BIM-based cable bridge routing autonomous optimization visualization system

Abstract

The invention, which relates to the technical field of engineering automation, discloses a BIM-based cable bridge routing autonomous optimization visualization system comprising a heterogeneous professional semantic modeling module, a path identification module, a path generation module, a path conflict evolution module, a path reconstruction module and an output module. The system identifies a cable thermosensitive area by constructing a cross-professional semantic map, constructs a multi-target path evaluation function and generates a high-quality candidate path by adopting a variable weight genetic algorithm; introducing a construction stage dynamic compression prediction mechanism to improve the path robustness; visual expression of the whole process of path design is realized through versioned path reconstruction and a three-dimensional animation evolution function; and finally outputting structured path data and an obstacle avoidance strategy which can be used for construction guidance. According to the invention, the intelligent level of cable bridge path planning, the engineering implementability and the system integration efficiency are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engineering automation, and in particular relates to a BIM-based cable bridge routing autonomous optimization visualization system. BACKGROUND

[0002] In building mechanical and electrical engineering, the cable bridge system as an important physical carrier for power and information transmission, its layout design not only involves mechanical and electrical professionals, but also needs to consider the space coordination between building structure, heating and ventilation, fire protection and other multi-professionals. However, the existing cable bridge routing design method generally relies on the manual drawing of the path by the designer based on the two-dimensional drawing or the BIM platform, lacks a unified path evaluation standard and a multi-objective optimization mechanism, resulting in long wiring path, frequent turning or frequent spatial conflict, which seriously affects the construction efficiency and later operation and maintenance.

[0003] Although there are BIM platform-based bridge modeling tools that can achieve a certain degree of three-dimensional layout assistance, such tools mostly stay at the "geometric visualization" level and do not have the ability to automatically optimize the path scheme, and cannot analyze the pros and cons of the path based on spatial layout constraints, construction process priority, material cost control and other dimensions, resulting in that the path design process still heavily relies on human experience.

[0004] In addition, the existing bridge system design lacks dynamic adjustment and feedback mechanism for path scheme, and the path optimization is usually one-time calculation, which cannot be quickly reconstructed according to the changes in project stage (such as equipment position adjustment, new discovery of professional collision). At the same time, the visualization module and the path optimization logic are decoupled, resulting in that the visualization result can only show the static path and does not support the synchronous feedback of path change and risk warning.

[0005] Therefore, there is an urgent need for a cable bridge routing system that can integrate multi-professional BIM data, have multi-dimensional constraint analysis, autonomous optimization ability and interactive visualization feedback mechanism, so as to realize the fundamental change of cable path design from "graph-driven" to "intelligent decision-driven". SUMMARY

[0006] The purpose of the present application is to provide a BIM-based cable bridge routing autonomous optimization visualization system to solve the problems in the background art.

[0007] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme: a BIM-based cable bridge routing autonomous optimization visualization system, comprising: a semantic modeling module, configured to extract geometric features and spatial constraint information of different professional components from a plurality of BIM sub-models, and construct a semantic constraint graph; a path identification module, configured to identify a path heat-sensitive section based on a cable load density, a device energy consumption heat flow, and a system power requirement, generate a dynamic heat spot distribution map, and participate in path preliminary screening as one of path feasibility determination factors; a path generation module, configured to construct a path multi-objective evaluation function based on a path length, a space passability, a construction complexity, a material cost, and a heat spot avoidance factor, and generate a plurality of path candidate schemes meeting a plurality of constraint conditions by a variable weight genetic algorithm; a path conflict evolution module, configured to simulate a dynamic space compression and a device change influence on the path in a construction stage, perform evolution prediction on path conflicts, and perform secondary screening on the path candidate scheme set based on a prediction result; a path reconstruction module, configured to embed the path candidate scheme in a BIM model in real time, support path version switching, scheme comparison, conflict prompting, and path contrast animation playing; an output module, configured to automatically generate a three-dimensional coordinate sequence, a construction installation step, a bridge quantity list, and a construction obstacle avoidance scheme from a final selected path, and output the final selected path to a construction platform in a data format.

