BIM-based electromechanical pipeline intelligent avoidance self-adjustment mounting system

Abstract

The invention belongs to the technical field of building construction automation and intelligent engineering management, and discloses a BIM-based electromechanical pipeline intelligent avoidance self-adjustment installation system, which comprises the steps of generating a three-dimensional point cloud model based on point cloud data and UWB positioning data, further constructing a four-dimensional space-time model, synchronously generating composite conflict quantification data, and performing BIM-based electromechanical pipeline intelligent avoidance self-adjustment installation. Correlation analysis is carried out in combination with construction specifications, and a space-time coupling semantic model is generated; fusing the penetrating scanning data of the millimeter wave radar and the UWB positioning data, and constructing a two-dimensional conflict thermodynamic diagram of a space margin-time window; generating a risk level conflict list in combination with a historical case library; generating a dynamic avoidance strategy list, refining the dynamic avoidance strategy list into an avoidance path execution scheme, verifying a strategy execution effect, judging a conflict elimination state, and generating an avoidance strategy execution state report; and evaluating the effectiveness of the avoidance strategy, dynamically adjusting the priority of the strategy, synchronously updating the dynamic BIM semantic model and the two-dimensional conflict thermodynamic diagram, and forming a strategy iteration closed loop.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction automation and intelligent engineering management, and more specifically, to a BIM-based intelligent avoidance and self-adjusting installation system for electromechanical pipelines. Background Art

[0002] With the rapid development of urban underground utility corridors, the construction complexity of densely packed areas where electromechanical pipelines intersect with drainage, power, communications, and other specialized pipelines has increased significantly. BIM-based pipeline collision detection technology has become a core tool in the design phase. However, in actual construction, dynamic issues such as unforeseen misalignment of reserved casing and structural settlement deviation frequently occur. Traditional BIM technology, due to its static model characteristics, is severely disconnected from the dynamic construction scenario, making it difficult to meet the needs of real-time avoidance and adjustment, resulting in low construction efficiency and increased quality risks.

[0003] Existing electromechanical pipeline installation systems suffer from multiple interrelated flaws that hinder their effectiveness in complex construction scenarios. First, they rely on static 3D BIM models, failing to integrate construction sequences with dynamic obstacle changes. This results in numerous conflicts requiring manual intervention and delayed responses. Second, conflict detection relies solely on geometric collision analysis, ignoring the combined effects of structural settlement and construction machinery trajectories, resulting in a high rate of missed detections. Furthermore, avoidance strategy generation relies on a rigid rule base, resulting in long update cycles and an inability to adapt to dynamic scenarios such as the intersection of multi-disciplinary pipelines. Furthermore, the control accuracy of construction machinery is insufficient, falling short of high-precision process requirements and resulting in a high rework rate. Finally, the data loop is broken, with a lack of real-time coordination between sensor data, machinery position, and BIM models, making it difficult to translate experience into system intelligence. These issues collectively lead to construction efficiency losses and cost overruns, urgently requiring systematic technological breakthroughs. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides the following technical solution: a BIM-based intelligent avoidance and self-adjusting installation system for electromechanical pipelines, comprising: Dynamic 4D modeling and composite conflict perception unit: Based on point cloud data, UWB positioning data, and construction schedule, a 3D point cloud model is generated, and then a 4D space-time model is constructed. The construction schedule is simultaneously accessed to predict future pipeline path conflicts. Combined with the corridor's structural settlement data, composite conflict detection is performed on the 4D space-time model, generating composite conflict quantitative data. This data is embedded in the 4D space-time model and combined with construction specifications for correlation analysis to generate a space-time coupling semantic model. Two-dimensional conflict heat map and priority ranking unit: Based on a spatiotemporal coupling semantic model, it integrates millimeter-wave radar penetration scanning data and UWB positioning data to construct a two-dimensional conflict heat map of spatial margin and time window. Combined with the historical case library, it matches priorities and generates a risk-level conflict list. Strategy Effectiveness Evaluation and Adaptive Optimization Unit: Based on the risk level conflict list and construction specifications, combined with the historical case library, a dynamic avoidance strategy list is generated and refined into an avoidance path execution plan. The strategy execution effect is verified through UWB positioning data, and the conflict elimination status is determined by comparing the four-dimensional space-time model. The avoidance strategy execution status report is generated; Closed-loop iteration and strategy self-evolution unit: Based on execution status reports and historical data from multiple projects, it evaluates the effectiveness of avoidance strategies and dynamically adjusts strategy priorities. It also synchronously updates the dynamic BIM semantic model and two-dimensional conflict heat map to form a closed-loop strategy iteration.

[0005] Furthermore, the three-dimensional point cloud model is generated by: Deploy mobile scanning equipment to collect high-precision point cloud data from the pipeline corridor, obtain point cloud data, and simultaneously use the UWB positioning module to obtain real-time posture data of the scanning equipment and construction machinery to generate UWB positioning data; Perform real-time registration of point cloud data to generate high-precision 3D point cloud models; Obtain the construction schedule and align the construction schedule with the timestamp of the 3D point cloud model to form a unified timeline; De-noising, downsampling and semantic segmentation are performed on the point cloud data, unforeseen obstacles are marked, and point cloud data with semantic labels is generated; Combined with the construction schedule, the construction period is set, the laying path and construction progress of each pipeline in the next construction period are marked in the point cloud data, and a processed 3D point cloud model is generated.

[0006] Furthermore, the generation method of the composite conflict quantization data includes: Based on the processed 3D point cloud model and the unified time axis, a timestamp is assigned to each point cloud data to construct a 4D space-time model, including spatial and temporal dimensions; Dynamically superimpose UWB positioning data into the four-dimensional space-time model to update the machine position and construction status in real time; Set conflict detection thresholds, including settlement thresholds and minimum safety distance thresholds; The FBG sensor is used to monitor the structural settlement data of the pipeline corridor in real time, generating a structural deformation layer that is fused into a four-dimensional space-time model. If the structural settlement data is greater than the settlement threshold, it is determined that there is structural settlement. Conduct composite conflict detection on the four-dimensional space-time model, including spatial conflict detection, temporal conflict detection and structural composite conflict detection; Spatial conflict detection is to detect the spatial intersection relationship between semantic labels and pipeline laying paths. If there are unforeseen obstacles, it is marked as a spatial conflict. Calculate the distance between pipelines and combine it with the minimum safe distance threshold. If the pipeline distance is less than the minimum safe distance threshold, it is determined that there is insufficient space margin, marked as a space conflict, and the insufficient space margin value is recorded. Time conflict detection is to predict potential conflicts in pipeline laying paths within the future construction period based on a unified time axis and mark them as time conflicts; Structural composite conflict detection is to associate spatial conflict and structural settlement data, identify areas where both spatial conflict and structural settlement exist, and mark them as composite conflicts; The labels and corresponding values ​​of spatial conflict, insufficient spatial margin, temporal conflict and composite conflict are integrated as composite conflict quantitative data.

