density collected within its expected installation area is lower than a preset threshold and the average distance exceeds the set installation tolerance (e.g., 20mm), the component is determined to be in an "uninstalled" state. If the point cloud as a whole exhibits the same geometric shape as the design model, but there are systematic translational or rotational deviations, it is marked as "misaligned installation," and its offset vector is recorded. Furthermore, based on the component's material type, length, weight, and other attribute information, combined with unit cost data from the ERP system, the system quantifies the direct material waste rate caused by non-installation or misaligned installation. For example, if a DN150 stainless steel elbow is determined to be completely uninstalled, its corresponding cost is included in the total waste amount for the current evaluation period.

[0081] In other alternative implementations, 3D spatial registration can employ feature-matching-based methods. For example, pipe axes, tee nodes, or flange contours from the BIM model can be extracted as features and matched with similar geometric features detected in the point cloud to improve registration robustness under low-density point cloud conditions. Alternatively, with the assistance of high-precision inertial navigation equipment, initial coarse registration can be achieved by scanning the equipment's built-in spatial positioning information, followed by fine optimization using ICP. Furthermore, the method for calculating spatial distance can be adjusted according to the application scenario. For instance, Hausdorff distance can be used to measure the maximum deviation between two point sets, suitable for detecting severe local deviations.

[0082] Through the above-described scheme, this embodiment can accurately identify and quantify the deviation between the on-site completed state and the design intent. It can not only automatically determine whether components are missing or incorrectly installed, but also provide traceable quality assessment data based on spatial geometric relationships. Compared with existing technologies, this embodiment avoids the subjectivity and missed detection risks inherent in traditional manual inspections by establishing a three-dimensional spatial comparison mechanism under a unified coordinate system, significantly improving the objectivity and timeliness of quality feedback. Simultaneously, the quantitative indicators based on spatial distance calculations provide reliable input for the dynamic updating of material waste rates, supporting the generation of subsequent resource recycling strategies and the closed-loop execution of the system's self-learning process.

[0083] In one specific implementation, after the system generates an optimized resource recycling instruction, it locates the specific pipe component associated with the identified material waste rate, such as a DN150 stainless steel elbow that is not installed or is misaligned. Specifically, the system initiates a full lifecycle data chain backtracking mechanism, starting from the component's design stage, sequentially retrieving its design parameters in the BIM model, MES execution records during the processing stage (such as cutting length and welding process parameters), quality inspection reports, outbound time, transport vehicle trajectory, and on-site receipt status, among other cross-system full-process operation logs.

[0084] The system then inputs this structured and semi-structured data into a predefined causal rule base for matching analysis. This causal rule base consists of multiple "condition-conclusion" logical rules, such as: "If a component has passed factory quality inspection and the transportation process is normal, but it is missing in the as-built point cloud, the root cause of the waste may be an omission during on-site installation"; or: "If the actual installation position deviation of the component exceeds the tolerance range, and there is design interference between adjacent supports and hangers, the root cause may be design conflict leading to installation difficulties." Through the rule engine's layer-by-layer reasoning, the system ultimately outputs one or more of the most likely technical root cause categories, such as "design interference," "out-of-tolerance processing dimensions," or "on-site construction errors."

[0085] In some other alternative implementations, the causal rule base can be replaced with a probabilistic inference model based on Bayesian networks, or a decision tree classifier can be used to perform pattern recognition on historical waste cases to improve diagnostic accuracy in complex scenarios; the collection of the entire lifecycle data chain can be synchronized periodically through ETL tools, or it can be captured in real time through an event-driven architecture.

[0086] Through the above solution, this embodiment can achieve in-depth technical attribution of material waste, not only the statistics of surface phenomena, but also establish the ability to trace back from the result to the process, providing reliable data for subsequent system-level parameter adjustment and process optimization, thereby enhancing the transparency and controllability of the entire optimization system.

[0087] In one specific implementation, once the system identifies the technical root cause of material waste through a causal rule base, the feedback analysis unit initiates a model self-correction process. First, the system transmits the technical root cause as an input signal to the update interface of the internal technical parameter model, triggering a dynamic adjustment mechanism for the model parameters. Specifically, this mechanism, based on a preset quantitative mapping relationship, converts the diagnosed technical root cause type into a corresponding parameter adjustment direction and magnitude coefficient. For example, if a certain type of connection structure is continuously identified as a high-risk installation mode, the system automatically calls the model update service to incrementally increase the weight factor of that structure in the processing risk prediction model, thereby improving the sensitivity of risk scoring for subsequent similar design changes.