[0008] Preferably, the semantic modeling module specifically comprises: performing dissection processing on a plurality of sub-models of structures, electromechanical devices, and heating and ventilation devices from the building information model, respectively extracting parameterized family definitions, constraint boundaries, and logical use attributes of each component, and constructing a standardized component tag set; based on a space topological relationship and a construction semantic dependency relationship between components, performing vectorization coding on component nodes by using a graph neural network model, and establishing a cross-professional semantic graph structure; introducing a relationship weighting strategy based on an attention mechanism, giving dynamic weights to node pairs of a path with a high conflict probability in the graph, and constructing a multi-dimensional semantic relationship graph for path generation constraint reasoning; automatically identifying a potential wiring path corridor and an obstacle avoidance strategy through relationship path deduction of structure class nodes and device class nodes in the semantic graph, and taking the identified path as an a priori input of the path optimization module.

[0009] Preferably, the path identification module specifically comprises: obtaining device operating parameters associated with the electrical system in the BIM model, combining device categories, nominal power, and predicted load curves, and constructing a cable energy flow density distribution model in a unit space of each region; introducing a composite heat perception model based on heat conduction and heat dissipation efficiency constraints, performing multi-dimensional temperature rise estimation on a bridge path candidate section, and combining a material heat capacity coefficient and a cooling condition to evaluate a thermal steady-state characteristic of the bridge path candidate section; generating a dynamic heat field sequence of the path section by using a time sequence variable load simulation, extracting a heat anomaly frequent area based on a convolutional spatio-temporal clustering algorithm, and generating a three-dimensional heat spot distribution map; The heat spot map is converted into a penalty factor matrix of a path cost map, and is input as a feasibility weight in an initial stage of path searching to automatically avoid high-risk heat-sensitive areas.

[0010] Preferably, the path generation module specifically comprises: Based on the spatial grid extracted from the BIM model, the cable laying specification is combined to construct a bridge candidate layout channel graph, and each candidate path unit is assigned a path length, a net height passability, a corner number, a heat spot overlap degree, and a required support number. A multi-objective path evaluation function is constructed, in which each target dimension is fused by linear normalization and dimension weighting to form a fitness function, and the initial weight is dynamically set according to the construction stage priority strategy; A diversity genetic coding structure with a variable intensity adaptive mechanism is designed, and a path segment sequence and a strike vector are jointly coded; Through multi-generation evolution and inferior solution elimination strategy, a set of global non-dominated path solutions is generated, and the optimal and suboptimal paths are selected as the candidate scheme set according to the heat spot avoidance score and the cost constraint.

[0011] Preferably, the path conflict evolution module specifically comprises: Based on the building construction plan model, the installation timing of each stage component, the temporary occupation information of the construction area, and the equipment position change log are extracted to generate a time-sequenced spatial state change model; A spatial compression prediction network is constructed, which combines the component intrusion probability and time span of the space where the path segment is located in different construction stages, and uses a Bayesian dynamic network model to evaluate the path compression risk level; Each path in the candidate path set is mapped by time period, and the compression coefficient and dynamic conflict probability of the path segment in each construction stage are aggregated into a conflict evolution score; According to the conflict evolution score, the path heat spot score, and the cost score, a comprehensive evaluation matrix is constructed to sort and select the candidate paths again, and the path scheme with a prominent conflict trend is eliminated.

[0012] Preferably, the path reconstruction module specifically comprises: A path embedding interface based on the BIM native model is constructed, and a plug-in path component family is automatically generated through the path segment three-dimensional coordinate sequence and the bridge parameter template; A path version control architecture is designed, a scheme management mechanism based on difference vector coding is used, the path change content is version aligned and incrementally stored, and multi-dimensional parameter comparison between any two path schemes is supported; A conflict response engine is constructed to real-time monitor the geometric interference, insufficient clearance, and equipment occlusion events between the current embedded path and the built components, and to visualize the conflicts through color coding or flashing prompt; Generate transition animation of the path from initial state to final scheme based on path component displacement sequence and timestamp information, for showing path optimization process and construction evolution track.

