Multi-constraint-driven intelligent arrangement method for rail transit station rooms

Abstract

The invention provides a multi-constraint-driven intelligent arrangement method for rail transit station rooms. Comprising the following steps of S1) analyzing a CAD arrangement base map and extracting semantic information, S2) establishing room configuration information and constructing a classification system, S3) defining decision variables and mathematical modeling, S4) analyzing an evacuation path and calculating an evacuation distance, S5) performing hard constraint modeling, S6) setting a soft constraint optimization objective and constructing a multi-objective function, S7) solving a constraint satisfaction problem, and S8) comparing and selecting multiple schemes and performing visual display. The method has the advantages of being high in automation degree, high in constraint processing capacity, remarkable in optimization effect, high in specialization degree and high in scheme comparison and selection capacity.

Description

Technical Field

[0001] This invention relates to the field of building design automation technology, specifically to a multi-constraint driven intelligent layout method for rooms in rail transit stations such as subway stations and high-speed rail stations. Background Technology

[0002] The layout of rooms inside rail transit stations is a crucial aspect of station design, involving the rational allocation of various room types, including equipment rooms, management rooms, and auxiliary rooms. During the station design phase, due to changing functional requirements, spatial constraints, and complex design specifications, designers often need to repeatedly adjust room layout plans, resulting in a large workload and a high risk of errors.

[0003] The existing methods for arranging station rooms have the following main problems: (1) Low efficiency of manual arrangement: Designers mainly rely on experience to arrange manually, which requires repeated trial and error and a long design cycle; (2) Difficulty in handling constraints: The arrangement of station rooms needs to meet multiple constraints such as area, location, evacuation, and fire prevention at the same time, and manual handling is prone to omissions; (3) Lack of optimization mechanism: Existing methods are difficult to achieve optimization goals such as maximizing space utilization under the premise of meeting all constraints; (4) Delay in evacuation analysis: Evacuation analysis is carried out only after the room arrangement is completed, and the layout needs to be readjusted when problems are found; (5) Difficulty in comparing schemes: There is a lack of quantitative indicators to objectively evaluate and compare different arrangement schemes. In particular, for rail transit stations, the room arrangement also needs to meet special fire compartment requirements, different evacuation distance restrictions for normal corridors and dead-end corridors, and special location requirements for equipment rooms, making the traditional manual arrangement method more difficult and inefficient. Summary of the Invention

[0004] The purpose of this invention is to provide an automatic, fast, and accurate intelligent layout method for rail transit station rooms driven by multiple constraints.

[0005] This invention achieves intelligent and automatic layout of rail transit station rooms through technical means such as CAD layout base map analysis, multi-constraint modeling, evacuation path analysis, constraint satisfaction problem solving, and multi-scheme comparison.

[0006] First, this invention uses CAD layout base map parsing and semantic information extraction technology to read CAD layout base map files in a specific format. This base map uses HATCH blocks to represent different functional areas and text labels to distinguish area types. The system automatically identifies and extracts functional area information, establishes a unified coordinate transformation relationship, and provides a standardized data foundation for subsequent processing.

[0007] Secondly, this invention establishes a hierarchical multi-constraint modeling system, including hard constraint modeling and soft constraint optimization objective setting. Hard constraint modeling establishes the constraints that must be strictly satisfied, defines the feasible domain of the solution, and covers multiple dimensions such as room area constraints, geometric constraints, location constraints, evacuation constraints, and door location constraints. Soft constraint optimization objective setting establishes the optimization objective function, used to find the optimal solution among feasible solutions that satisfy the hard constraints. This includes objectives such as area optimization, layout optimization, and evacuation optimization, and provides a dynamic weight adjustment mechanism to achieve personalized multi-objective optimization configuration.

[0008] Furthermore, this invention employs evacuation path analysis technology, identifies evacuation route areas based on CAD vector data, constructs an evacuation network diagram, calculates evacuation distances using the shortest path algorithm, and combines fire compartment constraints to ensure the compliance of evacuation paths, thus providing evacuation safety assurance for room layout.

[0009] Then, this invention transforms the multi-constraint building layout problem into a constraint satisfaction problem, which is solved using a constraint programming solver. A hierarchical optimization strategy of hard and soft constraints is implemented, prioritizing the satisfaction of hard constraints to ensure the feasibility of the solution, and then optimizing the objective function of soft constraints to generate multiple feasible layout schemes.

[0010] Finally, this invention provides a multi-scheme comparison and visualization technology to comprehensively evaluate multiple feasible solutions, establish a scheme evaluation index system including area utilization rate, constraint satisfaction rate, evacuation efficiency, etc., realize the scientific ranking and screening of schemes, and provide users with intuitive scheme comparison support through visualization.