[0007] Furthermore, the generation method of the space-time coupling model includes: Embed the composite conflict quantification data into a four-dimensional space-time model to generate a dynamic BIM semantic model; Identify and extract entities from the dynamic BIM semantic model, define relationships between entities based on construction specifications and historical data, and combine entities and relationships to generate a knowledge graph with a triple structure; Associate the semantic labels of the dynamic BIM semantic model with the triples of the knowledge graph, expand the knowledge graph through rule reasoning, and generate implicit relationships; Combine the dynamic BIM semantic model with the knowledge graph to generate a spatiotemporal coupling model.

[0008] Furthermore, the generation method of the dual-dimensional conflict heat map includes: Millimeter-wave radar collects penetrating scanning data within the tunnel, extracts obstacle data, retains unforeseen obstacles in the extracted obstacle data, converts the millimeter-wave radar coordinate system into a unified coordinate system, and then locates unforeseen obstacles in real time; Align the positions of unforeseen obstacles detected by the millimeter-wave radar with the point cloud data of the spatiotemporal coupling model to generate a dynamic obstacle layer; Bind UWB positioning data to the time axis of the spatiotemporal coupling model to predict potential intersections between machine trajectories and pipeline laying paths, and generate a machine-obstacle conflict layer based on the locations of unforeseen obstacles. Combined with the quantitative data of composite conflicts, the conflict areas and conflict area levels are marked; The areas with complex conflicts are marked as high-conflict areas; the areas with insufficient space margin are marked as medium-conflict areas; the areas with complex conflicts and insufficient space margin are marked as low-conflict areas; The dynamic update mechanism of the conflict area is set to trigger the automatic refresh of the conflict area when the UWB positioning module detects a change in the trajectory of the construction machinery; Construct a two-dimensional conflict heat map. Overlay the dynamic obstacle layer and the machinery-obstacle conflict layer into the two-dimensional conflict heat map. Use pipeline spacing as the horizontal axis and mark the minimum safe spacing threshold and actual spacing. With the construction period as the vertical axis, set fixed time windows and mark the conflict prediction results of each fixed time window within a future construction period.

[0009] Furthermore, the risk level conflict list is generated in the following manner: Based on the dual-dimensional conflict heat map and combined with the historical case library, the currently detected conflict areas and types in the dual-dimensional conflict heat map are matched with the historical case scenarios in the historical case library to determine the priority and the corresponding priority strategy; Assess the difficulty of conflict resolution through expert experience and generate technical feasibility; integrate conflict area levels, priorities, priority strategies, and technical feasibility to generate a risk-level conflict list; According to the preset response time mechanism, the response time for different conflict area levels is set, and the risk level conflict list is updated in real time according to the response time adjustment mechanism.

[0010] Furthermore, the method of generating a dynamic avoidance strategy list and refining it into an avoidance path execution plan includes: Based on the risk level conflict list, define targeted avoidance strategies according to conflict type; For spatial conflicts and insufficient space margins, the strategy is to adjust the trajectory of construction machinery to avoid detours. For time conflicts, the strategy is defined as optimizing the construction sequence; For complex conflicts, the strategy is to adjust the trajectory of construction machinery to avoid them and initiate reinforcement plans. Drive strategy execution based on priority definitions; For high-conflict areas, enforce shutdown operations and immediately stop construction; For medium conflict areas, adjust the working order of construction machinery and delay the cutting time for conflicting pipelines; For low-level conflict areas, record the conflict but do not intervene for now; Combining construction specifications with a historical case library, a specific avoidance path plan is generated based on defined targeted avoidance strategies, including: adjusting pipeline intersection locations according to construction specifications and recommending strategies based on matching historical case scenarios in the historical case library; Integrate conflict type, priority, avoidance strategy and execution time to generate a dynamic avoidance strategy list; Based on the dynamic avoidance strategy list, combined with the conflict type and construction specifications, the specific avoidance path plan is refined into specific parameters to generate an avoidance path execution plan, including the mechanical trajectory adjustment path, pipeline layout optimization plan and reinforcement plan.

[0011] Furthermore, the avoidance strategy execution status report is generated in the following manner: The dynamic avoidance strategy list and avoidance path execution plan are transmitted to the control system of the construction machinery in real time to automatically adjust the machine posture; The UWB positioning module updates the location of construction machinery in real time to verify the effectiveness of the avoidance strategy. Compare the four-dimensional space-time models before and after the strategy is executed to determine whether the conflict has been resolved; If the conflict is not resolved, the emergency response mechanism is triggered, the alarm mechanism is triggered, and alternative strategies are recommended based on the historical case library, and alternative plans are generated and sent to the control system of the construction machinery; Integrate the actual trajectory of construction machinery, conflict resolution status and emergency response records to generate an avoidance strategy execution status report.

[0012] Furthermore, the method of evaluating the effectiveness of the avoidance strategy and dynamically adjusting the strategy priority includes: After executing the avoidance strategy, the construction machinery execution data is collected. Based on the avoidance strategy execution status report, the number of successful conflict eliminations and the total number of conflict eliminations are extracted, and the conflict elimination success rate is calculated. The strategy effectiveness score is calculated by combining the conflict elimination success rate and the construction machinery execution data. Set a score threshold interval and prioritize the avoidance strategy whose effectiveness score is greater than the maximum value of the score threshold interval; The avoidance strategy with an effectiveness score less than the minimum of the score threshold interval is prioritized lower; For avoidance strategies whose effectiveness score is less than or equal to the maximum value of the score threshold interval and greater than or equal to the minimum value of the score threshold interval, the priority remains unchanged; Based on the execution data of construction machinery, if the execution data of construction machinery is greater than the corresponding threshold, the threshold self-calibration mechanism is triggered to automatically tighten the conflict detection threshold; If the conflict resolution success rate of a single avoidance strategy is lower than the preset success rate threshold, it will be automatically marked as a pending avoidance strategy and suspended; If a new conflict type is detected and there is no corresponding historical case scenario, a new historical case scenario is generated based on the pattern of the conflict type.

[0013] Furthermore, the method of synchronously updating the dynamic BIM semantic model and the two-dimensional conflict heat map to form a closed loop of strategy iteration includes: Add semantic tags based on new conflict types to expand the dynamic BIM semantic model; Update the conflict area of ​​the two-dimensional conflict heat map according to the adjusted conflict detection threshold; According to the avoidance strategy with improved priority, the highest priority avoidance strategy is embedded into the knowledge graph triple to expand the knowledge graph; According to the suspended avoidance strategy to be verified, the logic of recommending alternative solutions is adjusted to form a closed-loop optimization.