[0013] Preferably, the output module specifically includes: Parse three-dimensional component information of the final path scheme, generate continuous path three-dimensional coordinate sequence based on path segment node coordinates and strike vector, and encode path structure in the form of topological linked list; According to the bridge installation process logic and the on-site operation sequence, generate a parameterized construction step list including installation starting point, segment connection, support and suspension point setting and terminal access, and attach the corresponding construction sequence number; Combined with the component distribution and clearance of the area passed by the path segment, extract high interference risk areas, and construct path obstacle avoidance strategy table, including avoidance section identification, adjustment angle suggestion and minimum clearance prompt; Pack the three-dimensional coordinate sequence, construction steps, bridge list and obstacle avoidance strategy into IFC extension format or custom JSON structure, and output to the construction platform synchronously.

[0014] In the above technical solution, the technical effects and advantages provided by the present application are: 1、The present application constructs a BIM semantic fusion system for cable bridge layout, integrates heterogeneous component semantic modeling, dynamic hot spot identification, path multi-objective optimization, construction phase conflict evolution prediction and three-dimensional path visualization reconstruction modules, significantly improves the intelligence, automation and construction adaptability level of path generation. Compared with traditional manual wiring or single geometric path generation tool, the present application can quickly generate high-quality path scheme under the conditions of meeting space passage, thermal safety, installation convenience and other multidimensional constraints, and interface with the construction platform in a data structured manner, significantly shortening the design cycle and improving the path execution stability.

[0015] 2、The present application realizes multiple breakthroughs in technical path: for the first time, it introduces component semantic graph based on graph neural network into bridge path planning to form a reasoning space constraint system; combined with convolutional spatio-temporal clustering algorithm to realize active identification and avoidance of cable heat-sensitive area; introduce Bayesian dynamic model to predict space compression trend in construction phase, effectively reduce path failure risk; and through component difference vector coding, version management and dynamic animation reconstruction, realize the whole cycle visualization management of path from design to construction. The integration of these technical means significantly improves the intelligent decision-making ability and engineering landing value of the system. BRIEF DESCRIPTION OF DRAWINGS

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a mind map of the system modules of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] For examples, please refer to Figure 1 As shown in this embodiment, the BIM-based cable tray routing autonomous optimization visualization system includes: The semantic modeling module is used to extract the geometric features and spatial constraint information of different professional components from multiple BIM sub-models and construct a semantic constraint map. The path identification module is used to identify heat-sensitive sections of the path based on cable load density, equipment energy consumption heat flow and system power requirements, generate a dynamic hot spot distribution map, and participate in the initial path screening as one of the path feasibility judgment factors. The path generation module constructs a multi-objective evaluation function for paths based on path length, spatial accessibility, construction complexity, material cost, and hot spot avoidance factor. It then generates multiple sets of candidate path schemes that meet multiple constraints using a variable-weight genetic algorithm. The path conflict evolution module is used to simulate the impact of dynamic spatial compression and equipment changes on paths during the building construction phase, predict the evolution of path conflicts, and perform secondary screening of the candidate path set based on the prediction results. The path reconstruction module is used to embed path candidate schemes into the BIM model in real time, and supports path version switching, scheme comparison, conflict prompts and path comparison animation playback. The output module is used to automatically generate a three-dimensional coordinate sequence, construction and installation steps, a list of cable tray quantities, and a construction obstacle avoidance plan for the final selected path, and output them to the construction platform in data format.

[0020] The semantic modeling module included in the system aims to solve the problems of semantic heterogeneity of multi-specialty components, geometric information fragmentation, and unclear spatial relationship in BIM models, and to construct a unified semantic data structure to provide a high readability and high constraint data basis for subsequent cable bridge path planning. The specific implementation steps are as follows: Firstly, the data of the structure, mechanical and electrical, and heating and ventilation sub-models in the building information model is divided and processed. The system automatically identifies the component types in different specialties and their respective specialties, and extracts the parameterized family definitions (such as component type, family parameter), constraint boundary information (such as shape size, spatial envelope), and logical use attributes (such as functional use, electrical grade, cold / heat medium attributes, etc.) of each component. After unified mapping rule processing, a set of standardized component label set with unified structure and consistent semantics is formed, which is used for subsequent semantic modeling.

