Data processing system based on BIM (Building Information Modeling) multi-specialty collaborative building design

Abstract

The invention discloses a data processing system based on BIM (Building Information Modeling) multi-specialty collaborative building design, and belongs to the technical field of calculation instruments. Data full-process processing of building, structure and electromechanical multi-specialty collaborative design is realized through modular design. The core process of the method comprises the steps of collecting and purifying multi-professional design parameters, extracting pipeline and building-structure collaborative conflict characteristic parameters, quantitatively generating two types of conflict indexes, performing risk grading, triggering grading visual early warning and corresponding disposal strategies, and realizing real-time data sharing and whole-process linkage based on a collaborative center of a unified standard. According to the system, the problems of insufficient model depth, low conflict processing efficiency, poor software interoperability, collaboration barriers and the like in traditional BIM application are solved, the data fine management level and the conflict processing efficiency are improved, the data consistency in the whole period of design-construction-operation and maintenance is guaranteed, and the system is suitable for various building engineering multi-specialty collaboration design scenes.

Description

Technical Field

[0001] This invention relates to the field of computing instrument technology, and more specifically to a data processing system for BIM-based multidisciplinary collaborative architectural design. Background Technology

[0002] Multidisciplinary collaboration in architectural design is a core element in improving the quality of building projects and controlling project implementation risks. The current application process of BIM technology in architectural design mainly involves: establishing parametric models and achieving efficient modeling based on family libraries; synchronously updating design content through a multidisciplinary collaboration platform to automatically generate plan, elevation, and section drawings and clash detection schemes; conducting 4D / 5D simulations during the construction phase to optimize resource scheduling by linking progress and cost data; enabling on-site model comparison via mobile devices; and driving the factory production of components using BIM models. In the operation and maintenance phase, efforts are being made to transform the design model into an operation and maintenance database to support asset management and fault prediction.

[0003] However, existing technologies still have the following prominent problems: Insufficient model data refinement: The model used in the construction phase lacks detailed data support, resulting in a lag in collaborative conflict detection and an inability to achieve real-time early warning during parameter adjustment, thus limiting the comprehensiveness and timeliness of conflict detection; Inefficient conflict handling: Conflict detection reports only indicate the location of conflicts and do not quantify and classify the risks of conflicts. Engineers need to manually select the priority of conflict handling, which leads to inefficient conflict handling and makes it easy to miss high-risk issues. Poor cross-software data compatibility: Different BIM tools have inconsistent compatibility standards for custom parameters, which can easily lead to the loss of attribute information during the conversion of complex component data, affecting the accuracy of multi-disciplinary data interaction; Barriers exist to the end-to-end collaboration: closed management of data from various disciplines creates information silos; model data in the design, construction, and operation and maintenance phases are fragmented; construction change information is not fed back to the design model in a timely manner, resulting in a large deviation between the as-built model and the actual site conditions.