[0014] The technical effects and advantages of the BIM-based intelligent avoidance and self-adjusting installation system for electromechanical pipelines of the present invention are as follows: By building a technical chain of dynamic perception, intelligent decision-making, and closed-loop optimization, this invention significantly improves the efficiency and quality controllability of multi-disciplinary pipeline construction in underground pipe corridors. Its technical effects and advantages are reflected in the following aspects: First, by collecting point cloud data and construction machinery trajectories in real time, integrating the construction schedule to build a four-dimensional spatiotemporal model, the system dynamically perceives unforeseen obstacles (such as casing misalignment) and structural settlement deviations, breaking through the limitations of traditional BIM static models and providing accurate spatiotemporal semantic support for conflict detection. Secondly, by generating and prioritizing a two-dimensional conflict heat map (spatial margin-temporal window), combined with millimeter-wave radar penetration scanning and UWB positioning data, complex conflicts (such as the spatiotemporal coupling of machinery trajectories and submerged pipelines) can be accurately identified, significantly improving conflict classification and risk assessment capabilities in complex scenarios. Then, a dynamic avoidance strategy list is generated, which is refined into parameterized instructions such as machine trajectory adjustment path and cutting angle compensation. The feasibility of the plan is verified with the help of digital twin pre-tests to ensure the scientific nature of the strategy and construction safety. Next, through the closed-loop feedback mechanism of UWB positioning and mechanical control systems, construction errors are monitored in real time and emergency responses are triggered. Combined with laser tracking and 3D vision to verify the conflict elimination status, the error accumulation caused by manual intervention is effectively reduced. Finally, a strategy self-evolution system is built based on execution logs and multi-project data to dynamically optimize conflict detection thresholds and avoidance priority rules, forming a closed-loop link from perception, decision-making, execution to iteration, enabling the system to have continuous learning and dynamic adaptation capabilities.

[0015] This invention systematically solves the core pain points of traditional construction, such as model rigidity, response lag, and experience dependence, and realizes intelligent, high-precision and adaptive optimization of electromechanical pipeline installation in complex scenarios of underground pipeline corridors. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of a BIM-based electromechanical pipeline intelligent avoidance and self-adjusting installation system of the present invention; Figure 2 A schematic diagram of a process flow for generating composite conflict quantification data for a BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation system according to the present invention; Figure 3 This is a schematic diagram of a BIM-based intelligent avoidance and self-adjusting installation method for electromechanical pipelines of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] Embodiment 1 of the present invention See also Figure 1 and Figure 2 As shown, the BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation system described in this embodiment includes: Dynamic 4D modeling and composite conflict perception unit: Based on point cloud data, UWB positioning data, and construction schedule, a 3D point cloud model is generated, and then a 4D space-time model is constructed. The construction schedule is simultaneously accessed to predict future pipeline path conflicts. Combined with the corridor's structural settlement data, composite conflict detection is performed on the 4D space-time model, generating composite conflict quantitative data. This data is embedded in the 4D space-time model and combined with construction specifications for correlation analysis to generate a space-time coupling semantic model. Two-dimensional conflict heat map and priority ranking unit: Based on a spatiotemporal coupling semantic model, it integrates millimeter-wave radar penetration scanning data and UWB positioning data to construct a two-dimensional conflict heat map of spatial margin and time window. Combined with the historical case library, it matches priorities and generates a risk-level conflict list. Strategy Effectiveness Evaluation and Adaptive Optimization Unit: Based on the risk level conflict list and construction specifications, combined with the historical case library, a dynamic avoidance strategy list is generated and refined into an avoidance path execution plan. The strategy execution effect is verified through UWB positioning data, and the conflict elimination status is determined by comparing the four-dimensional space-time model. The avoidance strategy execution status report is generated; Closed-loop iteration and strategy self-evolution unit: Based on execution status reports and historical data from multiple projects, it evaluates the effectiveness of avoidance strategies and dynamically adjusts strategy priorities. It also synchronously updates the dynamic BIM semantic model and two-dimensional conflict heat map to form a closed-loop strategy iteration.

