BIM model-based lighting equipment automatic arrangement method and system

Abstract

The invention relates to the technical field of BIM building design, in particular to an automatic lighting equipment arrangement method and system based on a BIM model. The method comprises the following steps that a BIM model of a target area is obtained, and a lamp light distribution curved surface mesh model is constructed; constructing a target area illumination characteristic model based on the target area BIM model and the lamp light distribution curved surface mesh model; identifying a functional region space in the target region BIM model to obtain a functional region space relationship network; determining a target area illumination demand distribution map based on the functional area space relation network; constructing an initial lamp layout scheme according to the target area illumination demand distribution diagram based on the target area illumination characteristic model; and carrying out structural conflict adjustment on the initial lamp layout scheme to obtain a corrected lamp layout scheme. According to the invention, accurate matching of the lamp layout and the functional area is realized, and the problem of uneven distribution caused by artificial experience adjustment is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of BIM building design, and particularly to an automatic lighting device layout method and system based on a BIM model. Background Art

[0002] In the lighting system design of clean operating rooms in medical buildings, when construction parties use BIM technology for lamp layout, they face special parameter calibration problems. In existing modeling standards, the physical property description of lighting devices has a lack of parameter dimensions - the IES files provided by lamp manufacturers contain complete light distribution curve data, while conventional BIM models only represent the lamp form with simplified geometric bodies. This parameter dimensionality reduction causes BIM software to be unable to accurately calculate the illuminance gradient change at the edge of the surgical area, affecting the accuracy of lighting design. In addition, existing BIM models usually rely on manual experience for adjustment in lamp layout planning, which is not only inefficient but also prone to uneven lamp distribution or unqualified lighting effects due to subjective judgment errors. Especially in complex spaces, the installation position, angle, and light intensity distribution of lamps need to comprehensively consider the spatial structure, functional requirements, and optical characteristics, and traditional methods are difficult to achieve efficient and accurate automatic layout. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide an automatic lighting device layout method and system based on a BIM model to solve at least one of the above technical problems.

[0004] To achieve the above object, an automatic lighting device layout method based on a BIM model includes the following steps:

[0005] Step S1: Obtain the BIM model of the target area and construct a lamp light distribution surface grid model; construct a target area lighting characteristic model based on the BIM model of the target area and the lamp light distribution surface grid model;

[0006] Step S2: Identify the functional area space in the BIM model of the target area to obtain a functional area space relationship network; determine the target area lighting demand distribution map based on the functional area space relationship network;

[0007] Step S3: Construct an initial lamp layout plan based on the target area lighting characteristic model according to the target area lighting demand distribution map; perform structural conflict adjustment on the initial lamp layout plan to obtain a modified lamp layout plan; perform installation path conflict adjustment on the modified lamp layout plan to obtain a feasible lamp layout plan;

[0008] Step S4: Perform environmental photon mapping simulation on the target area based on the feasible lamp layout plan to obtain an illuminance distribution heat map; identify the light gradient change gradient field in the illuminance distribution heat map to generate an illuminance change rate distribution map; determine the lamp layout optimization parameter set based on the illuminance change rate distribution map;

[0009] Step S5: Translate the construction parameters of the optimized parameter set of the lighting fixture layout to obtain the lighting fixture installation guidance data; predict the lighting impact based on the lighting fixture installation guidance data to obtain the illuminance impact prediction data; generate a fault tolerance interval for the lighting fixture installation guidance data according to the illuminance impact prediction data to obtain a complete construction plan for the lighting system.

[0010] By constructing a lighting fixture light distribution surface grid model and a lighting characteristic model, the present invention solves the problem that the existing BIM model cannot accurately describe the physical properties of lighting fixtures, enables the lighting design to accurately simulate the illuminance gradient change at the edge of the surgical area, and significantly improves the design accuracy. Through the identification of the spatial relationship network of functional areas and the generation of the lighting demand distribution map, the precise matching of the lighting fixture layout and the functional areas is realized, avoiding the uneven distribution problem caused by manual experience adjustment, and at the same time improving the rationality of the lighting fixture layout in complex spaces. By introducing structural conflict adjustment and installation path conflict adjustment, the feasibility of the lighting fixture layout plan is ensured, and the risk of rework during the construction stage is reduced. Through environmental photon mapping simulation and the generation of the illuminance change rate distribution map, the lighting fixture layout parameters are further optimized, making the lighting effect more uniform and meeting the functional requirements. By translating the construction parameters and generating the fault tolerance interval, detailed guidance data for construction are provided, ensuring the efficiency and acceptance pass rate of the construction process. In summary, the present invention not only improves the efficiency and accuracy of the lighting design of medical buildings, but also significantly reduces the construction cost and risk, providing a comprehensive solution for the automatic layout of lighting systems in complex spaces.

[0011] Preferably, in step S1, constructing the lighting fixture light distribution surface grid model includes:

[0012] Obtain the IES light distribution file of the lighting fixture; verify the data integrity of the IES light distribution file of the lighting fixture to obtain a valid IES light distribution file of the lighting fixture;

[0013] Analyze the geometric structure of the BIM model of the target area to obtain a spatial component topological relationship diagram;

[0014] Extract and standardize the parameters of the valid IES light distribution file of the lighting fixture to obtain the standard light distribution data of the lighting fixture;

[0015] Convert the C-γ polar coordinate data in the standard light distribution data of the lighting fixture to Cartesian coordinates to obtain the point cloud of the three-dimensional spatial light intensity distribution of the lighting fixture;

[0016] Perform triangular meshing on the point cloud of the three-dimensional spatial light intensity distribution of the lighting fixture to obtain the lighting fixture light distribution surface grid model.

[0017] Through data integrity verification and parameter standardization, the present invention ensures the reliability and consistency of IES photometric files. By converting the C-polar coordinate luminous intensity data of the luminaire into a point cloud of three-dimensional spatial luminous intensity distribution and further triangulating it into a photometric surface mesh model, the optical characteristics of the luminaire can be presented intuitively and accurately in the BIM environment. This not only improves the accuracy of lighting design but also provides a reliable basis for optimizing the luminaire layout in complex spaces. By combining the optical characteristics of the luminaire with the topological relationship of spatial components in the target area, the distribution of light in the actual building environment can be better simulated, thereby improving the prediction accuracy of lighting effects.