[0004] Therefore, a refined model management approach, intelligent conflict classification, highly compatible data interaction, and a closed-loop collaborative process are needed to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned shortcomings of existing technologies, this invention provides a data processing system for multi-disciplinary collaborative architectural design based on BIM. This method achieves end-to-end data processing for collaborative design across architecture, structure, and MEP disciplines through modular design. Its core process involves: collecting and refining multi-disciplinary design parameters; extracting characteristic parameters of pipeline and building-structure collaboration conflicts; quantifying and generating two types of conflict indices and classifying risks; triggering tiered visual early warnings and corresponding response strategies; and achieving real-time data sharing and end-to-end linkage through a unified standard collaborative hub, thereby addressing the problems existing in the aforementioned background technologies.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a data processing system for multi-disciplinary collaborative architectural design based on BIM, comprising a multi-disciplinary parameter acquisition unit, a collaborative conflict analysis unit, a conflict quantification calculation unit, a risk level determination unit, a graded early warning response unit, and a BIM cross-disciplinary collaborative hub; Multi-disciplinary parameter acquisition unit: Real-time acquisition of building space layout parameters, structural component geometric parameters, and electromechanical system routing parameters; after data purification, the acquired data is transmitted to the collaborative conflict analysis unit. Collaborative Conflict Analysis Unit: Extracts features from multi-disciplinary parameters after purification to generate pipeline collaborative conflict feature parameters and building-structure collaborative conflict feature parameters; Conflict quantification calculation unit: Based on two types of collaborative conflict characteristic parameters and combined with the weight rules in the data repository, pipeline conflict quantification index and building-structure conflict quantification index are generated respectively; Risk level determination unit: Based on the preset threshold range, the risk level of the two types of conflict quantification indices is defined, and the three-level risk determination result is output; Tiered early warning response unit: Based on the risk assessment results, it executes tiered visual early warnings and matches the corresponding level of handling strategy instructions; BIM cross-disciplinary collaboration hub: Adopting a unified data compatibility standard, it enables real-time sharing, parallel editing, and version synchronization of design data across multiple disciplines, and receives and responds to policy instructions and execution status. The pipeline coordinated conflict characteristic parameters include a conflict influence intensity coefficient, which is calculated as follows: When in a conflict scenario involving physical intersections, the conflict impact intensity coefficient is a fixed value, specifically: Ki=1.6; When in a conflict scenario with insufficient spacing, the specific conflict impact intensity coefficient is: Ki=(AS) / A; Where Ki is the influence intensity coefficient of the i-th type of pipeline conflict, A is the standard value of the safety distance in the industry standard, and S is the actual measured distance value; When the actual measured spacing value equals the standard safe spacing value, Ki=0, indicating no risk of conflict. When the actual measured spacing value is 0, Ki = 1.1, indicating the risk of extreme deviation. In special cases, if the actual measured spacing value is <0, it indicates a physical intersection or a deviation exceeding 100%, and should be handled as a physical intersection conflict scenario. The pipeline conflict quantification index is specifically: Q = Σ(Pi × Vi × Ki); where Q is the pipeline conflict quantification index, Pi is the number of points of the i-th type of pipeline conflict, and Vi is the risk weight coefficient of the i-th type of pipeline conflict, with the weight priority as follows: high-risk medium pipeline conflict > gravity flow pipeline conflict > general pressure pipeline conflict; for the risk weight coefficient, a medium hazard bonus is implemented: the weight of pipelines transporting flammable / explosive / toxic media is increased; a function dependency bonus is implemented: the weight of pipelines affecting the core functions of the building is increased.

[0007] Preferably, the building-structure coordination conflict characteristic parameters include spatial adaptation deviation coefficient and functional adaptation influence coefficient; The spatial adaptation deviation coefficient is calculated as follows: Ej = δj / Δj; where Ej is the spatial adaptation deviation coefficient, δj is the actual measured deviation value of the j-th type of building-structure conflict, and Δj is the maximum allowable deviation limit of the corresponding component according to the specification. The functional compatibility impact coefficient is a level coefficient assigned based on the degree of interference of the conflict with the core functions of the building. The specific assignment rules are as follows: Level I is complete functional failure, Level II is limited functional use, and Level III is affected by the appearance.

[0008] Preferably, the building-structure conflict quantification index is: Z=Σ(Qj×Ej×Fk); where Z is the building-structure conflict quantification index, Qj is the number of points of the j-th type of building-structure conflict, Ej is the spatial adaptation deviation coefficient, and Fk is the functional adaptation influence coefficient.

[0009] Preferably, the risk level classification of the two types of conflict quantification indices specifically involves: Risk levels are determined by a quantitative index for pipeline conflict: When Q≤a1, the risk is low; When a1 < Q ≤ a2, it is considered medium risk; When Q > a2, it is considered high risk; Risk level classification based on the quantitative index of building-structure conflict: When Z≤b1, the risk is low; When b1 < Z ≤ b2, it is considered medium risk; When Z > b2, it is considered high risk; The values ​​of a1 range from 4.8 to 6.2, a2 ​​range from 14.5 to 16.5, b1 range from 2.8 to 4.2, and b2 range from 7.5 to 9.5.

[0010] Preferably, the purification process of the multi-disciplinary parameter acquisition unit includes: using box plot outlier screening criteria to remove deviation data from the multi-disciplinary parameters; filling data gaps using cubic spline interpolation to ensure data integrity; achieving multi-source parameter synchronization based on the system's unified timestamp, with a time alignment error ≤3ms; using median filtering to eliminate environmental interference for geometric parameter data; and improving parameter consistency through data standardization.