[0019] Deploy mobile scanning equipment (such as mobile scanning robots) to collect high-precision point cloud data from the pipeline corridor. Simultaneously, the UWB positioning module is used to obtain real-time posture data of the scanning equipment and construction machinery to obtain UWB positioning data. Use SLAM algorithms to perform real-time registration of point cloud data, eliminate coordinate offsets caused by device movement, and generate high-precision three-dimensional point cloud models; Obtain a construction schedule (e.g., pipeline laying schedule) and align the construction schedule with the timestamps of the point cloud data in the 3D point cloud model (using a timestamp matching algorithm such as a Kalman filter) to form a unified timeline (e.g., "T+0 to T+24 hours"), providing time dimension support for subsequent conflict prediction. The point cloud data is denoised, downsampled, and semantically segmented (e.g., using a YOLOv8+Transformer model). Unforeseen obstacles (e.g., "dynamic obstacle_misaligned" casing) are annotated and added as semantic tags to the point cloud data, generating semantically labeled point cloud data. Furthermore, the construction schedule is combined with a set construction period (e.g., 24 hours as a construction period). The pipeline laying paths and construction progress for the next construction period are marked in the point cloud data (e.g., "drainage pipes will intersect at T+8 hours") to generate a processed 3D point cloud model. Based on the binding of the processed 3D point cloud model to a unified timeline, a timestamp is assigned to each point cloud data point to construct a 4D spatiotemporal model (e.g., dynamic pipeline trajectory from T+0 to T+24 hours). This model includes the spatial dimension (3D point cloud model and construction machinery trajectory) and the temporal dimension (construction status at each time point in a fixed timeline (e.g., drainage pipe laying at T+8 hours)). For example, if the construction plan requires laying a drainage pipe at T+8 hours, the 4D model will mark that time point and associate the drainage pipe path in the point cloud data; The UWB positioning data is then dynamically superimposed onto the four-dimensional space-time model to provide real-time updates of the machine position and construction status (e.g., "hydraulic cutting machine is operating at position T+6 hours"). For example, if a hydraulic cutting machine starts operating ahead of time (T+5 hours) due to construction adjustments, the model will automatically correct its trajectory and trigger relevant conflict detection; Set conflict detection thresholds, including settlement thresholds and minimum safety distance thresholds; The FBG sensor is used to monitor the structural settlement data of the pipeline corridor in real time. If the structural settlement data is greater than the settlement threshold, it is determined that there is structural settlement (for example, "the verticality error of a certain support section is greater than 0.8°"), and a structural deformation layer is generated and fused into the four-dimensional space-time model; For example: If a support in a certain area has a verticality error of 0.9° due to foundation settlement, the model will be marked as an "abnormal structural settlement area" and associated with the relevant pipeline path; The final four-dimensional spatiotemporal model contains dynamic trajectories, semantic labels, point cloud coordinates and timestamps; Conduct composite conflict detection on the four-dimensional space-time model, including spatial conflict detection, temporal conflict detection and structural composite conflict detection; Spatial conflict detection detects the spatial intersection between semantic labels and pipeline laying paths. If there are unforeseen obstacles (such as a 3cm axis offset due to construction errors), it is marked as a spatial conflict. For example, if a section of drainage pipe has an axis offset of 3cm due to construction errors, the model will be marked as a "spatial conflict" and trigger an alert. The distance between pipelines is calculated using the Euclidean distance formula and combined with the minimum safe distance threshold (e.g., "multi-pipeline spacing ≥ 15 cm"). If the pipeline spacing is less than the minimum safe distance threshold, it is determined to be insufficient space margin, marked as a space conflict, and the insufficient space margin value (e.g., 4.8 cm) is recorded. Example: When the distance between the communication cable and the fire protection pipe is less than 15 cm, it is marked as "Insufficient space margin"; Time conflict detection is to predict potential conflicts in pipeline laying paths within the future construction period based on a unified timeline (i.e., time overlap, such as "the drainage pipe and the power pipe intersect at T+8 hours"). If a potential conflict exists, it is marked as a time conflict. For example, if the drainage pipe and the power pipe intersect at T+8 hours, the model will mark it as a "time conflict" and indicate the conflict time window (e.g., T+7.5 to T+8.5 hours); Structural composite conflict detection involves correlating and analyzing spatial conflicts with structural settlement data, identifying areas with both spatial conflicts and structural settlements, and marking them as composite conflicts (e.g., "casing misalignment + settlement composite conflict"). Integrate the markers of spatial conflict, insufficient spatial margin, temporal conflict, and composite conflict, and combine them with specific values ​​(such as insufficient spatial margin value and structural settlement data) as quantitative data for composite conflict; Embed the composite conflict quantification data into a four-dimensional space-time model to generate a dynamic BIM semantic model (including time-space coupling features and supporting real-time updates); It should be noted that the method for generating dynamic BIM semantic models is to use DynamicCity's 4D to 2D feature dimensionality reduction technology, use variational autoencoders (VAE) to compress four-dimensional scenes, and combine them with the diffusion model (DiT) to generate an efficient dynamic model as the dynamic BIM semantic model; Example: Use HexPlane to characterize the dynamic deformation field, predict the Gaussian kernel displacement, rotation, and scale, and drive the generation of a dynamic model for each frame; The dynamic effects of the dynamic BIM semantic model are: An incremental update strategy is designed for the dynamic BIM semantic model to refresh only the affected areas (such as the area where the mechanical trajectory changes), reducing the computational load; Dynamically modify the semantic labels of the dynamic BIM semantic model. When the trajectory of construction machinery changes (e.g., "the hydraulic cutter will start work at T+5 hours ahead of schedule"), the dynamic BIM semantic model is triggered to automatically modify the conflict prediction results (e.g., updating "the drainage pipe and the power pipe will intersect at T+8 hours" to "they will intersect at T+6 hours"). For example, if a section of support has a verticality error of 0.9° due to settlement, the dynamic BIM semantic model will automatically associate the pipeline path in this area and mark it as a "structural settlement abnormality area"; Identify and extract entities from dynamic BIM models (using named entity recognition (NER) to extract entities such as "support," "pipeline," and "hydraulic cutter"), and define relationships between entities based on construction specifications and historical data (using rule templates or deep learning models (such as BERT) to extract relationships such as "support → load → pipeline," "hydraulic cutter → operation at → T+6 hours"); It should be noted that construction specifications refer to a series of standards, rules, and guidelines that must be followed during the construction process. These specifications are usually formulated by industry organizations, government agencies, or professional associations to ensure construction quality and safety. Specifically for pipeline construction, construction specifications may include but are not limited to: Standard spacing, depth and safe distance between pipelines and other structural elements; Regulations on the use of specific materials, such as the type, specifications and connection methods of pipes; Technical requirements and implementation standards for different types of mechanical operations (such as cutting and welding); Safety measures required, such as the use of personal protective equipment and emergency response procedures; Historical data refers to a collection of information collected from multiple completed or ongoing similar engineering projects. This type of data includes but is not limited to the following: Detailed design drawings, construction plans and their execution status of previous projects; Records of problems encountered during construction, such as location and type of conflicts and their resolution; Evaluation of the effectiveness of various avoidance strategies, including which ones have proven effective and which have been less successful; data analysis results on cost control, schedule scheduling, etc., help predict potential risk points for future projects; A knowledge graph that combines entities and relations into a triple structure (subject-predicate-object), for example: (bracket A, settlement, 0.9°); (hydraulic cutting machine, operation time, T+6 hours); (casing, misalignment, 3cm); Storing triples in a graph database (such as Neo4j) or RDF format to support efficient query and reasoning; Associating semantic labels in the dynamic BIM semantic model with triples in the knowledge graph, and then expanding the knowledge graph through rule reasoning (or logical reasoning) (e.g., "settlement > 0.8° → reinforcement required") to generate implicit relationships (e.g., "bracket A → reinforcement required → T+6 hours"); Combine the dynamic BIM semantic model with the knowledge graph to generate a spatiotemporal coupling model; It should be noted that the characteristics of the spatiotemporal coupling model: support conflict prediction (e.g., “high-risk conflict at T+6 hours”), root cause analysis (e.g., “casing misalignment + settlement → insufficient bearing capacity”); Provide semantic input for subsequent avoidance decisions (e.g., conflict location, risk level, and repair strategy); Millimeter-wave radar collects penetrating scanning data within the tunnel and extracts obstacle data, including quantity, distance, and speed. Unforeseen obstacles are retained in the extracted obstacle data. The millimeter-wave radar coordinate system is converted to a unified coordinate system using a spatial synchronization algorithm, allowing for real-time positioning of unforeseen obstacles. Align the obstacle positions detected by the millimeter-wave radar with the point cloud data in the spatiotemporal coupling model to generate a dynamic obstacle layer; Bind UWB positioning data to the time axis of the spatiotemporal coupling model to predict potential intersections between machine trajectories and pipeline laying paths (e.g., where the hydraulic cutting machine operating area intersects with the power pipeline at T+6 hours). Combined with obstacle locations detected by millimeter-wave radar, a machine-obstacle conflict layer is generated. Then, combined with the quantitative data of complex conflicts in the spatiotemporal coupling model, the conflict areas and their levels are marked (the level of the conflict area can be represented by a color gradient, such as green-yellow-red). Areas with complex conflicts are marked as high-conflict areas (such as red areas), areas with insufficient spatial margin are marked as medium-conflict areas (such as yellow areas), and areas with complex conflicts and insufficient spatial margin are marked as low-conflict areas (such as green areas). The dynamic update mechanism of the conflict area is set to trigger the automatic refresh of the conflict area when the UWB positioning module detects a change in the trajectory of the construction machinery; Construct a two-dimensional conflict heat map by overlaying the dynamic obstacle layer and the machinery-obstacle conflict layer into the two-dimensional conflict heat map. Use pipeline spacing as the horizontal axis, mark the minimum safe spacing threshold and actual spacing, and use a color gradient to indicate the conflict area level. With the construction period as the vertical axis, set fixed time windows and mark the conflict prediction results from the first fixed time window to the last fixed time window in the future construction period (for example, "the hydraulic cutting machine operation area intersects with the pipeline at T+6 hours"), and mark the time window where the conflict occurs with a timestamp; It should be noted that the fusion of millimeter-wave radar and UWB data ensures real-time synchronization of the thermal map’s spatial dimension (obstacle location) and temporal dimension (machine trajectory); Composite conflict quantitative data in the spatiotemporal coupling model (e.g., “settlement 0.9°”) directly drives the generation of red areas in the heat map, and risk labels are expanded through rule-based reasoning (e.g., “settlement > 0.8° → reinforcement required”). The role of the spatiotemporal coupling semantic model: Provide quantitative data on composite conflicts (e.g., "casing misalignment + settlement" and "insufficient pipeline spacing") as the basis for judging red / yellow areas in the thermal map; By associating obstacles detected by millimeter-wave radar with structural settlement data through knowledge graph triples (e.g., "bracket A → settlement → 0.9°"), semantic labeling of conflict areas is achieved. The role of millimeter wave radar data: Real-time detection of unforeseen obstacles (e.g., “casing misalignment”) to supplement the missing semantically labeled point cloud data in the spatiotemporal coupling model; Provide millimeter-level obstacle positioning through high resolution, ensuring the accuracy of the thermal map's spatial dimension; The role of UWB posture data: High-precision positioning of construction machinery trajectories to predict conflicts within future time windows (e.g., "the work area will intersect with the pipeline in T+6 hours"); Dynamically update the machinery-obstacle conflict layer, triggering real-time refresh of the heat map (e.g. yellow → red); In general, the spatiotemporal coupling model: provides conflict type determination and semantic association (e.g., “casing misplacement + settlement”); Millimeter-wave radar: Supplements dynamic obstacle detection and high-precision spatial positioning; UWB data: predicts machine trajectories and conflict time windows, driving the time dimension update of the heat map; After the three are integrated, the dual-dimensional conflict heat map can not only reflect current spatial conflicts (such as red areas) but also predict future temporal conflicts (such as yellow areas), providing an accurate decision-making basis for subsequent priority sorting. Perform lightweight encoding (such as ROI extraction) on the two-dimensional conflict heat map and transmit it to the central decision maker in real time via the 5G-TSN network; Leveraging 5G-TSN's time-sensitive networking (TSN) to ensure dual-dimensional