[0021] After completing the label standardization, the system constructs an initial component relationship graph across specialties according to the spatial adjacency relationship (such as connection, nesting, coplanar) and construction semantic dependency relationship (such as installation sequence, common slot layout) between components. Subsequently, a graph neural network (GNN) model is introduced to embed and encode the component nodes, generating a vector representation with dimension constraint and context awareness capability. This representation not only contains the attributes of the component itself, but also implicitly its semantic role in the building space and its relationship with other components.

[0022] To enhance the recognition ability of high-risk path sections (such as collision-prone and space-compressed areas), the system further introduces a relationship weighting strategy based on attention mechanism (Attention) to dynamically assign edge weights to path-related node pairs in the graph. The system learns and trains according to the historical conflict probability and path passing success rate between components in past projects, giving higher attention weights to node connection relationships with high conflict probability, thereby forming a multi-dimensional weighted semantic graph with semantic reasoning capability.

[0023] Finally, the system identifies potential wiring path corridors with good passing potential in the building space through path deduction algorithms between structure class component nodes and device class component nodes in the graph, and analyzes their spatial characteristics and connection feasibility. At the same time, combined with the high-weight edges in the attention graph and the conflict hot zone information, the system automatically generates a set of path obstacle avoidance strategies, including recommended avoidance path segments, candidate passing channels, etc. These structured information serves as prior input for the path optimization module, providing semantic support and spatial guidance for subsequent path search and selection.

[0024] The path identification module aims to identify the areas in the building space that have a thermal risk impact on the bridge layout from the electrical system operating parameters, and to complete the dynamic avoidance judgment of the high-thermal-sensitive area in the path planning stage. This module starts from the spatial distribution of cable thermal load, combines material properties and environmental conditions, and generates a three-dimensional thermal spot distribution map for path preliminary screening, which mainly includes the following steps: The system first extracts the equipment parameters associated with the electrical system from the BIM model, including equipment categories (such as lighting, power, fire protection, etc.), nominal power, operating load level, periodic start-stop characteristics, etc. Combined with cable layout rules and equipment power supply association, a cable energy flow density distribution model is established in the unit space of the area around each path segment, i.e. the time-varying distribution of current load intensity in a given space unit, which provides input basis for subsequent thermal impact analysis.

[0025] The specific implementation of this model is as follows: The area in the BIM model where the path is to be laid is divided into regular cubic space unit grids (such as one unit per 0.5m³) to facilitate load distribution calculation in a three-dimensional coordinate system. Each space unit is numbered and bound to the center coordinates, volume information, and contained components, devices, or cable path segments.

[0026] According to the device load list in the system (including device nominal power, peak current, operating time, loop number, etc.) and the bridge wiring logic, the mapping relationship between the cable segment and the power supply device is established. The system automatically identifies the devices served by each cable path, and then obtains the expected load curve (obtained from the device type or historical project experience library).

[0027] For each cable path segment li, based on the operating state of the served device, the energy transmission amount of the segment in unit time (unit can be W or A·m) is calculated: ; where Ii is the cable load current and Li is the path segment length. Project the energy flow of all path segments to the space unit they are in, and accumulate the energy flow values contributed by each path segment in the same unit to form the total energy flow density of the unit.

[0028] For each space unit Sj, its unit volume energy flow density Dj can be defined as: ; where represents the total energy flow of all path segments passing through the unit, and Vj is the unit volume. This index is used to measure the cable concentration and thermal accumulation potential.

[0029] For the cable current density distribution model, the system introduces a composite thermal perception model based on heat conduction theory and heat dissipation condition constraints. Combined with the heat capacity coefficient and thermal conductivity of the selected bridge material for the path segment, as well as the cooling capacity of the local space (such as ventilation conditions, shielding density, etc.), the system estimates the steady-state temperature rise and temperature distribution curve of each path segment, forming a path thermal stability evaluation model.