[0011] Preferably, the system further includes a data repository, a visualization interaction unit, and a conflict propagation path analysis module. The data repository includes a standard collaborative parameter reserve and a working condition-parameter mapping library, storing standard collaborative design parameters, conflict characteristics and handling parameter mapping relationships, full-process operation data and control records, supporting data query and traceability analysis. The visualization interaction unit includes a real-time collaborative status panel, a risk level pop-up window, a conflict early warning prompt bar, and a historical data traceability interface, displaying the real-time collaborative status of multiple disciplines, dynamic conflict quantification index, risk level results, and early warning alarm information, supporting the backtracking of original parameters, conflict quantification curves, and handling record reports by time range, building area, professional type, and other conditions. The conflict propagation path analysis module is used to: analyze the transmission path of conflict impact based on BIM topology relationships; predict the chain risks that conflicts may trigger; and provide path optimization suggestions for handling strategies.

[0012] Preferably, the hierarchical visual early warning executed by the hierarchical early warning response unit has its early warning identifier and push rules configured differently according to the risk level: Low-risk warnings are indicated by a light green label and sent to the construction team leader, who is required to complete on-site adaptation and fine-tuning within a preset working day. Medium-risk warnings are indicated by an amber color and pushed to project managers and professional designers, with a time limit of initiating design scheme adaptation and optimization within a preset working day. High-risk warnings are indicated by a bright red symbol and accompanied by audio and visual alerts. The alerts are sent to senior management of the company, supervision units, and relevant authorities, and trigger a freeze order on construction permits in the relevant areas, requiring the immediate activation of emergency plans.

[0013] The technical effects and advantages of this invention are as follows: 1. Improve the level of data refinement management: Through the purification and real-time acquisition functions of multi-professional parameter acquisition units, enhance the data detail dimension of BIM models, support real-time conflict warning during parameter adjustment, and solve the problem of detection lag caused by insufficient data depth in traditional models. 2. Achieve intelligent hierarchical handling of conflicts: Based on the quantitative index generated by the conflict quantification calculation unit, the risk level is automatically defined, replacing manual priority screening and greatly improving the efficiency of conflict handling; 3. Enhance cross-platform data compatibility: Built-in cross-software parameter compatibility engine ensures lossless transfer of custom parameters between different BIM tools and solves the problem of attribute loss caused by poor software interoperability; 4. Build a closed-loop collaborative process: Break down professional data barriers through the BIM cross-professional collaboration hub to achieve dynamic data linkage between the design, construction and operation and maintenance stages, and avoid information silos and as-built deviations; 5. Optimize human-computer interaction experience: Visual interaction units present key information in real time, and the full-process data storage and traceability function facilitates quick location of the root cause of the problem and reduces rework costs. Attached Figure Description

[0014] Figure 1 This is a diagram of the overall system architecture of the present invention.

[0015] Figure 2 This is a flowchart of the conflict handling process of the present invention. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The automatic unloading device for rotary kiln with self-cooling function involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Reference Figure 1 This invention provides a data processing system for multi-disciplinary collaborative architectural design based on BIM, including a multi-disciplinary parameter acquisition unit, a collaborative conflict analysis unit, a conflict quantification calculation unit, a risk level determination unit, a graded early warning response unit, a BIM cross-disciplinary collaborative hub, a data storage unit, and a visualization interaction unit.

[0018] Multi-disciplinary parameter acquisition unit: Real-time acquisition of building space layout parameters, structural component geometric parameters, and electromechanical system routing parameters; after data purification, the acquired data is transmitted to the collaborative conflict analysis unit. Furthermore, in the above technical solution, the purification process of the multi-professional parameter acquisition unit includes: using box plot outlier screening criteria to remove deviation data in multi-professional parameters; filling data gaps through cubic spline interpolation to ensure data integrity; achieving multi-source parameter synchronization based on the system's unified timestamp, with a time alignment error ≤3ms; using median filtering to eliminate environmental interference for geometric parameter data; and improving parameter consistency through data standardization.

[0019] It should be further explained that the building space layout parameters include space layout dimensions (such as room clear height, bay width and depth), functional zoning boundary parameters (such as evacuation passage width, equipment room floor area); structural component geometric parameters include component cross-sectional dimensions (such as beam cross-sectional height and width, column cross-sectional diameter), installation positioning coordinates (such as component axis coordinates, elevation parameters); electromechanical system routing parameters include pipeline laying paths (such as pipeline direction coordinates, turning node positions), pipe diameter specifications (such as water supply pipe diameter, air duct cross-sectional dimensions) and installation elevations (such as pipeline center elevation, pipe bottom elevation).

