A method for mechanical and electrical installation construction simulation and collision detection based on BIM model lightweight

Abstract

The application discloses a kind of mechanical and electrical installation construction simulation and collision detection method based on BIM model light weight, it is related to building informatization technical field.The present application includes differentiating light weight compression to BIM model;Through delay prediction trigger end side cooperation realizes accelerated loading;Construct space-time priority rendering queue output progressive data;Integrate digital twin coordinate field and carry out virtual-real fusion adaptive correction;Based on role permission and QoS protocol, incremental collaborative transmission is realized;Real-time collision monitoring and hierarchical early warning are carried out in pipeline dense area, closed-loop optimization path is formed by fusing field feedback, and dynamic correction instruction is generated;According to terminal performance and network state, the rendering accuracy is adaptively refreshed;Second-level loading of mobile terminal, millimeter-level space correction and collision risk closed-loop management are realized, which effectively reduces the rework rate and supports whole-process fine management and control.

Description

Technical Field

[0001] This invention relates to the field of building information technology, specifically a lightweight electromechanical installation construction simulation and collision detection method based on BIM models. Background Technology

[0002] Against the backdrop of digital transformation in the construction industry, Building Information Modeling (BIM) technology has become an important pillar for promoting efficient management and refined construction of engineering projects. It integrates information from the entire process of design, construction, and operation and maintenance through digital means, and has irreplaceable value in improving project quality and efficiency. However, many current solutions have revealed significant shortcomings in practical applications, especially when dealing with complex engineering projects. They often fail to meet the needs of real-time on-site operations and multi-party collaboration due to limitations in technical architecture, which leads to frequent problems during construction and affects project progress and cost control.

[0003] Looking deeper, the core challenges in this field lie primarily in balancing the contradiction between efficient information processing and real-time application. The first key factor is the mismatch between the massive volume of complex engineering information and the processing capabilities of terminal devices. Especially on mobile devices or ordinary web pages, information loading and display are often extremely slow, or even impossible, due to limitations in computing resources. Another key factor arising from this is the insufficient dynamism of information interaction. Because information processing speed cannot keep up with actual needs, multi-party collaboration and real-time adjustments at the construction site become extremely difficult, and relevant personnel cannot obtain the latest construction status or discover potential problems in a timely manner. For example, during electromechanical installation, on-site workers may be unable to quickly assess the spatial relationship between pipes and equipment, leading to installation deviations and subsequent conflicts and rework in later processes. Summary of the Invention

[0004] The purpose of this invention is to provide a lightweight electromechanical installation construction simulation and collision detection method based on BIM models, which solves the technical problems in the prior art, such as slow terminal loading due to the large size of BIM models, spatial deviation between the design model and the construction site, and difficulty in closed-loop handling of collision risks, thereby affecting the construction progress and causing rework.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] This application provides a lightweight electromechanical installation construction simulation and clash detection method based on BIM model, including the following steps:

[0007] S1. Extract the geometric and attribute information of the electromechanical installation project from the building information model database, and use a semantically aware data compression algorithm to distinguish key components from auxiliary components for differentiated dimensionality reduction processing to obtain simplified engineering information data.

[0008] S2. The simplified engineering information data is loaded on the terminal device. The loading delay prediction model is used to predict the risk of exceeding the threshold. If the threshold is exceeded, the edge-end collaboration mechanism is triggered to unload the rendering task to the edge node, thereby obtaining the engineering information data that is loaded faster.

[0009] S3. Obtain accelerated loading engineering information data, construct a spatiotemporal priority rendering queue based on the temporal relationship and spatial hierarchy of electromechanical installation procedures, prioritize rendering of critical path components and dynamically adjust the accuracy to obtain progressive rendering data.

[0010] S4. For the spatial relationships in the progressive rendering data, the digital twin coordinate field information of the construction site is integrated. By comparing the design coordinates with the on-site collected coordinates, if the deviation exceeds the threshold, the error backpropagation algorithm is used for local adaptive correction to obtain high-fidelity spatial relationship correction data.

[0011] S5. From the spatial relationship correction data, incremental update segments are extracted based on the multi-party collaborative role permissions and viewpoint changes. Differentiated data is pushed to the terminal in a targeted manner using a QoS-based real-time transmission protocol to obtain bandwidth-adaptive synchronous collaborative data.

[0012] S6. Real-time collision monitoring is performed on dense electromechanical pipeline areas based on synchronous collaborative data. If a collision risk is detected, a graded early warning mechanism is triggered, and the early warning information is integrated with the on-site feedback data to form a closed-loop optimization path, resulting in early warning optimization data with dynamic correction instructions.

[0013] S7. By calling the mixed precision rendering module through early warning optimization data, adaptive refresh is performed. The model display details and refresh frequency are dynamically adjusted according to terminal performance and network status to obtain the final optimized collaborative display data.

[0014] The beneficial effects of this invention are as follows:

[0015] This invention addresses the mismatch between the massive volume of complex engineering information and the processing capabilities of terminal devices through steps S1 to S3. By employing a semantically aware data compression algorithm to perform differentiated dimensionality reduction on key and auxiliary components, and combining a loading delay prediction model to predict the risk of exceeding the threshold and trigger an edge-end collaboration mechanism to dynamically unload the rendering task to the edge node, and then constructing a spatiotemporal priority rendering queue based on the temporal relationship and spatial hierarchy of electromechanical installation procedures, it achieves second-level loading and smooth interaction of large BIM models on mobile terminals, effectively avoiding construction delays caused by slow information loading.

[0016] Steps S4 to S5 resolved the issues of deviation between the design model and the actual on-site pose, as well as difficulties in multi-party collaboration. By integrating digital twin coordinate field information from the construction site, and utilizing an error backpropagation algorithm based on an elastic deformation model to transmit the weight of deviation influence along the topological connection relationship of components, local adaptive correction parameters containing translation, rotation, and scale corrections were generated, achieving high-fidelity spatial alignment between the design model and the actual construction scene. Simultaneously, incremental update segments were extracted based on role permissions and viewpoint changes, and a QoS-based real-time transmission protocol was used to dynamically adjust the bandwidth allocation strategy, enabling real-time synchronous collaboration of multiple terminals under differentiated network conditions. This avoided installation deviations and rework caused by inconsistent spatial information from the source.

[0017] Steps S6 and S7 addressed the problem of frequent collision risks and the difficulty in closed-loop management during construction. A spatial clustering algorithm dynamically identified densely packed electromechanical pipeline areas, and a hierarchical collision detection algorithm based on spatial indexing was used for frame-by-frame analysis. Tiered warnings were triggered based on collision type and urgency, and spatiotemporal alignment and fusion of on-site feedback data formed a closed-loop optimized path to generate dynamic correction instructions. Simultaneously, terminal hardware performance and network status were collected, and a mixed-precision rendering module differentiated rendering of critical and non-critical areas, implementing a tiered degradation or progressive recovery strategy. This achieved proactive warning, closed-loop handling, and adaptive visualization of collision risks, significantly reducing rework rates and supporting refined management throughout the entire process, from pre-construction simulation and rehearsal to post-construction review and analysis. Attached Figure Description

[0018] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0019] Figure 1 A flowchart illustrating a lightweight electromechanical installation construction simulation and collision detection method based on a BIM model, provided in Embodiment 1 of this application;

[0020] Figure 2 This is a flowchart illustrating step S4 in a lightweight electromechanical installation construction simulation and collision detection method based on a BIM model provided in Embodiment 1 of this application.

[0021] Figure 3 This is a flowchart illustrating step S6 of a lightweight electromechanical installation construction simulation and collision detection method based on a BIM model, as provided in Embodiment 1 of this application. Detailed Implementation

[0022] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0023] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used herein are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0024] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0025] Example 1, please refer to Figures 1-3 This embodiment provides a lightweight electromechanical installation construction simulation and clash detection method based on BIM model, including the following steps:

[0026] S1. Extract the geometric and attribute information of the electromechanical installation project from the building information model database, and use a semantic-aware data compression algorithm to differentiate between key components and auxiliary components for dimensionality reduction processing, so as to obtain simplified engineering information data that retains the core features.

