Electromechanical pipeline installation quality management system based on data analysis

Abstract

The invention discloses an electromechanical pipeline installation quality management system based on data analysis, and relates to the related technical field of installation quality management, and the system comprises a scene construction module which is used for constructing a target scene and configuring an electromechanical pipeline component distribution database; the initialization module is used for initializing an installation task set of the electromechanical pipeline; the acquisition monitoring module is used for configuring sampling nodes based on the installation task set and executing installation data acquisition; the quality scoring module is used for synchronizing the installation data set and the target scene to a quality scoring model, executing electromechanical pipeline installation quality scoring and establishing a quality scoring result; and the packaging management module is used for carrying out associated storage management on the quality scoring result and the installation task set. The technical problems that in the prior art, electromechanical pipeline installation quality management efficiency is low, and real-time monitoring and dynamic evaluation are difficult are solved, and the technical effects that real-time monitoring and dynamic scoring are achieved, the automation level of installation quality management is improved, and data traceability is enhanced are achieved.

Description

Technical Field

[0001] This application relates to the technical field of intelligent installation management, specifically to a data analysis-based electromechanical pipeline installation quality management system. Background Technology

[0002] The installation quality of electromechanical pipelines, including water supply and drainage, electrical, HVAC, and fire protection systems, directly affects the safety and reliability of engineering projects. Large-scale projects involve numerous components and complex spatial distributions in their electromechanical pipeline systems. Traditional quality management methods for these systems are inefficient, subjective, and difficult to trace, resulting in insufficient comprehensive coverage and a high risk of accumulated installation errors and rework. With the development of Building Information Modeling (BIM) and the Internet of Things (IoT), real-time quality assessment of the installation process through dynamic data collection and analysis has become crucial. However, existing management systems lack dynamic monitoring and quantitative evaluation of installation activities, making it difficult to identify and correct problems promptly during construction. Furthermore, the varying installation standards for different electromechanical systems hinder multi-dimensional and automated quality scoring, leading to low management efficiency and data traceability.

[0003] Therefore, current technologies suffer from low efficiency in the quality management of electromechanical pipeline installation and difficulties in real-time monitoring and dynamic evaluation. Summary of the Invention

[0004] This application provides a data analysis-based electromechanical pipeline installation quality management system, which solves the technical problems of low efficiency, difficulty in real-time monitoring and dynamic evaluation in existing electromechanical pipeline installation quality management, and achieves the technical effects of real-time monitoring and dynamic scoring, improving the automation level of installation quality management, and enhancing data traceability.

[0005] This application provides a data analysis-based electromechanical pipeline installation quality management system. The system includes: a scenario construction module, used to construct a target scenario and configure a distribution database of electromechanical pipeline components within the target scenario; an initialization module, used to read the pipeline component distribution database and initialize an installation task set for electromechanical pipelines; a data acquisition and monitoring module, used to configure sampling nodes based on the installation task set after the user selects an installation task, execute installation data acquisition, and establish an installation dataset, the installation dataset including an installation behavior dataset and an installation effect dataset; a quality scoring module, used to receive the installation dataset, synchronize the installation dataset and the target scenario to a quality scoring model, execute electromechanical pipeline installation quality scoring, and establish a quality scoring result; and an encapsulation and management module, used to associate and store the quality scoring result and the installation task set.

[0006] In a possible implementation, the electromechanical pipeline installation quality management system based on data analysis further includes: activating the behavior processing layer in the quality scoring model; synchronizing the installation behavior dataset and target scene in the installation dataset to the behavior processing layer; after calling the calibration rule base according to the installation task, using the behavior processing layer to perform a behavior comparison between the calibration rule base and the installation behavior dataset to establish abnormal behavior identifiers; extracting scene features of the target scene using the spatial semantic channel of the behavior processing layer, the scene features including spatial dimensions, construction space constraints, cross-operation features, and risk area identifier features; performing behavior authentication compensation for the abnormal behavior identifiers based on the scene features, establishing an installation behavior score based on the behavior authentication compensation result, and obtaining an electromechanical pipeline installation quality score based on the installation behavior score.