[0020] The data cleansing process includes: using box plot outlier screening criteria, setting the interquartile range (IQR) to Q3-Q1, and filtering out deviation data that are less than Q1-1.5IQR or greater than Q3+1.5IQR; filling data gaps using cubic spline interpolation to ensure data integrity; achieving multi-source parameter synchronization based on the system's unified timestamp, with a time alignment error ≤3ms; using median filtering to eliminate environmental interference for geometric parameter data (with the filter window size set to 5×5), and improving parameter consistency through data standardization (converting parameters to the [0,1] interval).

[0021] It should be noted that for a set of data, Q1 (the first quartile) is located at the 25th percentile of the dataset, Q2 (the second quartile, i.e., the median) is located at the 50th percentile, and Q3 (the third quartile) is located at the 75th percentile.

[0022] In this embodiment, the parameter acquisition device is a Trimble X7 3D laser scanner with a scanning accuracy of ±0.03mm and a distance range of 0.6-120m from the scanned object. It collects 3D point cloud data of multiple professional parameters in real time and generates standardized parameters through built-in algorithms. The electromechanical system routing parameters are supplemented by a FARO Focus S70 laser scanner, which performs precise scanning in areas with dense pipelines to ensure the accuracy of the routing parameters.

[0023] Collaborative Conflict Analysis Unit: Extracts features from multi-disciplinary parameters after purification to generate pipeline collaborative conflict feature parameters and building-structure collaborative conflict feature parameters; Furthermore, in the above technical solution, the pipeline cooperative conflict characteristic parameter includes a conflict influence intensity coefficient, which is specifically calculated as follows: When in a conflict scenario involving physical intersections, the conflict impact intensity coefficient is a fixed value, specifically: Ki=1.6; When in a conflict scenario with insufficient spacing, the specific conflict impact intensity coefficient is: Ki=(AS) / A; Where Ki is the influence intensity coefficient of the i-th type of pipeline conflict, such as i=1 indicating insufficient spacing between gas pipes and cable pipes, i=2 indicating weakened cross-section of water supply pipes passing through beams, and i=3 indicating intersection of air ducts and fire protection pipes. The classification is based on the combination of pipeline type and conflict form. A represents the industry-standard safe clearance value, and S represents the actual measured clearance value. When the actual measured spacing value equals the standard safe spacing value, Ki=0, indicating no risk of conflict. When the actual measured spacing value is 0, Ki = 1.1, indicating the risk of extreme deviation. In special cases, if the actual measured spacing value is <0, it indicates a physical intersection or a deviation exceeding 100%, and should be treated as a physical intersection conflict scenario.

[0024] Furthermore, in the above technical solution, the building-structure coordination conflict characteristic parameters include spatial adaptation deviation coefficient and functional adaptation influence coefficient; The spatial adaptation deviation coefficient is calculated as follows: Ej = δj / Δj; where Ej is the spatial adaptation deviation coefficient, δj is the actual measured deviation value of the j-th type of building-structure conflict, and Δj is the maximum allowable deviation limit of the corresponding component according to the specification. The functional adaptability impact coefficient is a level coefficient assigned based on the degree of interference of the conflict with the core functions of the building. The specific assignment rules are as follows: Level I is a complete functional failure, such as when evacuation routes are blocked by structural components. The functional adaptation impact coefficient ranges from 1.4 to 1.6. Level II is characterized by limited functionality, such as insufficient corridor height leading to passage obstruction. The functional adaptation impact coefficient ranges from 1.1 to 1.3. Level III is affected by appearance and form, such as structural convex corners affecting the visual integrity of the space, and the functional adaptation impact coefficient ranges from 0.7 to 0.9.