[0027] Further, step S1 specifically includes:

[0028] S11. Obtain geometric data and attribute information of the electromechanical installation project from the building information model database. Based on the functional importance, spatial positioning sensitivity and collision detection priority of the components during the installation process, divide the components into key components and auxiliary components through a semantic classifier to form an original dataset with semantic labels. Among them, key components include main pipelines, core equipment and complex node components, and auxiliary components include supports, insulation layers and non-structural accessories.

[0029] The semantic classifier employs a two-layer classification architecture combining rules and machine learning. The first layer quickly categorizes components in the standard component library using preset component type mapping rules. The second layer uses a classification model trained on historical engineering data to perform semantic recognition on non-standard or custom components. The functional importance is quantitatively evaluated based on the component's functional level in the electromechanical system. The spatial positioning sensitivity is weighted and calculated based on the component's installation accuracy requirements and the degree of spatial correlation with adjacent components. The collision detection priority is dynamically ranked based on the component's criticality in the construction sequence and its spatial overlap risk with surrounding components. Finally, the comprehensive classification threshold for each component is determined through a weighted fusion method.

[0030] S12. For key components, a semantically aware data compression algorithm is used to retain high-priority features such as geometric contours, spatial poses, and connectivity relationships, while removing fine textures and internal redundant meshes that have low contribution to collision detection, generating concise key component data that retains core features. For auxiliary components, a differentiated dimensionality reduction strategy is implemented, replacing fine geometric descriptions with parametric expressions, and aggregating and compressing redundant information to obtain a low-granularity auxiliary component dataset.

[0031] The semantically aware data compression algorithm first parses the semantic labels of components, identifies the functional roles and spatial constraints of components in the collision detection scenario, and then constructs a feature importance evaluation matrix. It assigns the highest fidelity weight to geometric contour features to ensure the accuracy of spatial interference judgment, maintains the integrity of the transformation matrix of spatial pose features to support precise positioning in construction simulation, and retains topological connection information for connection relationship features to maintain the correlation between components. The differentiated dimensionality reduction strategy uses a parameterized template library to replace the geometric representation of auxiliary components, maps the complex mesh model into parameterized instances defined by key parameters such as length, diameter, and thickness, and merges similar auxiliary components with dense spatial distribution through a spatial clustering algorithm. Under the premise of ensuring acceptable visual effects, the data volume is compressed to less than 5% of the original model.

[0032] S13. Integrate the simplified key component data with the compressed auxiliary component dataset to construct a unified lightweight engineering information framework; use automated verification tools to detect the geometric integrity, attribute correlation and semantic consistency of the data. If missing or biased data is found, backtrack to the original dataset for local completion and correction, and finally output simplified engineering information data that meets the requirements of electromechanical installation construction simulation and collision detection.

[0033] The unified lightweight engineering information framework employs a hierarchical index structure to organize data. The top layer stores overall project metadata and spatial bounding box information; the middle layer divides data into blocks according to construction area and system type; and the bottom layer stores simplified geometric data and associated attributes using components as basic units. The automated verification tool incorporates three sets of verification rules: geometric integrity rules check whether component faces are closed and whether normal vectors are consistent; attribute association rules verify the completeness of the mapping between component IDs and attribute information and the bidirectional consistency of connections between components; and semantic consistency rules check the degree of matching between component classification labels and actual geometric features. When data missing or deviation is detected, the verification tool automatically generates a backtracking completion task, locates the latest version of the corresponding component in the original dataset, extracts the missing data, performs format conversion and compression processing, and incrementally updates it into the lightweight framework to ensure the completeness and accuracy of the output data.

[0034] Specifically, a semantic classifier is used to divide components into key components and auxiliary components based on functional importance, spatial positioning sensitivity, and collision detection priority. Key components retain high-priority features such as geometric contours, spatial poses, and connection relationships, while auxiliary components are reduced in a differentiated manner using parametric representation and aggregation compression. This solves the technical problem of the huge size of the BIM model causing difficulties in terminal loading, and achieves the effect of compressing the data volume to less than 5% of the original model while retaining the core features required for collision detection.

[0035] S2. The simplified engineering information data is loaded on a resource-constrained terminal device. The loading delay prediction model is used to predict the risk of exceeding the threshold. If the threshold is exceeded, the edge-end collaboration mechanism is triggered. Based on the task dependency relationship, the computationally intensive rendering task is dynamically unloaded to the edge node to obtain the engineering information data with accelerated loading.

[0036] Further, step S2 specifically includes:

[0037] S21. Deploy a lightweight data processing module on the terminal device to parse the simplified engineering information data and collect the current terminal's computing resource status, memory usage, and network bandwidth parameters in real time. Input the above parameters into the loading delay prediction model to evaluate the estimated time required to complete the full rendering loading under the current resource constraints, and compare it with the preset loading timeout threshold to determine whether there is a risk of exceeding the threshold.

[0038] The loading delay prediction model employs a lightweight temporal prediction network trained on historical loading records. This network uses multi-dimensional state parameters of the terminal device (CPU utilization, available memory, network bandwidth fluctuation coefficient) and feature parameters of the data to be loaded (data volume, geometric complexity, texture density) as input features. After input layer normalization, these features are fed into a multilayer perceptron structure containing two hidden layers. Each hidden layer includes 32 neurons. Through layer-by-layer forward propagation calculation, the output layer (linear activation) outputs continuous estimated loading time values. The model establishes differentiated prediction parameter sets for different terminal types (mobile tablets, on-site industrial control computers, remote command terminals) in electromechanical installation construction scenarios and supports incremental fine-tuning based on actual loading results during construction to continuously improve prediction accuracy. When the estimated loading time exceeds a preset threshold, the model simultaneously outputs the over-threshold confidence level and risk level, providing a quantitative basis for triggering subsequent edge-end collaboration mechanisms.

[0039] S22. If the loading delay prediction model determines that there is a risk of exceeding the threshold, the edge collaboration mechanism is triggered. Based on the spatial hierarchy and task dependency relationship of the electromechanical installation model, the computationally intensive rendering task is divided into independently executable sub-task units. The optimal edge node is dynamically selected through the edge node selection strategy, and the sub-task units and their dependent geometric data are unloaded to the edge node through the high-bandwidth channel for parallel rendering calculation.

[0040] The task dependencies are based on the BIM tree structure of the electromechanical installation model. A directed acyclic graph is constructed according to the construction area division (e.g., floors, fire compartments), system type (e.g., water supply and drainage, HVAC, electrical), and the degree of spatial association of components. Nodes in the graph represent independently renderable data blocks, and directed edges represent the dependency constraints between rendering results. The edge node selection strategy comprehensively considers the network latency between nodes and terminals, the current computing load of edge nodes, the hit rate of cached data stored in nodes, and the geographical proximity of nodes to the construction site. A multi-objective optimization algorithm is used to dynamically calculate the comprehensive score of each candidate node, and the node with the best score is selected as the task unloading target. The high-bandwidth channel adopts a WebRTC-based data transmission protocol. During transmission, geometric data is compressed in blocks and piped, and the sending window size is automatically adjusted when the network fluctuates to ensure the timeliness of task unloading.

[0041] S23. The terminal device receives the rendering results returned by the edge node and merges them with the locally loaded engineering information data to form a complete accelerated loading data structure. The data verification tool is used to check the geometric integrity, rendering consistency and timestamp alignment of the merged data. If data abnormality or missing data is found, the edge node incremental retransmission mechanism is triggered to make local corrections. Finally, the accelerated loading engineering information data that meets the real-time interaction requirements of construction simulation and collision detection is output.

