A data optimization processing method and system for edge computing in architectural design

Abstract

The application provides a data optimization processing method and system for edge computing in architectural design. The method obtains geometric deviation and attribute change characteristics between architectural design models, generates a version conflict identification map of a high conflict area through topological relationship comparison, deploys an incremental synchronization controller on a physical layer edge computing node, dynamically intercepts a difference data segment associated with the high conflict area and compresses it into an incremental data packet, analyzes a conflict propagation path predicted based on a dependent topological relationship between the models, converts the path into a merging operation instruction conforming to a design rule through a collaborative modeling engine, integrates the differences by executing the instruction, and outputs a conflict-free multi-architectural design integrated model after fusing the models. The application realizes second-level dynamic resolution and automatic fusion of architectural model conflicts, improves design iteration efficiency, and eliminates the risk of construction rework.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data optimization processing, in particular to a data optimization processing method and system for edge computing in architectural design. BACKGROUND

[0002] In the field of intelligent architectural design, edge computing needs to process real-time dynamic fusion of multi-source heterogeneous data (such as environmental sensor temperature and humidity, equipment operating status, crowd density, energy consumption, etc.). At the same time, it faces three core challenges: high timeliness of data (security monitoring needs millisecond-level response), strong spatial correlation (such as cross-floor energy consumption linkage needs dynamic coordination), and edge resource constraints (terminal device computing power and storage are limited). Traditional centralized cloud computing cannot meet the sudden scene (such as fire needs to immediately fuse smoke sensing and fire water pressure data to trigger local evacuation instructions), and it is urgent to realize the chain processing of data cleaning-compression-analysis on the edge side to support local real-time decision-making, and to ensure that privacy-sensitive data (such as facial information) are desensitized and securely isolated at the edge layer.

[0003] The current targeted solution is an edge data hierarchical processing framework based on spatio-temporal correlation modeling. This scheme constructs a spatio-temporal feature model of building data (such as the correlation rules of crowd patterns and energy consumption peaks and valleys), deploys lightweight data filtering and compression modules (such as removing redundant data for stable temperature and humidity, and using inter-frame difference coding for video streams) on the edge gateway layer; At the same time, perform cross-system data correlation analysis (such as cooperative judgment of fire water pressure anomaly and fire alarm signal) on the fog computing node layer, and schedule edge resources based on dynamic priority strategy (such as reserving computing power for security tasks). However, the core deficiency is that the spatio-temporal correlation model relies on pre-set static rules of building function partitioning, which is difficult to adapt to dynamic switching of spatial functions, and the cross-layer cooperative mechanism does not deeply integrate real-time resource state feedback, resulting in a decrease in data processing timeliness in sudden scenes. SUMMARY

[0004] The present application provides a data optimization processing method and system for edge computing in architectural design, to solve the problems of full-quantity synchronization bandwidth redundancy, conflict propagation out of control and artificial fusion efficiency black hole in the prior art.

[0005] In a first aspect, the present application provides a data optimization processing method for edge computing in architectural design, comprising:

[0006] Obtaining geometric deviation and attribute change features between architectural design models, and performing topological relationship comparison on the geometric deviation and attribute change features to generate a version conflict identification map, the version conflict identification map marking high conflict areas of different architectural design conflicts;

[0007] By deploying an incremental synchronization controller at a physical layer edge computing node, a difference data segment associated with the high conflict area and the version conflict identification map is dynamically intercepted, and the difference data segment is compressed to form an incremental data packet;

[0008] Analyzing the dependency topological relationship between different building design models, and predicting the propagation path of building design conflicts between multiple building design models according to the dependency topological relationship;

[0009] Converting the propagation path into a merge operation instruction that adapts to the design rules between building design models through a collaborative modeling engine;

[0010] Executing the merge operation instruction to realize the difference integration between building information models, and fusing the building design models after integrating the differences to output a conflict-free multi-building design integrated model.

[0011] Optionally, geometric deviations and attribute change features between building design models are obtained, and the geometric deviations and attribute change features are compared in topological relationship to generate a version conflict identification map, which labels high conflict areas of different building design conflicts, including:

[0012] Scanning point-line-surface coordinates of different building design model geometries, calculating offset distances of point-line-surface coordinates between different building design models as geometric deviations;

[0013] Obtaining component attribute records of different building design models, and extracting material parameters and physical property parameters in the component attribute records, and comparing the change amount of the same building design model components in the material parameters and physical property parameters as attribute change features;

[0014] Mapping the geometric deviations and attribute change features to the topological grid cells of the building design models, and detecting topological grid cells that simultaneously exceed the preset geometric tolerance threshold and the preset attribute change threshold as abnormal grid cells to complete topological relationship comparison;

[0015] Statistically analyzing the aggregation density of the abnormal grid cells in the topological grid cells, and marking the area with an aggregation density exceeding a preset conflict threshold as a high conflict area;

[0016] Mapping point-line-surface coordinates of high conflict areas to surfaces of the building design models to generate a version conflict identification map labeling positions of high conflict areas.

[0017] Optionally, by deploying an incremental synchronization controller at a physical layer edge computing node, a difference data segment associated with the high conflict area and the version conflict identification map is dynamically intercepted, and the difference data segment is compressed to form an incremental data packet, including:

[0018] Deploy an incremental synchronization controller on the physical layer edge computing node, enabling the incremental synchronization controller to monitor data changes in the version conflict identifier graph in real time;

[0019] When a data change is detected in a high-conflict area of ​​the version conflict identifier map, the incremental synchronization controller extracts the spatial coordinate range of the high-conflict area and the corresponding geometric deviation and attribute change characteristics to form a difference data segment.

[0020] The three-dimensional spatial coordinates of the differential data fragments are converted into two-dimensional parameterized planar coordinates to perform spatial coordinate compression processing, and the differential data fragments after spatial coordinate compression processing are integrated into an incremental data packet.

[0021] Optionally, the dependency topology between different architectural design models is parsed, and the propagation path of architectural design conflicts among multiple architectural design models is predicted based on the dependency topology, including:

[0022] Extract the component connection relationships and spatial adjacency relationships between different architectural design models, and construct a dependency topology graph describing the dependency topology relationships between architectural design models based on the component connection relationships and spatial adjacency relationships.

[0023] In the dependency topology graph, the locations of the detected high-conflict regions are marked, and the components where the high-conflict regions are located are marked as conflict source nodes;

[0024] Locate the neighboring nodes connected to the conflict source node through the dependency topology relationship, and use the directed line segment between the conflict source node and the neighboring node as the propagation path between multiple building design models.

[0025] Optionally, the component connection relationships and spatial adjacency relationships between different architectural design models are extracted, and a dependency topology graph describing the dependency topology relationships between architectural design models is constructed based on the component connection relationships and spatial adjacency relationships, including:

[0026] Scan the physical connection points between components in different architectural design models, and obtain the component connection relationships based on the physical connection points;

[0027] Measure the spatial coordinates of adjacent components in different architectural design models, and calculate the distance between the surfaces of the components as the spatial interval value of the spatial adjacency relationship. When the spatial interval value is less than the preset adjacency determination threshold, establish a spatial adjacency relationship marking line between the corresponding components.

[0028] The physical connection points of the component connection relationship are integrated with the spatial adjacency relationship marking lines, so that the physical connection points are used as topological nodes and the spatial adjacency relationship marking lines are used as topological edges.