[0011] To achieve the above objectives, the present invention is implemented through the following technical solution: A multi-constraint-driven intelligent layout method for rail transit station rooms includes the following steps: Step S1) CAD layout base map analysis and semantic information extraction Input the CAD layout base map DXF file of the station, establish a file parsing and entity recognition mechanism for the CAD layout base map, use HATCH fill blocks to represent different functional areas, HATCH entity data format includes geometric boundary coordinates and fill attribute information, distinguish area types by TEXT text labels, TEXT entity data includes text content, position coordinates and font attribute information, establish area key value mapping relationship to determine the one-to-one correspondence between text labels and fill areas, and convert CAD vector data into a standardized raster image format by performing coordinate system transformation and data standardization processing. The system can automatically parse out the building outline, functional zoning, fire compartments, traffic areas, evacuation facilities and structural elements; Step S2) Establishing Room Configuration Information and Building a Classification System Based on the actual needs of the station, a multi-dimensional room classification system was established. The main classification dimensions include classification by personnel usage characteristics, classification by functional attributes, classification by professional system, and classification by architectural features. This classification system realizes the systematic management of multi-dimensional room identification and constraints by establishing a room attribute tag library. Each room is configured with detailed information and parameters, including basic information configuration, geometric constraint parameter settings, position constraint parameter settings, evacuation constraint parameter settings, and door configuration parameter settings, which are stored and managed through a standardized data structure. Step S3) Defining decision variables and mathematical modeling Establish a complete data structure for decision variables and door location decision variables for each room; Step S4) Evacuation route analysis and evacuation distance calculation Based on the HATCH filling entity data obtained in step S1) and the established regional key-value mapping relationship, the relevant areas of the evacuation route are identified from the CAD layout base map. The system converts the evacuation route into a grid map representation, extracts the key nodes of the evacuation route as the basic elements of the evacuation network, and merges the evacuation exit coordinate information with the key nodes to form a complete evacuation network topology. The system constructs network adjacency relationships based on fire compartment constraints, and uses the Floyd-Warshall algorithm to calculate the shortest path distance between all node pairs in the network, generating a lookup table of the shortest distance from each key node to all evacuation exits. Step S5) Hard Constraint Modeling Users can set area constraints, geometric dimension constraints, room non-overlap constraints, evacuation distance constraints, fire compartment constraints, room adjacency constraints, and door position constraints, and selectively enable or disable various constraints according to project requirements. Step S6) Setting soft-constraint optimization objectives and constructing multi-objective functions Set optimization targets for area, room layout, water supply, professional system rooms, and structural coordination, and construct the final comprehensive optimization model. The system provides an independent slider control for each soft constraint optimization objective, which can adjust the position of each slider in real time and supports personalized multi-objective optimization configuration; Step S7) Solving the constraint satisfaction problem The CP-SAT constraint programming solver of OR-Tools is used to efficiently solve multi-constraint building layout problems, and feasible layout schemes are generated through hierarchical optimization strategies. Step S8) Comparison and Visualization of Multiple Options Establish a scientific evaluation index system for the layout schemes to achieve comprehensive evaluation and ranking of the schemes, and provide users with professional scheme comparison and decision support through an intuitive visual interface.

[0012] Furthermore, in step S2), the multi-dimensional room classification system specifically includes: (1) Classification by personnel usage characteristics: This classification dimension is mainly used for evacuation constraint modeling and safety management constraint setting. Among them, occupied rooms refer to rooms that require staff to be stationed or used frequently. Such rooms need to meet stricter evacuation distance requirements and environmental comfort constraints. Unoccupied rooms are mainly used for equipment installation, item storage, etc., where no personnel need to be stationed. The evacuation requirements for such rooms are relatively relaxed, but they need to meet the accessibility constraints for equipment maintenance. (2) Classification by functional attributes: This classification dimension is mainly used for functional relevance constraints and centralized layout optimization target setting. Management rooms undertake the station operation management function. These rooms need to be centrally arranged to improve management efficiency. Equipment rooms are used to install various equipment systems. These rooms need to consider equipment load and maintenance space constraints. Auxiliary rooms provide auxiliary service functions. The layout of these rooms needs to take into account both service convenience and space utilization. (3) Classification by professional system: This classification dimension is mainly used for setting location constraints and adjacent relationship constraints related to professional systems. Rooms with water involve water supply and drainage systems. Such rooms need to be located close to the main water supply and drainage pipes. Rooms with electricity involve high-voltage systems. Such rooms need to consider electrical safety distance and cable laying path constraints. Rooms with low-voltage systems involve low-voltage systems. Such rooms need to avoid high-voltage interference and meet signal transmission distance requirements. (4) Classification by building characteristics: This classification dimension is mainly used for setting building structure constraints and spatial adaptability constraints. Cable wells are used for vertical spaces for cable laying. Such spaces need to meet the requirements of vertical penetration and fire separation. Annex area rooms provide supporting services for main functional rooms. The layout of such rooms needs to consider service radius and circulation organization.

[0013] Furthermore, in step S2), configuring information and setting parameters for each room specifically includes: (1) Basic information configuration: including room name as the unique identifier of the room, functional area affiliation indicating the building functional area to which the room belongs, and characteristic attributes covering characteristic parameters such as frequency of use by personnel and type of equipment; (2) Geometric constraint parameter settings: including minimum bay and minimum depth as the lower limit of size based on functional requirements and building module, maximum bay-to-depth ratio used to ensure the geometric constraint of room proportion, and area range determined according to functional requirements as upper and lower limits of area for area constraint modeling; (3) Location constraint parameter settings: including adjacent relationship requirements based on functional association and professional system requirements, functional zoning to ensure that the room must be located in the specified functional zone, and floor location restrictions based on usage characteristics and equipment requirements. (4) Evacuation constraint parameter settings: including evacuation distance limits based on building fire protection codes and room occupancy characteristics, as well as different evacuation distance standards for normal corridors and dead-end corridors; (5) Door configuration parameter settings: including the number of doors based on evacuation requirements and usage functions, the door opening size parameters to meet the requirements of personnel evacuation and equipment handling, and the door opening direction constraints based on evacuation direction and spatial layout requirements.

[0014] Furthermore, in step S5), hard constraint modeling specifically includes: (1) Area constraint settings: The minimum area constraint and maximum area constraint checkboxes in the configuration interface are used to control the enabling status, ensuring that the actual area of ​​each room is within the specified minimum and maximum values; (2) Geometric dimension constraint settings: The minimum bay depth constraint and maximum bay depth ratio constraint checkboxes in the configuration interface are used to control the enabled status, including the size limit of the minimum bay and minimum depth of the room, as well as the reasonable control of the bay depth ratio; (3) Room non-overlapping constraint setting: The two-dimensional interval non-overlapping algorithm is used to ensure that there is no spatial conflict between rooms, and to ensure that no two rooms will overlap in the layout space, thus maintaining the spatial rationality of the room layout. (4) Evacuation distance constraint settings: The enabled status can be controlled by the evacuation distance constraint checkbox in the configuration interface. Different maximum evacuation distance limits can be set according to the corridor type to ensure that the room layout meets the evacuation requirements of the building fire protection code. (5) Fire compartment constraint settings: The enabled status is controlled by the fire compartment constraint checkbox in the configuration interface to ensure that different types of rooms are arranged in the corresponding fire compartments and to establish the ownership relationship constraint between rooms and fire compartments. (6) Room adjacency constraint settings: The enabled state is controlled by the adjacent room constraint checkbox in the configuration interface. This handles room pairs with special location relationship requirements and ensures that the specified rooms can be arranged adjacently in space, such as the vehicle control room being adjacent to the stationmaster's office. (7) Door position constraint settings: The door orientation constraint and door spacing constraint checkboxes in the configuration interface are used to control the enabled state to ensure that the position of the room door complies with the building design code, including the position restriction of the door on the room boundary, the requirement of the door facing the corridor, and the spacing requirement between doors in a multi-door room.