[0018] Preferably, in step S1, constructing a lighting characteristic model for the target area based on the BIM model of the target area and the photometric surface mesh model of the luminaire includes:

[0019] Fitting a mathematical expression to the photometric surface mesh model of the luminaire to obtain a continuous photometric function of the luminaire;

[0020] Extracting the optical characteristics of the material of the topological relationship diagram of spatial components to obtain a surface reflectivity distribution map of the components;

[0021] Constructing a fast illuminance calculation unit based on the continuous photometric function of the luminaire and the surface reflectivity distribution map of the components;

[0022] Evaluating the parameter sensitivity of the fast illuminance calculation unit to obtain a table of lighting parameter influence factors; constructing a digital twin model of the luminaire according to the table of lighting parameter influence factors;

[0023] Performing spatial positioning and matching on the digital twin model of the luminaire and the BIM model of the target area to obtain an initial integrated model;

[0024] Performing geometric-optical coupling conflict detection and optimization on the initial integrated model to obtain a lighting characteristic model for the target area.

[0025] The present invention accurately describes the optical characteristics of the luminaire by fitting a mathematical expression to the photometric surface mesh model of the luminaire to generate a continuous photometric function. By extracting the optical characteristics of the material of spatial components to generate a surface reflectivity distribution map of the components, the authenticity and reliability of lighting simulation are further enhanced. The introduction of the fast illuminance calculation unit greatly improves the calculation efficiency, while the parameter sensitivity evaluation and the construction of the table of lighting parameter influence factors provide a scientific basis for the digital twin model of the luminaire, ensuring the accuracy and adaptability of the model. Through the spatial positioning and matching of the digital twin model and the BIM model, the precise combination of the luminaire and the building space is achieved, and the geometric-optical coupling conflict detection and optimization effectively avoid potential conflicts in design and construction.

[0026] Preferably, step S2 includes the following steps:

[0027] Step S21: Identify the functional area spaces in the target area BIM model to obtain the functional area spatial relationship network;

[0028] Step S22: Perform workflow path simulation optimization on the functional area spatial relationship network to obtain the optimal functional area configuration plan; Generate three-dimensional boundary surfaces according to the optimal functional area configuration plan to obtain the functional area boundary surfaces;

[0029] Step S23: Obtain the illuminance requirement parameters for different functional areas in the functional area spatial relationship network from the preset medical lighting standard library to generate a standard illuminance parameter set;

[0030] Step S24: Generate spatial sampling points according to the functional area boundary surfaces to obtain the target area illuminance calculation grid;

[0031] Step S25: Perform parameter mapping on the target area illuminance calculation grid based on the standard illuminance parameter set to obtain the initial illuminance requirement distribution data;

[0032] Step S26: Perform boundary constraints on the initial illuminance requirement distribution data to obtain the boundary-coordinated illuminance requirement map; Perform lighting quality parameter expansion on the boundary-coordinated illuminance requirement map to obtain the target area lighting requirement distribution map.

[0033] By identifying the functional area spaces and constructing the spatial relationship network, the present invention can accurately grasp the functional layout of the building space, providing a reasonable spatial basis for the layout of lighting fixtures. The workflow path simulation optimization and three-dimensional boundary surface generation further optimize the functional area configuration, ensuring the coordination between the lighting design and the actual use scenario. By obtaining the illuminance requirement parameters from the medical lighting standard library to generate the standard illuminance parameter set, it ensures that the design complies with professional specifications and reduces the design errors caused by inaccurate parameters. The generation of spatial sampling points and parameter mapping provide detailed data support for the lighting requirement distribution, making the layout of lighting fixtures more in line with the actual needs. The boundary constraints and lighting quality parameter expansion ensure the uniformity and high quality of the lighting effect, avoiding lighting blind spots or over-illumination problems caused by uncoordinated boundaries.

[0034] Preferably, constructing the initial lighting fixture layout plan based on the target area lighting requirement distribution map according to the target area lighting characteristic model in step S3 includes:

[0035] Perform grid discretization on the target area lighting requirement distribution map to obtain lighting requirement dot matrix data; Extract the light distribution intensity distribution curve of the lighting fixtures according to the target area lighting characteristic model;

[0036] Construct an illuminance optimization target expression based on the light distribution intensity distribution curve of the lighting fixtures and the lighting requirement dot matrix data;

[0037] Parametrically model the ceiling structure of the target area to obtain the lamp installation constraint surface;

[0038] Obtain the physical dimensions of the lamps and the requirements for installation spacing; Screen the lamp installation points on the lamp installation constraint surface according to the physical dimensions of the lamps and the requirements for installation spacing to obtain a set of candidate lamp layout points;

[0039] Construct an illumination layout optimization model based on the illumination optimization objective expression and the set of candidate lamp layout points;

[0040] Implement a hybrid optimization strategy for the illumination layout optimization model to obtain an initial lamp layout plan, where the implementation of the hybrid optimization strategy includes the collaborative optimization of the genetic algorithm and the simulated annealing algorithm.

[0041] Through grid discretization and the extraction of the light distribution intensity curve of the lamps, the present invention can transform complex lighting requirements into computable lattice data. The illumination layout optimization model constructed based on the illumination optimization objective expression and the set of candidate lamp layout points ensures the scientificity and adaptability of the lamp layout, and avoids the problem of unqualified lighting effects caused by unreasonable layout. The introduction of the hybrid optimization strategy (the collaborative optimization of the genetic algorithm and the simulated annealing algorithm) not only improves the optimization efficiency, but also effectively avoids local optimal solutions and ensures the globally optimal lamp layout plan. By fully considering the physical dimensions of the lamps and the requirements for installation spacing through parametric modeling and installation point screening, the feasibility of the layout plan is ensured, and the risk of rework during the construction stage is reduced.