[0007] In a possible implementation, the electromechanical pipeline installation quality management system based on data analysis further includes: activating the quality evaluation layer in the quality scoring model; inputting the installation effect dataset from the installation dataset into the quality evaluation layer; configuring a standard effect comparison chart according to the installation task, performing a feature comparison between the standard effect comparison chart and the installation effect dataset, and establishing a feature comparison result; using the feature comparison result to establish an installation effect score, and obtaining an electromechanical pipeline installation quality score based on the installation effect score and the installation behavior score.

[0008] In a possible implementation, the electromechanical pipeline installation quality management system based on data analysis further includes: synchronizing the abnormal behavior identifier and the feature comparison result to the consistency verification layer in the quality scoring model; using the consistency verification layer to perform behavior-effect consistency verification and generate a quality deviation factor; and constructing an electromechanical pipeline installation quality score after correcting the installation effect score and the installation behavior score through the quality deviation factor.

[0009] In a possible implementation, the electromechanical pipeline installation quality management system based on data analysis further includes: a task identification submodule, used to read the installation task, perform risk identification of the task execution steps, and establish a risk action set; and a visual reminder submodule, used to identify the installation behavior data flow in real time, and then provide visual reminders based on the risk action set matched to the installation node.

[0010] In a possible implementation, the electromechanical pipeline installation quality management system based on data analysis further includes: an active identification submodule, used to perform real-time action anomaly analysis on the installation behavior data stream and establish a predicted quality impact factor; and an active reminder submodule, used to execute an active reminder of action anomalies if the predicted quality impact factor meets a preset threshold.

[0011] In a possible implementation, the electromechanical pipeline installation quality management system based on data analysis further includes: a calling submodule for obtaining a user's construction behavior profile; and a scoring submodule for scoring the installation behavior data stream for risk operation tendency based on the construction behavior profile, so as to complete real-time action anomaly analysis.

[0012] In a possible implementation, the electromechanical pipeline installation quality management system based on data analysis further includes: a traceability management submodule, used to establish defect traceability paths based on the quality scoring results, and to perform task execution defect traceability path clustering using associated installation tasks to establish a defect attribution dataset; and a feedback management submodule, used to perform feedback management for construction optimization based on the defect attribution dataset.

[0013] In a possible implementation, the electromechanical pipeline installation quality management system based on data analysis further includes: a component scoring module, used to update the quality scoring results to the electromechanical pipeline component level and establish a component installation quality score; and a scene scoring map generation module, used to identify the target scene based on the component installation quality score and establish a quality score heatmap.

[0014] In a possible implementation, the electromechanical pipeline installation quality management system based on data analysis further includes: the sampling nodes in the acquisition and monitoring module are configured with a sensor group, which includes a vision sensor, an inertial measurement sensor, a radio frequency sensor, a 3D laser scanner, a distance measuring sensor, and a thermal imaging sensor.

[0015] This application proposes a data analysis-based electromechanical pipeline installation quality management system, comprising: a scenario construction module for building target scenarios and configuring a database of electromechanical pipeline component distribution; an initialization module for initializing the installation task set of electromechanical pipelines; a data acquisition and monitoring module for configuring sampling nodes based on the installation task set and executing installation data acquisition; a quality scoring module for synchronizing the installation dataset and target scenarios to a quality scoring model, performing electromechanical pipeline installation quality scoring, and establishing quality scoring results; and an encapsulation and management module for associating and storing the quality scoring results and the installation task set. This system solves the technical problems of low efficiency and difficulty in real-time monitoring and dynamic evaluation in existing electromechanical pipeline installation quality management technologies, achieving the technical effects of real-time monitoring and dynamic scoring, improving the automation level of installation quality management, and enhancing data traceability. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a schematic diagram of a data analysis-based electromechanical pipeline installation quality management system provided in an embodiment of this application.