[0029] The topological nodes and topological edges are combined to form a dependency topology graph that describes the dependencies between architectural design models.

[0030] Optionally, the propagation path is transformed into a merging operation instruction that adapts to the design rules between architectural design models through a collaborative modeling engine, including:

[0031] Analyze the component identifier and conflict propagation direction parameters corresponding to each node in the propagation path;

[0032] The pre-defined design rule library of the architectural design model is retrieved based on the component identifier;

[0033] The allowed spatial position adjustment range and attribute modification constraints of the corresponding component are obtained from the preset design rule library as rule matching parameters;

[0034] The conflict propagation direction parameters and rule matching parameters are input into the collaborative modeling engine to generate a merging operation instruction that adapts to the design rules between architectural design models.

[0035] Optionally, the merging operation instruction is executed to achieve the integration of differences between building information models, and the architectural design models after the difference integration are merged to output a conflict-free multi-architectural design integrated model, including:

[0036] In the architectural design model, the corresponding component is moved to the target coordinate position specified by the merge operation command to achieve the integration of differences in component positions;

[0037] Update the material parameters and physical property parameters of the corresponding components in the architectural design model to the values ​​specified in the merge operation command to complete the integration of differences in the construction attributes;

[0038] The architectural design model, which integrates the differences in component positions and construction attributes, is then matched and aligned according to the spatial coordinates of the components.

[0039] The matched and aligned architectural design models are merged to generate a conflict-free integrated model of multiple architectural designs.

[0040] Secondly, this application provides a data optimization processing system for edge computing in architectural design, including:

[0041] The comparison module is used to obtain the geometric deviations and attribute change characteristics between architectural design models, and to perform topological relationship comparison on the geometric deviations and attribute change characteristics to generate a version conflict identification map, which marks the high conflict areas of different architectural design conflicts.

[0042] The interception module is used to dynamically intercept the difference data segments associated with the version conflict identification map and the high conflict area by deploying an incremental synchronization controller on the physical layer edge computing node, and to compress the difference data segments to form an incremental data packet;

[0043] The parsing module is used to parse the dependency topology between different architectural design models and predict the propagation path of architectural design conflicts among multiple architectural design models based on the dependency topology.

[0044] The conversion module is used to convert the propagation path into a merging operation instruction that adapts to the design rules between architectural design models through the collaborative modeling engine;

[0045] The output module is used to execute the merging operation instructions to realize the integration of differences between building information models, and to merge the building design models after the differences are integrated to output a conflict-free multi-building design integrated model.

[0046] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the data optimization processing method for edge computing in architectural design as described in the first aspect above.

[0047] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a data optimization processing method for edge computing in architectural design as described in the first aspect.

[0048] This application's technical solution achieves intelligent conflict resolution among multiple Building Information Models (BIMs) through topological relationship analysis and incremental synchronization technology. Specifically, a visual version conflict identification map is constructed based on precise comparison of geometric deviations and attribute changes; propagation path prediction relying on topological relationships significantly improves the ability to predict conflict impacts; and incremental data compression and instruction conversion by the collaborative modeling engine enable efficient integration of differences. This method overcomes the efficiency bottleneck of traditional manual verification, achieves automated fusion of multi-disciplinary design models, ensures the integrity and consistency of BIM integration, and significantly improves the efficiency and quality of cross-disciplinary collaborative design in large-scale construction projects.

[0049] Furthermore, precise location of conflicts in Building Information Modeling (BIM) is achieved through multi-dimensional feature fusion and intelligent grid analysis. Specifically, a comprehensive conflict assessment system is constructed based on geometric deviation calculations using point, line, and surface coordinates and changes in material parameters; an anomaly detection mechanism using topological grid cells effectively identifies complex conflict areas; and a clustering density algorithm enables automated annotation and visualization of high-conflict areas. This method overcomes the limitations of traditional single-dimensional comparisons, significantly improving the accuracy and efficiency of conflict identification across multiple professional models. It provides reliable technical support for collaborative design and version management of BIM, ensuring data consistency throughout the entire lifecycle of large-scale construction projects.

[0050] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart of a data optimization processing method for edge computing in architectural design provided in this application is shown;

[0053] Figure 2 This application provides a schematic diagram of the structure of a data optimization processing system for edge computing in architectural design.

[0054] Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0055] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0056] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0057] Researchers have discovered that edge computing in smart buildings faces fundamental flaws: while a hierarchical processing framework based on static rules can achieve basic data compression, its lack of spatial dynamic adaptability and rigid cross-layer collaboration mechanisms lead to a double loss of control. Specifically, when building functional zones dynamically switch (e.g., a conference room instantly transforms into an exhibition hall), the pre-defined spatiotemporal correlation model cannot reconstruct the rules linking pedestrian flow and energy consumption, resulting in delays in the fusion of smoke and water pressure data in sudden fire scenarios; furthermore, edge resource scheduling does not deeply integrate real-time computing power fluctuations, causing security response disruptions due to CPU preemption in video stream desensitization tasks. This contradiction stems from a blind spot in the decoupling of dynamic conflict propagation paths in the building information model, necessitating the construction of a closed-loop resolution architecture of "conflict topology - incremental synchronization - intelligent merging."

[0058] To address the aforementioned challenges, this invention proposes a data optimization processing method for edge computing in architectural design. Its innovation lies in breaking through the limitations of static rules by collaboratively generating version conflict topology interpretation and merging instructions. Specifically: a heatmap is constructed to label high-conflict areas by comparing the topological relationships of geometric deviations and attribute change characteristics; an incremental synchronization controller is deployed at edge nodes to dynamically extract conflict-related data fragments and compress them into lightweight incremental packages; the topology of multiple BIM models is analyzed to predict the cross-model propagation path of conflicts; and a merging operation instruction is generated based on path features to drive a collaborative modeling engine, achieving intelligent ablation of discrepancies and fusion of conflict-free models. This method overturns traditional processing paradigms: the conflict heatmap achieves millisecond-level localization of conflict sources at the building component level for the first time; propagation path prediction compresses the rule mismatch rate caused by spatial function switching through dynamic topology modeling; and the incremental synchronization mechanism reduces edge resource consumption, forming a closed-loop control chain of "conflict perception - incremental compression - path deduction - intelligent merging," providing a real-time decision-making center for large-scale intelligent buildings, from data conflict resolution to dynamic resource optimization.

[0059] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] Figure 1 This application provides a flowchart of a data optimization processing method for edge computing in architectural design, as shown in the embodiments of this application. Figure 1 As shown, the method includes:

[0061] 101. Obtain the geometric deviations and attribute change characteristics between architectural design models, and perform topological relationship comparison on the geometric deviations and attribute change characteristics to generate a version conflict identification map, wherein the version conflict identification map marks the high conflict areas of different architectural design conflicts.

[0062] Optionally, step 101 may specifically include the following steps:

[0063] 1011. Scan the point, line, and surface coordinates of the geometric structure of different architectural design models, and calculate the offset distance of the point, line, and surface coordinates between different architectural design models as the geometric deviation.

[0064] 1012. Obtain component attribute records for different architectural design models, and extract the material parameters and physical property parameters from the component attribute records. At the same time, compare the changes in the material parameters and physical property parameters of components of the same architectural design model as attribute change features.

[0065] 1013. Map the geometric deviation and attribute change features to the topological mesh cells of the architectural design model, and detect topological mesh cells that simultaneously exceed the preset geometric tolerance threshold and the preset attribute change threshold as abnormal mesh cells, so as to complete the topological relationship comparison.