conflict heat map update latency is less than 10ms (e.g., changes in machine trajectory are synchronized to the central decision maker within 10ms). After receiving the two-dimensional conflict heat map, the central decision maker combines it with the knowledge graph triples (such as "bracket A → settlement → 0.9°") to construct a global conflict topology: Mark high-risk nodes (such as the red area corresponding to "Stand A"); Associate conflicting paths (e.g., “hydraulic cutting machine → T+5 hours operation area → pipeline intersection”); Set up a dynamic feedback mechanism: if an edge node (such as an on-site sensor) detects a new obstacle (such as an unforeseen casing), it triggers a local refresh of the two-dimensional conflict heat map and synchronizes it to the central decision maker; Based on the two-dimensional conflict heat map and combined with a historical case library (which stores priority processing strategies for similar scenarios in the past (e.g., "power pipelines take precedence over sewer pipes")), the currently detected conflict areas (e.g., "the hydraulic cutting machine operation area intersects with power pipelines at T+6 hours") and types in the two-dimensional conflict heat map are matched with historical case scenarios in the historical case library to determine the priority and corresponding priority strategy (this can be achieved through a branch demand prioritization method, with conflicts in high-conflict areas as the first-level priority, conflicts in medium-conflict areas as the second-level priority, and conflicts in low-conflict areas as the third-level priority). A dynamic adjustment mechanism is set up. If a new conflict is detected (such as a new red zone added in T+4 hours), similar scenario processing rules in the historical case library are called to adjust the conflict level. Expert experience is used to assess the difficulty of resolving conflicts and generate technical feasibility (e.g., comparing the difficulty of resolving the conflict of "casing misalignment requiring reinforcement" with the difficulty of resolving the "pipeline spacing adjustment" conflict). This is combined with conflict area levels, priorities, priority strategies, and technical feasibility to generate a risk-level conflict list. According to the preset response time mechanism, set the response time for different conflict area levels, add them to the risk level conflict list, and update the risk level conflict list in real time according to the response time adjustment mechanism; Example of the resulting risk level conflict list: Advanced: T+6 hours Power pipeline intersection → Immediately adjust the hydraulic cutting machine trajectory (30-minute response); Medium: T+5 hours, insufficient drainage pipe spacing → arrange for inspection (1 hour response); The response time mechanism is the response time required to resolve different conflict area levels set through the SMART target principle; For example: a high-conflict area requires a response within 30 minutes (e.g., "immediately adjust the trajectory of the hydraulic cutter"); Moderate conflict areas are allowed to be handled within 1 hour (e.g., "arrange personnel to check the spacing between drainage pipes"); Low-conflict areas are allowed to be monitored within 2 hours (e.g. recording conflict status); The response time adjustment mechanism is that if a new conflict area is detected (such as a new red area added in T+4 hours), the conflict will be moved up the risk level conflict list in real time and an alarm of the corresponding level will be triggered; A dual-dimensional conflict heat map is synchronized via the 5G-TSN network. If a new conflict is detected (e.g., a hydraulic cutter operating ahead of schedule at T+4 hours), the risk level conflict list is automatically refreshed. Combine the priority strategies for repairs in the historical case library (e.g., “prioritized reinforcement of power pipelines”) to optimize conflict resolution logic (achievable through iteration of the Logit-QRE model); Provides manual adjustment functions (e.g., project managers can temporarily increase the priority of a conflict) to ensure flexibility; Based on the risk level conflict list, define targeted avoidance strategies according to the conflict type (spatial / temporal / complex); For spatial conflicts and insufficient space margins, a strategy is defined to adjust the trajectory of construction machinery to avoid them (for example, a hydraulic cutter avoids high-conflict areas). The RVO2 algorithm is used to predict potential collisions between machines and calculate the obstacle avoidance speed for subsequent avoidance. For time conflicts (such as "pipeline intersection at T+6 hours"), we define a strategy to optimize the construction sequence (for example, advancing the laying of drainage pipes to T+4 hours). We also use the A* and DWA fusion algorithm to plan the optimal path, balancing global efficiency and local obstacle avoidance. For complex conflicts, the strategy is to adjust the trajectory of construction machinery to avoid them and simultaneously initiate reinforcement plans (such as deploying temporary supports). Priorities are recommended based on a historical case library (such as "prioritizing the reinforcement of power pipelines"). Priority-driven strategy generation includes mandatory shutdowns for high-conflict areas, immediately halting construction machinery (e.g., pausing hydraulic cutters). UWB high-precision positioning monitors machinery position in real time to ensure avoidance path execution. For moderate conflict areas, adjust the working order of construction machinery and delay the cutting time of conflicting pipelines. Apply dynamic window methods (such as DWA) to calculate the feasible speed range to avoid path conflicts. For low-level conflict areas, record the conflict but do not intervene for now, and continuously monitor the evolution of the conflict through a four-dimensional space-time model; Integrating construction specifications with a historical case library, the system generates specific avoidance path plans based on defined, targeted avoidance strategies. This includes adjusting pipeline intersections (e.g., routing a DN150 water pipe around a DN500 pipe) based on pipeline avoidance principles in construction specifications (e.g., "small pipes give way to large pipes" and "pressurized pipes give way to unpressurized pipes"), and recommending strategies based on matching historical case scenarios in the historical case library. Specific dynamic algorithm reasoning includes: Calculate the Reciprocal Velocity Obstacle (RVO) between machines using the RVO2 algorithm (Reciprocal Velocity Obstacle), predict future conflicts and adjust speeds; Based on the fusion algorithm of A* and DWA, the A* algorithm is used to plan the global path, and DWA dynamically adjusts the local speed to avoid sudden obstacles; Through VISSIM priority rules, set minimum clearance times in narrow construction areas (such as bridge width restrictions) to ensure the orderly passage of construction machinery; The dynamic adjustment mechanism of the avoidance path plan is that if a new conflict is detected (such as a new red zone at T+4 hours), the strategy iteration is triggered (such as upgrading a low-level conflict zone to a high-level one); and conduct multi-objective optimization to balance efficiency and safety (e.g., prioritizing high-risk conflicts while ensuring construction progress); According to the specific avoidance path plan, the conflict type, priority, avoidance strategy and execution time are integrated to generate a dynamic avoidance strategy list; For example, [conflict type / priority / avoidance strategy / execution time / technical support]; [power pipeline intersection / level 1 / adjust hydraulic cutting machine trajectory to bypass high-conflict area / T+0 minutes]; [insufficient drainage pipe spacing / level 2 / advance drainage pipe laying to T+4 hours / T+10 minutes]; [structural settlement >0.8° / level 1 / deploy temporary support reinforcement / T+5 minutes]; Based on the dynamic avoidance strategy list, combined with conflict types and construction specifications, the specific avoidance path plan is refined into specific parameters, and an avoidance path execution plan is generated, including the machine trajectory adjustment path, pipeline layout optimization plan, and reinforcement plan; Exemplary methods of refining a specific avoidance path plan into specific parameters include: Mechanical trajectory adjustment path: The hydraulic cutter was moved from the T+6 hour operation area to the T+7 hour safety area (path length increased by 10m, taking 30 minutes); Pipeline layout optimization plan: According to the principle of "small pipes give way to large pipes", the DN150 water pipe is turned up and bypassed around the DN500 pipe, with the slope controlled at 1:6; Implementation of reinforcement plan: Deploy temporary supports in high-conflict areas with structural settlement, with support spacing ≤ 2m and made of Q345 steel; It should be noted that the RVO2 algorithm is combined with the A*+DWA fusion algorithm to achieve dynamic obstacle avoidance between construction machines; the pipeline avoidance principle is combined with structural monitoring data to ensure that the solution complies with engineering specifications; The dynamic avoidance strategy list and avoidance path execution plan are transmitted to the construction machine's control system in real time. The control system then automatically adjusts the machine's position based on the dynamic avoidance strategy list and avoidance path execution plan (e.g., "a hydraulic cutter moves from the T+6 hour operation zone to the T+7 hour safety zone"); The UWB positioning module updates the location of the construction machinery in real time to verify the effectiveness of the avoidance strategy (e.g., through CloudTrail event tracking, such as "the hydraulic cutter has detoured to a high-level conflict area"); Verify whether existing conflicts have been resolved by comparing the four-dimensional space-time models before and after strategy execution (A* and DWA fusion algorithms can be used to dynamically assess path safety to confirm whether conflicts have been resolved, such as "the pipeline intersection conflict has been resolved in T+6 hours"); If the conflict persists (e.g., the construction machine's trajectory deviates from the strategy (e.g., "the hydraulic cutter fails to detour as planned")), the emergency response mechanism is triggered, including an alarm (e.g., "the probability of conflict in the high-conflict area rises to 80%)." Simultaneously, an alternative strategy is recommended based on the historical case database, and an alternative plan is dynamically generated (e.g., "activate the backup hydraulic cutter to perform the detour") and sent to the construction machine's control system. Integrate the actual execution trajectory of construction machinery, conflict elimination status and emergency response records to generate an avoidance strategy execution status report; Dynamically annotate the avoidance strategy execution results in the four-dimensional space-time model (e.g., "high conflict area (red area) → low conflict area (green area)"); Analyze execution efficiency through Athena log queries and calculate resource consumption of avoidance strategies (e.g., "hydraulic cutting machine detour increases energy consumption by 10%"). Associate the avoidance strategy execution results with the knowledge graph (e.g., add the triple “hydraulic cutting machine → detour → T+7 hours”); Generate new rules based on historical case libraries (e.g., “If settlement > 0.8° → prioritize reinforcement over adjusting machine trajectory”); Feedback the results of avoidance strategy execution to the four-dimensional spatiotemporal model (e.g., "adding the semantic label 'T+7 hours safe detour'"); update the two-dimensional conflict heat map (e.g., "increasing the weight of red areas with settlement > 0.8°"); After executing the avoidance strategy, the construction machine execution data (including the position error of the construction machine's manipulator arm and the flatness of the pipeline's cut surface) is collected. Based on the avoidance strategy execution status report, the number of successful conflict resolutions and the total number of conflict resolutions are extracted. The conflict resolution success rate (the ratio of the number of successful conflicts to the total number of conflicts resolved) is calculated. The strategy effectiveness score is calculated by combining the conflict resolution success rate with the construction machine execution data. The strategy effectiveness score is calculated as: ; Among them, the error threshold and flatness threshold are preset values. If the posture error is less than or equal to the error threshold, it is qualified. If the flatness of the cutting surface is less than or equal to the flatness threshold, it is qualified. Set a score threshold interval and increase the priority of avoidance strategies (such as "bypass high-conflict areas") whose effectiveness scores are greater than the maximum value of the score threshold interval (e.g., level 2 to level 1); Lower the priority of the avoidance strategy whose effectiveness score is less than the minimum of the score threshold interval (e.g., level 1 → level 2); For avoidance strategies whose effectiveness score is less than or equal to the maximum value of the score threshold interval and greater than or equal to the minimum value of the score threshold interval, the priority remains unchanged; Based on the execution data of construction machinery, if the execution data of construction machinery is greater than the corresponding threshold, the threshold self-calibration mechanism is triggered to automatically tighten the conflict detection threshold (for example, the settlement threshold is changed from 0.8° to 0.7°); If the conflict elimination success rate of a single avoidance strategy is less than the preset success rate threshold (e.g., the success rate of "suspend construction" is less than 30%), it will be automatically marked as a pending avoidance strategy and suspended. If the conflict type does not have a corresponding historical case scenario in the historical case library, a new historical case scenario (e.g., "preferential deployment of temporary supports") will be generated directly based on the conflict type pattern (e.g., "casing misalignment + settlement"). Based on new conflict types (such as "casing misalignment + settlement"), new semantic tags (such as "T+7 hours safe detour") are added to expand the dynamic BIM semantic model; Update the conflict area of ​​the two-dimensional conflict heat map based on the adjusted conflict detection threshold; Based on the prioritized avoidance strategies (e.g., "deploy temporary supports first at bridge width limits"), the highest priority avoidance strategy is embedded into the knowledge graph triple (e.g., "hydraulic cutting machine → deploy temporary supports → T+7 hours") to expand the knowledge graph. Based on the suspended avoidance strategy to be verified, the logic for recommending alternative solutions (such as the "conditions for activating backup machinery") is adjusted, ultimately forming an iterative closed loop of strategies to drive the system to continuously adapt to complex scenarios and sudden interference.