[0015] Furthermore, in step S6), the setting of the soft-constraint optimization objective specifically includes: (1) Area optimization target setting: For rooms with a suitable area, the layout result is made as close as possible to the suitable area value, so as to achieve precise control of the room area; (2) Optimize the layout of occupied rooms. For occupied rooms that are not toilets, the layout is concentrated by minimizing the distance between such rooms. The goal of placing occupied rooms near the vehicle control room is to ensure that occupied rooms are placed as close as possible to the vehicle control room. (3) Optimize the layout of rooms with water by minimizing the distance between toilet-type rooms with water to achieve centralized layout of such rooms; (4) Optimize the layout of professional system rooms to ensure that the low-voltage rooms are arranged in a linear manner, which facilitates the laying of cables and maintenance of equipment in the low-voltage system and improves the integrity and maintainability of the low-voltage system; (5) Structural coordination optimization target setting: By optimizing the room location, structural columns are prevented from passing through the interior space of the room, reducing the impact on the room's function and improving the integrity and efficiency of the room space; (6) Construction of comprehensive optimization model: Based on all soft constraint optimization objectives, the final comprehensive optimization model is constructed. The system integrates multiple optimization objectives into a comprehensive optimization objective by weighted summation. The slider control corresponding to the weight of each objective is adjusted in real time.

[0016] Furthermore, in step S7), the specific layout scheme includes the following steps: S701) Based on the hard constraints established in step S5), the soft constraints established in step S6), and the evacuation analysis results generated in step S4), initialize the CP-SAT constraint programming solver of OR-Tools, configure the solution algorithm parameters, and set the upper limit of the solution time to 150 seconds to prevent the solution process from being extended indefinitely. S702) Based on the initialized solver, start the branch and bound algorithm to search the feasible solution space, prioritize satisfying all hard constraints defined in step S5) to ensure the feasibility of the solution, and prune directly if hard constraints are violated. S703) Based on satisfying the hard constraints, optimization is performed based on the soft constraint objective function defined in step S6). The optimization objectives include maximizing the area, optimizing the concentration, and minimizing the evacuation distance. A heuristic search strategy is adopted to improve the solution efficiency. S704) Configure a solution collector, set to collect a maximum of 15 feasible solutions, monitor the solution status in real time through a callback function mechanism, record the current optimal solution and solution progress, and terminate the solution process when the time limit is reached, a satisfactory solution is found, or the optimization gap is less than the convergence threshold. S705) Collect and organize all feasible solutions found, including complete solution information such as room location coordinates, geometric dimensions, and objective function values. Sort the solutions according to the merits of their objective function values ​​to provide basic data for subsequent solution comparison.

[0017] Compared with the prior art, the present invention has the following advantages: This invention presents a multi-constraint-driven intelligent layout method for rail transit station rooms, which is automatic, fast, and accurate. Specifically, it features high automation: achieving fully automatic conversion from CAD drawings to layout schemes, significantly improving design efficiency; strong constraint handling capability: supporting complex multi-constraint modeling to ensure the compliance and rationality of the scheme; significant optimization effect: employing a multi-objective optimization strategy and supporting dynamic weight adjustment to achieve personalized optimal layout while satisfying constraints; high professionalism: specifically designed for the characteristics of rail transit buildings, with strong adaptability; and strong scheme comparison capability: generating multiple feasible schemes and providing a scientific comparison mechanism to offer users the optimal choice. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0019] Figure 2 This is an example diagram of the CAD layout base map of the present invention.

[0020] Figure 3 This is a schematic diagram of the room type configuration system of the present invention.

[0021] Figure 4 This is an example diagram of room configuration information according to the present invention.

[0022] Figure 5 This is a schematic diagram of room decision variable modeling in this invention.

[0023] Figure 6 This is a schematic diagram of the room adjacency constraint modeling of the present invention.

[0024] Figure 7 This is a diagram of the hard constraint configuration interface of the present invention.

[0025] Figure 8 This is a diagram of the soft constraint configuration interface of the present invention.

[0026] Figure 9 This is a visualization of the results for a single room.

[0027] Figure 10 This is a schematic diagram of the interface for comparing multiple options. Detailed Implementation

[0028] The embodiments of the present invention will now be described in further detail with reference to the accompanying drawings.

[0029] A multi-constraint-driven intelligent room layout method for rail transit stations, taking a subway station as an example, such as... Figure 1 As shown, the specific steps include the following.

[0030] Step S1) CAD layout base map analysis and semantic information extraction This embodiment provides a method for parsing and extracting semantic information from CAD layout base maps, achieving automatic conversion from DXF format CAD files to standardized data structures, providing accurate spatial foundation data for subsequent constraint modeling and optimization solutions. Specifically, the input is a DXF file of the CAD layout base map of the station in this embodiment. This base map uses a specific format—using HATCH blocks to represent different functional areas and text labels to distinguish area types. The system automatically parses and obtains: (1) Building outline: Information on the outer boundary outline of the station building; (2) Functional zoning: Functional zoning such as public areas, room layout areas, and auxiliary areas identified by the HATCH entity; (3) Fire compartments: Boundary and attribute information of various fire compartments identified by text labels; (4) Traffic areas: Traffic evacuation spaces such as corridors and stairwells identified by HATCH entities; (5) Evacuation facilities: Location of evacuation exits and related evacuation routes; (6) Structural elements: the location and geometric information of building structural components such as structural columns.