[0042] Preferably, in step S3, the structural conflict adjustment of the initial lamp layout plan includes:

[0043] Simulate the lighting effect according to the initial lamp layout plan to obtain lighting effect prediction data; Quantitatively evaluate the glare of the lighting effect prediction data to obtain the lamp glare evaluation result;

[0044] Obtain the ceiling installation conditions; Detect spatial conflicts based on the initial lamp layout plan and the ceiling installation conditions to obtain an installation feasibility evaluation report;

[0045] Adjust the constraints of the initial lamp layout plan according to the installation feasibility evaluation report to obtain a revised lamp layout plan, where the constraint adjustment is specifically:

[0046] Extract the ceiling keel topology data based on the BIM model of the target area;

[0047] Generate a ceiling structure avoidance buffer zone according to the ceiling keel topology data, and offset the lamp layout points in the initial lamp layout plan to the non-keel area, where the offset distance ≥ 50 mm, and the non-keel area is the extension area of the ceiling structure avoidance buffer zone;

[0048] Use a laser 3D scanner to scan the ceiling installation surface to generate a topological point cloud of the ceiling installation surface;

[0049] Determine a universal hinge type installation bracket according to the installation space conflict area in the installation feasibility evaluation report;

[0050] Deploy a universal hinge type installation bracket at the lamp installation nodes in the initial lamp layout plan to generate a set of bracket installation angle adaptation parameters, where the normal direction of the universal hinge type installation bracket has a deviation of ≤2° from the ceiling surface;

[0051] Perform a layout space remapping on the initial lamp layout plan based on the topological point cloud of the ceiling installation surface to generate a 3D space remapped layout diagram;

[0052] Insert redundant installation nodes into the 3D space remapped layout diagram to obtain redundant installation node distribution data, where the spacing of the redundant installation nodes is ≤1.5m;

[0053] Construct a digital twin scenario for ceiling installation, import the 3D space remapped layout diagram and the set of bracket installation angle adaptation parameters into the digital twin scenario for ceiling installation to simulate the installation process, thereby generating an installation conflict detection report;

[0054] Dynamically eliminate nodes from the redundant installation node distribution data according to the installation conflict detection report to generate an optimized set of installation nodes;

[0055] Deploy a magnetic adsorption type installation base in the optimized set of installation nodes to generate a base magnetic suction distribution table, where the magnetic adsorption strength of the base magnetic suction distribution table is ≥80N and the disassembly torque is ≤3N·m;

[0056] Modify the initial lamp layout plan according to the ceiling structure avoidance buffer zone, the set of bracket installation angle adaptation parameters, the optimized set of installation nodes, and the base magnetic suction distribution table to obtain a corrected lamp layout plan.

[0057] Through lighting effect simulation and glare quantification evaluation, the present invention can identify potential lighting quality problems in advance, providing a scientific basis for layout optimization. By combining spatial conflict detection of ceiling installation conditions, it can accurately locate potential conflict points during the installation process, ensuring the matching of the design plan with the actual construction conditions. By extracting ceiling keel topological data based on the BIM model and generating an avoidance buffer zone, it can effectively avoid conflicts between lamp layout and building structure, ensuring the smooth progress of construction. By applying laser 3D scanning technology and universal hinge type installation brackets, it further improves the flexibility and accuracy of installation, while reducing the loss of lighting effect caused by installation angle deviation. By dynamically eliminating redundant installation nodes and deploying magnetic adsorption type installation bases, it not only enhances the stability of installation, but also simplifies the construction process, reducing the construction difficulty and cost.

[0058] Preferably, in step S3, adjusting the installation path conflict of the corrected lighting fixture layout plan includes:

[0059] Verifying the maintainability reachability of the corrected lighting fixture layout plan to obtain a maintainability feasibility evaluation result;

[0060] Performing constraint-driven fine-tuning on the corrected lighting fixture layout plan according to the maintainability feasibility evaluation result to obtain a feasible lighting fixture layout plan, where the constraint-driven fine-tuning is specifically:

[0061] Generating a maintenance operation path conflict area according to the maintainability feasibility evaluation result;

[0062] Inserting maintenance channel nodes into the corrected lighting fixture layout plan according to the maintenance operation path conflict area to generate maintenance channel three-dimensional space occupancy data;

[0063] According to the maintenance tool compatibility list in the maintainability feasibility evaluation result, deploying a six-degree-of-freedom quick-release base at the lighting fixture installation node in the corrected lighting fixture layout plan, where the six-degree-of-freedom quick-release base includes XYZ-axis sliding rails and a ball joint structure, and replacing the traditional bolt fixing points with magnetic quick-release bases to generate a lighting fixture module quick-release unit mapping table;

[0064] Based on the maintenance channel three-dimensional space occupancy data and the lighting fixture module quick-release unit mapping table, constructing a maintenance operation digital twin scenario in the DELMIA simulation platform to simulate the passage and operation process of maintenance personnel, thereby generating a maintainability reachability dynamic simulation report;

[0065] Iteratively optimizing the maintenance channel nodes and the six-degree-of-freedom quick-release base according to the maintainability reachability dynamic simulation report, and finally generating a feasible lighting fixture layout plan.

[0066] Through the maintainability feasibility evaluation of the present invention, the maintenance path conflict area is identified in advance, providing a scientific basis for layout optimization. The insertion of maintenance channel nodes and the generation of three-dimensional space occupancy data ensure that maintenance personnel can easily reach the lighting fixture position, avoiding maintenance difficulties caused by path conflicts. Through the application of the six-degree-of-freedom quick-release base, combined with the XYZ-axis sliding rails and the ball joint structure, the detachability and installation flexibility of the lighting fixtures are greatly improved, reducing the time and cost of maintaining and replacing the lighting fixtures. Through the simulation of the maintenance operation digital twin scenario in the DELMIA simulation platform, the maintenance path and the layout of the quick-release base are further optimized, ensuring the efficiency and operability of the maintenance process. Through the deep integration of digital simulation and actual construction, the risk of rework in the construction stage is significantly reduced, improving the construction efficiency and the maintenance convenience of the lighting fixture system, providing an efficient and reliable solution for the lighting fixture installation in complex building environments.

[0067] Preferably, step S4 includes the following steps:

[0068] Step S41: Extract the lamp spatial position data from the feasible lamp layout schemes, and obtain the component surface reflectivity distribution map;

[0069] Step S42: Establish a ray tracing virtual scene for the target area based on the lamp spatial position data and the component surface reflectivity distribution map;

[0070] Step S43: Perform Monte Carlo photon tracing on the ray tracing virtual scene of the target area to obtain the original photon mapping data;

[0071] Step S44: Perform spatial clustering on the original photon mapping data to obtain the photon density distribution map;

[0072] Step S45: Reconstruct the luminance field based on the photon density distribution map to obtain the surface luminance distribution data; perform ray tracing according to the surface luminance distribution data to obtain the initial illuminance distribution data;

[0073] Step S46: Perform bilinear interpolation on the initial illuminance distribution data to obtain the continuous illuminance distribution data; calculate the illuminance gradient field based on the continuous illuminance distribution data to obtain the illuminance change rate distribution map;

[0074] Step S47: Determine the lamp layout optimization parameter set based on the illuminance change rate distribution map.