[0020] Embodiment 2 of the present invention See also Figure 3 As shown, for the parts not described in detail in this embodiment, please refer to the description of Example 1. A BIM-based intelligent avoidance and self-adjusting installation method for electromechanical pipelines is provided, comprising: S1: Generate a 3D point cloud model based on point cloud data, UWB positioning data, and construction schedule, and then construct a 4D space-time model. Synchronously integrate the construction schedule to predict future pipeline path conflicts. Combined with the corridor's structural settlement data, perform composite conflict detection on the 4D space-time model, generate composite conflict quantitative data, embed it into the 4D space-time model, and perform correlation analysis based on construction specifications to generate a space-time coupling semantic model. S2: Based on a spatiotemporal coupling semantic model, the millimeter-wave radar penetration scanning data and UWB positioning data are integrated to construct a dual-dimensional conflict heat map of spatial margin and time window. Combined with the historical case library, priority matching is performed to generate a risk-level conflict list. S3: Based on the risk level conflict list and construction specifications, combined with the historical case library, a dynamic avoidance strategy list is generated and refined into an avoidance path execution plan. The strategy execution effect is verified through UWB positioning data, and the conflict elimination status is determined by comparing the four-dimensional space-time model. The avoidance strategy execution status report is generated; S4: Based on execution status reports and historical data from multiple projects, the effectiveness of avoidance strategies is evaluated and strategy priorities are dynamically adjusted. The dynamic BIM semantic model and two-dimensional conflict heat map are simultaneously updated to form a closed loop of strategy iteration.