[0088] The parameter update process employs a version-based management strategy. Each correction generates a new model version snapshot, along with metadata recording the reason for the change, timestamp, and scope of impact, ensuring the model evolution process is traceable. After the model update is complete, the system automatically deploys it to a test sandbox environment, using historical data replay to verify its logical consistency and stability. Only after confirming no abnormal behavior is it switched to the production environment.

[0089] In other alternative implementations, the methods for modifying the intrinsic technical parameter model include, but are not limited to: retraining the feature weights of the machine learning model, adjusting the constraint boundary conditions in the optimization algorithm, updating the default parameter thresholds in the process knowledge base; or injecting newly discovered technical root sample samples into the training data stream through an online learning mechanism to achieve continuous iteration of the model.

[0090] Through the above solution, this embodiment can realize the automatic and quantitative correction of the internal technical model of the system, so that the optimization strategy can dynamically evolve with the accumulation of project experience, effectively improve the system's ability to prevent recurring technical problems, and enhance the adaptability and long-term effectiveness of the whole process control logic.

[0091] In one specific implementation, modifying the intrinsic technical parameter model includes: When the feedback analysis unit identifies the root cause of material waste as a design margin issue through the causal rule base, the system automatically lowers the standard loss coefficient in the bill of materials generation model. Specifically, the system first extracts actual material consumption data from multiple completed projects that match the current component type, connection form, and installation environment from the historical project database, and calculates their average actual loss rate. If this value is consistently lower than the originally set standard loss coefficient (for example, originally set at 5%, but the statistical result is 2.3%), the system determines that the original design margin is too high, triggering a parameter correction process: dynamically adjusting the standard loss coefficient in the bill of materials generation model from 5% to 2.5%, and marking this change for manual review.

[0092] When the root cause of the technical issue is identified as a machining accuracy problem, the system tightens the risk assessment threshold used in the decision tree algorithm to identify high-risk changes. First, the system extracts all historical event records related to the machining accuracy problem from the entire lifecycle data chain, including corresponding BIM change feature vectors, machine machining parameter logs, and dimensional deviation data discovered during quality inspection. Then, this data is used to locally retrain the existing GBDT risk prediction model, adjusting the weights of features strongly correlated with machining accuracy (such as material weldability and wall thickness tolerance grades). Simultaneously, during the inference phase, the original second preset threshold is lowered from 0.70 to 0.65, thereby increasing the model's sensitivity to potential machining failures. This adjustment allows subsequent similar design changes to be intercepted earlier before entering the machining process, triggering stricter process control instructions.

[0093] In some other alternative implementations, the standard loss coefficient can be adjusted by introducing a sliding time window mechanism, which is dynamically updated only based on data from similar projects within the last N months; or, the risk assessment threshold tightening strategy can adopt a segmented adjustment method, which is divided into three levels—mild, moderate, and severe—based on the degree of processing accuracy deviation, corresponding to quantitative correction rules of threshold reduction of 5%, 10%, and 15%, respectively.

[0094] Through the above solution, this embodiment enables adaptive and quantitative correction of the system's internal technical parameter model, allowing the material planning and risk assessment logic to continuously evolve with actual engineering performance. Compared with existing technologies, this embodiment not only accurately identifies the root causes of resource waste but also transforms diagnostic results into executable model parameter optimization actions. This improves the accuracy and robustness of future decisions without human intervention, forming a true knowledge loop and effectively reducing the probability of repetitive errors.

[0095] In one specific implementation, the path optimization algorithm is the A* algorithm. First, the system models the urban road network as a weighted directed graph, where nodes represent road intersections or locations, and edges represent connecting road segments. The weights of the edges are dynamically calculated from real-time traffic data, including travel distance, historical traffic speed, current congestion index, and estimated transit time. Specifically, when searching for paths, the A* algorithm uses Euclidean distance as a heuristic function to estimate the shortest straight-line distance from the current node to the target node, and combines this with the known actual path cost for a comprehensive evaluation, thus prioritizing the expansion of nodes with the lowest estimated total cost.