[0030] To more realistically simulate the impact of device operation on cable load, the system simulates the thermal field response of the path segment over time based on the time-series load data during the project operation period, generating a dynamic thermal field sequence. Then, using a convolutional spatio-temporal clustering algorithm, the system automatically identifies areas where thermal abnormalities frequently occur, extracts the path segments that form the temperature rise threshold, and outputs a three-dimensional structured thermal spot distribution map.

[0031] Finally, the system converts the temperature rise intensity, duration, and coverage area in the thermal spot map into a penalty factor matrix in the path cost map, which is used as one of the weight input variables in the path search stage. High-thermal-sensitivity areas will be assigned higher path costs, automatically determining them as low-priority paths during path generation, thus actively avoiding thermal risks.

[0032] The path generation module is responsible for constructing a multi-objective path evaluation mechanism based on semantic modeling and thermal risk analysis, integrating spatial, engineering, and cost constraints, and generating high-quality bridge path candidate schemes using an adaptive genetic algorithm. This module includes the following steps: The system first divides the building space into a three-dimensional space grid based on the structural geometry and clearance area data extracted from the BIM model, and constructs a bridge candidate channel map that meets the installation standards by combining cable installation specifications (such as minimum clearance, maximum corner, etc.). For each passable path unit in the map, the following multi-dimensional evaluation parameters are assigned: path length, clearance passability, number of path corners, overlap with thermal spot sections, required support installation quantity and type, etc. This step forms a high-dimensional path evaluation basic data set.

[0033] For the above multi-dimensional parameters, a unified multi-objective path evaluation function is constructed. After linear normalization processing, each evaluation factor is combined into a path fitness function through weighted fusion. Different stages (such as scheme design, construction drawing design, and construction preparation) can define different weight strategies, and the system supports dynamic adjustment of weight distribution to achieve deep coupling between path planning and actual engineering needs.

[0034] Path schemes are expressed in the genetic algorithm using a combination of path segment sequences and direction vectors. Path segments are represented by three-dimensional grid numbers, and direction vectors identify path turning trends. The coding structure is combined with a variable strength adaptive mechanism, which automatically adjusts the gene mutation probability based on the current population diversity and evolution progress to enhance search diversity and prevent local optimal traps.

[0035] Through multiple rounds of genetic evolution process, including selection, crossover, mutation and poor solution elimination operation, the system generates a large number of path schemes. After completing the fitness evaluation, a global non-dominated solution set (Pareto front) is constructed, that is, the optimal path set which is not suppressed on multiple objectives by other schemes. The system then selects 1-3 groups of optimal and suboptimal paths based on the hot spot avoidance index and construction cost constraints as the final candidate scheme set, which is input into the path reconstruction and comparison module.

[0036] The path conflict evolution module is used to simulate the path compression and potential collision risk caused by component installation progress, equipment position adjustment or temporary construction occupation in the construction process, and to predict and optimize the conflict trend of the candidate path scheme. The module specifically includes the following steps: The system first extracts the construction installation timing information of each component in the project, the temporary area occupation plan of each construction stage and the dynamic change log of equipment position from the 4D-BIM construction plan model. The above information is mapped to the three-dimensional space model to generate a time-labeled space state change model, that is, the spatial form and occupation change of components in different time periods, which is used to describe the dynamic evolution process of the construction environment.

[0037] For the area passed by the candidate path, the system constructs a space compression prediction network, taking the component intrusion probability and intrusion duration of the space where the path segment is located in different construction stages as input variables, and introducing a Bayesian dynamic network (Bayesian Dynamic Network) model to probabilistically model and predict the risk level of the space compression degree faced by the path segment. The model supports effective simulation of construction environments with strong time dependence and complex spatial changes.

[0038] For each path in the candidate path set, the system projects its path segments into the space state model of each construction stage, calculates its compression coefficient (reduction ratio of spatial clearance) and dynamic conflict probability in different time periods, and aggregates to form the conflict evolution score of the path. The score reflects the degree of spatial interference of the path in the whole construction cycle, and is an important indicator for evaluating the robustness of the path.