[0021] Embodiment 3 of the present invention This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the BIM-based electromechanical pipeline intelligent avoidance and self-adjusting installation method provided above is implemented.

[0022] Since the electronic device introduced in this embodiment is an electronic device used to implement a BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation method in the embodiment of this application, based on the BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation method introduced in the embodiment of this application, technical personnel in this field can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application will not be introduced in detail here. As long as technical personnel in this field implement the electronic device used in the BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation method in the embodiment of this application, it falls within the scope of protection of this application.

[0023] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0024] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for users of ordinary skill in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A BIM-based intelligent avoidance and self-adjusting installation system for electromechanical pipelines, characterized by: include: Dynamic 4D modeling and composite conflict perception unit: Based on point cloud data, UWB positioning data, and construction schedule, a 3D point cloud model is generated, and then a 4D space-time model is constructed. The construction schedule is simultaneously accessed to predict future pipeline path conflicts. Combined with the corridor's structural settlement data, composite conflict detection is performed on the 4D space-time model, generating composite conflict quantitative data. This data is embedded in the 4D space-time model and combined with construction specifications for correlation analysis to generate a space-time coupling semantic model. Two-dimensional conflict heat map and priority ranking unit: Based on a spatiotemporal coupling semantic model, it integrates millimeter-wave radar penetration scanning data and UWB positioning data to construct a two-dimensional conflict heat map of spatial margin and time window. Combined with the historical case library, it matches priorities and generates a risk-level conflict list. Strategy Effectiveness Evaluation and Adaptive Optimization Unit: Based on the risk level conflict list and construction specifications, combined with the historical case library, a dynamic avoidance strategy list is generated and refined into an avoidance path execution plan. The strategy execution effect is verified through UWB positioning data, and the conflict elimination status is determined by comparing the four-dimensional space-time model. The avoidance strategy execution status report is generated; Closed-loop iteration and strategy self-evolution unit: Based on execution status reports and historical data from multiple projects, it evaluates the effectiveness of avoidance strategies and dynamically adjusts strategy priorities. It also synchronously updates the dynamic BIM semantic model and two-dimensional conflict heat map to form a closed-loop strategy iteration.

2. The BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation system according to claim 1 is characterized in that: The three-dimensional point cloud model is generated by: Deploy mobile scanning equipment to collect high-precision point cloud data from the pipeline corridor, obtain point cloud data, and simultaneously use the UWB positioning module to obtain real-time posture data of the scanning equipment and construction machinery to generate UWB positioning data; Perform real-time registration of point cloud data to generate high-precision 3D point cloud models; Obtain the construction schedule and align the construction schedule with the timestamp of the 3D point cloud model to form a unified timeline; De-noising, downsampling and semantic segmentation are performed on the point cloud data, unforeseen obstacles are marked, and point cloud data with semantic labels is generated; Combined with the construction schedule, the construction period is set, the laying path and construction progress of each pipeline in the next construction period are marked in the point cloud data, and a processed 3D point cloud model is generated.

3. The BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation system according to claim 2 is characterized in that: The generation method of the composite conflict quantization data includes: Based on the processed 3D point cloud model and the unified time axis, a timestamp is assigned to each point cloud data to construct a 4D space-time model, including spatial and temporal dimensions; Dynamically superimpose UWB positioning data into the four-dimensional space-time model to update the machine position and construction status in real time; Set conflict detection thresholds, including settlement thresholds and minimum safety distance thresholds; The FBG sensor is used to monitor the structural settlement data of the pipeline corridor in real time, generating a structural deformation layer that is fused into a four-dimensional space-time model. If the structural settlement data is greater than the settlement threshold, it is determined that there is structural settlement. Conduct composite conflict detection on the four-dimensional space-time model, including spatial conflict detection, temporal conflict detection and structural composite conflict detection; Spatial conflict detection is to detect the spatial intersection relationship between semantic labels and pipeline laying paths. If there are unforeseen obstacles, it is marked as a spatial conflict. Calculate the distance between pipelines and combine it with the minimum safe distance threshold. If the pipeline distance is less than the minimum safe distance threshold, it is determined that there is insufficient space margin, marked as a space conflict, and the insufficient space margin value is recorded. Time conflict detection is to predict potential conflicts in pipeline laying paths within the future construction period based on a unified time axis and mark them as time conflicts; Structural composite conflict detection is to associate spatial conflict and structural settlement data, identify areas where both spatial conflict and structural settlement exist, and mark them as composite conflicts; The labels and corresponding values ​​of spatial conflict, insufficient spatial margin, temporal conflict and composite conflict are integrated as composite conflict quantitative data.