[0075] By extracting the lamp spatial position data and the component surface reflectivity distribution map, the present invention establishes a highly realistic ray tracing virtual scene. Through the application of Monte Carlo photon tracing and spatial clustering techniques, the reconstruction of the photon density distribution and the luminance field is made more accurate, thereby improving the accuracy of the illuminance distribution data. Through bilinear interpolation and the calculation of the illuminance gradient field, the details of the illuminance change are further refined, providing detailed data support for the optimization of the lamp layout. The lamp layout optimization parameter set determined based on the illuminance change rate distribution map not only ensures the uniformity and high quality of the lighting effect, but also provides a flexible adjustment basis for the lamp arrangement in complex environments, reducing the design and construction errors caused by inaccurate optical simulation, and significantly improving the overall performance and construction efficiency of the lighting system.

[0076] Preferably, step S5 includes the following steps:

[0077] Step S51: Standardize the data format of the lamp layout optimization parameter set to obtain the standard lamp layout parameter table;

[0078] Step S52: Perform three-dimensional coordinate transformation on the lamp installation positions in the feasible lamp layout schemes based on the standard lamp layout parameter table to obtain the lamp positioning data under the building construction reference system;

[0079] Step S53: Calculate the installation fixing points for the lighting fixture positioning data to obtain the coordinate set of the hoisting and anchoring points of the lighting fixture; generate the installation measurement reference data for the lighting fixture according to the coordinate set of the hoisting and anchoring points of the lighting fixture;

[0080] Step S54: Plan the electrical connection path for the installation measurement reference data of the lighting fixture to obtain the layout diagram of the power supply line for the lighting fixture;

[0081] Step S55: Generate a mapping table of lighting fixture control parameters based on the layout diagram of the power supply line for the lighting fixture; integrate the mapping table of lighting fixture control parameters and the installation measurement reference data of the lighting fixture to obtain a comprehensive construction guidance document;

[0082] Step S56: Predict the lighting impact on the target area based on the comprehensive construction guidance document to obtain the predicted data of the illuminance impact;

[0083] Step S57: Generate a fault tolerance interval for the lighting fixture installation guidance data according to the predicted data of the illuminance impact to obtain a complete construction plan for the lighting system.

[0084] Through data format standardization and three-dimensional coordinate transformation, the present invention ensures the accuracy and consistency of construction data, and provides clear guidance for construction personnel. The calculation of installation fixing points and the planning of electrical connection paths provide a detailed implementation plan for construction, reducing errors and risks during construction. The generation and integration of the mapping table of control parameters further optimize the construction process, improving construction efficiency and the accuracy of lighting fixture installation. The prediction of lighting impact and the generation of fault tolerance intervals provide a scientific basis for construction, ensuring that the performance and quality of the lighting system meet the design requirements, and at the same time reducing the risk of rework during construction.

[0085] Preferably, the present invention also provides a lighting equipment automatic layout system based on a BIM model for executing the lighting equipment automatic layout method based on a BIM model as described above. The lighting equipment automatic layout system based on a BIM model includes:

[0086] A model initialization module, configured to obtain a BIM model of the target area and construct a light distribution surface mesh model of the lighting fixture; construct a lighting characteristic model of the target area based on the BIM model of the target area and the light distribution surface mesh model of the lighting fixture;

[0087] A requirement analysis module, configured to identify the functional area space in the BIM model of the target area to obtain a functional area space relationship network; determine a lighting requirement distribution map of the target area based on the functional area space relationship network;

[0088] The layout planning module is used to construct an initial lighting fixture layout plan based on the lighting demand distribution map of the target area according to the lighting characteristics model of the target area; adjust the structural conflicts of the initial lighting fixture layout plan to obtain a revised lighting fixture layout plan; adjust the installation path conflicts of the revised lighting fixture layout plan to obtain a feasible lighting fixture layout plan.

[0089] The layout optimization module is used to perform environmental photon mapping simulation on the target area based on the feasible lighting fixture layout plan to obtain an illuminance distribution heat map; identify the light gradient change gradient field in the illuminance distribution heat map and generate an illuminance change rate distribution map; determine the lighting fixture layout optimization parameter set based on the illuminance change rate distribution map.

[0090] The layout integration module is used to translate the construction parameters of the lighting fixture layout optimization parameter set to obtain lighting fixture installation guidance data; perform lighting impact prediction based on the lighting fixture installation guidance data to obtain illuminance impact prediction data; generate a fault tolerance interval for the lighting fixture installation guidance data according to the illuminance impact prediction data to obtain a complete lighting system construction plan.

[0091] In the present invention, the model initialization module ensures the reliability of optical simulation by accurately constructing the lighting distribution surface grid model and the lighting characteristics model. The requirement analysis module realizes the high degree of fit between the lighting fixture layout and the building function by identifying the functional areas and generating the lighting demand distribution map, avoiding the design errors caused by manual experience. The layout planning module ensures the feasibility of the lighting fixture layout plan through double adjustment of structural conflicts and installation path conflicts, reducing the risk of rework during the construction stage. The layout optimization module further optimizes the lighting fixture layout parameters through environmental photon mapping simulation and illuminance change rate analysis, ensuring the uniformity and high quality of the lighting effect. The layout integration module provides detailed guidance data for the construction layout through construction parameter translation and fault tolerance interval generation, ensuring the efficiency of the construction process and the acceptance qualification rate. Description of the Drawings

[0092] Other features, objects, and advantages of the present invention will become more apparent by reading the following detailed description with reference to the accompanying drawings.

[0093] Figure 1 Shows the step flow diagram of the lighting equipment automatic layout method based on the BIM model in an embodiment.

[0094] Figure 2 Shows the detailed step flow diagram of step S2 in an embodiment.

[0095] Figure 3 Shows the detailed step flow diagram of step S5 in an embodiment. Detailed Embodiment

[0096] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments in the present invention without creative work belong to the scope of protection of the present invention.