[0039] The system constructs a multi-objective comprehensive evaluation matrix according to the conflict evolution score, path hot spot score and construction cost score, and uses a weight weighted fusion method to perform secondary sorting and elimination on the candidate paths. Paths with prominent conflict trends will be determined as low-priority or unqualified paths and eliminated from the final candidate set to ensure that the output paths have higher spatial adaptability and execution stability in the construction implementation stage.

[0040] The path reconstruction module is used to realize the embedded visual expression, version management and interactive feedback of the candidate path scheme in the BIM model, support multi-version comparison, conflict response and dynamic evolution display of the path scheme, and is a key visual interface for the path planning result to design verification and construction delivery. It specifically includes the following steps: Based on the native interface protocol of the BIM platform, the system constructs a path embedding channel. For each path candidate scheme, the system extracts its three-dimensional coordinate sequence (including the spatial position of the path node) and the bridge parameter template (including the bridge type, width, height, connector form, etc.), and automatically generates a path component family that meets the Revit, IFC and other standards. This component family supports segment insertion, node connection and attribute rendering, realizing the pluggable three-dimensional component expression of the path in the BIM model.

[0041] To support multi-scheme comparison and historical path tracking, the system designs a path version control architecture, which uses a difference vector coding mechanism to archive and incrementally store each path change. Path difference data includes node changes, path length changes, and hot spot avoidance index changes. Through a multi-dimensional parameter comparison interface, the visual difference display and comparison analysis of any two versions of the path in terms of spatial orientation, risk factors and material consumption can be realized.

[0042] After the path is embedded, the system starts the conflict response engine to detect whether there is a geometric interference, insufficient clearance or blocking of key equipment between the embedded path and the existing components. After conflict identification, the system provides immediate conflict visualization prompts in the form of layer annotations, color coding or path segment flashing, and marks the problem path segment as "needs adjustment" for user decision reference or path correction process.

[0043] The system records the coordinate change sequence and timestamp information of all path segments in each version of the path scheme, and generates a component animation transition sequence from the initial scheme to the final scheme based on this data, realizing the three-dimensional dynamic demonstration of the path optimization process. This function supports users to track the path evolution logic from a visual perspective, which helps project managers to trace back the path optimization results and verify the decision.

[0044] The output module is used to structure the bridge path scheme selected by the path generation module, convert the construction process, and output the compatible format, realizing seamless data connection from path design to construction execution. This module specifically includes the following steps: The system first analyzes the final determined path scheme in the path candidate set, extracts the three-dimensional component information of the path segment, including the spatial coordinates of the starting and ending nodes, the path segment heading vector, the turning node and connection point data, etc. Based on this, the continuous path three-dimensional coordinate sequence is generated, and the topological linked list data structure is used to encode the logical order between the path segments, and a path data model with spatial continuity and topological traceability is constructed.

[0045] According to the standard bridge construction process and the on-site installation logic, the system automatically generates a parameterized construction step list. The list includes path installation starting point identification, installation order of each bridge segment, segment connection component setting, support and suspension point position and type labeling, terminal access method, etc., and assigns a construction order number to each construction action to form a structured construction instruction set that can be used for on-site operation guidance.

[0046] The system combines the existing component distribution and clearance data of the area passed by the path segment to identify key segments with high collision probability or space compression risk, and constructs a path obstacle avoidance strategy table. The table contains information such as the location of the section to be avoided, the recommended detour angle or path adjustment suggestion, and the minimum clearance in the area, which is used to guide construction personnel to install reasonably in narrow areas.

[0047] The above-mentioned three-dimensional path coordinates, construction steps, bridge material list and obstacle avoidance strategy content are packaged into a standardized data format, which can be IFC extension format (Industry Foundation Classes), or exported as a custom structured JSON file according to the actual use platform requirements. The system supports automatic pushing of data packets to BIM collaboration platforms, intelligent construction terminals or material prefabrication systems, realizing the digital connection and task-driven docking of design results and construction platforms.