[0096] To further enhance the adaptability of route planning, the system introduces a dynamic adjustment term into the cost function of the A* algorithm, which is related to the unloading window at the construction site. For example, if the estimated arrival time of a route falls within a period when hoisting equipment is unavailable, a penalty value will be added to the total cost of that route, thus reducing its selection priority. Then, the system periodically (e.g., every 5 minutes) retrieves the latest traffic updates and re-triggers the A* algorithm to recalculate routes, ensuring that the delivery plan is always based on the latest environmental conditions.

[0097] In other alternative implementations, the path optimization algorithm can be replaced by Dijkstra's algorithm, which is suitable for small-scale road network scenarios with strict requirements for the global optimal path and without considering heuristic acceleration; or the ant colony algorithm can be used to deal with complex logistics scheduling problems with multiple objectives and strong uncertainties. It gradually converges to a better solution set by simulating the pheromone positive feedback mechanism in the foraging behavior of ant colonies, and is especially suitable for dynamic environments of multi-vehicle collaborative delivery.

[0098] Through the above solution, this embodiment can flexibly select the appropriate path optimization algorithm according to different application scenarios and computing requirements. While ensuring transportation efficiency, it can effectively avoid invalid waiting caused by traffic changes or on-site receiving conditions, and significantly improve the accuracy and robustness of logistics response.

[0099] In one specific implementation, the method further includes: after identifying and handling material waste according to optimized resource recycling instructions, the system further initiates a consistency verification and closed-loop archiving mechanism for the entire process data status. First, the system collects the latest updated real-time data from various business modules, including but not limited to inventory adjustment records in the ERP system, processing completion confirmations in the MES system, outbound execution logs in the WMS system, transportation receipt information in the TMS system, and virtual-to-physical comparison result reports generated by the feedback analysis unit. Specifically, this data is aggregated to the central data coordination service through a unified data access interface and linked across systems based on the unique component identifier (such as BIM ID).

[0100] Then, the system performs a data synchronization necessity judgment. This judgment is based on a preset closed-loop consistency rule set, such as: whether all prefabricated components related to the installed area have been marked as "delivered" or "consumed"; whether there are any abnormal logistics events or quality inspection alarms with a status of "pending processing"; and whether the on-site feedback data has been registered and compared with the design model and a final deviation report has been generated. When the data in all stages meets the predefined integrity and consistency conditions, the system determines that no further synchronization operation is needed; otherwise, if any data breakpoint or status conflict is detected (for example, a component is shown as completed in MES, but there is no corresponding outbound record in WMS), the incremental data synchronization process is triggered, sending data completion or status correction requests to the relevant subsystems.

[0101] Next, once all data reaches a logically consistent state, the system packages all the integrated data from this iteration cycle into an immutable closed-loop dataset. This dataset is labeled with metadata using timestamps and project stage tags and stored in a distributed file system (such as HDFS) or object storage service, serving as a historical benchmark dataset for reuse in subsequent projects and a data source for model self-learning.

[0102] In other alternative implementations, the closed-loop consistency rule set can be configured to support multiple verification strategies, such as blockchain-based multi-party state consensus verification, or chain binding of data nodes using lightweight message digests (such as SHA-256 hashes) to enhance the anti-tampering capability of data traceability; the timing of the generation of the process closed-loop dataset can also be set as needed to be triggered by project stage milestones, or generated periodically according to a fixed time window.

[0103] Through the above solution, this embodiment enables systematic verification and final consolidation of data status at each stage of the entire fire protection pipeline prefabrication process, ensuring a closed loop of information flow from design changes to on-site installation feedback. Compared with existing technologies, this embodiment not only achieves data aggregation at the operational level, but also ensures the semantic and temporal consistency of multi-source heterogeneous data through a structured synchronous judgment mechanism, providing a high-quality and highly reliable data foundation for continuous system optimization, knowledge accumulation, and cross-project experience transfer.