[0042] The data fusion process is based on the spatial index of the electromechanical installation model. The rendering results returned by the edge nodes are aligned with the local data frame according to the component ID and spatial coordinate position. For overlapping areas, the latest rendering data is retained according to the timestamp priority principle, and for boundary connection areas, a geometric smoothing interpolation algorithm is used to eliminate splicing gaps. The data verification tool has three sets of lightweight verification operators: the geometric integrity operator identifies data corruption by detecting normal vector consistency and vertex closure; the rendering consistency operator identifies rendering anomalies by comparing pixel differences between adjacent frames; and the timestamp alignment operator identifies data expiration by checking the difference between the data generation time and the terminal reception time. The incremental retransmission mechanism only initiates retransmission requests for specific data blocks that fail verification, rather than reloading the entire task unit. During the retransmission process, breakpoint resumption and redundant coding techniques are used to minimize network overhead while ensuring data integrity.

[0043] Specifically, by predicting the risk of exceeding the threshold through a loading delay prediction model and triggering an edge-to-edge collaboration mechanism, the rendering task is dynamically unloaded to edge nodes for parallel computing. This solves the loading delay problem caused by insufficient computing power when loading large BIM models on resource-constrained terminals, and realizes second-level loading and real-time interactive response of large electromechanical models on mobile terminals.

[0044] S3. Obtain accelerated loading engineering information data, construct a spatiotemporal priority rendering queue based on the temporal and spatial relationships of electromechanical installation procedures, prioritize the rendering of components on the critical path, and dynamically adjust the rendering accuracy to obtain progressive rendering data that meets the needs of real-time collaborative interaction.

[0045] Furthermore, step S3 specifically includes:

[0046] S31. Obtain the engineering information data after accelerated loading, extract the temporal relationship of electromechanical installation procedures and the spatial hierarchy of components from the data; establish a time priority sequence based on the construction schedule, and construct a spatial priority hierarchy by combining the physical space division of the installation area and the collision detection sensitivity, forming a comprehensive priority evaluation framework that integrates the time dimension and the spatial dimension.

[0047] The temporal relationship is based on the construction organization design of the electromechanical installation project. By analyzing the sequential logic between processes, the parallel operation relationship, and the schedule constraints on the critical path, a directed acyclic graph with process nodes as the basic unit is constructed. Each node in the graph is associated with a corresponding set of components, and the directed edges represent the completion-start dependency constraints between processes. The spatial hierarchy is based on the spatial positioning information in the building information model. The components are spatially indexed according to three levels of granularity: construction zoning, floor division, and system affiliation. The collision detection sensitivity is quantified based on the functional role of the component in the electromechanical system and the probability of spatial overlap with the surrounding civil structures and other professional pipelines. Areas with higher sensitivity receive higher weights in the spatial priority level. The comprehensive priority evaluation framework adopts a weighted fusion strategy, which integrates time priority and spatial priority through adjustable weight coefficients. The weight coefficients are dynamically adjusted according to the progress of the construction stage. In the early stage of construction, spatial priority is emphasized to avoid design conflicts, while in the peak construction period, time priority is emphasized to ensure the controllable progress of key processes.

[0048] S32. Based on the spatiotemporal priority framework, identify critical path components in the electromechanical installation process, including main pipeline intersections, core equipment installation areas, and sensitive parts of process connections; assign the highest rendering priority to critical path components, sort non-critical components in descending order of spatial hierarchy, generate a dynamically adjustable rendering queue, and update the queue order in real time according to changes in the terminal interaction perspective.

[0049] The identification of critical path components employs a two-layer judgment mechanism. The first layer, based on process critical path analysis, marks components corresponding to processes located on the critical path of construction progress as time-series critical components. The second layer, based on spatial collision risk analysis, marks components in areas where spatial overlap complexity exceeds a preset threshold as spatial critical components. When a component simultaneously meets both judgment conditions, it is assigned the highest priority identifier. The dynamically adjustable rendering queue adopts a priority queue data structure. Each element in the queue contains a component identifier, current priority score, dependency index, and rendering status marker. The queue order is dynamically reordered based on real-time changes in priority scores. Changes in the terminal interaction perspective are captured in real-time using the terminal device's gyroscope, touch interaction, and viewpoint movement trajectory. Combined with view frustum clipping technology, the set of components within the current field of view is calculated. The priority of components in the central area of ​​the field of view and the extended area in the direction of the line of sight is dynamically increased, while the priority of components that have not entered the field of view for a long time is gradually decreased, ensuring that rendering resources are always focused on the construction area currently being observed by on-site personnel.

[0050] S33. Execute component rendering according to the rendering queue order, allocate sufficient rendering resources for high-priority components to ensure geometric accuracy and texture clarity, and adopt a simplified rendering strategy for low-priority components; monitor the interaction response time and rendering frame rate in real time, and dynamically reduce the rendering accuracy of non-visible areas or non-critical components if performance fluctuations occur during collaborative interaction, and continuously output progressive rendering data that meets the needs of real-time collaborative interaction, so as to ensure that on-site personnel obtain smooth and focused visual feedback during electromechanical installation simulation and collision detection.

[0051] The simplified rendering strategy includes a three-level detail model. The highest detail level retains complete geometric contours and texture maps for displaying key components. The middle detail level uses polygon reduction and texture compression for secondary priority components. The lowest detail level replaces the original geometry with bounding boxes or simplified primitives for displaying background components. Real-time smooth switching between the three levels of models is supported. The real-time monitoring of interaction response time and rendering frame rate is obtained through the performance monitoring interface of the terminal device. When the rendering frame rate is detected to be continuously lower than a preset threshold or the interaction response delay exceeds the user's acceptable range, an adaptive degradation mechanism is triggered. First, components in the edge area of ​​the field of view are switched to a lower detail level. Second, the material and lighting calculations of non-critical components are simplified to solid color rendering. Finally, in extreme cases, the loading and rendering of components in non-visible areas are paused. The progressive rendering data adopts a block-based streaming transmission strategy. A low-precision placeholder model is output first to ensure interface responsiveness, and then it is gradually replaced with a high-precision model. This allows on-site personnel to obtain an overview of spatial relationships in the electromechanical installation simulation and collision detection process as soon as possible, and then obtain refined visual feedback in continuous interaction, effectively supporting on-site collaborative decision-making and rapid problem localization.

[0052] Specifically, by integrating the temporal and spatial relationships of electromechanical installation procedures to construct a spatiotemporal priority rendering queue, the rendering of critical path components is prioritized and the rendering accuracy is dynamically adjusted. This solves the technical problems of model loading lag and untimely presentation of key information under fluctuations in terminal performance and network status. It enables on-site personnel to obtain smooth and focused visual feedback and real-time collaborative interaction experience during electromechanical installation simulation and collision detection.

[0053] S4. For the spatial relationships in the progressive rendering data, the digital twin coordinate field information of the construction site is integrated. By comparing the design coordinates with the on-site collected coordinates in real time, if the deviation exceeds the preset tolerance threshold, the error backpropagation algorithm is used to perform local adaptive correction on the data points to obtain high-fidelity spatial relationship correction data.

[0054] Furthermore, step S4 specifically includes:

[0055] S41. Obtain real-time physical coordinate field information from the construction site digital twin system and establish a spatial reference mapping relationship with the design coordinates in the progressive rendering data; compare the spatial differences between the design coordinates and the on-site collected coordinates point by point, calculate the three-dimensional offset of each component, and if the offset exceeds the preset tolerance threshold of the electromechanical installation project, mark the point as a deviation point and record its spatial neighborhood relationship and component topology connection information.