[0048] Example 2: To verify the technical advantages of the BIM-based cable bridge routing autonomous optimization visualization system proposed in the present application in terms of path optimization efficiency, obstacle avoidance ability and thermal stability, a certain complex project BIM model is selected as the test scene to verify the system function and conduct comparative experiments. The experimental content includes: Implementation scenario: Building area: about 42,000 square meters, 10 floors above ground, 2 floors underground; Including strong electricity, weak electricity, fire protection, air conditioning and water supply and drainage and other multi-specialty BIM sub-models; Experimental path: select a typical cable bridge path from the underground equipment layer to the floor power distribution room, with a path length of about 60 meters, involving 17 corners and crossing 8 types of component areas.

[0049] Comparison system: The system of the present application (system A); Traditional BIM visual modeling + manual routing method (B system); Commercial plug-in automatic routing (C system, without thermal evaluation and conflict prediction capabilities).

[0050] Experimental data and effect comparison:

[0051] Result analysis and technical advantages: Path quality is significantly better than existing methods: the path generated by system A is not only the shortest, but also has zero thermal spot crossing times, avoiding high temperature risks; the paths generated by systems B and C cannot avoid some high-risk areas.

[0052] Obstacle avoidance and dynamic compression prediction accuracy: the unique "conflict evolution module" of the invention successfully predicts possible space compression areas in later construction, ensuring sufficient path reserved clearance; traditional systems may fail in path construction in later stages.

[0053] Optimization efficiency is significantly improved: from about 90 minutes of manual path layout to second-level response, and the system automatically outputs component lists and construction guidance documents.

[0054] Three-dimensional path reconstruction and evolution visualization: system A supports complete path version animation playback and comparison, which is helpful for engineering collaboration and scheme review.

[0055] The above examples fully verify the comprehensive technical advantages of the system in terms of path planning intelligence, thermal sensitivity avoidance, construction adaptability, and platform compatibility, providing an efficient, safe, and highly visual interactive path design means for practical engineering applications.

[0056] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A BIM-based cable tray routing autonomous optimization visualization system, characterized in that: The method comprises the following steps: A semantic modeling module is used to extract geometric features and spatial constraint information of different professional components from multiple BIM sub-models, and construct a semantic constraint graph; A path recognition module is used to identify path heat-sensitive sections based on cable load density, equipment energy consumption heat flow and system power demand, generate a dynamic heat spot distribution map, and participate in path preliminary screening as one of the path feasibility judgment factors; A path generation module is used to construct a path multi-objective evaluation function based on path length, space passability, construction complexity, material cost and heat spot avoidance factors, and generate multiple sets of path candidate schemes that meet multiple constraint conditions through a variable weight genetic algorithm; A path conflict evolution module is used to simulate the influence of dynamic space compression and equipment changes on the path during the construction phase, predict the evolution of path conflicts, and implement secondary screening on the candidate path set based on the prediction results; A path reconstruction module is used to embed path candidate schemes in the BIM model in real time, support path version switching, scheme comparison, conflict prompting and path contrast animation playback; An output module is used to automatically generate three-dimensional coordinate sequences, construction and installation steps, bridge quantity lists and construction obstacle avoidance schemes from the final selected path, and output them to the construction platform in data format. 2.The BIM-based cable tray routing autonomous optimization visualization system according to claim 1, wherein: The semantic modeling module specifically comprises the following steps: The structure, mechanical and electrical, and HVAC sub-models from the building information model are subjected to subdivision processing, and the parameterized family definitions, constraint boundaries and logical use attributes of each component are extracted to construct a standardized component tag set; Based on the spatial topological relationship and construction semantic dependency relationship between components, a graph neural network model is used to vectorize the component nodes, and a cross-professional semantic graph structure is established; A relationship weighting strategy based on attention mechanism is introduced to assign dynamic weights to node pairs in the graph with high conflict probability, and a multi-dimensional semantic relationship graph for path generation constraint reasoning is constructed; Through the relationship path deduction of structure class nodes and equipment class nodes in the semantic graph, potential wiring path corridors and obstacle avoidance strategies are automatically identified as prior inputs to the path optimization module. 3.The BIM-based cable tray routing autonomous optimization visualization system of claim 1, wherein: The path recognition module specifically comprises the following steps: The device operating parameters associated with the electrical system in the BIM model are obtained, and combined with the device category, nominal power and expected load curve, a cable energy flow density distribution model is constructed in each unit space in the region; A composite heat perception model based on heat conduction and heat dissipation efficiency constraints is introduced to estimate the multi-dimensional temperature rise of the bridge path candidate section, and its thermal steady-state characteristics are evaluated based on the material heat capacity coefficient and cooling conditions; A dynamic thermal field sequence of the path section is generated by using time-series variable load simulation, and a three-dimensional heat spot distribution map is generated based on a convolutional spatio-temporal clustering algorithm; The heat spot map is converted into a penalty factor matrix of the path cost map, and used as the feasibility weight input in the initial stage of path search to automatically avoid high-risk areas with heat sensitivity.