[0097] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions of them will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0098] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0099] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides an automatic layout method for lighting devices based on a BIM model, including the following steps:

[0100] Step S1: Obtain the BIM model of the target area and construct a light distribution surface grid model of the lighting fixture; construct a lighting characteristic model of the target area based on the BIM model of the target area and the light distribution surface grid model of the lighting fixture;

[0101] Step S2: Identify the functional area space in the BIM model of the target area to obtain a functional area space relationship network; determine a lighting demand distribution map of the target area based on the functional area space relationship network;

[0102] Step S3: Construct an initial lighting fixture layout plan based on the lighting characteristic model of the target area according to the lighting demand distribution map of the target area; perform structural conflict adjustment on the initial lighting fixture layout plan to obtain a revised lighting fixture layout plan; perform installation path conflict adjustment on the revised lighting fixture layout plan to obtain a feasible lighting fixture layout plan;

[0103] Step S4: Perform environmental photon mapping simulation on the target area based on the feasible lighting layout scheme to obtain an illuminance distribution heat map; identify the light gradient change gradient field in the illuminance distribution heat map to generate an illuminance change rate distribution map; determine the lighting layout optimization parameter set based on the illuminance change rate distribution map;

[0104] Step S5: Translate the lighting layout optimization parameter set into construction parameters to obtain lighting installation guidance data; perform illumination impact prediction based on the lighting installation guidance data to obtain illuminance impact prediction data; generate a fault tolerance interval for the lighting installation guidance data according to the illuminance impact prediction data to obtain a complete lighting system construction plan.

[0105] In this embodiment, the BIM model of the target area is opened using Revit software, which details the three-dimensional structure of the operating room, including the layout of walls, ceilings, floors, and various medical equipment. Using DIALux lighting design software, the IES light distribution file of the luminaire is imported to construct a light distribution surface grid model of the luminaire, which accurately describes the light intensity distribution characteristics of the luminaire. By combining the BIM model with the light distribution surface grid model of the luminaire and using the "model import" function of DIALux, a lighting characteristic model of the target area is constructed, which comprehensively considers the spatial structure and luminaire optical characteristics. In Revit, the "space analysis" plug-in is used to identify functional area spaces, such as the surgical area, instrument area, and anesthesia area, to generate a spatial relationship network of functional areas, which describes the spatial relationships and work processes between functional areas. Based on this relationship network and referring to the "Medical Building Lighting Design Standard", the illuminance requirements for each functional area are determined. For example, the illuminance requirement for the surgical area is 500 lux, and for the instrument area is 300 lux, to generate an illuminance requirement distribution map of the target area. Based on the lighting characteristic model and the illuminance requirement distribution map, an initial lighting layout scheme is constructed in DIALux, and the initial scheme is adjusted through the collaborative optimization of the genetic algorithm and the simulated annealing algorithm to ensure that the lighting layout has no conflicts with the building structure (such as ceiling keels, air ducts), resulting in a revised lighting layout scheme. Further, a laser scanner is used to scan the ceiling installation surface to generate an installation surface topological point cloud, and in combination with the BIM model, the installation path conflict of the revised scheme is adjusted to ensure that the lighting installation path has no conflicts with the maintenance passage, finally obtaining a feasible lighting layout scheme. Based on the feasible lighting layout scheme, environmental photon mapping simulation is performed in DIALux, and the number of photons is set to 10 6, the tracking depth is 5 reflections to generate an illuminance distribution heat map, which intuitively shows the illuminance distribution in the operating room. By identifying the light gradient changes in the heat map, the "gradient analysis" function of DIALux is used to generate a distribution map of the illuminance change rate, which shows the rate of illuminance change in the form of contour lines. Based on this distribution map, an optimized parameter set for the lamp layout is determined, including parameters such as fine-tuning the installation position of the lamps and adjusting the light intensity distribution. The optimized parameter set is translated into construction parameters, and Excel is used to convert the parameters into lamp installation guidance data, including information such as the exact position of the lamps, installation angles, and light intensity distribution. Based on these guidance data, a prediction of the lighting impact is carried out in DIALux to generate illuminance impact prediction data and evaluate the impact of construction errors on the final illuminance. According to the prediction data, a Python script combined with the NumPy library is used to calculate the tolerance interval, generating the tolerance interval of the lamp installation guidance data to ensure that the illuminance deviation during the construction process is controlled within the allowable range, and finally a complete construction plan for the lighting system is obtained, which details the construction details such as the layout installation position, angle, and electrical connection path of the lamps.

[0106] Preferably, in step S1, constructing a grid model of the lamp light distribution surface includes:

[0107] Obtain the IES light distribution file of the lamp; verify the data integrity of the IES light distribution file of the lamp to obtain a valid IES light distribution file of the lamp;

[0108] Analyze the geometric structure of the BIM model of the target area to obtain a topological relationship diagram of the spatial components;

[0109] Extract and standardize the parameters of the valid IES light distribution file of the lamp to obtain the standard light distribution data of the lamp;

[0110] Convert the C-γ polar coordinate data in the standard light distribution data of the lamp into Cartesian coordinates to obtain a point cloud of the three-dimensional spatial light intensity distribution of the lamp;

[0111] Perform triangular meshing on the point cloud of the three-dimensional spatial light intensity distribution of the lamp to obtain a grid model of the lamp light distribution surface.

[0112] In this embodiment, in the lighting design project of a medical building, first, extract the IES photometric file from the CD provided by the luminaire manufacturer, and use an MD5 verification tool (such as the `md5sum` command-line tool) to verify the data integrity of the file. After confirming that the file is intact by comparing the generated MD5 value with the value provided by the manufacturer, import it into the lighting design software (such as DIALux) for subsequent processing. Open the BIM model of the target area using BIM modeling software (such as Revit), extract the functional areas of the operating room (such as the operating table, instrument area, anesthesia area, etc.) through the "space analysis" function of Revit, and export the topological relationship diagram of the space components, including the geometric information of components such as walls, ceilings, and floors, using the API interface of Revit. Subsequently, import the topological relationship diagram into DIALux to provide spatial structure data for optical simulation. Use a Python script to read the IES file, extract the C-polar coordinate data (such as luminous intensity values and angular distributions), and standardize the data using the NumPy library to ensure that the unit of the luminous intensity value is unified as candela. The standardized data is imported into DIALux to generate the standard light distribution data of the luminaire. Use the Python script again to convert the C-polar coordinate data into Cartesian coordinates (x, y, z), and import the converted three-dimensional spatial luminous intensity distribution point cloud into a visualization tool (such as MATLAB) for preliminary verification. The verified point cloud data is exported in PLY format. Use MATLAB to perform triangular meshing on the point cloud data and set the mesh density parameter (such as side length ≤ 0.5 meters) to ensure that the mesh can accurately describe the luminous intensity distribution of the luminaire. The generated triangular meshed model is exported in STL file format for visualization and further processing in BIM software (such as Revit). Through the above steps, the luminous intensity distribution of the luminaire is transformed into an accurate geometric model.