[0056] The spatial reference mapping relationship adopts a coordinate transformation model based on common control points. By selecting control points that are uniformly distributed and stable over a long period of time on the construction site (such as structural column corner points and intersections of reference axes) as reference benchmarks, the rotation matrix and translation vector between the design coordinate system and the on-site physical coordinate system are calculated using the least squares matching algorithm. The point-by-point comparison process performs high-density sampling on key feature points such as the endpoints of electromechanical pipelines, the center of equipment installation bases, and pipe fitting connection interfaces. For curved pipe sections, a combination of equidistant sampling and curvature adaptive sampling is used to obtain the feature point set. The spatial neighborhood relationship is expressed by constructing a k-nearest neighbor graph based on Euclidean distance. Each deviation point is associated with adjacent component points within a certain radius around it. The component topology connection information is established by establishing directed associated edges based on the physical connection relationship of the electromechanical system (such as flange connections, threaded connections, and welded interfaces), forming a topological network structure that reflects the differences between rigid and flexible connections between components, providing accurate spatial constraint boundaries for subsequent error propagation.

[0057] S42. Starting from the marked deviation points, the error backpropagation algorithm is used to propagate the deviation influence weight layer by layer along the topological connection relationship of the components to generate local adaptive correction parameters. The correction parameters are applied to the deviation points and their associated neighborhood point sets. Under the premise of maintaining the geometric continuity of the components and the consistency of spatial topology, the coordinates of the points are locally fine-tuned to obtain the spatial relationship data after preliminary correction.

[0058] The error backpropagation algorithm employs a multi-layer transmission mechanism based on an elastic deformation model. It treats the connection relationships of components as elastic constraints and deviation points as points of application of external forces. The algorithm calculates the deviation attenuation coefficient layer by layer along the topological connection path. The attenuation coefficient is dynamically set according to the component type; rigid connection components (such as welded steel pipes and flange connections) exhibit smaller deviation transmission attenuation, while flexible connection components (such as rubber joints and expansion joints) exhibit larger attenuation. The local adaptive correction parameters include three components: translation correction to eliminate point offset, rotation correction to adjust component orientation deviation, and dimensional correction to compensate for manufacturing errors between the actual and designed dimensions of the component. Local fine-tuning is performed while maintaining the geometric continuity and spatial topological consistency of the components. This is achieved by constructing a local energy optimization function. This function uses the smoothness of the corrected points, the cost of deviation from the original design coordinates, and the continuous constraints at the connection points as optimization objectives. The gradient descent method is used to solve for the optimal correction parameters that minimize the energy function, ensuring that the corrected components are close to their actual on-site positions while maintaining coordinated connections with adjacent components, avoiding new spatial conflicts caused by local adjustments.

[0059] S43. Perform geometric continuity verification on the initially corrected spatial relationship data, analyze the smoothness and connection consistency between the corrected points and the surrounding uncorrected points, and perform secondary local smoothing if discontinuous distortion is detected; synchronously update the corrected spatial relationship data to the progressive rendering data framework to form a closed-loop spatial correction feedback mechanism, and finally output high-fidelity spatial relationship correction data that meets the accuracy requirements of electromechanical installation construction simulation and collision detection.

[0060] The geometric continuity verification includes three core indicators: tangent continuity verification detects the degree of angle abruptness at pipeline bends, curvature continuity verification evaluates the smooth transition state of curved pipe sections, and connection point consistency verification verifies the coordinate coincidence and axial alignment at component interfaces. The secondary local smoothing process targets the detected distortion areas, employing a Bézier curve-based local reconstruction method. It retains the corrected endpoint positions and tangent directions at both ends of the distortion area, inserts new control points to refit the curve, and achieves a smooth curvature transition while ensuring passage through the endpoints. The closed-loop spatial correction feedback mechanism establishes a bidirectional synchronous channel between the corrected spatial relationship data and the digital twin system. On one hand, it pushes the correction results to the digital twin system to update the physical coordinate field information; on the other hand, it receives new rounds of on-site coordinate data collected by the digital twin system, forming a continuously iterative correction loop. The high-fidelity spatial relationship correction data is ultimately organized in a hierarchical storage manner, retaining the mapping relationship between the original design coordinates and the corrected coordinates, and marking the correction amount and correction timestamp. This provides accurate spatial reference for quality traceability during electromechanical installation and subsequent processes (such as equipment connection and insulation construction).

[0061] Specifically, by integrating digital twin coordinate field information and using the error backpropagation algorithm to transmit deviation weights along the component topology connection relationship to generate adaptive correction parameters, the technical problem of insufficient installation accuracy caused by spatial deviation between the design model and the construction site was solved. This achieved millimeter-level high-fidelity spatial alignment of key parts, effectively eliminating the potential for conflicts and rework in subsequent processes caused by positional deviations.

[0062] S5. From the spatial relationship correction data, based on the changes in multi-party collaborative role permissions and viewpoints, incremental update segments are extracted, and differentiated data is pushed to different terminals at the construction site using a QoS-based real-time transmission protocol to obtain bandwidth-adaptive synchronous collaborative data.

[0063] Furthermore, step S5 specifically includes:

[0064] S51. Monitor the viewpoint changes and interactive behaviors of multi-party collaborative terminals at the construction site, extract incremental update fragments related to the current viewpoint from the spatial relationship correction data; combine the permission configuration of multi-party collaborative roles, perform differentiated content separation on the incremental fragments, and mark the core data (such as the spatial relationship of key components and collision warning information) and auxiliary data (such as the texture of non-critical areas and auxiliary information) separately to form a set of data fragments with permission tags and priority identifiers;

[0065] The viewpoint changes and interactive behaviors of the multi-party collaborative terminals are captured in real time through a sensor array and interactive event listener built into the terminal devices. The sensor array includes a gyroscope for sensing device attitude changes, an accelerometer for detecting movement trajectories, and a touchscreen for capturing zoom and rotation operations. The interactive event listener records user collaborative operations such as clicks, selections, and annotations on the model. The incremental update segments employ a dual filtering mechanism based on timestamps and spatial bounding boxes. First, new or changed data is filtered out based on the timestamp of the last data push. Second, data in the visible area is filtered out based on the frustum range and depth of view corresponding to the current viewpoint. Finally, a subset of data that meets both timeliness requirements and is relevant to the current viewpoint is extracted. The permission configuration is based on a role-based access control model, dividing on-site collaborative personnel into project teams. The system includes roles such as manager, technical lead, on-site installer, and quality inspector, each with a different data access level. Project managers have full data access, on-site installers can only access component data within their assigned construction area, and quality inspectors additionally have access to collision warnings and deviation data. The separation of core data and auxiliary data is based on the criticality of the data in collision detection and construction simulation. Core data includes the precise geometric pose of key components, connection topology, real-time collision warning information, and deviation correction records. Auxiliary data includes decorative textures, non-structural accessories, annotation information, and historical version data. The two types of data are marked with different priority indicators. Core data is given the highest priority to ensure transmission reliability, while auxiliary data is given a lower priority so that it can be dynamically downgraded when bandwidth is limited.

[0066] S52. Based on real-time service quality monitoring indicators, assess the current network bandwidth usage status and the transmission quality requirements of each terminal; adopt a QoS-based real-time transmission protocol, dynamically adjust the bandwidth allocation strategy according to the priority of data segments, prioritize the transmission quality of core data segments, and dynamically reduce the transmission rate of auxiliary data or delay the transmission of non-urgent data when bandwidth resources are tight, generating bandwidth-adaptive differentiated transmission queues.