[0104] This invention provides a BIM-based optimization system for the entire prefabrication process of fire protection piping. For example... Figure 2 As shown, the system includes: The data processing unit M100 is configured to acquire updated BIM design change records from the design unit's BIM collaboration platform via a computer and integrate data sources from multiple heterogeneous systems through a real-time synchronization mechanism. The M100 listens for model version change events in a subscription mode through a data acquisition plugin deployed on the BIM platform. Upon detecting a new version submission, it immediately extracts the change content and encapsulates it into a standardized data format. Simultaneously, this unit also connects to the Enterprise Resource Planning (ERP) system, Manufacturing Execution System (MES), and Transportation Management System (TMS), using a distributed message queue as a unified message channel to receive status updates pushed by each system. When multiple data points from different sources but pointing to the same entity are received, the system selects the latest data by comparing timestamp information to resolve conflicts, ultimately generating a structured, non-redundant, and consistent unified change dataset as the basis for subsequent data input.

[0105] The machining instruction generation unit M200, communicatively connected to the data processing unit M100, receives a unified change dataset and performs analysis and judgment based on the design change information contained therein. This unit incorporates a logic engine based on a decision tree algorithm, capable of quantitatively evaluating the magnitude of design changes according to preset technical parameter thresholds. When a change is determined to exceed a first preset threshold, the unit automatically triggers control logic to adjust operating parameters to match the physical processing devices in the machining plant, such as CNC cutting machines and automatic welding equipment. It generates an optimized machining instruction set containing updated process parameters and sends this instruction to the corresponding equipment control system via a standard industrial communication protocol, thereby ensuring that the machining process adapts to the latest design requirements.

[0106] The material scheduling unit M300, coupled with the processing instruction generation unit M200, parses the material requirement information implicit in the optimized processing instruction set to form an accurate material requirement list. Based on this, the unit invokes an operations research algorithm to perform a global analysis of the current inventory status. Under the premise of meeting production needs, it comprehensively considers inventory availability, procurement cycle, and cost factors to solve for the optimal allocation strategy. The output is a dynamic material allocation plan, covering the specific quantities to be transferred from existing inventory and details of materials requiring replenishment. This plan can directly drive the warehouse management system to execute outbound operations or automatically generate electronic purchase orders.

[0107] The logistics planning unit M400 establishes a data interface with the material scheduling unit M300 to initiate the transportation task planning process after confirming that materials are ready. This unit integrates external real-time logistics information services to obtain vehicle resource status and road conditions, and combines this with receiving capacity information from the construction site. It then uses a route optimization algorithm to calculate or dynamically recalculate the transportation route from the prefabrication plant to the construction site. The resulting efficient logistics and distribution plan includes not only recommended driving routes but also estimated arrival time windows and loading sequence suggestions to improve on-site unloading efficiency and reduce waiting time.

[0108] The feedback analysis unit M500 is used to acquire on-site installation feedback data reflecting the actual completion status of components after on-site installation. This unit verifies the consistency of the feedback data with the dynamic material allocation plan generated by the system in the early stage through data comparison. When a discrepancy is found, the system reassesses the difference between actual consumption and plan, updates the material waste rate calculation, and generates optimized resource recovery instructions to guide surplus material recycling, inventory write-off, or accountability.

[0109] The aforementioned functional units achieve loosely coupled communication through clearly defined internal API interfaces, operating together on a centralized or distributed cloud computing platform. The data processing unit M100 serves as the data entry point for the entire system, ensuring the real-time nature and consistency of information flow; the processing instruction generation unit M200 enables intelligent response from design changes to physical processing; the material scheduling unit M300 ensures the economy and feasibility of resource allocation; the logistics planning unit M400 improves the timeliness and coordination of the supply chain; and the feedback analysis unit M500 completes the information flow from the physical world to the digital system, forming a complete data closed loop.

[0110] In one embodiment, the path optimization algorithm employs a heuristic search framework, enabling rapid convergence to near-optimal solutions in complex road networks. In other alternative implementations, path optimization can also be based on ant colony optimization or Dijkstra's algorithm, the specific choice depending on the computational efficiency and path accuracy requirements of the application scenario. Similarly, decision tree algorithms can be replaced by ensemble learning methods such as random forests or gradient boosting trees to enhance the ability to model nonlinear relationships; operations research algorithms can also be extended to mixed integer programming or other multi-objective optimization forms to support more complex constraints.

[0111] Through the above solution, this embodiment enables seamless transmission and intelligent response of design change information throughout the entire prefabrication process, effectively avoiding rework and material waste caused by information delays or miscommunication. Cross-stage collaboration between units is achieved based on a unified data platform, creating a linkage mechanism between materials, processing, logistics, and on-site installation, significantly improving resource allocation efficiency and project execution certainty. Most importantly, by collecting actual on-site installation results and comparing them with the initial plan, the system establishes a reverse feedback path from physical execution to the digital system. This enables the entire optimization system to self-correct based on actual execution results, thereby gradually reducing systematic errors and continuously improving the overall operational quality of the entire process.