[0031] The specific implementation process is as follows: S101) Initialize the DXF file reading and entity filtering system, establish a file parsing and entity recognition mechanism for CAD layout base maps, and prepare for the subsequent extraction of HATCH entities and TEXT text entities and spatial relationship analysis. S102) Using the entity recognition mechanism established in step S101), obtain all HATCH filling entities. These entities are used to represent the boundaries of different functional areas in the layout base map. The obtained HATCH entity data format includes geometric boundary coordinates and filling attribute information. S103) Based on the entity recognition mechanism in step S101), obtain all TEXT text entities, identify the region type through text annotation content, and obtain TEXT entity data including text content, position coordinates and font attribute information; S104) Establish a region key-value mapping relationship, and match the text annotations in CAD with the region types inside the system. Based on the spatial positional relationship between the HATCH entities obtained in step S102) and the TEXT entities obtained in step S103), a mapping table is established. The system adopts the nearest distance matching algorithm, calculates the Euclidean distance between the insertion point coordinates of the TEXT text entity and the seed point coordinates of the HATCH fill entity, and finds the fill region closest to each text annotation within the set distance threshold range, thus establishing a one-to-one correspondence between text annotations and fill regions. For HATCH entities with the same fill pattern, the system classifies them into the same region type to achieve unified identification and management of regions. S105) Perform coordinate system transformation and data standardization processing. Based on the HATCH filling entity data obtained in step S102) and the region mapping relationship established in step S104), convert the CAD vector data into a standardized raster image format and establish a mapping relationship between the CAD world coordinate system and the image pixel coordinate system. Since CAD uses the actual physical coordinates of the world coordinate system, while subsequent evacuation analysis, constraint calculation and other processing need to be based on the image pixel coordinate system, the coordinate transformation matrix is ​​used to achieve accurate conversion between the two coordinate systems, ensuring the coordinate consistency of the entire technical process and providing a unified spatial data foundation for subsequent steps.

[0032] Step S2) Establishing Room Configuration Information and Building a Classification System This embodiment provides a method for establishing and classifying room configuration information in rail transit stations, enabling systematic management and standardized configuration of room requirements, and providing complete room attribute data for constraint modeling. The method includes the following steps: S201) Establishment of Room Category Configuration and Classification System Based on the actual needs of rail transit stations in the implementation example, a multi-dimensional room classification system is established, such as... Figure 3 As shown, this classification system was established to support the handling of classification constraints and the setting of optimization objectives in subsequent constraint modeling. Different classification dimensions correspond to different constraint conditions and optimization strategies. The main classification dimensions include: (1) Classification by personnel usage characteristics: This classification dimension is mainly used for evacuation constraint modeling and safety management constraint setting. Manned rooms refer to rooms that require staff to be stationed or used frequently, such as vehicle control rooms and station service rooms. These rooms need to meet stricter evacuation distance requirements and environmental comfort constraints. Unmanned rooms are mainly used for equipment installation, item storage, and other rooms that do not require staff to be stationed, such as equipment rooms and storage rooms. The evacuation requirements for these rooms are relatively relaxed, but they need to meet the accessibility constraints for equipment maintenance. (2) Classification by functional attributes: This classification dimension is mainly used for functional relevance constraints and centralized layout optimization target setting. Management rooms undertake the station operation and management functions, such as offices and meeting rooms. These rooms need to be centrally arranged to improve management efficiency. Equipment rooms are used to install various equipment systems, such as power distribution rooms and machine rooms. These rooms need to consider equipment load and maintenance space constraints. Auxiliary rooms provide auxiliary service functions, such as toilets and storage rooms. The layout of these rooms needs to take into account both service convenience and space utilization. (3) Classification by professional system: This classification dimension is mainly used for setting location constraints and adjacency constraints related to professional systems. Rooms with water involve water supply and drainage systems, such as toilets and pump rooms. These rooms need to be located close to the main water supply and drainage pipes. Rooms with electricity involve high-voltage systems, such as power distribution rooms and substations. These rooms need to consider electrical safety distance and cable laying path constraints. Rooms with low-voltage systems involve low-voltage systems, such as communication equipment rooms and control rooms. These rooms need to avoid high-voltage interference and meet signal transmission distance requirements. (4) Classification by building characteristics: This classification dimension is mainly used for setting building structure constraints and spatial adaptability constraints. Cable wells are vertical spaces for cable laying, such as power wells and weak current wells. Such spaces need to meet the requirements of vertical connection and fire separation. Annex area rooms provide supporting services for main functional rooms, such as anterooms and corridors. The layout of such rooms needs to consider the service radius and circulation organization.

[0033] This classification system establishes a room attribute tag library to achieve multi-dimensional identification and systematic management of room constraints, providing a classification basis and optimization target setting foundation for subsequent constraint modeling.