[0066] 1014. Calculate the cluster density of the abnormal mesh cells in the topological mesh cells, and mark the areas where the cluster density exceeds a preset conflict threshold as high conflict areas.

[0067] 1015. Map the point, line, and surface coordinates of the high-conflict areas to the surface of the architectural design model to generate a version conflict marker map that indicates the location of the high-conflict areas.

[0068] In the above scheme, the architectural design model refers to a digital model used for architectural design. Geometric deviation refers to the difference in geometric shape between models. Attribute change characteristics refer to the characteristics of changes in model attributes. Topological relationship comparison refers to comparative analysis based on topological structure. Version conflict identification diagram refers to a graphic marking conflict areas. High conflict area refers to an area with severe conflict. Point, line, and surface coordinates refer to the coordinate data of geometric elements. Offset distance refers to the distance difference between geometric elements. Component attribute record refers to the attribute data of architectural components. Material parameters refer to parameters of material properties. Physical property parameters refer to parameters of physical performance. Change amount refers to the numerical value of parameter change. Topological mesh cell refers to the mesh cell divided by the model. Preset geometric tolerance threshold refers to the maximum allowable geometric deviation. Preset attribute change threshold refers to the maximum allowable attribute change. Abnormal mesh cell refers to a mesh cell that exceeds the threshold. Cluster density refers to the density of abnormal meshes. Preset conflict threshold refers to the critical value for judging conflict.

[0069] In this embodiment, the system first scans the point, line, and surface coordinates of the geometric structures of different architectural design models: spatial data of building components is extracted using a 3D model analysis engine (such as the geometry scanning module in BIM software), including coordinate information of vertex positions (points), edge orientations (lines), and surface contours (surfaces). Subsequently, a coordinate alignment module spatially matches corresponding components from different versions of the model, calculates the displacement difference of the same components in 3D space, and generates quantified geometric deviations (such as the positional offset of beam-column joints). This process relies on a spatial registration algorithm to ensure that different models are in the same coordinate system, providing a geometric basis for subsequent conflict analysis.

[0070] Subsequently, the system acquires component attribute records from different architectural design models: extracting non-geometric attributes of components from the Building Information Modeling (BIM) database, including material parameters (such as concrete grade and steel type) and physical property parameters (such as thermal conductivity and load-bearing strength). The attribute analyzer compares the parameter differences of the same component in different versions, calculates the change amount (such as the percentage change in material strength from C30 to C35), and generates attribute change features. This step captures non-geometric attribute changes in design changes through version difference tracking technology, supplementing conflicting dimensions not covered by geometric deviations.

[0071] Next, the system maps geometric deviations and attribute change characteristics to topological mesh elements: based on finite element meshing technology, the surface of the building model is discretized into subdivided topological mesh elements (such as triangular or quadrilateral meshes). The conflict detection engine superimposes geometric deviation values ​​and attribute change characteristic values ​​within each mesh element. If the following conditions are met simultaneously within an element: the geometric deviation value exceeds a preset geometric tolerance threshold (such as the upper limit of displacement tolerance) and the attribute change exceeds a preset attribute change threshold (such as the upper limit of material strength change tolerance), then the element is marked as an abnormal mesh element. This process accurately locates comprehensive design conflict areas through a dual-threshold cross-validation mechanism.

[0072] Then, the system statistically analyzes the clustering density of anomalous grid cells: spatial density clustering algorithms (such as density calculation based on neighborhood search) are used to analyze the distribution density of adjacent anomalous grid cells, generating clustering density values ​​(such as the number of anomalous cells per unit volume). The region labeling module compares the clustering density with a preset conflict threshold (such as a density critical value). If the density of a local area consistently exceeds the threshold, the geometric boundary of that area is extracted and marked as a high-conflict area (such as the intersection of walls and pipelines in the core tube area). This step visualizes the degree of conflict concentration using heatmap generation technology.

[0073] Finally, the system generates a version conflict identification map highlighting high-conflict areas: the coordinate mapper projects the point, line, and surface coordinates of the high-conflict areas back onto the surface of the original building model, and the graphics rendering engine marks these areas with bright color blocks (such as a red semi-transparent overlay) and overlays contour lines to indicate the conflict intensity gradient, forming the version conflict identification map. This map is displayed in real time through a 3D visualization pipeline, allowing designers to interactively view conflict details (such as rotation and sectioning), achieving intuitive early warning and location of design risks.

[0074] In practical applications, in the intelligent fire protection system design collaboration platform, edge computing nodes first scan the point, line, and surface coordinates of the geometric structure of different building design models (such as the three-dimensional orientation coordinates of the sprinkler network and the positioning coordinates of the fire hydrant), calculate the offset distance of the point, line, and surface coordinates between different building design models as geometric deviations (for example, the coordinate offset of a certain section of the network in the new design causes a conflict with the network of the adjacent fire compartment) (step 1011); at the same time, they acquire the component attribute records of different building design models (such as the fire resistance rating and pipe diameter of the pipe), extract the material parameters and physical property parameters (such as the change in the bearing coefficient caused by replacing stainless steel pipe with PVC pipe), and compare the changes in the material parameters and physical property parameters of the components of the same building design model as attribute change features (step 1012). Subsequently, geometric deviations and attribute change features are mapped to the topological grid cells of the building design model (e.g., mapping pipeline coordinate offsets to the gridded space of the building BIM model). Topological grid cells that simultaneously exceed preset geometric tolerance thresholds and preset attribute change thresholds are identified as abnormal grid cells (e.g., pipeline offsets within a certain grid exceed limits and pipe pressure bearing coefficients do not meet standards) (step 1013). Next, the clustering density of abnormal grid cells in the topological grid cells is calculated (e.g., 30% of cells in the fire compartment grid of an underground parking garage have conflicts), and areas with clustering density exceeding a preset conflict threshold are marked as high-conflict areas (step 1014). Finally, the point, line, and surface coordinates of the high-conflict areas are mapped to the surface of the building design model (e.g., highlighted conflicting pipeline segments in red in the underground parking garage BIM model), generating a version conflict identification map that marks the location of the high-conflict areas (step 1015). This diagram drives collaborative design optimization: edge computing nodes automatically push conflict areas to the terminals of each design party, prompting adjustments to the pipeline path or restoration of pipe specifications; when the attribute change feature shows pipe degradation, the system links with the fire water pressure simulation model to verify feasibility, avoiding fire protection acceptance failure due to design conflicts.

[0075] The solution described in step 101 above achieves intelligent identification and visual localization of conflicts in architectural design models. Through a dual comparison of geometric deviations and attribute change characteristics, an innovative conflict detection mechanism based on topological meshes is constructed. This technology employs a multi-dimensional threshold determination method to transform abstract model differences into quantifiable conflict marker maps, accurately pinpointing the spatial locations of high-conflict areas. Through cluster density analysis and conflict threshold determination, intelligent aggregation from discrete anomalies to concentrated conflict areas is achieved, providing an intuitive conflict visualization solution for multi-model collaborative design and significantly improving the efficiency and accuracy of design conflict identification.

[0076] 102. By deploying an incremental synchronization controller on the physical layer edge computing node, the differential data segments associated with the version conflict identification map and the high conflict area are dynamically extracted, and the differential data segments are compressed to form incremental data packets.