[0018] Figure 2 This is a schematic diagram illustrating the execution process of a quality scoring module in a data analysis-based electromechanical pipeline installation quality management system, as provided in an embodiment of this application.

[0019] Figure labeling: Scene construction module 10, initialization module 20, data acquisition and monitoring module 30, quality scoring module 40, and encapsulation management module 50. Detailed Implementation

[0020] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0023] This application provides a data analysis-based electromechanical pipeline installation quality management system, such as... Figure 1 As shown, the system includes: Scene building module 10 is used to configure the distribution database of electromechanical pipeline components in the target scene after the target scene is built.

[0024] Preferably, constructing a target scenario refers to determining the physical environment where electromechanical pipeline installation is required, such as buildings, industrial facilities, and subway tunnels. This is typically presented as a digital model such as a Building Information Modeling (BIM) system, containing information on building structure, spatial layout, and equipment location. Then, component data is imported from AutoCAD or point cloud data is obtained from the site using a 3D laser scanner. A database of electromechanical pipeline component distribution within the target scenario is configured to store detailed information on all electromechanical pipeline-related components in the target scenario, including component type and attributes, spatial distribution relationships, and construction constraints. Component types may include water supply and drainage pipes, cable trays, ventilation ducts, and fire sprinkler pipes. Attributes include material specifications, connection methods, construction standards, and spatial coordinate parameters. Spatial distribution relationships refer to the precise location of components in the scenario, such as 3D coordinates and floor distribution; as well as the topological relationships between components, such as the connection between pipes and supports, and the laying path of cables and cable trays. Construction constraints include requirements for high-altitude work areas, explosion-proof requirements, collaborative work needs, and construction requirements for high-risk areas such as high-voltage electricity and flammable / explosive environments. This provides spatial data support for the installation task, ensuring that the construction plan matches the actual situation.

[0025] Initialization module 20 is used to initialize the installation task set of electromechanical pipelines after reading the pipeline component distribution database.

[0026] Preferably, information such as component type, location, and construction specifications is read from the pipeline component distribution database to initialize the installation task set for electromechanical pipelines. This automatically generates a structured construction task list to guide and manage the entire installation process. Specifically, the process is decomposed by professional type to identify subsystems such as water supply and drainage, electrical, and HVAC. It is also divided by spatial area to obtain floors, rooms, and pipe shafts. The tasks are then sorted according to the construction sequence, such as main pipes before branch pipes and concealed works before terminal equipment. Next, task parameters are configured, which means binding corresponding construction standards, resource allocation, and time to each task. At the same time, based on the construction constraints in the pipeline component database, high-risk tasks are automatically marked and safety operation guidelines are attached. Finally, the installation task set for electromechanical pipelines is obtained, ensuring that the installation task of each pipeline is associated with resources, specifications, and risks and is executed according to standards.

[0027] The data acquisition and monitoring module 30 is used to configure sampling nodes based on the installation task set after the user selects an installation task, perform installation data acquisition, and establish an installation dataset, which includes an installation behavior dataset and an installation effect dataset.

[0028] Furthermore, the specific configuration of the acquisition and monitoring module 30 also includes that the sampling nodes in the acquisition and monitoring module are equipped with a sensor group, which includes a vision sensor, an inertial measurement sensor, a radio frequency sensor, a three-dimensional laser scanner, a ranging sensor, and a thermal imaging sensor.

[0029] Preferably, after the user selects a specific installation task, construction data is collected in real time through configured sampling nodes. These sampling nodes are equipped with a sensor group, including a vision sensor, an inertial measurement sensor, an RF sensor, a 3D laser scanner, a ranging sensor, and a thermal imaging sensor. Specifically, the vision sensor may be an industrial camera used to capture construction actions or component appearance, such as the trajectory of a welding torch or flange alignment; the inertial measurement sensor is used to monitor operational posture, such as whether the wrench angle is compliant; the RF sensor is used to automatically identify component numbers and associate them with construction records; the 3D laser scanner is used to scan installed components and generate point cloud models for comparison with design accuracy; the ranging sensor is used to measure in real time whether pipeline spacing meets safety standards; and the thermal imaging sensor is used to detect the temperature distribution at welding points and determine whether it is uniform.