[0034] S202) Room Information Configuration and Parameter Settings Based on the classification system established in step S201), such as Figure 4 As shown, detailed information configuration and parameter settings are performed for each room: (1) Basic information configuration: The basic information configuration includes the room name as the unique identifier of the room, the room type label based on the multi-dimensional label combination of the classification system in step S201), the functional area affiliation indicating the building functional area to which the room belongs, and the usage characteristic attributes covering characteristic parameters such as the frequency of use by personnel and the type of equipment; (2) Geometric constraint parameter settings: Geometric constraint parameter settings include minimum bay and minimum depth as the lower limit of size based on functional requirements and building module, maximum bay-to-depth ratio as geometric constraint to ensure the rationality of room proportion, and area range as the upper and lower limits of area determined according to functional requirements, used for area constraint modeling; (3) Location constraint parameter settings: Location constraint parameter settings include adjacent relationship requirements based on functional association and professional system requirements, functional zoning to ensure that the room must be located in the specified functional zone, and floor location restrictions based on usage characteristics and equipment requirements. (4) Evacuation constraint parameter settings: Evacuation constraint parameter settings include evacuation distance limits based on building fire protection codes and room occupancy characteristics, as well as different evacuation distance standards for normal corridors and dead-end corridors. (5) Door configuration parameter settings: Door configuration parameter settings include the number of doors based on evacuation requirements and usage functions, the door opening size parameters to meet the requirements of personnel evacuation and equipment handling, and the door opening direction constraints based on evacuation direction and spatial layout requirements.

[0035] Room configuration information is stored and managed through a standardized data structure, supporting dynamic adjustment and batch processing of configuration parameters, and providing a complete data foundation for subsequent decision variable definition (step S3), hard constraint modeling (step S4), and soft constraint optimization objective setting (step S5).

[0036] Step S3) Defining decision variables and mathematical modeling like Figure 5 As shown, this embodiment provides a method for defining room layout decision variables and mathematical modeling, realizing a structured expression of the room layout problem and providing a data foundation for subsequent constraint construction. Specifically, it includes the following steps: S302) Definition of Room Decision Variables Based on the room information configured in step S202), establish a complete decision variable data structure for each room (e.g., ...). Figure 5 As shown in the figure, the room decision variables include basic geometric parameters and location boundary parameters; the basic geometric parameters include the room's starting coordinate position and width and height dimensions, which are used to determine the room's position and occupied space in the layout space; the location boundary parameters include the room's ending coordinate position, which are used to simplify the calculation of the room's boundaries.

[0037] S303) Definition of Door Decision Variables Based on the number of doors configured in room S202), door location decision variables are established for each room. These door decision variables include the coordinate position parameters of the door, used to determine the specific location of the doorway on the room boundary. Depending on the functional requirements of the room, each room can have one or more doorways, and the system establishes location parameters for each doorway separately.

[0038] Step S4) Evacuation route analysis and evacuation distance calculation This embodiment provides a method for evacuation path analysis and evacuation distance calculation based on CAD vector data. It enables automatic construction of evacuation networks and accurate calculation of the shortest paths. Combined with fire compartment constraints, it ensures the compliance of evacuation paths and provides evacuation safety assurance for room layout. Specifically, it includes the following steps: S401) Evacuation network construction and node identification: Based on the HATCH filling entity data obtained in step S102) and the regional mapping relationship established in step S104), the system identifies relevant areas of evacuation routes from the CAD layout base map, including evacuation spaces such as corridor areas and stairwell areas; the system converts the evacuation routes into a grid map representation, extracts the key nodes of the routes as the basic elements of the evacuation network, and merges the evacuation exit coordinate information converted in step S105) with the key nodes to form a complete evacuation network topology; S402) Shortest distance calculation of evacuation routes: Based on the evacuation network topology constructed in step S401), network adjacency relationships are constructed in combination with fire compartment constraints. The fire compartment constraints require that evacuation routes can only proceed from lower-level fire compartments to higher-level fire compartments. The system uses the Floyd-Warshall algorithm to calculate the shortest path distance between all pairs of nodes in the network and generates a lookup table of the shortest distance from each key node to all evacuation exits. S403) Evacuation distance assessment and result output: Based on the evacuation distance lookup table generated in step S402), the shortest evacuation distance from any point in the building to the evacuation exit is calculated, and the corridor type is determined according to the topological characteristics of the evacuation path. The system integrates the evacuation distance value, corridor type and other information into a unified data format, providing a basis for evacuation safety assessment for subsequent hard constraint modeling.

[0039] Step S5) Hard Constraint Modeling This embodiment provides a multi-dimensional hard constraint modeling and constraint construction method. Based on the decision variables defined in step S3), it realizes systematic management of constraints to ensure that the generated layout scheme meets building codes and safety requirements. The system uses a hard constraint configuration interface (such as...) Figure 7 As shown, each constraint is controlled by a checkbox, allowing users to selectively enable or disable various constraints according to project needs, thus achieving flexibility and customizability in constraint configuration. The specific steps include: S501) Area constraint setting: Based on the area parameters of the room configuration information in step S202) and the room geometric parameters defined in step S302), room area constraints are added. The enabling status of these constraints can be controlled by the minimum area constraint and maximum area constraint checkboxes in the configuration interface, ensuring that the actual area of ​​each room is within the specified minimum and maximum values, thus meeting the room's functional requirements and building code requirements. S502) Geometric dimension constraint setting: Using the geometric constraint parameters configured in step S202) and the room dimension parameters defined in step S302), set the room geometric dimension constraints. The enabling status of these constraints can be controlled by the minimum span-depth constraint and maximum span-depth ratio constraint checkboxes in the configuration interface. This includes the size limits of the minimum span and minimum depth of the room, as well as the reasonable control of the span-depth ratio, to ensure that the room proportions meet the architectural design requirements. S503) Room non-overlapping constraint setting: Based on all room location parameters defined in step S302), room non-overlapping constraints are established. This constraint uses a two-dimensional interval non-overlapping algorithm to ensure that there is no spatial conflict between rooms, and to ensure that no two rooms will overlap in the layout space, thus maintaining the spatial rationality of the room layout. S504) Evacuation distance constraint setting: Based on the evacuation distance calculation results generated in step S4) and the room location parameters defined in step S302), evacuation distance constraints are added. The enabling status of this constraint can be controlled by the evacuation distance constraint checkbox in the configuration interface. Different maximum evacuation distance limits are set according to the corridor type to ensure that the room layout meets the evacuation requirements of the building fire protection code. S505) Fire compartment constraint setting: Using the fire compartment information parsed in step S1) and the room area affiliation configuration in step S202), fire compartment constraints are set. The constraint can be enabled by the fire compartment constraint checkbox in the configuration interface to ensure that different types of rooms are arranged in the corresponding fire compartments and establish the affiliation constraint between rooms and fire compartments. S506) Room adjacency constraint settings: Based on the adjacency requirements of the room configuration in step S202) and the room location parameters defined in step S502), add room adjacency constraints (such as...). Figure 6 As shown in the diagram, this constraint can be enabled by checking the adjacent room constraint checkbox in the configuration interface. It handles room pairs with special location requirements and ensures that the specified rooms can be arranged adjacently in space, such as the vehicle control room being adjacent to the stationmaster's office. S507) Door position constraint setting: Based on the door configuration parameters defined in step S302) and the door position parameters defined in step S303), door position constraints are set. The enabling status of these constraints can be controlled by the door orientation constraint and door spacing constraint checkboxes in the configuration interface, respectively, to ensure that the position of the room door complies with the building design code, including the position restrictions of the door on the room boundary, the requirements for the door to face the corridor, and the spacing requirements between doors in a multi-door room.