[0077] Optionally, step 102 may specifically include the following steps:

[0078] 1021. Deploy an incremental synchronization controller on the physical layer edge computing node, so that the incremental synchronization controller can monitor the data changes of the version conflict identifier graph in real time.

[0079] 1022. When a data change is detected in a high-conflict area of ​​the version conflict identifier map, the incremental synchronization controller extracts the spatial coordinate range of the high-conflict area and the corresponding geometric deviation and attribute change characteristics to form a difference data segment.

[0080] 1023. Convert the three-dimensional spatial coordinates of the difference data fragments into two-dimensional parameterized planar coordinates to perform spatial coordinate compression processing, and integrate the spatial coordinate compressed difference data fragments into incremental data packets.

[0081] In the above scheme, physical layer edge computing nodes refer to edge computing devices located close to the data source. Incremental synchronization controllers manage incremental data synchronization. Difference data fragments refer to the difference data between different versions. Incremental data packages are compressed packages containing incremental data. Data change status refers to detailed information about data changes. Spatial coordinate range refers to the spatial range of high-conflict areas. Two-dimensional parametric plane coordinates refer to parametric coordinates on a two-dimensional plane. Spatial coordinate compression processing refers to methods for reducing the amount of coordinate data. Real-time monitoring refers to continuously monitoring data changes.

[0082] In this embodiment, the system first deploys an incremental synchronization controller at the physical layer edge computing node. This controller is embedded into the physical layer node of the building design data management system (such as a local server at a construction site) via an edge computing framework. The controller continuously monitors changes to version conflict identifier data in the BIM model through a real-time monitoring module. The monitoring mechanism employs an event-driven architecture. When designers modify high-conflict areas (such as adjusting component coordinates or material parameters), the controller automatically captures the change event and triggers subsequent processing, establishing underlying support for dynamically capturing discrepancies.

[0083] Subsequently, the system extracts differential data fragments associated with high-conflict areas: when the incremental synchronization controller detects data changes (such as structural beam displacement or wall material updates) in high-conflict areas of the version conflict identifier map, the data extraction engine immediately locates the spatial coordinate range of that area and extracts its corresponding geometric deviations (such as coordinate offsets) and attribute change features (such as material strength changes). This process uses spatial topology mapping technology to accurately associate the changed content with the original conflict area, generating differential data fragments containing only the changed elements, ensuring minimal transmission content.

[0084] Finally, the system compresses and integrates the discrepancy data fragments into incremental data packets: the coordinate transformation module converts the three-dimensional spatial coordinates (such as the X / Y / Z values ​​of beam-column nodes) of the discrepancy data fragments into two-dimensional planar coordinates (such as the UV coordinate system) using a parametric projection algorithm, eliminating redundant dimensions to achieve spatial coordinate compression; subsequently, the data encapsulator sorts the compressed fragments according to the conflict area number, adds timestamps and version identifiers, and integrates them into a lightweight incremental data packet. This data packet is uploaded to the cloud BIM database through a dedicated communication channel of the edge nodes, achieving efficient synchronization of conflict changes.

[0085] In practical applications, in the energy optimization scenario of smart buildings, the system first deploys an incremental synchronization controller (such as an edge gateway deployed in the floor distribution room) at the physical layer edge computing node. This controller monitors data changes in the version conflict identification map in real time (such as the coordinate update of pipeline path conflict areas in the building BIM model due to the modification of the air conditioning system) (step 1021). When a data change occurs in a high conflict area in the version conflict identification map (such as a conflict between a new photovoltaic device and the original structural beam position of an energy pipeline on a certain floor), the incremental synchronization controller immediately extracts the spatial coordinate range of the high conflict area and the corresponding geometric deviation and attribute change characteristics (such as the three-dimensional coordinate offset of the conflicting pipeline and the change value of the thermal conductivity of the material), forming a difference data segment (step 1022). Then, the three-dimensional spatial coordinates of the difference data segment are converted into two-dimensional parametric plane coordinates (by orthogonal projection, the pipeline conflict coordinates are mapped to the floor plan), and spatial coordinate compression processing is performed (eliminating redundant elevation information in the three-dimensional data). Finally, the compressed difference data segments are integrated into an incremental data package (step 1023). The data packet is transmitted to the cloud energy management platform via the LoRa wireless network, updating only the conflict area data instead of the full model, thus reducing the transmission load. At the same time, the edge nodes retain the compressed parametric coordinates for local real-time calculation (such as conflict area energy efficiency simulation), avoiding the impact of network latency on air conditioning system scheduling decisions.

[0086] The solution described in step 102 above achieves efficient compression and incremental synchronization of conflicting data. By deploying edge computing nodes at the physical layer, an innovative dynamic truncation and compression mechanism for differential data fragments is designed. This technology, which converts three-dimensional spatial coordinates into two-dimensional parametric planar coordinates, significantly reduces data transmission volume while preserving key conflict information. The incremental data packet generation mechanism ensures efficient transmission of conflicting information, providing a low-latency data synchronization solution for distributed collaborative design and effectively solving the bandwidth bottleneck problem during large-scale building information model synchronization.

[0087] 103. Analyze the dependency topology between different architectural design models, and predict the propagation path of architectural design conflicts among multiple architectural design models based on the dependency topology.

[0088] Optionally, step 103 may specifically include the following steps:

[0089] 1031. Extract the component connection relationships and spatial adjacency relationships between different architectural design models, and construct a dependency topology graph describing the dependency topology relationships between architectural design models based on the component connection relationships and spatial adjacency relationships.

[0090] Step 1031 may specifically include the following processes: scanning the physical connection points between components in different architectural design models and obtaining the component connection relationship based on the physical connection points; measuring the spatial position coordinates of adjacent components in different architectural design models to calculate the distance between the component surfaces as the spatial interval value of the spatial position adjacency relationship; when the spatial interval value is less than a preset adjacency determination threshold, establishing a spatial adjacency relationship marking line between the corresponding components; integrating the physical connection points of the component connection relationship with the spatial position adjacency relationship marking line, so that the physical connection points are used as topological nodes and the spatial position adjacency relationship marking line is used as topological edges; combining the topological nodes and topological edges to form a dependency topology graph describing the dependency relationship between architectural design models.

[0091] 1032. Mark the location of the detected high-conflict regions in the dependency topology graph, and mark the component where the high-conflict region is located as the conflict source node.

[0092] 1033. Locate the neighboring nodes connected to the conflict source node through the dependency topology relationship, and use the directed line segment between the conflict source node and the neighboring node as the propagation path between multiple building design models.

[0093] In the above scheme, dependency topology refers to the dependency connections between models. Propagation path refers to the path along which conflicts are transmitted between models. Component connection refers to the physical connection between components. Spatial adjacency refers to the spatial proximity between components. Dependency topology graph refers to the topology graph reflecting dependencies. Physical connection point refers to the connection location between components. Spatial interval value refers to the distance value between components. Preset adjacency threshold refers to the critical distance for judging adjacency. Spatial adjacency marker line refers to the line segment that identifies adjacency. Topology node refers to a key point in the network. Topology edge refers to the line segment connecting nodes. Conflict source node refers to the node where a conflict begins. Directed line segment refers to a directional connection line. High-conflict region refers to the location of areas with severe conflicts. Adjacent node refers to a directly connected node.