[0112] In one specific implementation, the processing instruction generation unit M200 is configured to parse a set of technical information from a unified change dataset. This set of technical information includes the geometric information, material information, and three-dimensional topological connection information of the components. The geometric information covers dimensional parameters such as the diameter, wall thickness, and length of the pipe components; the material information includes physical properties such as material type, yield strength, and weldability grade; and the three-dimensional topological connection information is quantified as a connection complexity index by analyzing the spatial connection methods, support constraints, and assembly sequence of the target component and its adjacent components in the BIM model. The processing instruction generation unit M200 combines the above multi-dimensional technical information into a structured feature vector and inputs it into a pre-trained machine learning model. This machine learning model adopts a gradient boosting decision tree (GBDT) algorithm architecture, and its training dataset comes from design change records and corresponding physical processing result data accumulated in historical projects, including successful processing, rework, or scrapping instances. The model output is a continuous value between 0 and 1, representing the probability that the current design change will cause physical processing failure, i.e., the risk assessment result. The processing instruction generation unit M200 further compares the risk assessment result with the preset second threshold. When the risk assessment result is greater than or equal to the second threshold, the system determines that the current change is a high-risk operation, and then interrupts the original routine processing instruction flow and triggers the generation logic of composite control instructions to adjust the operating parameters and quality inspection requirements of the subsequent processing equipment.

[0113] In other alternative implementations, the machine learning model can be replaced with a support vector machine (SVM), random forest, or multilayer perceptron (MLP) neural network model, as long as its input feature space contains geometric information, material information, and three-dimensional topological connectivity information. The extraction of three-dimensional topological connectivity information can also be based on the embedding representation of the component connection diagram in the BIM model using graph neural networks (GNNs), thereby capturing more complex nonlinear dependencies. The application of risk assessment results also includes dynamically adjusting processing priorities or initiating expert review processes, rather than being limited to interrupting or adjusting processing instructions.

[0114] Through the above-described solution, this embodiment enables quantitative assessment and intelligent decision-making response to processing risks arising from design changes. It considers not only explicit geometric and material changes but also implicit factors related to the complexity of three-dimensional assembly, thus improving the accuracy and comprehensiveness of risk prediction. Compared to existing technologies, this embodiment proactively identifies potential process conflicts before physical processing, automatically intervenes in the processing flow based on data-driven risk assessment results, effectively reducing component rework rates and material waste caused by design changes, thereby improving the stability and resource utilization efficiency of prefabrication production.

[0115] In one specific implementation, the project schedule assessment unit is configured to receive an efficient logistics and distribution plan from the logistics planning unit M400 and further integrate real-time process data from the processing instruction generation unit M200, the material scheduling unit M300, and the on-site feedback data stream. Internally, this unit deploys a time-series forecasting model service, which is trained and continuously updated based on historical project execution data to dynamically simulate the overall progress trend of the current project. Specifically, the model uses the planned start and finish times of each component, actual processing start and end times, material arrival times, estimated transportation arrival times, and on-site installation status as input variables. By analyzing the time correlation and lag effects of these variables, it generates quantitative indicators regarding potential project delays. These quantitative indicators are expressed as the probability of on-time project completion or the number of days of deviation from the estimated total project duration.

[0116] When the system detects that a quantitative indicator exceeds a preset warning threshold, such as an on-time completion probability below 85% or an expected delay exceeding 3 days, the project progress assessment unit automatically triggers a notification mechanism, pushing warning information to the mobile terminal of designated management personnel or the project management platform. Simultaneously, the unit invokes its built-in schedule rescheduling engine, combining the actual progress status of each process and resource availability, to recalculate the scheduling logic for subsequent tasks, generating a revised end-to-end timeline, and synchronously updating it to the scheduling interface of relevant collaborative systems to support decision-making adjustments.

[0117] In other alternative implementations, the time series forecasting model may employ an ARIMA model, a SARIMA model, or a Long Short-Term Memory (LSTM) neural network model; the warning threshold may be set as a fixed threshold, a dynamically floating threshold, or an adaptive threshold weighted based on project size; and the automatic notification mechanism may integrate an SMS gateway, an instant messaging interface, or an email push service to achieve multi-channel alarms.