[0067] The real-time service quality monitoring indicators include, but are not limited to: round-trip latency between the terminal and edge nodes, packet loss rate, available bandwidth, jitter amplitude, and terminal local buffer occupancy rate. These indicators are collected through a combination of periodically sending probe packets and passively monitoring the transmission link status. The collection period is dynamically adjusted according to network stability; the collection period is extended under stable networks to reduce overhead, and shortened under fluctuating networks to respond quickly to changes. The QoS-based real-time transmission protocol is a customized extension based on the standard WebRTC protocol framework, adding a data fragment priority marking field and a transmission scheduling strategy negotiation mechanism. This enables the protocol stack to identify the priority information of data fragments and adopt differentiated retransmission strategies and congestion control parameters based on priority differences. The dynamic bandwidth allocation strategy adopts a token bucket-based approach. The traffic shaping algorithm configures a higher token generation rate and a larger bucket capacity for core data segments, ensuring that core data still gets priority transmission when bandwidth resources are tight. A lower token generation rate is configured for auxiliary data segments, and a transmission waiting queue is set up. When bandwidth usage exceeds a preset threshold, the transmission rate of auxiliary data segments is actively reduced. If bandwidth remains tight, the transmission of auxiliary data is temporarily suspended and cached at edge nodes, resuming transmission once bandwidth is restored. The bandwidth-adaptive differentiated transmission queue adopts a multi-level queue structure. Core data segments are placed in a high-priority queue using an immediate transmission strategy, while auxiliary data segments are placed in a low-priority queue using a backlog transmission strategy. The two queues are coordinated through a weighted fair queue scheduler to ensure that the service quality of the high-priority queue is not affected by the backlog in the low-priority queue.

[0068] S53. Based on the role permissions and viewpoint range of each terminal, differentiated data fragments are pushed to the corresponding terminals at the construction site through dynamically scheduled transmission channels. After receiving the data, the terminal uses an incremental data verification mechanism to check the integrity and timing consistency of the fragments. If data is missing or timing is disordered, a retransmission request is triggered. In the end, collaborative data with real-time synchronization and bandwidth adaptation between multiple terminals is formed to support multi-party collaborative operations on site for electromechanical installation construction simulation and collision detection.

[0069] The dynamically scheduled transmission channel employs multi-path transmission technology, establishing two physical transmission paths simultaneously at the construction site using both 5G cellular networks and local Wi-Fi networks. A path quality assessment module monitors the transmission quality of each path in real time, distributing high-priority data fragments to the highest-quality path and low-priority data fragments to the next highest-quality path. When the quality of a transmission path deteriorates, traffic is automatically switched to another path. The incremental data verification mechanism uses a dual verification method based on content block hashes and sequence numbers. The data sender divides each data fragment into fixed-size content blocks, calculates the hash value of each content block, and sends it along with the data fragment. The receiver recalculates the hash value upon receiving the data and compares it to verify content integrity. Simultaneously, the data is processed via data fragments. The system uses a globally incrementing sequence number to detect out-of-order data or packet loss. The retransmission request employs a combination of selective acknowledgment and negative feedback. When the receiver detects missing data, it only sends a retransmission request for the missing content block, avoiding bandwidth waste caused by retransmitting the entire data segment. An exponential backoff strategy is used to control the sending frequency of the retransmission request, preventing request storms when packet loss is severe. Real-time synchronization between multiple terminals is achieved through a global data version number maintained by edge nodes. When any terminal performs a data modification operation (such as marking collision points or recording processing measures), the edge node increments the global version number and encapsulates the modified data into an incremental update segment. It then determines whether to push the data based on the roles, permissions, and viewpoints of other terminals, ensuring that each terminal obtains a consistent and up-to-date collaborative data view within its respective permission scope.

[0070] Specifically, incremental update segments are extracted by monitoring the viewpoint changes and interaction behaviors of multi-party collaborative terminals. Differentiated content is separated and prioritized based on role permissions. Based on real-time service quality monitoring indicators, a QoS protocol is used to dynamically adjust bandwidth allocation strategies to prioritize the transmission of core data. After multi-path transmission and incremental verification, the data is pushed to a specific target. This solves the technical problems of low synchronization efficiency of multi-party collaborative data and insufficient guarantee of core information transmission under the limited network resources at construction sites. It enables multiple terminals to obtain low-latency and highly reliable synchronized collaborative data under differentiated network conditions, supporting real-time collaborative operations of multiple parties at electromechanical installation sites.

[0071] S6. Based on the synchronous collaborative data, real-time collision monitoring is carried out at the dense area of ​​electromechanical pipelines and equipment interfaces. If a potential collision risk is detected, a graded early warning mechanism is triggered, and the early warning information is integrated with the feedback data of the on-site processing to form a closed-loop optimization path, resulting in early warning optimization data with dynamic correction instructions.

[0072] Furthermore, step S6 specifically includes:

[0073] S61. Extract spatial pose information of dense electromechanical pipeline areas and equipment interfaces from synchronous collaborative data, and use real-time collision detection algorithm to analyze the spatial interference relationship between components frame by frame; if a potential collision risk is detected, classify the risk level according to the collision type (hard collision, soft collision, insufficient clearance) and urgency level (immediate intervention, pre-process warning, observation prompt), trigger the corresponding graded warning mechanism, and generate initial warning information with risk level label;

[0074] The densely populated areas of electromechanical pipelines are dynamically identified using a spatial clustering algorithm. Pipeline components in the synchronous collaborative data are clustered based on the intersection degree and proximity density of their bounding boxes. When the pipeline density in a certain spatial area exceeds a preset threshold or the minimum spacing between pipelines is less than the construction specification requirements, that area is marked as a dense monitoring area. The real-time collision detection algorithm employs a hierarchical detection strategy based on spatial indexing. First, it quickly filters potentially colliding component pairs through spatial grid division. Then, it uses a precise detection method based on the separating axis theorem to perform geometric interference calculations on the filtered component pairs. Simultaneously, it combines the dynamic motion trajectory of the components (such as equipment hoisting paths and pipeline installation sequences) to predict temporal collisions. The collision type is determined based on the spatial relationship and functional constraints between components. Hard collision refers to the direct penetration or overlap of the geometric entities of the components. Soft collision refers to the distance between components being less than the minimum safe distance required for construction operations. Insufficient clearance refers to the situation where, although there is no direct contact between components, the reserved space cannot meet the implementation requirements of subsequent processes (such as insulation wrapping, maintenance passages). The classification of the urgency level comprehensively considers the timeliness and scope of impact of the collision risk. Immediate intervention is for situations where the current process is about to be executed and the risk is unacceptable. Pre-process warning is for risks that may occur in subsequent processes. Observation and prompting are for situations where the risk probability is low or can be avoided by adjusting the construction process. The three types of risks correspond to different warning push methods and response time limits.

[0075] S62. Push the initial early warning information to the relevant responsible terminals at the construction site, and simultaneously collect feedback data from the on-site handling process, including manual handling records, equipment adjustment parameters and environmental change information; use data fusion algorithms to perform spatiotemporal alignment and correlation analysis on the early warning information and on-site feedback, identify early warning response deviations and handling effects, form a closed-loop optimization path, and generate dynamic correction instructions for remaining risks or derivative problems.

[0076] The feedback data from the on-site processing stage is acquired through multimodal acquisition. Manual handling records combine structured forms with voice input to record personnel, time, measures, and results. Equipment adjustment parameters are read in real-time via IoT interfaces, showing the positional and attitude changes of electromechanical equipment (such as fans, pumps, and pipe supports). Environmental change information, including temperature, humidity, vibration, and other environmental parameters that may affect the spatial orientation of components, is collected through a wireless sensor network deployed in densely populated areas. The data fusion algorithm employs a time-window and spatial anchor-point-based association matching strategy to align early warning information and feedback data according to their occurrence time and spatial location, forming a complete event chain of early warning, response, and result. Analysis of the early warning... The effectiveness and deviation of the early warning response are quantitatively evaluated by considering the matching degree between response time, handling measures and early warning type, and the degree of improvement in spatial relationships after handling. The closed-loop optimization path starts with the early warning event and ends with the elimination or acceptance of risk. The path records the decision node, execution node and verification node of each early warning response. When the same area or the same type of risk is detected to recur, the path optimization algorithm automatically adjusts the early warning strategy, such as increasing the early warning level, shortening the response time, or suggesting alternative handling solutions. The dynamic correction instructions include three types of content: fine-tuning parameters of spatial pose for remaining risks, suggestions for adjusting construction technology for subsequent procedures, and prompts for key areas of concern for on-site workers. The instructions are encapsulated in a structured data format for easy parsing and execution by downstream systems.