[0040] Step S6) Setting soft-constraint optimization objectives and constructing multi-objective functions This embodiment provides a method for setting soft-constraint optimization objectives and constructing multi-objective functions, achieving layout optimization while satisfying hard constraints. The system utilizes... Figure 8 The interface provides an independent slider control for each soft constraint optimization objective. Users can adjust the position of each slider in real time by dragging it with the mouse. The value of each slider ranges from 0 to 100, with larger values ​​indicating higher importance of the corresponding optimization objective. Personalized multi-objective optimization configurations are supported, including: S601) Area optimization target setting: Based on the room geometric parameters defined in step S302), set area-related optimization targets. The optimal area target targets rooms with a suitable area, making the layout result as close as possible to its suitable area value, thus achieving precise control of the room area. The target of maximizing the layout area takes the maximum total area as the optimization direction, improving the overall space utilization by maximizing the total area of ​​all rooms, and ensuring that the layout area is fully utilized.

[0041] S602) Setting optimization goals for occupied room layout: Using the room classification information in step S201) and the room location parameters defined in step S302), optimization goals for the layout of occupied rooms are set. The goal of centralized layout of occupied rooms targets non-toilet occupied rooms, achieving centralized layout by minimizing the distance between such rooms, thus improving management efficiency and ease of use; the goal of placing occupied rooms near the vehicle control room ensures that occupied rooms are located as close as possible to the vehicle control room, facilitating unified management and rapid response.

[0042] S603) Setting optimization goals for the layout of rooms with water: Based on the classification information of rooms with water in step S201), setting optimization goals for the layout of rooms with water. The goal of centralized layout of toilet-type rooms with water is to achieve centralized layout of these rooms by minimizing the distance between them.

[0043] S604) Setting optimization goals for the layout of professional system rooms: Based on the classification information of professional system rooms in step S201), setting optimization goals for the layout of professional system rooms; The goal of arranging low-voltage rooms in rows ensures that low-voltage rooms are arranged in a linear manner, which facilitates the laying of cables and maintenance of equipment in the low-voltage system and improves the integrity and maintainability of the low-voltage system.

[0044] S605) Structural Coordination Optimization Goal Setting: Based on the structural column location information extracted in step S1), an optimization goal is set to ensure that structural columns are kept as far away from the room as possible. This goal optimizes the room location, preventing structural columns from penetrating the interior space, reducing the impact on the room's functionality, and improving the integrity and efficiency of the room space.

[0045] S606) Comprehensive Optimization Model Construction Based on all the soft-constraint optimization objectives defined in steps S601) to S605), the final comprehensive optimization model is constructed; the system integrates multiple optimization objectives into a comprehensive optimization objective through weighted summation, and the weights of each objective are determined by... Figure 8 The corresponding slider control in the interface is adjusted in real time.

[0046] Step S7) Solving the constraint satisfaction problem and generating the layout scheme This embodiment provides a method for solving constraint satisfaction problems and generating layout schemes. It utilizes the CP-SAT constraint programming solver from OR-Tools to efficiently solve multi-constraint building layout problems, and generates feasible layout schemes through a hierarchical optimization strategy. The specific steps include: S701) Based on the hard constraints established in step S5), the soft constraints optimization model established in step S6), and the evacuation analysis results generated in step S403, initialize the CP-SAT constraint programming solver of OR-Tools, configure the solution algorithm parameters, and set the upper limit of the solution time to 150 seconds to prevent the solution process from being extended indefinitely. S702) Based on the initialized solver, start the branch and bound algorithm to search the feasible solution space, and prioritize satisfying all hard constraints (area constraints, geometric constraints, non-overlapping constraints, sparse constraints, etc.) defined in step S5) to ensure the feasibility of the solution. If hard constraints are violated, prune directly. S703) Based on satisfying the hard constraints, optimization is performed based on the soft constraint objective function defined in step S6). The optimization objectives include maximizing the area, optimizing the concentration, and minimizing the evacuation distance. A heuristic search strategy is adopted to improve the solution efficiency. S704) Configure a solution collector, set to collect a maximum of 15 feasible solutions, monitor the solution status in real time through a callback function mechanism, record the current optimal solution and solution progress, and terminate the solution process when the time limit is reached, a satisfactory solution is found, or the optimization gap is less than the convergence threshold. S705) Collect and organize all feasible solutions found, including complete solution information such as room location coordinates, geometric dimensions, and objective function values. Sort the solutions according to the merits of their objective function values ​​to provide basic data for subsequent solution comparison.