4. The BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation system according to claim 3 is characterized in that: The generation method of the space-time coupling model includes: Embed the composite conflict quantification data into a four-dimensional space-time model to generate a dynamic BIM semantic model; Identify and extract entities from the dynamic BIM semantic model, define relationships between entities based on construction specifications and historical data, and combine entities and relationships to generate a knowledge graph with a triple structure; Associate the semantic labels of the dynamic BIM semantic model with the triples of the knowledge graph, expand the knowledge graph through rule reasoning, and generate implicit relationships; Combine the dynamic BIM semantic model with the knowledge graph to generate a spatiotemporal coupling model.

5. The BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation system according to claim 4 is characterized in that: The generation method of the dual-dimensional conflict heat map includes: Millimeter-wave radar collects penetrating scanning data within the tunnel, extracts obstacle data, retains unforeseen obstacles in the extracted obstacle data, converts the millimeter-wave radar coordinate system into a unified coordinate system, and then locates unforeseen obstacles in real time; Align the positions of unforeseen obstacles detected by the millimeter-wave radar with the point cloud data of the spatiotemporal coupling model to generate a dynamic obstacle layer; Bind UWB positioning data to the time axis of the spatiotemporal coupling model to predict potential intersections between machine trajectories and pipeline laying paths, and generate a machine-obstacle conflict layer based on the locations of unforeseen obstacles. Combined with the quantitative data of composite conflicts, the conflict areas and conflict area levels are marked; The areas with complex conflicts are marked as high-conflict areas; the areas with insufficient space margin are marked as medium-conflict areas; the areas with complex conflicts and insufficient space margin are marked as low-conflict areas; The dynamic update mechanism of the conflict area is set to trigger the automatic refresh of the conflict area when the UWB positioning module detects a change in the trajectory of the construction machinery; Construct a two-dimensional conflict heat map. Overlay the dynamic obstacle layer and the machinery-obstacle conflict layer into the two-dimensional conflict heat map. Use pipeline spacing as the horizontal axis and mark the minimum safe spacing threshold and actual spacing. With the construction period as the vertical axis, set fixed time windows and mark the conflict prediction results of each fixed time window within a future construction period.

6. The BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation system according to claim 5 is characterized in that: The method for generating the risk level conflict list includes: Based on the dual-dimensional conflict heat map and combined with the historical case library, the currently detected conflict areas and types in the dual-dimensional conflict heat map are matched with the historical case scenarios in the historical case library to determine the priority and the corresponding priority strategy; Assess the difficulty of conflict resolution through expert experience and generate technical feasibility; integrate conflict area levels, priorities, priority strategies, and technical feasibility to generate a risk-level conflict list; According to the preset response time mechanism, the response time for different conflict area levels is set, and the risk level conflict list is updated in real time according to the response time adjustment mechanism.

7. The BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation system according to claim 6 is characterized in that: The method of generating a dynamic avoidance strategy list and refining it into an avoidance path execution plan includes: Based on the risk level conflict list, define targeted avoidance strategies according to conflict type; For spatial conflicts and insufficient space margins, the strategy is to adjust the trajectory of construction machinery to avoid detours. For time conflicts, the strategy is defined as optimizing the construction sequence; For complex conflicts, the strategy is to adjust the trajectory of construction machinery to avoid them and initiate reinforcement plans. Drive strategy execution based on priority definitions; For high-conflict areas, enforce shutdown operations and immediately stop construction; For medium conflict areas, adjust the working order of construction machinery and delay the cutting time for conflicting pipelines; For low-level conflict areas, record the conflict but do not intervene for now; Combining construction specifications with a historical case library, a specific avoidance path plan is generated based on defined targeted avoidance strategies, including: adjusting pipeline intersection locations according to construction specifications and recommending strategies based on matching historical case scenarios in the historical case library; Integrate conflict type, priority, avoidance strategy and execution time to generate a dynamic avoidance strategy list; Based on the dynamic avoidance strategy list, combined with the conflict type and construction specifications, the specific avoidance path plan is refined into specific parameters to generate an avoidance path execution plan, including the mechanical trajectory adjustment path, pipeline layout optimization plan and reinforcement plan.

8. The BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation system according to claim 7 is characterized in that: The method for generating the avoidance strategy execution status report includes: The dynamic avoidance strategy list and avoidance path execution plan are transmitted to the control system of the construction machinery in real time to automatically adjust the machine posture; The UWB positioning module updates the location of construction machinery in real time to verify the effectiveness of the avoidance strategy. Compare the four-dimensional space-time models before and after the strategy is executed to determine whether the conflict has been resolved; If the conflict is not resolved, the emergency response mechanism is triggered, the alarm mechanism is triggered, and alternative strategies are recommended based on the historical case library, and alternative plans are generated and sent to the control system of the construction machinery; Integrate the actual trajectory of construction machinery, conflict resolution status and emergency response records to generate an avoidance strategy execution status report.

9. The BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation system according to claim 8 is characterized in that: The method of evaluating the effectiveness of the avoidance strategy and dynamically adjusting the strategy priority includes: After executing the avoidance strategy, the construction machinery execution data is collected. Based on the avoidance strategy execution status report, the number of successful conflict eliminations and the total number of conflict eliminations are extracted, and the conflict elimination success rate is calculated. The strategy effectiveness score is calculated by combining the conflict elimination success rate and the construction machinery execution data. Set a score threshold interval and prioritize the avoidance strategy whose effectiveness score is greater than the maximum value of the score threshold interval; The avoidance strategy with an effectiveness score less than the minimum of the score threshold interval is prioritized lower; For avoidance strategies whose effectiveness score is less than or equal to the maximum value of the score threshold interval and greater than or equal to the minimum value of the score threshold interval, the priority remains unchanged; Based on the execution data of construction machinery, if the execution data of construction machinery is greater than the corresponding threshold, the threshold self-calibration mechanism is triggered to automatically tighten the conflict detection threshold; If the conflict resolution success rate of a single avoidance strategy is lower than the preset success rate threshold, it will be automatically marked as a pending avoidance strategy and suspended; If a new conflict type is detected and there is no corresponding historical case scenario, a new historical case scenario is generated based on the pattern of the conflict type.

10. The BIM-based electromechanical pipeline intelligent avoidance self-adjusting installation system according to claim 9 is characterized in that: The method of synchronously updating the dynamic BIM semantic model and the two-dimensional conflict heat map to form a closed loop of strategy iteration includes: Add semantic tags based on new conflict types to expand the dynamic BIM semantic model; Update the conflict area of ​​the two-dimensional conflict heat map according to the adjusted conflict detection threshold; According to the avoidance strategy with improved priority, the highest priority avoidance strategy is embedded into the knowledge graph triple to expand the knowledge graph; According to the suspended avoidance strategy to be verified, the logic of recommending alternative solutions is adjusted to form a closed-loop optimization.

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