[0118] Through the above solution, this embodiment can achieve non-linear and interconnected identification of overall project schedule risks, avoid misjudgments caused by traditional linear superposition of delay times, accurately locate factors affecting the path in the early stage, and automatically generate response strategies, significantly improving the predictability and control capabilities of project management.

[0119] In one specific implementation, the feedback analysis unit M500 is configured to receive physical point cloud data, representing the actual spatial state of the installed fire ducts, collected by a 3D laser scanning device deployed at the construction site, and input this physical point cloud data into the system as feedback data for on-site installation. The feedback analysis unit M500 is further configured to invoke a 3D spatial registration module to rigidly align the physical point cloud data with the latest version of the design model derived from BIM design change records in a unified world coordinate system to eliminate initial positional deviations. After registration, the system calculates the shortest Euclidean distance from each data point in the physical point cloud data to the corresponding geometric surface of the design model using either the nearest point iteration (ICP) algorithm or a voxel-based fast registration method, and generates a spatial deviation heatmap for the entire area. Based on a preset installation accuracy tolerance threshold (e.g., 20mm), the system identifies component areas exceeding the tolerance range or instances of completely missing components, thereby accurately determining installation differences such as misaligned installation or omissions.

[0120] The M500 feedback analysis unit also integrates a lifecycle data traceability engine. Its configuration automatically traces the complete lifecycle data chain of a component from design changes, material allocation, processing execution, logistics delivery to on-site acceptance, based on the component identifier corresponding to the identified installation discrepancies. This traceability process combines multiple diagnostic rules stored in the built-in causal rule library, such as "If a component has been accepted but not installed and there is significant spatial interference between adjacent components, the technical reason is a design conflict" or "If the component's processing dimensions are consistent with the design model but cannot be assembled on-site, the reason is on-site construction error," using logical reasoning to pinpoint the technical reasons leading to material waste or rework. After identifying the technical cause, the feedback analysis unit M500 triggers an adaptive parameter update mechanism, configured to quantitatively adjust one or more technical parameter models within the system based on different root cause types: when the technical cause is attributed to insufficient design rationality of a specific connection node, the system automatically increases the weight coefficient of that type of node in the processing risk prediction model; when the cause is unstable processing deformation control of a certain type of material, the system adds a safety margin to the recommended set of machine operation parameters associated with the process knowledge base; when the cause is unreasonable design margin settings, the system dynamically corrects the standard loss coefficient in the bill of materials generation model.

[0121] In other alternative implementations, the 3D scanning device can be replaced with a structured light scanner or a mobile scanning system mounted on a drone to adapt to construction site environments of different sizes and accessibility; the spatial registration algorithm can use SIFT-3D or FPFH descriptors based on feature point matching to assist in initial registration, improving registration efficiency and robustness in complex scenarios; the reasoning mechanism of the causal rule base can be extended from a rule-based expert system to a probabilistic reasoning model based on Bayesian networks to handle multi-factor coupling scenarios with uncertainty; the update method of the technical parameter model can introduce an incremental learning mechanism, using the results of each closed-loop feedback as new samples to fine-tune the machine learning model online, achieving a more fine-grained self-evolution capability.

[0122] Through the above scheme, this embodiment can achieve high-precision virtual-physical comparison from the physical completion state to the digital design model, automatically identify installation deviations and accurately trace them to specific technical links, thereby driving the quantitative correction of internal system parameters, enabling the optimization strategy to have the ability to continuously evolve based on actual execution results, effectively improving the quality stability and system-level intelligence level of the entire process of fire pipeline prefabrication and installation.

[0123] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A BIM-based optimization method for the entire process of prefabrication of fire protection pipes, characterized in that, include: Updated data is obtained from BIM design change records using a computer, and the updated data from different sources is processed through a real-time synchronization mechanism to obtain a unified change dataset. Based on the unified change dataset, the design changes are analyzed using a decision tree algorithm. When the magnitude of the design change exceeds a first preset threshold, the processing parameters of the processing equipment are adjusted to obtain an optimized processing instruction set. The material requirement list is parsed from the optimized processing instruction set, and the material inventory is allocated using a linear programming algorithm based on the material requirement list to obtain a dynamic material allocation scheme. Based on the dynamic material allocation scheme, real-time logistics data is obtained, and based on the real-time logistics data, the transportation route is calculated or recalculated using a path optimization algorithm to obtain an efficient logistics distribution plan. The system acquires feedback data from on-site installations and compares this data with the dynamic material allocation scheme using a data comparison method. When the on-site installation feedback data does not match the dynamic material allocation scheme, the system recalculates the material waste rate to obtain an optimized resource recycling instruction.