[0030] Preferably, installation data is collected using multiple sensors configured at sampling nodes to obtain an installation behavior dataset and an installation effect dataset. The installation behavior dataset records dynamic operational data during construction, including tool parameters, construction time, operator ID, and other operational parameters; it compares these data with a standard process library, marking anomalies such as failure to tighten bolts in sequence, and environmental factors that may affect construction, such as temperature, humidity, and lighting. The installation effect dataset records static quality data after construction, including actual pipeline coordinates and design deviations obtained through a laser scanner, sealing test data and pipeline pressure values, and visual inspection of weld appearance and coating integrity. Finally, the installation behavior dataset and the installation effect dataset are combined to form an installation dataset, supporting risk identification, anomaly detection, and visual alerts.

[0031] Furthermore, the specific configuration of the data acquisition and monitoring module 30 also includes a task identification submodule, which is used to identify risks in the task execution steps after reading the installation task and establish a set of risk actions; and a visual reminder submodule, which is used to identify the installation behavior data stream in real time and then provide visual reminders based on the risk action set matched with the installation node.

[0032] Preferably, the task identification submodule is responsible for automatically analyzing the execution steps of the task after the user selects it. This involves calling a predefined rule base for matching or using machine learning models to predict high-risk actions to identify potential risks during task execution and generate a standardized risk management list, i.e., a set of risk actions. For example, in a stainless steel pipe welding task, high-risk operations are marked, such as "prolonged arc exposure may cause material deformation," and associated with the corresponding safety specification that the welding temperature must not exceed 300℃. The visual alert submodule monitors the behavioral data flow during construction in real time, such as welding current and operating speed collected by sensors. It compares the installation behavioral data flow with the risk action set in real time. If a match is detected between the current action and the risk action set, a visual alert is immediately pushed through AR glasses, industrial control screens, or mobile devices, such as flashing warning icons or pop-up operation instructions, reminding workers to make timely adjustments. The task identification submodule and the visual alert submodule work together to achieve proactive risk control for high-altitude operations, high-pressure welding, and construction in confined spaces.

[0033] Furthermore, the specific configuration of the data acquisition and monitoring module 30 also includes an active identification submodule, used to perform real-time action anomaly analysis on the installation behavior data stream and establish a predicted quality impact factor; and an active reminder submodule, used to execute an active reminder of action anomalies if the predicted quality impact factor meets a preset threshold.

[0034] Preferably, the proactive identification submodule is used to perform real-time analysis of behavioral data streams during the installation process, such as tool sensor data and motion capture data, to dynamically detect abnormal actions during construction. For example, it can train a machine learning model based on the correlation records of abnormal behaviors and quality defects in similar past tasks to predict their potential impact on the final installation quality and generate a predicted quality impact factor, which is the probability and severity of quality defects that the abnormal behavior may cause, quantifying the potential harm of the abnormal behavior, such as a score of 0-100, where a higher value indicates a greater quality risk. The proactive reminder submodule is used to automatically trigger multi-level reminders when the predicted quality impact factor exceeds a preset threshold to prevent low-quality construction. This includes providing vibration alerts to the operator through AR glasses or smartwatches for minor risks, and automatically pausing the installation equipment and pushing alarms to the management terminal for severe risks.

[0035] Furthermore, the specific configuration of the proactive identification submodule also includes a calling submodule for obtaining a user's construction behavior profile; and a scoring submodule for scoring the installation behavior data stream for risk operation tendency based on the construction behavior profile, so as to complete real-time action anomaly analysis.