[0094] In this embodiment, the system first extracts the component connection relationships and spatial adjacency relationships between different architectural design models: A 3D model scanning engine identifies physical contact points between architectural components (such as beam-column welding points and wall panel joints), generating component connection relationship data. Simultaneously, a spatial position analysis module measures the surface coordinate point cloud of adjacent components and calculates the minimum distance between components as the spatial interval value. If this value is less than a preset adjacency threshold, a spatial adjacency relationship marker line is established between the components. Subsequently, a topology integrator maps physical connection points to topological nodes and spatial adjacency relationship marker lines to topological edges. A graph structure generation algorithm combines all nodes and edges to form a complete dependency topology graph expressing the inter-model dependency structure. This graph presents the spatial and logical connections between components in the form of a directed network.

[0095] Subsequently, the system marks high-conflict areas and identifies conflict source nodes in the dependency topology graph: the conflict mapping module reads the version conflict identification map generated in step 1015, locates the building components (such as load-bearing walls with excessive displacement) corresponding to the high-conflict areas in the graph, finds the corresponding topology nodes in the dependency topology graph, and marks them as conflict source nodes. This process uses spatial coordinate matching technology to ensure the precise association between conflict areas and topology nodes, and adds special identifiers (such as red highlighting) to the conflict source nodes, providing a starting point for subsequent propagation analysis.

[0096] Finally, the system locates the neighboring nodes of the conflict source node and generates propagation paths: the path tracing engine starts from the conflict source node and searches for directly connected neighboring nodes (such as floor slabs connected to conflicting walls or partition walls between adjacent rooms) along the topological edges of the dependent topology graph. The propagation path generator extracts the directed line segments from the conflict source node to neighboring nodes as propagation paths and uses dynamic arrows to mark the direction of conflict propagation (such as the transfer of structural stress from load-bearing walls to floor slabs) through path visualization pipelines. This path reveals the transmission chain of design conflicts between models, providing a key basis for collaborative design modifications.

[0097] In practical applications, in medical complex construction projects, the system first extracts the component connection relationships and spatial adjacency relationships between different architectural design models (e.g., the physical connection between the operating room wall in the architectural model and the load-bearing beam in the structural model, and the parallel arrangement of the air ducts in the HVAC model and the cable trays in the electrical model within the ceiling space). Specifically, the connection relationships between components are obtained by scanning the physical connection points between components in different architectural design models (e.g., the bolt anchoring points between steel beams and concrete columns); at the same time, the spatial coordinates of adjacent components are measured (e.g., the surface distance between the sterile corridor wall of the operating room and the wall of the adjacent equipment room), and the distance between the component surfaces is calculated as the spatial interval value of the spatial adjacency relationship. When the spatial interval value is less than the preset adjacency judgment threshold (e.g., the distance between two walls is less than the safe clearance for equipment installation), a spatial adjacency relationship marking line is established between the corresponding components (step 1031). Subsequently, the physical connection points are used as topological nodes (e.g., the bolt anchoring points are marked as node N1), and the spatial adjacency relationship marking lines are used as topological edges (e.g., the distance between the walls of the equipment room is marked as edge E1), and these are combined to form a dependency topology graph describing the dependencies between architectural design models (step 1031). Next, the system marks the locations of detected high-conflict areas in the dependency topology graph (such as areas in the sterile corridor of the operating room where the space for duct installation is insufficient due to changes in the structural beam elevation), and marks the components at these locations as conflict source nodes (e.g., conflicting duct components are marked as node C1) (step 1032). Subsequently, the system locates the adjacent nodes connected to the conflict source node through dependency topology relationships (e.g., load-bearing beam node N1 and adjacent electrical cable tray node N2 connected to C1 through topological edges), and uses directed line segments as propagation paths between multiple building design models (e.g., C1→N1 indicates that the conflict between the duct and the beam will affect structural safety, C1→N2 indicates that the displacement of the duct will squeeze the space of the electrical cable tray) (step 1033). This path drives collaborative optimization: when the conflict source node (operating room duct) triggers displacement, the system automatically warns associated nodes along the propagation path (e.g., notifying the electrical model to adjust the cable tray route), preventing the conflict chain from spreading to critical systems such as medical gas pipelines.

[0098] The scheme described in step 103 above enables intelligent prediction and tracking of design conflict propagation paths. By constructing a dependency topology graph, the physical connections and spatial adjacencies of building components are transformed into a networked topology. This innovative conflict source node marking method and path propagation algorithm can accurately predict the spread path of conflicts among multiple models, overcoming the limitations of traditional static analysis in conflict detection. This dynamic propagation analysis based on topology provides a scientific basis for locating the root causes and assessing the scope of impact of design conflicts, significantly improving the predictability and systematic nature of conflict resolution.

[0099] 104. The propagation path is transformed into a merging operation command that adapts to the design rules between architectural design models through the collaborative modeling engine.

[0100] Optionally, step 104 may specifically include the following steps:

[0101] 1041. Parse the component identifier and conflict propagation direction parameters corresponding to each node in the propagation path.

[0102] 1042. Retrieve the preset design rule library of the architectural design model based on the component identifier.

[0103] 1043. Obtain the allowed spatial position adjustment range and attribute modification constraints of the corresponding component from the preset design rule library as rule matching parameters.

[0104] 1044. Input the conflict propagation direction parameters and rule matching parameters into the collaborative modeling engine to generate a merging operation command that adapts to the design rules between architectural design models.

[0105] In the above scheme, the collaborative modeling engine refers to a software engine that supports collaborative design of multiple models. Merging operation commands refer to operation commands that coordinate differences between models. Component identifiers refer to unique codes that identify components. Conflict propagation direction parameters refer to the direction information of conflict propagation. Preset design rule base refers to a database that stores design rules. Spatial location adjustment range refers to the allowed range of location changes. Attribute modification constraints refer to the restrictions on attribute changes. Rule matching parameters refer to parameters that match the design rules. Adaptation refers to the process of making operations conform to the design rules.

[0106] In this embodiment, the system first parses the component identifier and conflict propagation direction parameters corresponding to each node in the propagation path: The propagation path generated in step 103 is scanned using a graph traversal algorithm (such as depth-first search), extracting the component identifier associated with each node in the path (e.g., beam / column number "B-203"). Simultaneously, the direction parsing module reads the topological relationship of directed line segments between nodes, quantifying the line segment directions into conflict propagation direction parameters (e.g., a vector identifier for "propagation from the core tube to the curtain wall"). This process transforms the abstract propagation path into a correspondence between specific components and conflict flow directions through node attribute extraction technology.

[0107] Subsequently, the system retrieves the pre-defined design rule base of the architectural design model based on the component identifiers: the rule retrieval engine uses the component identifiers extracted in step 1041 as query keywords and performs index matching in the pre-defined design rule base of the BIM system. This rule base adopts a relational database architecture and stores design constraint entries for various components (e.g., a design constraint entry could be "steel beam spacing ≥ 800mm"). The retrieval process returns all rule entries associated with the component identifiers through API calls, providing a data foundation for rule matching.

[0108] Next, the system retrieves rule matching parameters from the preset design rule base: the rule parser performs semantic decomposition on the rule entries retrieved in step 1042, extracting two types of key constraints: one is the spatial location adjustment range, and the other is the attribute modification constraint (such as "concrete grade shall not be lower than C30"). This process uses constraint classification technology to transform text rules into machine-readable rule matching parameters, forming a set of boundary conditions for component modification.