[0077] S63. Embed dynamic correction instructions into the early warning optimization dataset and distribute them to on-site operators and managers through a collaborative synchronization mechanism to guide subsequent construction adjustments; continuously monitor collisions in areas that have been corrected, and automatically trigger a new cycle of early warning, feedback and correction if new risk points are detected or corrections are not implemented properly, thereby achieving closed-loop dynamic optimization of collision risks during electromechanical installation and ultimately outputting early warning optimization data to support precise on-site operations.

[0078] The dynamic correction instructions embedded in the early warning optimization dataset employ a versioned management mechanism. Each correction instruction is associated with its corresponding original early warning event identifier, generation timestamp, expected effect description, and verification conditions, forming a traceable correction instruction chain for easy subsequent quality traceability and effect evaluation. The collaborative synchronization mechanism divides the early warning optimization dataset into operational data for workers and supervisory data for managers. The operational data includes specific correction parameters and operational guidelines, which are intuitively displayed on the construction site via mobile terminals using augmented reality overlay. The supervisory data includes risk statistics, handling efficiency, and trend analysis, which are visualized through a management dashboard. The continuous collision tracking monitoring adopts an incremental monitoring strategy, setting a monitoring frequency attenuation factor in areas where corrections have been completed, and increasing the frequency after correction. Initially, high-frequency monitoring is used to verify the correction effect. After the risk stabilizes, the monitoring frequency is gradually reduced to save computing resources. When new spatial pose changes or abnormal environmental parameters are detected, high-frequency monitoring is automatically resumed and the risk status is reassessed. The criteria for determining inadequate correction include: the spatial pose of the component still exceeds the tolerance threshold after correction; new collision risks arise after correction; the correction operation is not completed within the specified time limit; or the correction record does not match the actual construction situation. All of the above situations trigger a new round of early warning cycle, and the risk level is increased in the new round of early warning to strengthen the intervention. The early warning optimization data is finally accumulated in the form of a knowledge graph to build a collision risk case library for electromechanical installation engineering, including risk characteristics, disposal plans, disposal effects, and experience summaries, providing a reference for risk prevention and control of similar projects in the future.

[0079] Specifically, spatial clustering and hierarchical collision detection algorithms are used to identify risks in densely packed electromechanical pipeline areas in real time. Early warnings are issued in stages based on collision type and urgency. Multimodal feedback data from the site is spatiotemporally fused to form a closed-loop optimization path, generating dynamic correction instructions and continuously tracking and monitoring until the risk is eliminated. This solves the technical problems of difficulty in closed-loop handling after collision risk is detected and lack of dynamic feedback mechanism for correction instructions. It realizes proactive early warning, precise correction and closed-loop management of collision risks, effectively reducing rework rate and supporting precise on-site operations.

[0080] S7. By optimizing data through early warning, the mixed-precision rendering module is called to adaptively refresh the pipeline equipment view. The model display details and refresh frequency are dynamically adjusted according to the terminal performance and network status to meet the requirements of fast loading and real-time multi-party collaboration, and finally optimize the collaborative display data to support the fine-grained management of the entire process of electromechanical installation construction simulation and collision detection.

[0081] Furthermore, step S7 specifically includes:

[0082] S71. Obtain early warning optimization data, and synchronously collect the current terminal's hardware performance parameters (including GPU capabilities and memory capacity) and real-time network status (including bandwidth, latency, and packet loss rate); input the above status parameters into the mixed precision rendering module, dynamically generate rendering strategies based on terminal capabilities and network conditions, and determine the precision level division and refresh frequency benchmark value of the pipeline device view in the current scene.

[0083] The hardware performance parameters are collected through the performance detection interface built into the terminal device. GPU capability assessment includes video memory capacity, number of rendering pipelines, and supported feature levels (such as shader model version and texture compression format support). Memory capacity assessment includes the ratio of available memory to total memory and memory bandwidth. The real-time network status is collected using a combination of active detection and passive monitoring. Active detection obtains approximate values ​​of end-to-end latency and available bandwidth by periodically sending lightweight probe packets. Passive monitoring analyzes changes in the congestion window and retransmission events at the transmission control protocol layer to statistically analyze packet loss rate and jitter amplitude. The mixed-precision rendering module internally constructs a rendering strategy decision-maker based on a combination of a rule engine and a lightweight neural network. The rule engine performs initial stratification (high-end terminal, mid-range terminal, low-end terminal) based on the absolute threshold of the terminal hardware performance. The lightweight neural network takes hardware performance parameters and network state parameters as input and outputs a precision level division coefficient and a refresh rate adjustment coefficient. The precision level division coefficient determines the selection threshold of the model's detail level, and the refresh rate adjustment coefficient determines the target frame rate and the triggering condition for dynamic frame reduction. The precision level division adopts a five-level detail level standard, decreasing sequentially from the highest precision primitive level model to the lowest precision primitive level model. Each level of the model has clear quantitative indicators in terms of the number of geometric faces, texture resolution, and material lighting computational complexity. The refresh rate benchmark value is determined comprehensively based on the refresh rate capability of the terminal display device and the dynamic level of the scene. A higher benchmark value is set for high refresh rate terminals in construction simulation interactive scenarios, and a lower benchmark value is set for ordinary terminals in static observation scenarios. The benchmark value serves as a reference origin for dynamic adjustment during subsequent operation.

[0084] S72. The mixed-precision rendering module is invoked to perform adaptive refresh. High-precision rendering is used in critical areas such as dense electromechanical pipelines and equipment interfaces to ensure the accuracy of collision detection, while low-precision rendering is used in non-critical areas to reduce the computational load. The terminal performance fluctuations and network status changes are monitored in real time. If resource shortage or network congestion is detected, the rendering precision of non-critical areas is automatically reduced and the refresh frequency is lowered. If sufficient resources are detected, high-precision display is restored to achieve a dynamic balance between rendering quality and interactive smoothness.

[0085] The identification of key areas employs a multi-source information fusion mechanism. First, high-attention areas are marked based on collision risk areas and deviation correction areas in the early warning optimization data. Second, active work areas are determined based on the current and next processes in the construction simulation. Third, user-focused areas are analyzed based on the terminal viewpoint and interactive heatmap. Finally, the above three types of areas are spatially superimposed and expanded to form a dynamically updated key area mask. High-precision rendering enables full geometric details, high-resolution textures, and real-time shadow and lighting calculations within key areas. In non-key areas, optimization methods such as geometric simplification, texture downsampling, static lighting baking, or complete omission of lighting calculations are used. The transition boundary between the two types of areas uses a gradual detail level blending technique to avoid visual abrupt changes. Real-time monitoring of terminal performance fluctuations and network status changes uses a sliding time window statistical method, maintaining historical sequences of performance and network indicators respectively. The difference between the current window mean and the historical window mean is compared. The system judges trend changes and triggers adjustment actions when the difference exceeds a preset threshold. The automatic reduction of rendering precision in non-critical areas adopts a step-by-step degradation strategy. The first step switches the model of non-critical areas to a lower level of detail and reduces texture resolution. The second step turns off real-time shadows and dynamic lighting in non-critical areas. The third step reduces the refresh rate of non-critical areas to half of the baseline value and uses asynchronous rendering. In extreme cases, the fourth step suspends rendering updates in non-critical areas and only retains real-time updates in critical areas. The restoration of high-precision display adopts a gradual upgrade strategy. After a period of stable operation with sufficient resources, the refresh rate, lighting calculation, and model detail level of non-critical areas are gradually restored to avoid performance fluctuations caused by frequent switching. The evaluation index of dynamic balance adopts a weighted comprehensive scoring model, which weights and integrates rendering frame rate, interaction response latency, collision detection update frequency, and user subjective experience score to drive continuous optimization of the rendering strategy with the goal of maximizing the comprehensive score.