[0113] Preferably, in step S1, constructing a lighting characteristic model of the target area based on the BIM model of the target area and the photometric surface mesh model of the luminaire includes:

[0114] Perform mathematical expression fitting on the photometric surface mesh model of the luminaire to obtain the continuous photometric function of the luminaire;

[0115] Extract the material optical characteristics of the topological relationship diagram of the space components to obtain the surface reflectivity distribution map of the components;

[0116] Construct a fast illuminance calculation unit based on the continuous photometric function of the luminaire and the surface reflectivity distribution map of the components;

[0117] Perform parameter sensitivity assessment on the fast illuminance calculation unit to obtain the lighting parameter influence factor table; construct a digital twin model of the luminaire according to the lighting parameter influence factor table;

[0118] Perform spatial positioning and matching on the digital twin model of the luminaire and the BIM model of the target area to obtain an initial integrated model;

[0119] Perform geometric-optical coupling conflict detection and optimization on the initial integrated model to obtain the lighting characteristic model of the target area.

[0120] In this embodiment, the Curve Fitting Toolbox of MATLAB is used to fit the mathematical expression of the light distribution surface grid model of the luminaire, and the fitted continuous light distribution function is exported as a MATLAB script file. Extract the material optical properties of the spatial components through BIM modeling software (such as Revit), export the material information as a CSV file using Revit's "Material Property Manager", and generate a component surface reflectivity distribution map in Excel, and then import it into lighting design software (such as DIALux) for verification. Based on these data, set the calculation parameters in DIALux (grid density 0.2 m, calculation area is the range of 3 m around the operating table), and run the fast illuminance calculation function to generate the initial illuminance distribution map. Use the Sensitivity Analysis Toolbox of MATLAB to perform sensitivity analysis on the parameters of the calculation unit (luminaire spacing 1 - 3 m, reflectivity 0.6 - 0.9), generate a table of lighting parameter influence factors and import it into DIALux for verification. Associate the BIM model with the optical data through the API interface of Revit, import the influence factor table, create a digital twin model of the luminaire in Revit and adjust the geometric parameters to match the optical properties. Import the digital twin model of the luminaire into the BIM model through Revit's "Import CAD" function, use the "Alignment Tool" for precise alignment (error range ≤ 2 mm), and verify the conflict situation through the "Collision Detection" function. Finally, use Revit's "Collision Detection" function to identify the conflict area between the luminaire model and the building structure, simulate the illuminance distribution of the conflict area in DIALux, adjust the luminaire layout according to the results (offset distance ≥ 50 mm), and re-run the illuminance calculation to verify whether the optimized layout meets the design requirements.

[0121] Preferably, step S2 includes the following steps:

[0122] Step S21: Identify the functional area space in the BIM model of the target area to obtain the functional area space relationship network;

[0123] Step S22: Perform workflow path simulation and optimization on the functional area space relationship network to obtain the optimal functional area configuration plan; generate a three-dimensional boundary surface according to the optimal functional area configuration plan to obtain the functional area boundary surface;

[0124] Step S23: Obtain the illuminance requirement parameters of different functional areas in the functional area spatial relationship network from a preset medical lighting standard library, and generate a standard illuminance parameter set;

[0125] Step S24: Generate spatial sampling points according to the functional area boundary surface to obtain an illuminance calculation grid for the target area;

[0126] Step S25: Perform parameter mapping on the illuminance calculation grid for the target area based on the standard illuminance parameter set to obtain initial illuminance requirement distribution data;

[0127] Step S26: Apply boundary constraints to the initial illuminance requirement distribution data to obtain a boundary - coordinated illuminance requirement map; perform lighting quality parameter expansion on the boundary - coordinated illuminance requirement map to obtain a lighting requirement distribution map for the target area.

[0128] In this embodiment, use a BIM modeling software (such as Revit) to open the BIM model of the target area, extract the functional areas of the operating room (such as the operation area, instrument area, anesthesia area, passage area, etc.) through the "space analysis" function of Revit, and use the API interface of Revit to export the spatial information of the functional areas, including the area name, location, and boundary. Use the NetworkX library of Python to convert the spatial information of the functional areas into a graph structure, construct a spatial relationship network of the functional areas, and export the spatial relationship network as a JSON file. Use AnyLogic simulation software to import the spatial relationship network of the functional areas (such as a JSON file), set the workflow path parameters (such as the movement path of the surgical team and the instrument transfer path), run the simulation to analyze the smoothness and conflict points of the path, and optimize the path layout. According to the optimization results, use the "surface modeling" tool of Revit to generate a three-dimensional boundary surface and import it into the BIM model. Use Excel to open the preset medical lighting standard library, filter out the functional areas related to the operating room (such as the operation area, instrument area, anesthesia area), extract the illuminance requirement parameters of each functional area (such as 500 lux for the operation area, 300 lux for the instrument area, 350 lux for the anesthesia area), and organize the extracted parameters into a standard illuminance parameter set and export it as a CSV file. Import the CSV file into lighting design software (such as DIALux) to verify the integrity and consistency of the parameters. Use the "point cloud generation" function of Revit to generate a regular sampling point grid on the functional area boundary surface, set the sampling point spacing to 0.5 meters to ensure that the sampling points cover the entire functional area, and export the generated sampling points as a PLY format file and import it into DIALux to verify whether the distribution of the sampling points is uniform. Based on the standard illuminance parameter set, set the illuminance calculation grid in DIALux, associate the sampling points with the functional areas, and perform parameter mapping on the sampling points according to the illuminance requirement parameters of the functional areas (such as 500 lux for the operation area, 300 lux for the instrument area), run the illuminance calculation function of DIALux to generate the initial illuminance requirement distribution data. Use a Python script to perform boundary constraint processing on the initial illuminance requirement distribution data to ensure that the illuminance values in the boundary area smoothly transition to the adjacent areas (such as setting the illuminance value in the boundary area to the average value of the adjacent areas), and add lighting quality parameters (such as uniformity, glare index) in DIALux to expand the lighting requirement distribution map. Run the comprehensive calculation function of DIALux to generate the lighting requirement distribution map of the target area and export it as a PNG format.