[0047] Step S8) Comparison and Visualization of Multiple Options This embodiment provides a method for comparing and visualizing multiple solutions, establishing a scientific evaluation index system for the solutions, realizing comprehensive evaluation and ranking of layout solutions, and providing users with professional solution comparison and decision support through an intuitive visualization interface. Specifically, it includes the following steps: S801) Based on the sorted solution sequence generated in step S705), obtain multiple feasible solutions, establish a solution evaluation index system, and calculate the index for each feasible solution; S802) Based on the calculated room occupancy rate and occupied room concentration index values, a weighted summation method is used to calculate the comprehensive score of each scheme: Comprehensive score = Σ(index value × weight), realizing a scheme ranking and filtering mechanism to provide users with an optimized scheme sequence; S803) Based on the scheme data and ranking results, generate a visual display of scheme comparison, such as... Figure 9 , Figure 10 This includes floor plans and indicator comparison tables for each option. The coordinate transformation matrix in step S1) is used to convert room coordinates into visual coordinates, and the final option comparison results and visualization report are output, providing users with comprehensive option comparison and decision support.

[0048] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements and modifications without departing from the concept of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multi-constraint-driven intelligent layout method for rail transit station rooms, characterized in that... Includes the following steps: Step S1) CAD layout base map analysis and semantic information extraction Input the CAD layout base map DXF file of the station, establish a file parsing and entity recognition mechanism for the CAD layout base map, use HATCH fill blocks to represent different functional areas, HATCH entity data format includes geometric boundary coordinates and fill attribute information, distinguish area types by TEXT text labels, TEXT entity data includes text content, position coordinates and font attribute information, establish area key value mapping relationship to determine the one-to-one correspondence between text labels and fill areas, and convert CAD vector data into a standardized raster image format by performing coordinate system transformation and data standardization processing. The system can automatically parse out the building outline, functional zoning, fire compartments, traffic areas, evacuation facilities and structural elements; Step S2) Establishing Room Configuration Information and Building a Classification System Based on the actual needs of the station, a multi-dimensional room classification system was established. The main classification dimensions include classification by personnel usage characteristics, classification by functional attributes, classification by professional system, and classification by architectural features. This classification system realizes the systematic management of multi-dimensional room identification and constraints by establishing a room attribute tag library. Each room is configured with detailed information and parameters, including basic information configuration, geometric constraint parameter settings, position constraint parameter settings, evacuation constraint parameter settings, and door configuration parameter settings, which are stored and managed through a standardized data structure. Step S3) Defining decision variables and mathematical modeling Establish a complete data structure for decision variables and door location decision variables for each room; Step S4) Evacuation route analysis and evacuation distance calculation Based on the HATCH filling entity data obtained in step S1) and the established regional key-value mapping relationship, the relevant areas of the evacuation route are identified from the CAD layout base map. The system converts the evacuation route into a grid map representation, extracts the key nodes of the evacuation route as the basic elements of the evacuation network, and merges the evacuation exit coordinate information with the key nodes to form a complete evacuation network topology. The system constructs network adjacency relationships based on fire compartment constraints, and uses the Floyd-Warshall algorithm to calculate the shortest path distance between all node pairs in the network, generating a lookup table of the shortest distance from each key node to all evacuation exits. Step S5) Hard Constraint Modeling Users can set area constraints, geometric dimension constraints, room non-overlap constraints, evacuation distance constraints, fire compartment constraints, room adjacency constraints, and door position constraints, and selectively enable or disable various constraints according to project requirements. Step S6) Setting soft-constraint optimization objectives and constructing multi-objective functions Set optimization targets for area, room layout, water supply, professional system rooms, and structural coordination, and construct the final comprehensive optimization model. The system provides an independent slider control for each soft constraint optimization objective, which can adjust the position of each slider in real time and supports personalized multi-objective optimization configuration; Step S7) Solving the constraint satisfaction problem The CP-SAT constraint programming solver of OR-Tools is used to efficiently solve multi-constraint building layout problems, and feasible layout schemes are generated through hierarchical optimization strategies. Step S8) Comparison and Visualization of Multiple Options Establish a scientific evaluation index system for the layout schemes to achieve comprehensive evaluation and ranking of the schemes, and provide users with professional scheme comparison and decision support through an intuitive visual interface.

2. The intelligent layout method for rail transit station rooms driven by multiple constraints according to claim 1, characterized in that: In step S2), the multi-dimensional room classification system specifically includes: (1) Classification by personnel usage characteristics: This classification dimension is mainly used for evacuation constraint modeling and safety management constraint setting. Among them, occupied rooms refer to rooms that require staff to be stationed or used frequently. Such rooms need to meet stricter evacuation distance requirements and environmental comfort constraints. Unoccupied rooms are mainly used for equipment installation, item storage, etc., where no personnel need to be stationed. The evacuation requirements for such rooms are relatively relaxed, but they need to meet the accessibility constraints for equipment maintenance. (2) Classification by functional attributes: This classification dimension is mainly used for functional relevance constraints and centralized layout optimization target setting. Management rooms undertake the station operation management function. These rooms need to be centrally arranged to improve management efficiency. Equipment rooms are used to install various equipment systems. These rooms need to consider equipment load and maintenance space constraints. Auxiliary rooms provide auxiliary service functions. The layout of these rooms needs to take into account both service convenience and space utilization. (3) Classification by professional system: This classification dimension is mainly used for setting location constraints and adjacent relationship constraints related to professional systems. Rooms with water involve water supply and drainage systems. Such rooms need to be located close to the main water supply and drainage pipes. Rooms with electricity involve high-voltage systems. Such rooms need to consider electrical safety distance and cable laying path constraints. Rooms with low-voltage systems involve low-voltage systems. Such rooms need to avoid high-voltage interference and meet signal transmission distance requirements. (4) Classification by building characteristics: This classification dimension is mainly used for setting building structure constraints and spatial adaptability constraints. Cable wells are used for vertical spaces for cable laying. Such spaces need to meet the requirements of vertical penetration and fire separation. Annex area rooms provide supporting services for main functional rooms. The layout of such rooms needs to consider service radius and circulation organization.