[0025] Conflict quantification calculation unit: Based on two types of collaborative conflict characteristic parameters and combined with the weight rules in the data repository, pipeline conflict quantification index and building-structure conflict quantification index are generated respectively; Furthermore, in the above technical solution, the pipeline conflict quantification index is specifically: Q=Σ(Pi×Vi×Ki); where Q is the pipeline conflict quantification index, Pi is the number of points of the i-th type of pipeline conflict, and Vi is the risk weight coefficient of the i-th type of pipeline conflict, with the weight priority as follows: high-risk medium pipeline conflict > gravity flow pipeline conflict > general pressure pipeline conflict; where the weight of high-risk medium pipeline conflict ranges from 1.1 to 1.4, the weight of gravity flow pipeline conflict ranges from 0.9 to 1.0, and the weight of general pressure pipeline conflict ranges from 0.7 to 0.8. For risk weighting coefficients, a medium hazard bonus is implemented: the weight of pipelines transporting flammable / explosive / toxic media is increased by 20%-30%; a function dependence bonus is implemented: the weight of pipelines affecting the core functions of a building is increased by 15%-25%.

[0026] Furthermore, in the above technical solution, the building-structure conflict quantification index is specifically: Z=Σ(Qj×Ej×Fk); where Z is the building-structure conflict quantification index, used to assess the coordination between building and structural design and identify potential safety hazards; Qj is the number of points of the j-th type of building-structure conflict, such as Q1 being the number of points of vertical component conflict (column / wall encroaching on door and window openings), Q2 being the number of points of horizontal component conflict (insufficient beam bottom elevation), and Q3 being the number of points of pipeline penetration conflict (illegal pipeline penetration through beams); Ej is the spatial adaptation deviation coefficient, and Fk is the functional adaptation influence coefficient.

[0027] Risk level determination unit: Based on the preset threshold range, the risk level of the two types of conflict quantification indices is defined, and the risk determination results of low, medium and high levels are output. Furthermore, in the above technical solution, the risk level classification of the two types of conflict quantification indices specifically refers to: Risk levels are determined by a quantitative index for pipeline conflict: When Q≤a1, the risk is low; When a1 < Q ≤ a2, it is considered medium risk; When Q > a2, it is considered high risk; Risk level classification based on the quantitative index of building-structure conflict: When Z≤b1, the risk is low; When b1 < Z ≤ b2, it is considered medium risk; When Z > b2, it is considered high risk; The values ​​of a1 range from 4.8 to 6.2, a2 ​​range from 14.5 to 16.5, b1 range from 2.8 to 4.2, and b2 range from 7.5 to 9.5.

[0028] Tiered early warning response unit: Based on the risk assessment results, it executes tiered visual early warnings and matches the corresponding level of handling strategy instructions; It should be further explained that the conflict handling and early warning process is as follows: Figure 2 As shown, the hierarchical visualization early warning process is as follows: Low risk: Light green markings are used, and conflict points are marked with semi-transparent dots. A visual interface displays the conflict location and deviation description. Medium risk: Amber color is used for the markings, and the conflict points are marked with bright solid dots. A pop-up box will indicate the conflict type and scope of impact. High risk: Uses crimson markings, with conflict points indicated by flashing solid dots and audio-visual cues. Automatically generates a semi-transparent overlay over the conflict impact area, displaying detailed location, deviation value, and handling priority.

[0029] Mobile push rules: Low risk: Push to the construction team leader, display conflict warning information and collaborative conflict quantitative index, requiring on-site adaptation and fine-tuning to be completed within 3 working days; Medium risk: Push to project manager and professional designer, showing the emergency status of the conflict, the scope of impact and optimization suggestions, requiring design scheme adaptation and optimization to be initiated within 1 working day; High risk: The message will be pushed to senior management of enterprises, supervision units and housing and construction authorities, displaying circuit breaker warning information and a collaborative conflict quantitative index, freezing construction permits in relevant areas, and requiring the immediate activation of emergency plans.

[0030] Push notification intelligent filtering mechanism: False alarm blocking: Three consecutive false alarms at the same location trigger AI verification to eliminate false warnings caused by device interference; Time-based filtering: Only high-risk alerts are pushed out between 22:00 and 6:00 the next day; medium and low-risk alerts are forwarded to the message center. Responsibility Association: Automatically associates conflicting professional and responsible personnel, such as pushing pipeline conflicts to electromechanical engineers, and structural conflicts to structural designers.