[0129] Preferably, in step S3, an initial lamp layout plan is constructed based on the target area lighting characteristic model according to the target area lighting requirement distribution map, including:

[0130] The lighting demand distribution map of the target area is discretized into a grid to obtain lighting demand dot matrix data; the light distribution intensity distribution curve of the luminaire is extracted according to the lighting characteristic model of the target area;

[0131] An illuminance optimization objective expression is constructed based on the light distribution intensity distribution curve of the luminaire and the lighting demand dot matrix data;

[0132] The ceiling structure of the target area is parametrically modeled to obtain the luminaire installation constraint surface;

[0133] The physical size and installation spacing requirements of the luminaire are obtained; the luminaire installation points are screened on the luminaire installation constraint surface according to the physical size and installation spacing requirements of the luminaire to obtain the luminaire layout candidate point set;

[0134] A lighting layout optimization model is constructed based on the illuminance optimization objective expression and the luminaire layout candidate point set;

[0135] The hybrid optimization strategy is implemented for the lighting layout optimization model to obtain the initial luminaire layout scheme, where the implementation of the hybrid optimization strategy includes the collaborative optimization of the genetic algorithm and the simulated annealing algorithm.

[0136] In this embodiment, in the lighting design project of the operating room, first use the DIALux software to open the lighting demand distribution map of the target area, set the grid parameters, divide the demand distribution map into regular grid dot matrices, and set the grid density to 0.2 meters to ensure that the illuminance requirements of each functional area can be accurately captured. Export the grid dot matrix data as a CSV file and import it into Python, and use the NumPy library to preprocess the data. Use MATLAB to import the IES light distribution file of the luminaire, and fit the light distribution data through the Curve Fitting Toolbox to generate the continuous light distribution intensity distribution curve of the luminaire. Set the fitting accuracy parameter (error range ≤ 3%) to ensure that the curve can accurately describe the light intensity distribution of the luminaire, and export the fitted curve as a MATLAB script file. Use the Python script to read the lighting demand dot matrix data (CSV file) and the light distribution intensity distribution curve of the luminaire (MATLAB script file), and combine the NumPy and SciPy libraries to match the light intensity distribution of the luminaire with the illuminance requirements of the grid points to construct the illuminance optimization objective expression. The expression form is:

[0137]

[0138] where, E i is the actual illuminance of the i-th grid point, E 需求，iis the required illumination of the i-th grid point, and N is the number of grid points. Set the optimization weight parameters (such as the weight of the operating table area is 1.5, and the weight of the instrument area is 1.0) to ensure that the illumination requirements of key areas are met first, and export the target expression as a Python script file. Use BIM modeling software (such as Revit) to open the BIM model of the target area, use the "Family Editor" in Revit to create a parametric model of the ceiling, set parameters including ceiling height (2.5 meters), keel spacing (600 mm) and ceiling material (aluminum plate), and use Revit's "Alignment Tool" to align the ceiling model with the spatial relationship network of the functional area to ensure the geometric accuracy of the model, and export the ceiling model as an IFC format file. Obtain the physical dimensions of the lamps (such as 600 mm in length and 300 mm in width) and installation spacing requirements (such as a minimum spacing of 1.2 meters) from the technical documents provided by the lamp manufacturer, use Revit's "Point Cloud Generation" function to generate a regular grid of installation points on the ceiling model, set the point spacing to 1.0 meters, and use Python scripts combined with the NumPy library to filter the generated installation points according to the physical dimensions and installation spacing requirements of the lamps, remove points that do not meet the requirements, and export the filtered installation points as a CSV file. Use Python scripts to read the illumination optimization target expression (Python script file) and the lamp layout candidate point set (CSV file), combine the SciPy optimization library, combine the target expression with the candidate point set, build a lighting layout optimization model, set optimization parameters (such as a population size of 100 and an iteration number of 500), ensure that the model can converge to the global optimal solution, run the optimization model, generate an initial lamp layout plan, and export the results as a JSON file. The genetic algorithm is implemented using Python's DEAP library, with the population size set to 100, the crossover rate to 0.8, and the mutation rate to 0.2. The simulated annealing algorithm is implemented using Python's SimulatedAnnealing library, with the initial temperature set to 1000 and the cooling rate set to 0.95. The genetic algorithm and the simulated annealing algorithm are combined to collaboratively optimize the optimization model through alternating iterations. The optimization process is run, the optimal solution of each iteration is recorded, and DIALux is used to verify whether the optimization results meet the design requirements.

[0139] Preferably, in step S3, the initial lamp layout scheme is adjusted for structural conflicts, including:

[0140] Perform lighting effect simulation according to the initial lighting fixture layout plan to obtain lighting effect prediction data; perform glare quantitative evaluation on the lighting effect prediction data to obtain the lighting fixture glare evaluation results;

[0141] Obtain the ceiling installation conditions; perform space conflict detection based on the initial lighting layout plan and ceiling installation conditions, and obtain an installation feasibility assessment report;

[0142] Adjust the initial lighting layout plan according to the installation feasibility assessment report to obtain a revised lighting layout plan. Specifically, the adjustment is as follows:

[0143] Extract the ceiling joist topology data based on the BIM model of the target area;

[0144] Generate a ceiling structure avoidance buffer based on the ceiling joist topology data, and offset the lighting layout points in the initial lighting layout plan to the non - joist area, where the offset distance ≥ 50mm, and the non - joist area is the extended area of the ceiling structure avoidance buffer;

[0145] Use a laser 3D scanner to scan the ceiling installation surface to generate a ceiling installation surface topology point cloud;

[0146] Determine the universal hinge type mounting bracket according to the installation space conflict area in the installation feasibility assessment report;

[0147] Deploy the universal hinge type mounting bracket at the lighting installation nodes in the initial lighting layout plan to generate a set of support installation angle adaptation parameters, where the normal of the universal hinge type mounting bracket deviates from the ceiling surface by ≤ 2°;