[0109] Finally, the system generates merge operation instructions adapted to the design rules: the collaborative modeling engine receives the conflict propagation direction parameters from step 1041 and the rule matching parameters from step 1043, and generates executable merge operation instructions through a conflict resolution algorithm (such as the conversion bridge method). Specifically, it determines the modification priority based on the conflict propagation direction parameters (e.g., prioritizing the adjustment of conflict source node components). It transforms the spatial location adjustment range and attribute modification constraints into parameterized operations. Finally, it outputs a set of operation instructions containing component numbers, modification types, and parameter thresholds via JSON structured data for the BIM collaborative platform to call and execute.

[0110] In practical applications, in the collaborative design of fire protection systems in medical complexes, after the system locates the conflict source node (such as the spatial collision between the operating room smoke exhaust duct and the structural beam) and its propagation path (such as the displacement of the duct compressing the space of the adjacent electrical cable tray) through the dependency topology graph, the collaborative modeling engine first parses the component identifier and conflict propagation direction parameters corresponding to each node in the propagation path (step 1041); then, based on the component identifier, it retrieves the preset design rule library of the building design model (such as the fire protection system design code library and the medical building space safety standard library) (step 1042) to obtain the allowable spatial position adjustment range and attribute modification constraints of the corresponding component. For the rule matching parameters (such as the upper limit of horizontal displacement of air ducts and the unmodifiable material properties of fireproof coatings for cable trays) (step 1043); finally, the conflict propagation direction parameters and rule matching parameters are input into the collaborative modeling engine to generate a merging operation instruction that adapts to the design rules between architectural design models (step 1044). This instruction drives the automatic synchronization of multi-disciplinary models: the edge computing nodes verify the rule matching parameters in real time (such as the safe distance between the air duct and the top plate after displacement). If the adjustment violates the rules (such as exceeding the net height limit of the operating room after lifting), the engine dynamically generates an alternative instruction (such as changing to an oblique beam scheme) to ensure conflict-free integration of the fire protection system with the architectural, structural, and electrical models.

[0111] The solution described in step 104 above achieves intelligent adaptation and conflict resolution of multi-model design rules. Through the innovative application of the collaborative modeling engine, abstract propagation paths are transformed into specific operational instructions that conform to design specifications. This technology deeply integrates component identifier retrieval, design rule base matching, and conflict propagation parameter analysis. The generated merging operation instructions consider both conflict propagation characteristics and strictly adhere to the design constraints of each model. This rule-driven conflict resolution solution ensures the standardization and consistency of the integration process, providing reliable technical support for the collaborative design of complex building systems.

[0112] 105. Execute the merging operation instruction to realize the integration of differences between building information models, and merge the building design models after the differences are integrated to output a conflict-free multi-building design integrated model.

[0113] Optionally, step 105 may specifically include the following steps:

[0114] 1051. Move the corresponding component in the architectural design model to the target coordinate position specified by the merge operation command to achieve the integration of differences in component positions.

[0115] 1052. Update the material parameters and physical property parameters of the corresponding components in the architectural design model to the values ​​specified in the merge operation command to complete the integration of differences in the construction attributes.

[0116] 1053, and then the architectural design model that integrates the differences in component positions and the differences in construction attributes is matched and aligned according to the spatial coordinates of the components.

[0117] 1054. Merge and match the aligned architectural design models to generate a conflict-free multi-architectural design integrated model.

[0118] In the above scheme, target coordinate position refers to the target position of the component after movement. Difference integration refers to the process of eliminating model differences. Attribute difference integration refers to the process of unifying component attributes. Spatial position coordinate matching and alignment refers to adjusting the model position to align it. Conflict-free multi-building design integrated model refers to an integrated model that eliminates conflicts. Material parameters refer to parameters of material properties. Physical property parameters refer to parameters of physical performance.

[0119] In this embodiment, the system first moves the corresponding component in the architectural design model to the target coordinate position specified by the merging operation command: the BIM model editing engine parses the merging operation command generated in step 104, locates the component to be adjusted, and the coordinate transformation module automatically moves the component to the new coordinates according to the target coordinate position (such as X / Y / Z axis values) in the command. This process uses a spatial transformation algorithm to ensure that the component displacement path accurately matches the design rules, for example, moving the offset load-bearing wall to within the preset seismic spacing range, realizing the integration of differences in component positions and eliminating geometric spatial conflicts.

[0120] Subsequently, the system updates the material and physical property parameters of the corresponding components in the architectural design model to the values ​​specified in the merge operation command: the attribute updater reads the parameter modification requirements in the merge operation command, locates the target component in the BIM database, and batch replaces its material parameters (such as steel type) and physical property parameters (such as thermal conductivity) with the values ​​specified in the command. This process synchronously updates the associated attributes of the components through parametric-driven technology. For example, after adjusting the wall material, its load-bearing strength is automatically recalculated, completing the integration of differences in component attributes and ensuring that material performance meets structural safety specifications.

[0121] Next, the system aligns the architectural design models, which have undergone location and attribute difference integration, according to the spatial coordinates of the components: the model alignment module extracts the spatial coordinates of all components (such as the coordinate set of floor slab vertices) and uses a spatial grid matching algorithm to unify the coordinate systems of different models to a global reference system. For example, it aligns the coordinate origins of the residential building model and the underground parking garage model, and automatically calibrates the relative positions of the components based on a preset grid (such as the coordinates of the intersection of axes). This process relies on topology verification technology to check the connection relationships of components (such as the connection points between pipes and walls) to ensure spatial topology consistency across models, laying the foundation for final fusion.

[0122] Finally, the system merges and aligns the architectural design models to generate a conflict-free multi-architectural design integrated model: the model fusion engine inputs the conflict resolution pipelines from the aligned multiple models, first using a duplicate component removal algorithm to remove overlapping parts (such as duplicate grid lines or floor slabs), and then connecting cross-model components (such as the interface between a residential building's elevator shaft and a garage ramp) through interface stitching technology. The final result is a conflict-free multi-architectural design integrated model, which is verified through real-time collision detection to ensure there are no geometric interferences or property contradictions, such as the spacing between structural beams and equipment pipelines complying with fire safety codes, and is output as a deliverable BIM integration file.

[0123] In practical applications, within the collaborative design platform for intelligent security systems in commercial complexes, after the system generates a merging operation command (such as adjusting the position of security monitoring equipment to avoid HVAC ducts) through the collaborative modeling engine, it first moves the corresponding component in the architectural design model to the target coordinate position specified by the merging operation command (e.g., moving a conflicting hemispherical camera from its original ceiling position to the side of an adjacent structural column) to achieve component position difference integration (step 1051). Subsequently, it updates the material parameters and physical property parameters of the corresponding component in the architectural design model to the values ​​specified by the merging operation command (e.g., changing the camera bracket material from aluminum alloy to carbon fiber to reduce the load and upgrading the seismic resistance level to the specified standard) to complete the construction attribute difference integration (step 1052). Next, the system matches and aligns the architectural design model, which has completed component position difference integration and construction attribute difference integration, according to the spatial coordinates of the components (e.g., recalibrating the camera coordinates of the security system and the duct coordinates of the HVAC system in three-dimensional space to ensure that the equipment spacing meets the installation specifications) (step 1053). Finally, the merged and aligned architectural design models (with the structural model verifying load distribution and the electrical model verifying power supply lines) are integrated to generate a conflict-free multi-architectural design integrated model (step 1054). This model drives cross-disciplinary collaboration: edge computing nodes verify the physical compliance of the merged model in real time (e.g., whether the camera view is obstructed by structural columns in new locations). If hidden conflicts are found (e.g., power supply distance exceeds limits), secondary instructions are automatically triggered (e.g., adding distribution box nodes) to ensure conflict-free deep integration of security, HVAC, structural, and electrical models.