2. The method according to claim 1, characterized in that, The process of processing the updated data from different sources via a real-time synchronization mechanism includes: Updated data from different sources are transmitted to a unified message channel via a distributed message queue; When a conflict is detected between the updated data, the updated data with the latest timestamp is selected and synchronized by comparing the timestamps of the updated data.

3. The method according to claim 1, characterized in that, The analysis and design changes also include: From the unified change dataset, a set of technical features associated with the design change are extracted, including geometric features, material features, and three-dimensional topological connection features. The set of technical features is input into a pre-trained machine learning model to output a risk value that represents the probability of physical processing failure.

4. The method according to claim 3, characterized in that, The step of adjusting the processing parameters of the processing equipment includes: When the risk value exceeds the second preset threshold, a composite control instruction is generated as the optimized processing instruction set. The composite control instruction includes machine operation parameters for controlling the processing equipment and enhanced physical quality inspection instructions for transmitting to the quality inspection station.

5. The method according to claim 1, characterized in that, The method of allocating material inventory using a linear programming algorithm includes: Construct a linear programming model with the objective function of minimizing inventory shortage costs and the constraint of the inventory level of the material; Solve the linear programming model to obtain the allocation results.

6. The method according to claim 1, characterized in that, The method further includes: Based on the efficient logistics and distribution plan, process data is integrated, and a time series forecasting model is used to assess the overall project progress in order to obtain quantitative indicators of potential delays. When the quantitative indicators of potential delays exceed the preset warning threshold, an automatic notification mechanism is triggered, and the process optimization strategy is updated to obtain a revised full-process timetable.

7. The method according to claim 1, characterized in that, The process of obtaining feedback data from on-site installation includes: acquiring physical point cloud data of the completed site using a 3D scanning device deployed at the construction site, and using the physical point cloud data as feedback data from on-site installation. The comparison using data comparison methods includes: The physical point cloud data and the design model derived from the BIM design change record are registered in three-dimensional space under a unified coordinate system. By calculating the spatial distance between the physical point cloud data and the model surface of the design model, physical components that are not installed or are misaligned are identified to quantify the material waste rate.

8. The method according to claim 1, characterized in that, After obtaining the optimized resource reclamation instruction, it also includes: Based on the material waste rate, trace back the entire lifecycle data chain of the components associated with the material waste rate, and use a causal rule base to locate the technical root cause of the waste.

9. The method according to claim 1, characterized in that, The method further includes: Based on the optimized resource recycling instructions, real-time data from each stage are integrated, and it is determined whether further synchronization of the real-time data from each stage is needed to obtain a closed-loop dataset of the process.

10. A BIM-based optimization system for the entire process of prefabrication of fire protection pipelines, characterized in that, include: The data processing unit is used to obtain updated data from BIM design change records via computer, and process the updated data from different sources through a real-time synchronization mechanism to obtain a unified change dataset. The processing instruction generation unit is used to analyze design changes using a decision tree algorithm based on the unified change dataset. When the magnitude of the design change exceeds a first preset threshold, the processing parameters of the processing equipment are adjusted to obtain an optimized processing instruction set. The material scheduling unit is used to parse the material demand list according to the optimized processing instruction set, and allocate the material inventory based on the material demand list using a linear programming algorithm to obtain a dynamic material allocation scheme. The logistics planning unit is used to acquire real-time logistics data according to the dynamic material allocation scheme, and to calculate or recalculate transportation routes based on the real-time logistics data using path optimization algorithms to obtain an efficient logistics distribution plan. The feedback analysis unit is used to acquire feedback data from on-site installation, compare the feedback data with the dynamic material allocation scheme using a data comparison method, and recalculate the material waste rate to obtain an optimized resource recycling instruction when the feedback data from on-site installation does not match the dynamic material allocation scheme.

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