[0086] S73. The pipeline equipment view data optimized by adaptive rendering is distributed to multiple terminals at the construction site through a collaborative synchronization mechanism to ensure that each terminal obtains a consistent interactive experience and key information visibility under different performance and network conditions. The final output optimized collaborative display data is deeply integrated with the collision detection module and construction simulation module to support the refined management and control of the entire process from pre-construction simulation and real-time monitoring during construction to post-construction review and analysis.

[0087] The collaborative synchronization mechanism adopts a publish-subscribe architecture based on edge nodes. Each terminal's rendering strategy and view state are reported to the edge nodes as subscription conditions. The edge nodes then personalize and transcode the optimized pipeline equipment view data according to these conditions, pushing data versions adapted to each terminal's capabilities to the corresponding terminals. The consistent interactive experience is achieved through a semantic consistency guarantee mechanism. Although the model precision and texture details displayed on different terminals may vary, key information (such as collision warning indicators, deviation correction instructions, and construction process status) maintains the same visual saliency across all terminals, ensuring that on-site workers and managers communicate based on the same semantic understanding during collaborative discussions. The visibility of key information employs enhanced display technology. When a terminal cannot render key areas with high precision due to performance limitations, methods such as overlaying outline highlighting, semi-transparent bounding boxes, or icon annotations are used to ensure that key information is not obscured by low-precision rendering, and temporary high-precision local loading capabilities are provided during user interaction. The deep integration with the collision detection module is reflected in… The rendering data and collision detection data share the same spatial index structure. The risk areas output by the collision detection module are directly used as input for rendering key areas. The latest spatial pose data output by the rendering module is synchronously fed back to the collision detection module for the next round of detection, forming a two-way data-driven closed loop. The deep integration with the construction simulation module is reflected in the rendering engine's built-in construction progress timeline control component, which can dynamically adjust the scope and rendering priority of key areas according to the process plan and current progress output by the construction simulation module. In the construction pre-study stage, the focus is on rendering the areas to be constructed. In the construction monitoring stage, the focus is on rendering the areas under construction. In the debriefing and analysis stage, it supports viewing the construction status and collision situation at any historical moment by time. The refined management and control of the entire process is ultimately achieved through a unified data platform, which aggregates and analyzes all interaction logs, performance data, rendering quality indicators, collision detection records and handling feedback generated by all terminals, providing construction management personnel with global situational awareness and decision support, while accumulating training data for rendering strategy optimization and performance tuning for similar projects in the future.

[0088] Specifically, by collecting terminal hardware performance parameters and real-time network status input, a mixed-precision rendering module dynamically generates rendering strategies. High-precision rendering is used for critical areas, while low-precision rendering is used for non-critical areas. Adaptive refresh is achieved by executing a step-down or progressive recovery strategy. This solves the technical problems of display lag and insufficient presentation of key information caused by differences in terminal performance and network fluctuations. It enables each terminal to obtain a smooth and focused collaborative display experience under differentiated conditions, supporting the refined management of the entire process of electromechanical installation construction simulation and collision detection.

[0089] Example 2 provides another method for implementing lightweight electromechanical installation construction simulation and clash detection based on BIM models, including the following:

[0090] A lightweight model oriented towards construction tasks is constructed. Geometric and attribute information of electromechanical installation engineering is extracted from a Building Information Modeling (BIM) database. Based on the construction tasks, the model is decomposed into several task units, each corresponding to an independent construction area or system type. A semantic feature-based progressive compression algorithm is employed to process the components within each task unit in layers: the first layer preserves the spatial contours and connectivity of components for collision detection; the second layer preserves the fine geometry of equipment interfaces and pipeline nodes for installation positioning; and the third layer aggregates and compresses non-critical auxiliary components using parametric representation. The compressed task unit model is stored in an edge node cache pool, and a lightweight model library indexed by construction tasks is established to enable on-demand loading of task-level models.

[0091] Based on process-driven construction simulation and collision prediction, this system obtains the temporal relationships of processes in the construction schedule, constructs a process dependency network diagram, and associates each process with a corresponding lightweight task unit model. During the construction simulation phase, task unit models for subsequent processes are dynamically loaded according to the current construction progress. A temporal collision detection algorithm is used to perform a pre-analysis of the spatial interference relationships between components of adjacent processes. When a potential collision risk is detected, a collision prediction report is automatically generated, marking the time node, spatial location, and associated processes of the collision. The collision area is highlighted in the 3D view, and adjustment schemes are recommended based on the collision type and impact range, including process sequence optimization, installation path adjustment, or component size correction.

[0092] The on-site installation positioning and deviation correction system integrates virtual and physical elements. Laser scanners or total stations are deployed at the construction site to collect the actual spatial coordinates of installed components in real time, which are then registered and aligned with the design coordinates of the lightweight model from step one. A deviation detection algorithm based on point cloud feature matching is used to calculate the three-dimensional offset of each component's installation point. For components with deviations exceeding the tolerance threshold, a spatial constraint optimization algorithm generates local correction parameters, and the corrected coordinates are fed back to the on-site operation terminal in real time. Simultaneously, the deviation data is updated to the digital twin platform, forming a closed-loop control link from design to construction to measurement to correction, ensuring that subsequent installed components can be positioned based on the corrected spatial reference.

[0093] The system enables multi-terminal collaborative early warning distribution and dynamic correction, establishing a role-based collaborative workspace that assigns differentiated data access and operation permissions to roles such as project managers, technical leads, on-site installers, and quality inspectors. When a collision risk or installation deviation is detected in step two or three, the system automatically generates tiered early warning information and pushes it to the corresponding role's mobile terminal based on the risk level and responsibility area. After receiving the early warning through their terminals, on-site personnel can fill in the handling measures online, upload on-site image data, and mark the correction location. The system associates and stores the feedback data with the original early warning information, forming a risk handling record chain. For complex issues requiring design changes or process adjustments, the system supports multi-terminal collaborative consultations. Participants can annotate, measure, and simulate solutions on a shared lightweight model, ultimately generating dynamic correction instructions and issuing them to the execution terminal.

[0094] The system features scene-adaptive, refined visualization, collecting hardware configuration parameters, network connection status, and user interaction behavior from each terminal to construct a terminal capability profile. Based on this profile, the system dynamically adjusts the rendering precision and data delivery strategy: for high-performance terminals in critical construction areas, it loads complete geometric details and real-time shadow effects; for medium- to low-performance terminals or in non-core areas, it employs texture compression, geometric simplification, and static lighting baking; for terminals with significant network fluctuations, it prioritizes pushing core component data within the current viewpoint and uses a progressive loading strategy to gradually supplement details. All terminals share a unified spatial coordinate system and collision detection results, ensuring consistent information understanding during on-site collaborative operations and supporting full-cycle visualized management and control from construction rehearsals and process monitoring to final delivery.