[0124] The solution described in step 105 above achieves lossless fusion and integrated output of multiple Building Information Models (BIMs). By precisely executing the merging operation instructions, it innovatively achieves dual integration of component positions and attributes. This technology employs a two-stage processing flow of spatial coordinate matching and model fusion, ensuring the geometric accuracy and attribute integrity of the integrated model. The generated integrated model not only eliminates design conflicts but also retains the design intent and technical features of each original model, providing a high-quality model foundation for multi-disciplinary collaboration in large-scale building projects and significantly enhancing the reusability and collaborative efficiency of BIMs.

[0125] The following are specific examples for steps 101 to 105:

[0126] In commercial complex projects, MEP engineers use edge computing nodes to acquire geometric deviations and attribute change characteristics between architectural design models. First, they scan the point, line, and surface coordinates of the geometric structures of different architectural design models (e.g., the 3D orientation coordinates of HVAC ducts and electrical cable trays), calculating the offset distance of these coordinates as geometric deviations (e.g., the increased cable tray elevation in the new electrical model causing overlap with the duct space). Simultaneously, they acquire component attribute records from different architectural design models (e.g., duct fire resistance rating, cable tray load parameters), extracting material parameters and physical property parameters (e.g., the change in load-bearing coefficient due to replacing galvanized steel plates with composite materials in ducts). They then compare the changes in material and physical property parameters of components within the same architectural design model as attribute change characteristics. Subsequently, the geometric deviations and attribute change characteristics are mapped to the topological mesh cells of the architectural design models (mapping coordinate offsets and material change values ​​to the meshed space of the BIM model). Topological mesh cells that simultaneously exceed preset geometric tolerance thresholds and preset attribute change thresholds are identified as abnormal mesh cells (e.g., insufficient vertical spacing between cable trays and ducts within a mesh, and duct load-bearing coefficients not meeting standards). Next, the clustering density of abnormal mesh cells in the topological mesh cells is statistically analyzed (e.g., densely packed conflicting cells in the electromechanical layer mesh group of an underground parking garage). Areas with a clustering density exceeding a preset conflict threshold are marked as high-conflict areas. The point, line, and surface coordinates of the high-conflict areas are mapped onto the surface of the architectural design model, generating a version conflict identification map that marks the location of the high-conflict areas (e.g., a cluster of conflicting pipelines highlighted in red in the 3D model of a basement). Based on this, the system deploys an incremental synchronization controller (e.g., an edge server deployed in the electromechanical control room) at the physical layer edge computing node to monitor data changes in the version conflict identification map in real time. When a data change is detected in a high-conflict area of ​​the version conflict identification map (e.g., an HVAC engineer adjusts the duct elevation), the incremental synchronization controller extracts the spatial coordinate range of the high-conflict area and the corresponding geometric deviation and attribute change characteristics to form a difference data segment (only capturing the coordinates of the conflicting ducts and new material parameters). The three-dimensional spatial coordinates of the differential data fragments are then converted into two-dimensional parametric planar coordinates (by orthogonal projection, the duct conflict coordinates are mapped to the floor plan), spatial coordinate compression is performed (to eliminate redundant data in the elevation dimension), and finally integrated into an incremental data package (the step corresponds to "dynamically extracting and compressing differential data fragments").

[0127] Simultaneously, the system analyzes the dependency topology relationships between different architectural design models—scanning the physical connection points between components in different architectural design models (such as the flange connection points between air ducts and air conditioning units) to obtain the component connection relationships; measuring the spatial coordinates of adjacent components (such as the surface distance between air ducts and adjacent fire pipes), calculating the distance between component surfaces as the spatial interval value of the spatial adjacency relationship, and establishing a spatial adjacency relationship marker line when the spatial interval value is less than the preset adjacency judgment threshold. The physical connection points are used as topology nodes, and the spatial adjacency relationship marker lines are used as topology edges to form a dependency topology graph (the steps correspond to "Constructing the Dependency Topology Graph"). The component where the high conflict area is marked in the graph is designated as the conflict source node (such as the conflicting air duct marked as node C1), and its adjacent nodes connected through dependency topology relationships are located (such as fire pipe node S1 and structural support node T1 connected to C1 through topology edges). Directed line segments are used as propagation paths (such as C1→S1 indicating that the air duct displacement will compress the fire pipe space).

[0128] The collaborative modeling engine transforms the propagation path into a merging operation command: it parses the component identifiers and conflict propagation direction parameters of the nodes in the propagation path (e.g., the identifier "DUCT-001" points to the fire-fighting pipeline, and the propagation direction is "horizontal displacement affects safety clearance"); it retrieves the preset design rule base (e.g., the design code for electromechanical integrated pipelines) based on the component identifier; it obtains the allowable spatial position adjustment range and attribute modification constraints of the corresponding component as rule matching parameters (e.g., the upper limit of horizontal displacement of the fire-fighting pipeline, and the fire resistance rating of the air duct cannot be lowered); finally, it inputs the conflict propagation direction parameters and rule matching parameters into the collaborative modeling engine to generate a merging operation command (e.g., "shift the air duct DUCT-001 horizontally by 300mm, simultaneously raise the fire-fighting pipeline S1, and lock the fire resistance attribute of the air duct").

[0129] The final merge operation command is executed as follows: In the architectural design model, the corresponding components are moved to the target coordinate positions (air ducts and fire protection pipes are moved to the new coordinates), achieving the integration of component position differences; the material parameters and physical property parameters of the components are updated to the specified values ​​(restoring the galvanized steel plate material of the air ducts to meet fire resistance requirements), completing the integration of construction attribute differences. The model with integrated position and attribute differences is then matched and aligned according to spatial coordinates (calibrating the coordinates of air ducts and fire protection pipes to a unified coordinate system), and merged to generate a conflict-free multi-architectural design integrated model (outputting a constructable MEP integrated model).

[0130] Figure 2 This application provides a schematic diagram of the structure of a data optimization processing system for edge computing in architectural design, as shown in the embodiments of this application. Figure 2 As shown, the system includes:

[0131] The comparison module 21 is used to obtain the geometric deviation and attribute change characteristics between architectural design models, and to perform topological relationship comparison on the geometric deviation and attribute change characteristics to generate a version conflict identification map, which marks the high conflict areas of different architectural design conflicts.

[0132] The interception module 22 is used to dynamically intercept the difference data segments associated with the version conflict identification map and the high conflict area by deploying an incremental synchronization controller on the physical layer edge computing node, and to compress the difference data segments to form an incremental data packet;

[0133] The parsing module 23 is used to parse the dependency topology between different architectural design models and predict the propagation path of architectural design conflicts among multiple architectural design models based on the dependency topology.

[0134] The conversion module 24 is used to convert the propagation path into a merging operation instruction that adapts to the design rules between architectural design models through the collaborative modeling engine;

[0135] Output module 25 is used to execute the merging operation instruction to realize the integration of differences between building information models, and to merge the building design models after the differences are integrated to output a conflict-free multi-building design integrated model.