[0031] The tiered response strategy is as follows: Low-risk handling: On-site adaptation and fine-tuning, the construction team marks the conflict points through the BIM mobile terminal, and adjusts the pipeline elevation or component installation position to complete the avoidance; the adjustment trajectory is recorded and uploaded to the collaborative center for record, and the supervising engineer reviews the adjustment results within 48 hours; Medium-risk handling: Design scheme adaptation and optimization, convening multi-disciplinary special coordination meetings, generating three-dimensional avoidance path diagrams or structural reinforcement schemes; for high-risk pipeline conflicts, leakage simulation verification needs to be added, and temporary monitoring equipment needs to be installed; High-risk response: Initiate a Level 3 emergency procedure; the project manager must halt work and set up an isolation zone within 15 minutes; the design institute and general contractor must arrange an expert team for on-site inspection within 1 hour; a third-party testing agency must submit a safety assessment report within 6 hours; and for structural reconstruction needs, adopt solutions such as steel-concrete transfer beams and cable-stayed reinforcement, while simultaneously verifying the foundation load.

[0032] BIM Cross-Disciplinary Collaboration Hub: Adopts a unified data compatibility standard to achieve real-time sharing, parallel editing and version synchronization of design data from multiple disciplines, and receives and responds to policy instructions and execution status.

[0033] It should be further explained that its core functions include: Cross-platform data compatibility transmission: Supports lossless transmission of geometric and non-geometric information (material properties, construction progress, etc.) from mainstream BIM tools such as Revit, Bentley, and Tekla, and defines model accuracy levels (LOD200-LOD500) to match the needs of different design stages; Intelligent collaborative task allocation: Design tasks are automatically allocated based on a responsibility matrix, task status is updated in real time and overdue warnings are pushed out; Design changes trigger an automatic notification mechanism, and related professionals need to confirm the impact of conflicts on the platform and modify the model synchronously. Refined access control: Configure model editing, viewing, and export permissions according to profession (architecture / structure / mechanical and electrical) and responsibility (design / review / construction); key operations (model acceptance, change confirmation) are stored on the blockchain to ensure traceability of responsibility; Full-process data linkage: The design model is linked with the construction progress and operation and maintenance data to provide a visual analysis interface for full life cycle management; the conflict quantification index is updated every 5 floors during the construction phase, and new conflicts are alerted by the model.

[0034] To verify the system's conflict detection, quantification, classification, and response capabilities in multi-disciplinary collaborative design scenarios, multiple rounds of experimental tests were conducted in a simulated building project. The experimental data are shown in Table 1. Table 1. Experimental Data of BIM-based Multidisciplinary Collaborative Architectural Design System ;

[0035] Data repository: Stores standard collaborative design parameters, mapping relationships between conflict characteristics and handling parameters, full-process operation data and control records, and supports data query and traceability analysis; Furthermore, in the above technical solution, the data storage repository includes a standard collaborative parameter reserve and a working condition-parameter mapping library.

[0036] It should be further explained that the standard collaborative parameter repository is used to store industry standard parameters (such as safety clearances and deviation limits), standard parameters for various components, and default process parameter combinations. The working condition-parameter mapping library is used to store the mapping relationship between 3D contours and process parameters, and the matching relationship between conflict features and handling solutions; Data update rules: If the conflict resolution is verified as qualified (low-risk resolution is approved, medium- and high-risk optimization scheme is accepted), and the optimized parameter combination is used this time, the mapping relationship between the conflict characteristic parameters and the resolution parameters is stored in the working condition-parameter mapping library; if the resolution is still unqualified, the defect type is analyzed and the resolution parameters are optimized, and a new mapping relationship is generated and stored in the library. Data traceability function: Stores full-process operation data and control records, and supports querying historical data, parameter change curves and handling reports by time, region, professional and other dimensions.

[0037] Visual interactive unit: Real-time display of multi-professional collaboration status, dynamic conflict quantification index, risk level results, and early warning alarm information.

[0038] Furthermore, in the above technical solution, the visual interaction unit includes a real-time collaborative status panel, a risk level pop-up window, a conflict early warning prompt bar, and a historical data traceability interface, which supports tracing back the original parameters, conflict quantification curves, and handling record reports according to conditions such as time range, building area, and professional type.

[0039] In a preferred embodiment of the present invention, the visualization interaction unit uses a 17-inch industrial touch screen as the hardware carrier and runs monitoring software developed based on the Vue framework, which includes a real-time collaborative status panel, a risk level pop-up window, a conflict warning prompt bar, and a historical data traceability interface.