[0095] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any brief modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A lightweight electromechanical installation construction simulation and clash detection method based on BIM model, characterized in that: Includes the following steps: S1. Extract the geometric and attribute information of the electromechanical installation project from the building information model database, and use a semantically aware data compression algorithm to distinguish key components from auxiliary components for differentiated dimensionality reduction processing to obtain simplified engineering information data. S2. The simplified engineering information data is loaded on the terminal device. The loading delay prediction model is used to predict the risk of exceeding the threshold. If the threshold is exceeded, the edge-end collaboration mechanism is triggered to unload the rendering task to the edge node, thereby obtaining the engineering information data that is loaded faster. S3. Obtain accelerated loading engineering information data, construct a spatiotemporal priority rendering queue based on the temporal relationship and spatial hierarchy of electromechanical installation procedures, prioritize rendering of critical path components and dynamically adjust the accuracy to obtain progressive rendering data. S4. For the spatial relationships in the progressive rendering data, the digital twin coordinate field information of the construction site is integrated. By comparing the design coordinates with the on-site collected coordinates, if the deviation exceeds the threshold, the error backpropagation algorithm is used for local adaptive correction to obtain high-fidelity spatial relationship correction data. Step S4 specifically includes: S41. Obtain the physical coordinate field information of the construction site from the digital twin system, establish a spatial reference mapping with the design coordinates, compare and calculate the three-dimensional offset point by point, and if the offset exceeds the tolerance threshold, mark the deviation point and record the spatial neighborhood relationship and topological connection information. S42. Starting from the deviation point, the error backpropagation algorithm is used to transmit the deviation influence weight along the topological connection relationship to generate local adaptive correction parameters. These parameters are applied to the deviation point and the neighboring point set for local fine-tuning, maintaining geometric continuity and topological consistency, and obtaining the spatial relationship data after preliminary correction. The error backpropagation algorithm adopts a multi-layer transmission mechanism based on the elastic deformation model, and dynamically sets the deviation attenuation coefficient according to the differences between rigid and flexible connection components; the local adaptive correction parameters include translation correction, rotation correction, and scale correction; the optimal correction parameters are solved by constructing an energy optimization function with smoothness, deviation cost, and connection point continuity constraints as objectives. S43. Perform geometric continuity verification on the corrected data. If discontinuous distortion is detected, perform secondary local smoothing. Synchronously update the corrected data to the rendering framework to form a closed-loop correction feedback mechanism and output high-fidelity spatial relationship correction data. S5. From the spatial relationship correction data, incremental update segments are extracted based on the multi-party collaborative role permissions and viewpoint changes. Differentiated data is pushed to the terminal in a targeted manner using a QoS-based real-time transmission protocol to obtain bandwidth-adaptive synchronous collaborative data. S6. Real-time collision monitoring is performed on dense electromechanical pipeline areas based on synchronous collaborative data. If a collision risk is detected, a graded early warning mechanism is triggered, and the early warning information is integrated with the on-site feedback data to form a closed-loop optimization path, resulting in early warning optimization data with dynamic correction instructions. S7. By calling the mixed precision rendering module through early warning optimization data, adaptive refresh is performed. The model display details and refresh frequency are dynamically adjusted according to terminal performance and network status to obtain the final optimized collaborative display data.

2. The method for lightweight electromechanical installation construction simulation and collision detection based on BIM model as described in claim 1, characterized in that: Step S1 specifically includes: S11. Obtain geometric and attribute information from the building information model database. Based on functional importance, spatial positioning sensitivity and collision detection priority, divide the components into key components and auxiliary components using a semantic classifier to form an original dataset with semantic labels. Key components include main pipelines, core equipment and complex node components, while auxiliary components include supports, insulation layers and non-structural accessories. S12. For key components, a semantically aware compression algorithm is used to preserve geometric contours, spatial poses, and connection features. For auxiliary components, parameterized expression and aggregation compression are used to perform differentiated dimensionality reduction, generating simplified key component data and low-granularity auxiliary component datasets. S13. The simplified key component data and auxiliary component dataset are integrated to construct a lightweight engineering information framework. After automated verification and local completion correction, the simplified engineering information data is output.

3. The method for lightweight electromechanical installation construction simulation and collision detection based on BIM model as described in claim 1, characterized in that: Step S2 specifically includes: S21. Deploy a lightweight data processing module on the terminal device to parse and simplify the data, collect terminal computing resource status, memory usage and network bandwidth parameters, input them into the loading delay prediction model, evaluate the estimated loading time and compare it with the preset threshold to judge the risk of exceeding the threshold. S22. If there is a risk of exceeding the threshold, the edge collaboration mechanism is triggered. The rendering task is split according to the model space hierarchy and task dependency relationship. The optimal node is dynamically selected through the edge node selection strategy, and the task is unloaded for parallel rendering. S23. Receive the rendering results returned by the edge nodes and fuse them with local data. After verifying geometric integrity, rendering consistency and timestamp alignment, correct abnormal data through incremental retransmission mechanism and output accelerated loading project information data.

4. The method for lightweight electromechanical installation construction simulation and collision detection based on BIM model as described in claim 1, characterized in that: Step S3 specifically includes: S31. Obtain accelerated loading data, extract the temporal relationship and spatial hierarchy of electromechanical installation procedures, establish a time priority sequence based on the construction schedule, construct a spatial priority hierarchy by combining physical space division and collision detection sensitivity, and form a comprehensive priority evaluation framework. S32. Based on the spatiotemporal priority framework, identify critical path components and assign them the highest rendering priority. For non-critical components, sort them according to spatial hierarchy to generate a dynamic rendering queue and update the queue order in real time according to the terminal interaction perspective. S33. Execute component rendering in the order of the rendering queue, allocate sufficient resources to high-priority components, adopt a simplified rendering strategy for low-priority components, monitor performance fluctuations in real time and dynamically adjust rendering accuracy, and output progressive rendering data.

5. The method for lightweight electromechanical installation construction simulation and collision detection based on BIM model as described in claim 1, characterized in that: Step S5 specifically includes: S51. Monitor changes in viewpoint and interaction behavior of multi-party collaborative terminals, extract incremental update fragments from spatial relationship correction data, separate differentiated content according to role permissions, mark core data and auxiliary data with priority respectively, and form a data fragment set. S52. Based on real-time service quality monitoring indicators, assess network bandwidth usage status, adopt QoS-based real-time transmission protocols to dynamically adjust bandwidth allocation strategies according to priority, prioritize core data transmission, and generate bandwidth-adaptive differentiated transmission queues. S53. Based on the terminal's role permissions and viewpoint range, differentiated data is pushed to the target audience through a dynamic scheduling transmission channel. After receiving the data, the terminal corrects it through an incremental verification and retransmission mechanism and outputs real-time synchronized collaborative data for multiple terminals.

6. The method for lightweight electromechanical installation construction simulation and collision detection based on BIM model as described in claim 1, characterized in that: Step S6 specifically includes: S61. Extract spatial pose information of dense electromechanical pipeline areas and equipment interfaces from synchronous collaborative data, and perform frame-by-frame analysis using a real-time collision detection algorithm. If a collision risk is detected, classify the level according to the collision type and urgency, and trigger the graded early warning mechanism to generate initial early warning information. S62. Push the initial warning information to the responsible terminal, collect feedback data from the on-site processing stage simultaneously, use data fusion algorithms to perform spatiotemporal alignment and correlation analysis, identify the warning response deviation and processing effect to form a closed-loop optimization path, and generate dynamic correction instructions. S63. Embed the dynamic correction instructions into the early warning optimization dataset and distribute them to on-site operators. Continuously track and monitor the correction area. If a new risk is detected or the correction is not in place, a new cycle of early warning, feedback and correction will be triggered, and early warning optimization data will be output.

7. The method for lightweight electromechanical installation construction simulation and collision detection based on BIM model as described in claim 6, characterized in that: The densely packed electromechanical pipeline areas are dynamically identified using a spatial clustering algorithm, which performs clustering analysis based on the degree of intersection of bounding boxes and the density of neighboring structures. The real-time collision detection algorithm adopts a hierarchical detection strategy based on spatial indexing, which filters potential collision component pairs through spatial grid division, performs geometric interference calculations using the separating axis theorem, and combines the dynamic motion trajectory of the components to predict temporal collisions.

8. The method for lightweight electromechanical installation construction simulation and collision detection based on BIM model as described in claim 1, characterized in that: Step S7 specifically includes: S71. Obtain early warning optimization data, collect terminal hardware performance parameters and real-time network status input to the mixed precision rendering module, dynamically generate rendering strategies to determine the precision level division and refresh frequency benchmark value. S72. Call the mixed precision rendering module to perform adaptive refresh, use high precision rendering for critical areas and low precision rendering for non-critical areas, monitor resource status in real time and perform step-by-step degradation or gradual recovery to achieve a dynamic balance between rendering quality and interactive smoothness. S73. The adaptive rendering optimized view data is distributed to multiple terminals through a collaborative synchronization mechanism, and deeply integrated with the collision detection module and construction simulation module to output optimized collaborative display data.

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