[0136] Figure 2 The aforementioned data optimization processing system for edge computing in architectural design can perform... Figure 1 The implementation principle and technical effects of the edge computing data optimization processing method in architectural design described in the illustrated embodiment will not be repeated here. The specific methods by which each module and unit of the edge computing data optimization processing system in architectural design performs its operations have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0137] In one possible design, Figure 2 The data optimization processing system for edge computing in architectural design, as shown in the embodiment, can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0138] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0139] The processing component 32 is used for the above Figure 1 The above embodiment describes a data optimization processing method for edge computing in architectural design.

[0140] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0141] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0142] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0143] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0144] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0145] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0146] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The illustrated embodiment is a data optimization processing method for edge computing in architectural design.

[0147] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0148] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0149] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for data optimization processing of edge computing in architectural design, characterized in that, The method comprises the following steps: Obtain the geometric deviation and attribute change characteristics between the building design models, and perform topological relationship comparison on the geometric deviation and attribute change characteristics to generate a version conflict identification map, which marks the high conflict areas of different building design conflicts; Deploy an incremental synchronization controller on a physical layer edge computing node to dynamically intercept the difference data segments associated with the high conflict areas of the version conflict identification map, and compress the difference data segments to form incremental data packets; Analyze the dependent topological relationship between different building design models, and predict the propagation path of building design conflicts between multiple building design models according to the dependent topological relationship; Convert the propagation path into a merging operation instruction that adapts to the design rules between the building design models through a collaborative modeling engine; Execute the merging operation instruction to realize the difference integration between the building information models, and fuse the building design models after the difference integration to output a conflict-free multi-building design integrated model.

2. The method of claim 1, wherein, Obtain the geometric deviation and attribute change characteristics between the building design models, and perform topological relationship comparison on the geometric deviation and attribute change characteristics to generate a version conflict identification map, which marks the high conflict areas of different building design conflicts, comprising: Scan the point-line-surface coordinates of the geometric structures of different building design models, and calculate the offset distance of the point-line-surface coordinates between different building design models as the geometric deviation; Obtain the component attribute records of different building design models, and extract the material parameters and physical property parameters in the component attribute records, and compare the change amount of the material parameters and physical property parameters of the components of the same building design model as the attribute change characteristics; Map the geometric deviation and attribute change characteristics to the topological grid cells of the building design models, and detect the topological grid cells that simultaneously exceed the preset geometric tolerance threshold and the preset attribute change threshold as abnormal grid cells to complete the topological relationship comparison; Statistical the aggregation density of the abnormal grid cells in the topological grid cells, and mark the areas with the aggregation density exceeding the preset conflict threshold as high conflict areas; Map the point-line-surface coordinates of the high conflict areas to the surface of the building design model to generate a version conflict identification map marking the location of the high conflict areas.

3. The method of claim 1, wherein, Deploy an incremental synchronization controller on a physical layer edge computing node to dynamically intercept the difference data segments associated with the high conflict areas of the version conflict identification map, and compress the difference data segments to form incremental data packets, comprising: Deploy an incremental synchronization controller on a physical layer edge computing node, so that the incremental synchronization controller can real-time monitor the data change of the version conflict identification map; When the incremental synchronization controller detects that the high conflict areas in the version conflict identification map have data changes, it intercepts the spatial coordinate range of the high conflict areas and the corresponding geometric deviation and attribute change characteristics to form a difference data segment; Convert the three-dimensional space coordinates of the difference data segment into two-dimensional parameterized plane coordinates to perform space coordinate compression processing, and integrate the difference data segment after space coordinate compression processing into an incremental data packet.

4. The method of claim 1, wherein, Analyzing dependency topological relations among different architectural design models, and predicting propagation paths of architectural design conflicts among the different architectural design models according to the dependency topological relations, comprising: extracting component connection relations and spatial position adjacent relations among different architectural design models, and constructing a dependency topological relation graph describing dependency topological relations among the different architectural design models according to the component connection relations and the spatial position adjacent relations; marking positions of detected high-conflict areas in the dependency topological relation graph, and marking components at which the positions of the high-conflict areas are located as conflict source nodes; locating adjacent nodes connected to the conflict source nodes by the dependency topological relations, and taking directed line segments between the conflict source nodes and the adjacent nodes as propagation paths among the different architectural design models.

5. The method of claim 4, wherein, extracting component connection relations and spatial position adjacent relations among different architectural design models, and constructing a dependency topological relation graph describing dependency topological relations among the different architectural design models according to the component connection relations and the spatial position adjacent relations, comprising: scanning physical connection points between components in different architectural design models, and obtaining component connection relations according to the physical connection points; measuring spatial position coordinates of adjacent components in different architectural design models to calculate distances between component surfaces as spatial interval values of spatial position adjacent relations, and establishing a spatial adjacent relation marking line between corresponding components when the spatial interval values are less than a preset adjacent judgment threshold; integrating the physical connection points of the component connection relations and the spatial position adjacent relation marking lines, so that the physical connection points are taken as topological nodes and the spatial position adjacent relation marking lines are taken as topological edges; combining the topological nodes and the topological edges to form a dependency topological relation graph describing dependency relations among the different architectural design models.

6. The method as claimed in claim 1, wherein, translating the propagation paths into merge operation instructions adapting to design rules among the different architectural design models by a collaborative modeling engine, comprising: analyzing component identifiers and conflict propagation direction parameters corresponding to each node in the propagation paths; retrieving a preset design rule library of the architectural design models according to the component identifiers; obtaining spatial position adjustment ranges and attribute modification constraint conditions allowed for corresponding components from the preset design rule library as rule matching parameters; inputting the conflict propagation direction parameters and the rule matching parameters into the collaborative modeling engine to generate the merge operation instructions adapting to design rules among the different architectural design models.

7. The method as claimed in claim 1, wherein, executing the merge operation instructions to realize difference integration among building information models, and fusing the architectural design models after the difference integration to output a conflict-free multi-architectural design integrated model, comprising: moving corresponding components in the architectural design models to target coordinate positions specified in the merge operation instructions to realize difference integration of component positions; updating material parameters and physical property parameters of corresponding components in the architectural design models to specified values in the merge operation instructions to complete difference integration of construction attributes; aligning the architectural design models after the difference integration of component positions and the difference integration of construction attributes according to spatial position coordinates of components; and fusing the aligned architectural design models to generate a conflict-free multi-architectural design integrated model.

8. A data optimization processing system for edge computing in architectural design, characterized by, The method comprises the following steps: A comparison module is used to obtain geometric deviation and attribute change features between building design models, and perform topological relationship comparison on the geometric deviation and attribute change features to generate a version conflict identification map, which labels high conflict areas of different building design conflicts; A clipping module is used to dynamically clip difference data segments associated with the high conflict areas of the version conflict identification map by deploying an incremental synchronization controller on a physical layer edge computing node, and compress the difference data segments to form an incremental data package; An analysis module is used to analyze the dependent topological relationship between different building design models, and predict the propagation path of building design conflicts between multiple building design models according to the dependent topological relationship; A conversion module is used to convert the propagation path into a merge operation instruction that adapts to the design rules between the building design models through a collaborative modeling engine; An output module is used to execute the merge operation instruction to realize difference integration between building information models, and fuse the building design models after the difference integration to output a conflict-free integrated model of multiple building designs.

9. A computing device, comprising: The method comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to realize the data optimization processing method for edge computing in building design according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer program is stored in the computer, and when the computer program is executed by the computer, the data optimization processing method for edge computing in building design according to any one of claims 1 to 7 is realized.

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