[0036] Preferably, the calling submodule is used to dynamically generate user construction behavior profiles. That is, by using historical construction data (such as operating habits, compliance rate, common error types) and real-time identity recognition (such as work badge RFID or facial recognition), a digital capability profile of each construction worker is constructed as a construction behavior profile. For example, a welder's construction behavior profile may include tags such as "high-frequency use of high current welding" and "occasional weld seam deviation". Then, the scoring submodule scores the risk operation tendency of the installation behavior data stream based on the construction behavior profile. If a worker has a history of "not tightening bolts in sequence", the monitoring sensitivity of his current operation is increased. If similar behavior is detected, it is immediately marked as high risk and triggers AR prompts or equipment lock, thereby significantly improving the accuracy of abnormal action recognition, avoiding false alarms or missed detections, and thus realizing the prediction of current operation risks, and finally completing the accurate analysis of real-time abnormal actions.

[0037] The quality scoring module 40 is used to receive the installation dataset, synchronize the installation dataset and the target scene to the quality scoring model, perform electromechanical pipeline installation quality scoring, and establish quality scoring results.

[0038] Preferably, the quality scoring model is an intelligent evaluation component based on multi-dimensional data analysis, used to automatically and standardizedly score the construction process and results of electromechanical pipeline installation, ultimately outputting a comprehensive quality score result. Specifically, the installation behavior dataset, installation effect dataset, and corresponding target scenario are synchronized to the quality scoring model, and the installation data is matched with the component positions and construction timelines in the target scenario. The quality scoring model performs electromechanical pipeline installation quality scoring, including behavior compliance analysis and effect scoring. Behavior compliance analysis refers to comparing installation behavior data with the process standard database, marking abnormal installation behaviors, and outputting installation behavior scores. Effect scoring is a result quality score that includes geometric accuracy detection and performance testing. Geometric accuracy detection is performed by calculating installation deviation values ​​through laser scanning of point clouds or BIM design models, and performance testing includes pressure testing, sealing testing, etc., thereby outputting an installation effect score. Finally, the installation behavior score and installation effect score are combined to determine the quality score result, which is used for installation management decisions and process optimization.

[0039] Furthermore, such as Figure 2 As shown, the specific configuration of the quality scoring module 40 also includes: activating the behavior processing layer in the quality scoring model; synchronizing the installation behavior dataset and target scene in the installation dataset to the behavior processing layer; after calling the calibration rule base according to the installation task, using the behavior processing layer to perform a behavior comparison between the calibration rule base and the installation behavior dataset to establish abnormal behavior identifiers; extracting scene features of the target scene using the spatial semantic channel of the behavior processing layer, the scene features including spatial dimensions, construction space constraints, cross-operation features, and risk area identifier features; performing behavior authentication compensation for the abnormal behavior identifiers based on the scene features; establishing an installation behavior score based on the behavior authentication compensation result; and obtaining an electromechanical pipeline installation quality score based on the installation behavior score.

[0040] Preferably, the behavior processing layer is the core analysis module in the quality scoring model. It is used to standardize the evaluation of operational behaviors during the construction process and dynamically correct the score based on scene characteristics. Specifically, when the installation behavior dataset and target scene data are input into the quality scoring model, the behavior processing layer is automatically activated. Then, the installation behavior dataset and target scene in the installation dataset are synchronized to the behavior processing layer, and the calibration rule base, i.e., the predefined process standard database, is called according to the installation task. Then, the behavior processing layer performs a behavior comparison between the calibration rule base and the installation behavior dataset to determine whether the installation behavior meets the installation process standards, and then establishes abnormal behavior identification. If the comparison result is compliant, it is marked in green; if the comparison result is slightly abnormal, it is marked in yellow; and if the comparison result is seriously abnormal, it is marked in red. For example, if bolts are not tightened in a diagonal order, it is marked as seriously abnormal.