3. A multi-constraint driven intelligent layout method for rail transit station rooms according to claim 1 or 2, characterized in that: Step S2) involves configuring information and setting parameters for each room, specifically including: (1) Basic information configuration: including room name as the unique identifier of the room, functional area affiliation indicating the building functional area to which the room belongs, and characteristic attributes covering characteristic parameters such as frequency of use by personnel and type of equipment; (2) Geometric constraint parameter settings: including minimum bay and minimum depth as the lower limit of size based on functional requirements and building module, maximum bay-to-depth ratio used to ensure the geometric constraint of room proportion, and area range determined according to functional requirements as upper and lower limits of area for area constraint modeling; (3) Location constraint parameter settings: including adjacent relationship requirements based on functional association and professional system requirements, functional zoning to ensure that the room must be located in the specified functional zone, and floor location restrictions based on usage characteristics and equipment requirements. (4) Evacuation constraint parameter settings: including evacuation distance limits based on building fire protection codes and room occupancy characteristics, as well as different evacuation distance standards for normal corridors and dead-end corridors; (5) Door configuration parameter settings: including the number of doors based on evacuation requirements and usage functions, the door opening size parameters to meet the requirements of personnel evacuation and equipment handling, and the door opening direction constraints based on evacuation direction and spatial layout requirements.

4. The intelligent layout method for rail transit station rooms driven by multiple constraints according to claim 1, characterized in that: In step S5), hard constraint modeling specifically includes: (1) Area constraint settings: The minimum area constraint and maximum area constraint checkboxes in the configuration interface are used to control the enabling status, ensuring that the actual area of ​​each room is within the specified minimum and maximum values; (2) Geometric dimension constraint settings: The minimum bay depth constraint and maximum bay depth ratio constraint checkboxes in the configuration interface are used to control the enabled status, including the size limit of the minimum bay and minimum depth of the room, as well as the reasonable control of the bay depth ratio; (3) Room non-overlapping constraint setting: The two-dimensional interval non-overlapping algorithm is used to ensure that there is no spatial conflict between rooms, and to ensure that no two rooms will overlap in the layout space, thus maintaining the spatial rationality of the room layout. (4) Evacuation distance constraint settings: The enabled status can be controlled by the evacuation distance constraint checkbox in the configuration interface. Different maximum evacuation distance limits can be set according to the corridor type to ensure that the room layout meets the evacuation requirements of the building fire protection code. (5) Fire compartment constraint settings: The enabled status is controlled by the fire compartment constraint checkbox in the configuration interface to ensure that different types of rooms are arranged in the corresponding fire compartments and to establish the ownership relationship constraint between rooms and fire compartments. (6) Room adjacency constraint settings: The enabled state is controlled by the adjacent room constraint checkbox in the configuration interface. This handles room pairs with special location relationship requirements and ensures that the specified rooms can be arranged adjacently in space, such as the vehicle control room being adjacent to the stationmaster's office. (7) Door position constraint settings: The door orientation constraint and door spacing constraint checkboxes in the configuration interface are used to control the enabled state to ensure that the position of the room door complies with the building design code, including the position restriction of the door on the room boundary, the requirement of the door facing the corridor, and the spacing requirement between doors in a multi-door room.

5. The intelligent layout method for rail transit station rooms driven by multiple constraints according to claim 1, characterized in that: In step S6), the setting of the soft constraint optimization objective specifically includes: (1) Area optimization target setting: For rooms with a suitable area, the layout result is made as close as possible to the suitable area value, so as to achieve precise control of the room area; (2) Optimize the layout of occupied rooms. For occupied rooms that are not toilets, the layout is concentrated by minimizing the distance between such rooms. The goal of placing occupied rooms near the vehicle control room is to ensure that occupied rooms are placed as close as possible to the vehicle control room. (3) Optimize the layout of rooms with water by minimizing the distance between toilet-type rooms with water to achieve centralized layout of such rooms; (4) Optimize the layout of professional system rooms to ensure that the low-voltage rooms are arranged in a linear manner, which facilitates the laying of cables and maintenance of equipment in the low-voltage system and improves the integrity and maintainability of the low-voltage system; (5) Structural coordination optimization target setting: By optimizing the room location, structural columns are prevented from passing through the interior space of the room, reducing the impact on the room's function and improving the integrity and efficiency of the room space; (6) Construction of comprehensive optimization model: Based on all soft constraint optimization objectives, the final comprehensive optimization model is constructed. The system integrates multiple optimization objectives into a comprehensive optimization objective by weighted summation. The slider control corresponding to the weight of each objective is adjusted in real time.

6. The intelligent layout method for rail transit station rooms driven by multiple constraints according to claim 1, characterized in that: In step S7), the specific layout scheme includes the following steps: S701) Based on the hard constraints established in step S5), the soft constraints established in step S6), and the evacuation analysis results generated in step S4), initialize the CP-SAT constraint programming solver of OR-Tools, configure the solution algorithm parameters, and set the upper limit of the solution time to 150 seconds to prevent the solution process from being extended indefinitely. S702) Based on the initialized solver, start the branch and bound algorithm to search the feasible solution space, prioritize satisfying all hard constraints defined in step S5) to ensure the feasibility of the solution, and prune directly if hard constraints are violated. S703) Based on satisfying the hard constraints, optimization is performed based on the soft constraint objective function defined in step S6). The optimization objectives include maximizing the area, optimizing the concentration, and minimizing the evacuation distance. A heuristic search strategy is adopted to improve the solution efficiency. S704) Configure a solution collector, set to collect a maximum of 15 feasible solutions, monitor the solution status in real time through a callback function mechanism, record the current optimal solution and solution progress, and terminate the solution process when the time limit is reached, a satisfactory solution is found, or the optimization gap is less than the convergence threshold. S705) Collect and organize all feasible solutions found, including complete solution information such as room location coordinates, geometric dimensions, and objective function values. Sort the solutions according to the merits of their objective function values ​​to provide basic data for subsequent solution comparison.

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