4. The BIM-based cable tray routing autonomous optimization visualization system of claim 1, wherein: The path generation module specifically comprises the following steps: Based on the space grid extracted from the BIM model, a bridge candidate laying channel graph is constructed in combination with the cable laying specifications, and multiple evaluation dimensions such as path length, net height passability, number of corners, heat spot overlap degree and number of required supports are assigned to each candidate path unit; A multi-objective path evaluation function is constructed, in which each objective dimension is fused by linear normalization and dimension weighting to form a fitness function, and the initial weight is dynamically set according to the construction stage priority strategy; A diversity genetic coding structure with a variable intensity adaptive mechanism is designed, and a path segment sequence and strike vector joint coding method is adopted; Through multi-generation evolution and inferior solution elimination strategy, a set of global non-dominated path solutions is generated, and the optimal and suboptimal paths are selected as the candidate scheme set according to the hot spot avoidance score and cost constraint.

5. The BIM-based cable tray routing self-optimization visualization system of claim 1, wherein: The path conflict evolution module specifically includes: Based on the building construction plan model, the component installation time sequence, construction area temporary occupation information and equipment position change log of each stage are extracted to generate a time-sequenced spatial state change model; A spatial compression prediction network is constructed, which combines the component intrusion probability and time span of the space where the path segment is located in different construction stages, and uses the Bayesian dynamic network model to evaluate the path compression risk level; The conflict evolution score of each path in the candidate path set is calculated by aggregating the compression coefficient and dynamic conflict probability of the path segment in each construction stage; According to the conflict evolution score, path hot spot score and cost score, a comprehensive evaluation matrix is constructed to sort and select the candidate paths again, and the path scheme with a prominent conflict trend is eliminated.

6. The BIM-based cable tray routing self-optimization visualization system of claim 1, wherein: The path reconstruction module specifically includes: A path embedding interface based on BIM native model is constructed to automatically generate a plug-in path component family through path segment three-dimensional coordinate sequence and bridge parameter template; A path version control architecture is designed, which uses a difference vector coding-based scheme management mechanism to perform version alignment and incremental storage of path change content, and supports multi-dimensional parameter comparison between any two path schemes; A conflict response engine is constructed to real-time monitor the geometric interference, insufficient clearance and equipment occlusion events between the embedded path and the built components, and visualize the conflicts through color coding or flashing prompt; Based on the path component displacement sequence and timestamp information, a transition animation from the initial state to the final scheme of the path is generated to show the path optimization process and construction evolution trajectory.

7. The BIM-based cable tray routing self-optimization visualization system of claim 1, wherein: The output module specifically includes: The three-dimensional component information of the final path scheme is parsed, and the continuous path three-dimensional coordinate sequence is generated based on the path segment node coordinates and strike vector, and the path structure is encoded in the form of a topological linked list; According to the bridge installation process logic and on-site operation sequence, a parameterized construction step list including installation starting point, segment connection, support and suspension point setting and terminal access is generated, and the corresponding construction sequence number is attached; Combined with the component distribution and clearance of the area passed by the path segment, a high interference risk area is extracted, and a path obstacle avoidance strategy table is constructed, including avoidance section identification, adjustment angle suggestion and minimum clearance prompt; The three-dimensional coordinate sequence, construction steps, bridge list and obstacle avoidance strategy are packaged into IFC extension format or custom JSON structure, and are output to the construction platform synchronously.

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