[0041] Preferably, the spatial semantic channel of the behavior processing layer is utilized to extract scene features of the target scene through semantic analysis. These features include spatial dimensions, such as insufficient working height within a suspended ceiling, which may lead to operational deformation; construction space constraints and installation operation limitations, such as narrow areas allowing for smaller operational errors; cross-operation characteristics, such as simultaneous construction with electrical teams, which increases operational complexity; and risk area identification characteristics, such as flammable areas in chemical plants where welding sparks must be strictly limited. Then, behavioral authentication compensation is applied to abnormal behavior identifiers based on scene features. This involves dynamically adjusting the weight of abnormal behaviors based on scene features. For example, in high-altitude work areas, the penalty for not wearing a safety belt increases from 10% to 30%, while in non-critical areas, minor operational deviations may be tolerated. Finally, based on the behavioral authentication compensation results—that is, the severity of the compensated abnormal behavior—an installation behavior score is established and serves as an important component of the comprehensive quality score for electromechanical pipelines. This reduces misjudgments caused by scene complexity and improves the objectivity of the scoring.

[0042] Furthermore, the specific configuration of the quality scoring module 40 also includes: activating the quality evaluation layer in the quality scoring model; inputting the installation effect dataset in the installation dataset into the quality evaluation layer; configuring a standard effect comparison chart according to the installation task, performing a feature comparison between the standard effect comparison chart and the installation effect dataset, and establishing a feature comparison result; using the feature comparison result to establish an installation effect score, and obtaining an electromechanical pipeline installation quality score based on the installation effect score and the installation behavior score.

[0043] Preferably, the quality evaluation layer is also the core analysis module in the quality scoring model. It is responsible for objectively evaluating the physical results of the electromechanical pipeline installation. The installation effect dataset, such as the 3D scan coordinates, sealing test data, and pressure test results from the installation dataset, is input into the quality evaluation layer. Then, a standard effect comparison map is configured based on the BIM model or design specifications of the installation task. This map may include the theoretical coordinates of the pipelines and the allowable deviation range of the flanges. The standard effect comparison map is then used as the benchmark data for the installation effect, and a feature comparison is performed between it and the installation effect dataset. That is, the actual installation effect is compared with the standard map item by item through point cloud registration or image recognition to generate feature comparison results, including geometric deviation, functional performance test results, and appearance quality. Then, an installation effect score is established based on the feature comparison results, such as 40% for geometric accuracy, 50% for functional performance, and 10% for appearance quality. Finally, the installation behavior score and the installation effect score are combined and weighted to generate the final quality score, such as 40% for installation behavior and 60% for installation effect, to obtain the final electromechanical pipeline installation quality score.

[0044] Furthermore, the specific configuration of the quality scoring module 40 also includes synchronizing the abnormal behavior identifier and the feature comparison result to the consistency verification layer in the quality scoring model; using the consistency verification layer to perform behavior-effect consistency verification and generate a quality deviation factor; and constructing an electromechanical pipeline installation quality score after correcting the installation effect score and the installation behavior score through the quality deviation factor.

[0045] Preferably, the consistency verification layer is a key logical verification module in the quality scoring model. It is used to resolve contradictory situations where the construction process is compliant but the results are substandard, or the results are good but the operation is not standardized. It corrects the quality score of electromechanical pipeline installation through cross-verification of behavior and effect. Specifically, it synchronizes abnormal behavior identification and feature comparison results to the consistency verification layer in the quality scoring model. The consistency verification layer is used to verify the consistency between behavior and effect, including establishing a causal relationship chain between behavior and effect. For example, if there is non-sequential bolt tightening and flange sealing test failure, it is determined to be directly related; if the welding parameters are compliant but the weld strength is insufficient, hidden factors are triggered. The analysis quantifies the mismatch between behavior and effect, generating a quality deviation factor, such as a quality deviation coefficient of 0-1. The larger the quality deviation factor, the worse the consistency verification result, such as a perfect score for installation behavior but an unqualified installation effect; conversely, the smaller the deviation in the consistency verification result, such as a small error in installation behavior that does not affect the installation effect. The installation effect score and installation behavior score are dynamically corrected through the quality deviation factor. For example, if the installation behavior is qualified but the installation effect is poor, the weight of the installation behavior score is reduced; if the installation behavior has minor flaws but the installation effect is excellent, the installation effect score is appropriately increased. Finally, a quality score for electromechanical pipeline installation is constructed to prevent misjudgment.