[0040] Real-time collaborative status panel: dynamically displays the design progress of each discipline, current process parameters, conflict quantification index change trend, and conflict point distribution heat map; Risk level pop-up: After the quality assessment is completed, the risk level result and core conflict information are displayed in a prominent color in the center of the screen; Conflict warning prompt bar: Records warning time, conflict type, parameter deviation value, and simultaneously triggers audible and visual alarm (high risk). Historical data traceability interface: Supports querying original parameters, conflict quantification curves, handling strategies and execution results by time range, building area, professional type, conflict level and other conditions, and exports PDF analysis reports.

[0041] Conflict propagation path analysis module: Analyzes the transmission path of conflict impacts based on BIM topology relationships; predicts potential chain risks triggered by conflicts; and provides path optimization suggestions for handling strategies.

[0042] It should be further explained that the implementation steps of the conflict propagation path analysis module are as follows: Step 1: Topology modeling. Based on the component connection relationships (such as beam-column connections, pipeline-equipment connections) of the BIM model, construct an undirected weighted graph (nodes are components, edges are connection relationships, and weights are component correlation coefficients), and use an adjacency matrix to store the topology structure.

[0043] Step 2: Propagation path calculation. Dijkstra's algorithm is used to find the shortest path from the "conflict source node" to other nodes (path weight = component correlation coefficient × conflict impact intensity), and the critical path of conflict propagation (the top 30% of paths with the highest weight values) is identified.

[0044] Step 3: Chain Risk Prediction. Based on the component types on the critical path (e.g., high-risk pipeline → equipment → structural components), and combined with historical data from the operating condition-parameter mapping library, a Bayesian probability model is used to predict the probability of chain risks occurring (e.g., the probability of gas pipeline leakage → equipment damage → structural corrosion). A secondary warning is triggered when the probability is ≥60%.

[0045] Step 4: Path Optimization Suggestions. For critical propagation paths, automatically generate "Avoidance Path Priority Ranking" (e.g., prioritize adjusting non-core functional pipelines and secondary structural components), and output a 3D optimized path diagram (in OBJ format, supports importing into Revit / Bentley software).

[0046] Through the above description of the embodiments, those skilled in the art can clearly understand that the various embodiments of this application can be implemented by means of software or software combined with necessary general-purpose hardware platforms, and of course, they can also be implemented by hardware functions; based on this understanding, the technical solution of this application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to cause a computer device, such as including but not limited to a personal computer, server, or network device, to execute all or part of the steps of the method described in any embodiment of this application.

[0047] The foregoing describes exemplary embodiments of this application. It should be understood that the above exemplary embodiments are not restrictive but illustrative, and the scope of protection of this application is not limited thereto. It should be understood that those skilled in the art can make modifications and variations to the embodiments of this application without departing from the spirit and scope of this application, and such modifications and variations should be within the scope of protection of this application.

Claims

1. A data processing system based on BIM multi-specialty collaborative building design, characterized in that, The BIM cross-professional collaborative hub adopts a unified data compatibility standard to realize real-time sharing, parallel editing and version synchronization of multi-professional design data, receives disposal strategy instructions and feeds back execution status. The pipeline collaborative conflict characteristic parameter includes a conflict influence intensity coefficient, and the calculation is specifically as follows: When in an entity intersection type conflict scenario, the conflict influence intensity coefficient is a fixed value, specifically: Ki=1.6; When in an insufficient spacing type conflict scenario, the conflict influence intensity coefficient is specifically: Ki=(A-S) / A; Wherein Ki is the influence intensity coefficient of the i-th pipeline conflict, A is the standard value of the industry specification safety spacing, and S is the actual measured spacing value; When the actual measured spacing value = the standard value of the specification safety spacing, Ki=0, indicating no conflict risk; When the actual measured spacing value = 0, Ki=1.1, indicating an extreme deviation risk; In special cases, the actual measured spacing value < 0 is entity intersection or deviation exceeding 100%, which is processed as an entity intersection type conflict scenario; The pipeline conflict quantization index is specifically: Q=Σ(Pi×Vi×Ki); wherein Q is the pipeline conflict quantization index, Pi is the point number of the i-th pipeline conflict; Vi is the risk weight coefficient of the i-th pipeline conflict, and the weight priority is: high-risk medium pipeline conflict > gravity flow pipeline conflict > general pressure pipeline conflict; For the risk weight coefficient, implement medium danger addition: pipeline conveying flammable / explosive / toxic medium weight up; Implement function-dependent addition: the weight of the pipeline affecting the building core function is improved. The building-structure collaborative conflict characteristic parameter includes a space adaptation deviation coefficient and a function adaptation influence coefficient; The space adaptation deviation coefficient calculation is specifically: Ej=δj / Δj; wherein Ej is the space adaptation deviation coefficient, δj is the actual measured deviation value of the j-th building-structure conflict, and Δj is the standard maximum deviation limit value of the corresponding component; The function adaptation influence coefficient is a level coefficient given according to the interference degree of the conflict on the building core function, and the assignment rule is specifically: level I is complete function failure, level II is limited function use, and level III is appearance form influence. ​ ​ ​ 2. The data processing system for BIM-based multi-specialty collaborative building design of claim 1, wherein: ​ ​ ​ 3. The data processing system for BIM-based multi-disciplinary collaborative building design of claim 1, wherein: The building-structure conflict quantitative index is specifically Z = Σ (Qj * Ej * Fk), wherein Z is the building-structure conflict quantitative index, Qj is the point number of the jth building-structure conflict, Ej is a spatial adaptation deviation coefficient, and Fk is a function adaptation influence coefficient.