[0046] The encapsulation management module 50 is used to associate and store the quality scoring results with the installation task set.

[0047] Preferably, structured data management is used to establish a two-way association between quality scoring results and corresponding installation tasks, forming a traceable and analyzable digital quality archive. Specifically, each quality scoring result is associated with a specific installation task through an ID, down to the scoring of each pipe fitting / equipment and stored. Then, complete quality data can be queried through the task ID, and construction records can be retrieved through the component number, ensuring support for the entire lifecycle traceability of installation construction and enhancing data traceability, thereby improving the automation level of installation quality management.

[0048] Furthermore, the specific configuration of the packaging management module 50 also includes a traceability management submodule, used to establish a defect traceability path based on the quality scoring results, and to cluster the task execution defect traceability paths using associated installation tasks to establish a defect attribution dataset; and a feedback management submodule, used to perform feedback management for construction optimization based on the defect attribution dataset.

[0049] Preferably, the source tracing management submodule is used to establish defect source tracing paths based on quality scoring results. This involves tracing back from low-scoring items layer by layer to the root cause installation tasks of specific construction defects, establishing a visual responsibility chain, and then using the associated installation tasks to cluster the defect source tracing paths. This includes classifying historical defects through machine learning, such as clustering sealing problems into subcategories like bolt torque, gasket installation, and surface cleanliness. This generates a defect attribution dataset. For example, a high-frequency root cause of poor pipe sealing is incorrect bolt sequence, and a high-frequency root cause of cable tray misalignment is excessive support spacing. The feedback management submodule is used for feedback management of construction optimization based on the defect attribution dataset. This involves transforming defect analysis results into actionable improvement measures, forming a closed loop for construction quality optimization. This includes generating optimization strategies and executing them through multi-channel feedback, completing the entire process management of structured defects, measures, and verification.

[0050] Furthermore, the specific configuration of the encapsulation management module 50 also includes a component scoring module, used to update the quality scoring results to the electromechanical pipeline component level and establish a component installation quality score; and a scene scoring map generation module, used to identify the target scene based on the component installation quality score and establish a quality score heatmap.

[0051] Preferably, the component scoring module is used to update the quality scoring results to the electromechanical pipeline component level, that is, to decompose the macro quality score to each electromechanical pipeline component, such as a single pipe, valve, or support, forming a component-level quality file to achieve accurate quality traceability. Specifically, the comprehensive quality score is bound to the component ID in the BIM model, and the sub-item scoring of composite components is decomposed. At the same time, the historical scores are iteratively updated using the newly added inspection data during the operation and maintenance phase, and then the component installation quality score is output. The scene scoring map generation module identifies the target scene based on the component installation quality score, that is, to associate the component score with the BIM model coordinates, use color gradients to represent the quality level, and generate a three-dimensional quality score heat map, which intuitively displays the quality distribution of each area in the target scene. The quality score heat map can be clicked to view the detailed component score, realizing quality coverage assessment.

[0052] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0053] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A data analysis-based electromechanical pipeline installation quality management system, characterized in that, The system includes: The scene construction module is used to construct the target scene and then configure the distribution database of electromechanical pipeline components within the target scene. The initialization module is used to read the pipeline component distribution database and initialize the installation task set of electromechanical pipelines; The data acquisition and monitoring module is used to configure sampling nodes based on the installation task set after the user selects an installation task, perform installation data acquisition, and establish an installation dataset, which includes an installation behavior dataset and an installation effect dataset. The quality scoring module is used to receive the installation dataset, synchronize the installation dataset and the target scene to the quality scoring model, perform electromechanical pipeline installation quality scoring, and establish quality scoring results. The encapsulation management module is used to associate and store the quality scoring results with the installation task set.