4. The data processing system for BIM-based multi-disciplinary collaborative building design of claim 1, wherein: The risk grade definition of the two types of conflict quantitative indexes is specifically: The risk grade definition of the pipeline conflict quantitative index is specifically: When Q ≤ a1, it is low risk; When a1 < Q ≤ a2, it is medium risk; When Q > a2, it is high risk; The risk grade definition of the building-structure conflict quantitative index is specifically: When Z ≤ b1, it is low risk; When b1 < Z ≤ b2, it is medium risk; When Z > b2, it is high risk; Wherein the value range of a1 is 4.8-6.2, the value range of a2 is 14.5-16.5, the value range of b1 is 2.8-4.2, and the value range of b2 is 7.5-9.

5.

5. The data processing system for BIM-based multi-disciplinary collaborative building design of claim 1, wherein: The purification process of the multi-specialty parameter acquisition unit includes: screening out deviated data in the multi-specialty parameters by using a box plot abnormal value screening criterion; filling in data gaps by using a cubic spline interpolation method to ensure data integrity; realizing multi-source parameter synchronization based on a system unified timestamp, with a time alignment error ≤3 ms; eliminating environmental interference by using median filtering on geometric parameter data, and improving parameter consistency through data standardization processing.

6. The data processing system for multi-disciplinary collaborative building design based on BIM according to claim 1, characterized in that: The system further includes a data storage library, a visual interaction unit and a conflict propagation path analysis module. The data storage library includes a standard collaborative parameter repository and a working condition-parameter mapping library, stores standard collaborative design parameters, conflict characteristics and processing parameter mapping relationships, whole-process operation data and control records, supports data query and traceability analysis; the visual interaction unit includes a real-time collaborative state panel, a risk grade pop-up window, a conflict early warning prompt bar and a historical data traceability interface, and real-time displays multi-specialty collaborative state, conflict quantitative index dynamics, risk grade results and early warning alarm information, supports traceability of original parameters, conflict quantitative curves and disposal records according to time range, building area, professional type and other conditions; the conflict propagation path analysis module is used for: analyzing the transmission path of conflict influence based on BIM topological relationship; predicting the chain risk that may be caused by the conflict; providing path optimization suggestions for disposal strategies.

7. The data processing system for multi-disciplinary collaborative building design based on BIM according to claim 1, characterized in that: The hierarchical visual early warning performed by the hierarchical early warning response unit has early warning identifiers and push rules differentiated according to risk grades: Low-risk early warning adopts a light green identifier, is pushed to the construction team leader, and is limited to completing on-site adaptation fine-tuning within a preset working day; Medium-risk early warning adopts an amber identifier, is pushed to the project manager and professional designers, and is limited to starting design scheme adaptation optimization within a preset working day; High-risk early warning adopts a red identifier with sound and light prompts, is pushed to enterprise executives, supervision units and competent departments, and triggers a freezing instruction for construction permission in the related area, requiring immediate start of an emergency plan.

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