2. The electromechanical pipeline installation quality management system based on data analysis as described in claim 1, characterized in that, The steps performed by the quality scoring module include: Activate the behavior processing layer in the quality scoring model; Synchronize the installation behavior dataset and target scenario in the installation dataset to the behavior processing layer; After calling the calibration rule base according to the installation task, the behavior processing layer is used to perform a behavior comparison between the calibration rule base and the installation behavior dataset to establish an abnormal behavior identifier; The spatial semantic channel of the behavior processing layer is used to extract scene features of the target scene, including spatial dimensions, construction space constraints, cross-operation features, and risk area identification features. Based on the scenario characteristics, behavioral authentication compensation is performed on the abnormal behavior identifiers. An installation behavior score is established based on the behavioral authentication compensation results. An electromechanical pipeline installation quality score is obtained based on the installation behavior score.

3. The electromechanical pipeline installation quality management system based on data analysis as described in claim 2, characterized in that, The steps performed by the quality scoring module include: Activate the quality evaluation layer in the quality scoring model; Input the installation effect dataset from the installation dataset into the quality evaluation layer; After configuring the standard effect comparison chart according to the installation task, perform feature comparison between the standard effect comparison chart and the installation effect dataset to establish feature comparison results; An installation effect score is established using the feature comparison results, and an electromechanical pipeline installation quality score is obtained based on the installation effect score and the installation behavior score.

4. The electromechanical pipeline installation quality management system based on data analysis as described in claim 3, characterized in that, The steps performed by the quality scoring module include: The abnormal behavior identifier and the feature comparison results are synchronized to the consistency verification layer in the quality scoring model. The consistency verification layer is used to verify the consistency between behavior and effect, and a quality deviation factor is generated. After correcting the installation effect score and the installation behavior score by the quality deviation factor, an electromechanical pipeline installation quality score is constructed.

5. The electromechanical pipeline installation quality management system based on data analysis as described in claim 1, characterized in that, The data acquisition and monitoring module includes: The task identification submodule is used to read the installation task, identify the risks of the task execution steps, and establish a set of risk actions. The visual alert submodule is used to identify the installation behavior data stream in real time and then display visual alerts based on the risk action set matched to the installation node.

6. The electromechanical pipeline installation quality management system based on data analysis as described in claim 5, characterized in that, The data acquisition and monitoring module also includes: The active identification submodule is used to perform real-time action anomaly analysis on the installation behavior data stream and establish a predictive quality impact factor. The proactive reminder submodule is used to proactively remind users of abnormal actions if the predicted quality impact factor meets a preset threshold.

7. The electromechanical pipeline installation quality management system based on data analysis as described in claim 6, characterized in that, The active identification submodule includes: Call the submodule to obtain the user's construction behavior profile; The scoring submodule is used to score the risk operation tendency of the installation behavior data stream based on the construction behavior profile, so as to complete the real-time action anomaly analysis.

8. The electromechanical pipeline installation quality management system based on data analysis as described in claim 1, characterized in that, The packaging management module includes: The source tracing management submodule is used to establish defect source tracing paths based on the quality scoring results, and to cluster the task execution defect source tracing paths using associated installation tasks to establish a defect attribution dataset. The feedback management submodule is used for feedback management of construction optimization based on the defect attribution dataset.

9. The electromechanical pipeline installation quality management system based on data analysis as described in claim 1, characterized in that, The system also includes: The component scoring module is used to update the quality scoring results to the electromechanical pipeline component level and establish a component installation quality score. The scene rating map generation module is used to identify the target scene based on the component installation quality rating and to establish a quality rating heat map.

10. The electromechanical pipeline installation quality management system based on data analysis as described in claim 1, characterized in that, The sampling nodes in the acquisition and monitoring module are equipped with a sensor group, which includes a vision sensor, an inertial measurement sensor, a radio frequency sensor, a 3D laser scanner, a distance measuring sensor, and a thermal imaging sensor.

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