[0148] Perform layout space remapping on the initial lighting layout plan based on the ceiling installation surface topology point cloud to generate a 3D space remapped layout diagram;

[0149] Insert redundant installation nodes into the 3D space remapped layout diagram to obtain redundant installation node distribution data, where the spacing of the redundant installation nodes ≤ 1.5m;

[0150] Construct a ceiling installation digital twin scenario, import the 3D space remapped layout diagram and the support installation angle adaptation parameter set into the ceiling installation digital twin scenario to simulate the installation process, and thus generate an installation conflict detection report;

[0151] Dynamically eliminate nodes from the redundant installation node distribution data according to the installation conflict detection report to generate an optimized installation node set;

[0152] Deploy magnetic adsorption type mounting bases in the optimized installation node set to generate a base magnetic suction distribution table, where the magnetic suction intensity of the base magnetic suction distribution table ≥ 80N and the disassembly torque ≤ 3N·m;

[0153] Modify the initial lighting layout plan according to the ceiling structure avoidance buffer, the support installation angle adaptation parameter set, the optimized installation node set and the base magnetic suction distribution table to obtain a revised lighting layout plan.

[0154] In this embodiment, in the lighting design project of the operating room, the initial lighting fixture layout plan is opened using DIALux software, and simulation parameters are set (including the luminous intensity distribution curve of the lighting fixture imported from the IES file, wall reflectivity of 0.7, floor reflectivity of 0.6, ceiling reflectivity of 0.8, and grid density of 0.2 meters). The lighting effect simulation function is run to generate prediction data, and the glare assessment tool of DIALux is used to quantitatively evaluate glare and generate an assessment report. In Revit, the ceiling installation conditions (height of 2.5 meters, keel spacing of 600 millimeters, and aluminum plate material) are extracted, the initial lighting fixture layout plan is imported, and the "collision detection" function of Revit is used to identify the conflict areas, and a conflict detection report in PDF format is exported. According to the report, the ceiling keel topology data is extracted using the Revit API, a 50-millimeter extension avoidance buffer zone is generated in Revit, and the lighting fixture layout points are offset through a Python script combined with the NumPy library to ensure that the offset distance is ≥50 millimeters, and the adjusted layout plan in DWG format is exported. The ceiling installation surface is scanned using a FARO laser 3D scanner with an accuracy set to 2 millimeters, the data is imported into CloudCompare for processing to generate a topological point cloud file in PLY format, and then imported into Revit for verification. The Kupo universal bracket is selected according to the conflict areas in the installation feasibility assessment report, a bracket model is created in Revit, the normal deviation is set to ≤2°, and the "parametric family editor" is used to adjust the angle, and a CSV format set of bracket installation angle adaptation parameters is generated and exported. The topological point cloud in PLY format is imported into Revit, and after adjustment using the "point cloud alignment" tool, a three-dimensional space remapping layout diagram is generated and an IFC format file is exported. On the remapping layout diagram, the "point cloud generation" function of Revit is used to generate an installation node grid with a node spacing of 1.5 meters, and redundant nodes are inserted through a Python script to ensure that each lighting fixture has at least two redundant nodes, and a DWG format file is exported. The digital twin platform is used to import the remapping layout diagram and the bracket parameter set, simulate the installation process to identify interference areas and blind spots, and generate an installation conflict detection report. According to the report, a Python script is used to read the conflict nodes, and the conflict nodes are removed in Revit to generate an optimized installation node set and exported as a CSV file. The Kupo magnetic base (magnetic attraction strength ≥80N, disassembly torque ≤3N·m) is selected, a model is created in Revit and deployed to the optimized node set, and a Python script is used to generate a magnetic force distribution table and exported as an Excel file for construction reference. Finally, in Revit, the avoidance buffer zone, the bracket parameter set, the optimized node set, and the magnetic force distribution table are integrated, the initial lighting fixture layout plan is modified using the "layout adjustment" tool, and after running the DIALux simulation for verification, the final corrected plan in DWG and IFC formats is exported for construction guidance.

[0155] Preferably, in step S3, the installation path conflict adjustment of the corrected lighting fixture layout plan includes:

[0156] Verify the maintainability reachability of the revised lamp layout plan to obtain the maintenance feasibility evaluation result;

[0157] Perform constraint-driven fine-tuning on the revised lamp layout plan according to the maintenance feasibility evaluation result to obtain a feasible lamp layout plan, where the constraint-driven fine-tuning is specifically:

[0158] Generate a conflict area for the maintenance operation path according to the maintenance feasibility evaluation result;

[0159] Insert maintenance channel nodes into the revised lamp layout plan according to the conflict area of the maintenance operation path to generate three-dimensional space occupancy data for the maintenance channel;

[0160] According to the maintenance tool compatibility list in the maintenance feasibility evaluation result, deploy a six-degree-of-freedom quick-release base at the lamp installation node in the revised lamp layout plan. The six-degree-of-freedom quick-release base includes XYZ-axis slide rails and a ball joint structure, and replace the traditional bolt fixing points with magnetic quick-release bases to generate a mapping table for the quick-release unit of the lamp module;

[0161] Based on the three-dimensional space occupancy data of the maintenance channel and the mapping table of the quick-release unit of the lamp module, construct a digital twin scenario of the maintenance operation in the DELMIA simulation platform to simulate the movement and operation process of maintenance personnel, thereby generating a dynamic simulation report on maintenance reachability;

[0162] Iteratively optimize the maintenance channel nodes and the six-degree-of-freedom quick-release base according to the dynamic simulation report on maintenance reachability, and finally generate a feasible lamp layout plan.

[0163] In this embodiment, in the lighting design project of the operating room, use BIM modeling software (such as Revit) to open the revised lighting fixture layout plan, and use the "maintenance accessibility analysis" plug-in to set the access paths of maintenance personnel and the positions of lighting fixtures. Run the plug-in to generate a maintenance accessibility evaluation report, and identify inaccessible areas and path conflict points. According to the evaluation report, use the "conflict area marking" tool in Revit to mark the inaccessible areas and path conflict points as maintenance operation path conflict areas, and export a CSV file containing the positions and dimensions of the conflict areas. Open the revised lighting fixture layout plan again using Revit, insert maintenance access nodes based on the data of the maintenance operation path conflict areas, and set the dimensions of the access nodes (width 1.2 meters, height 2.0 meters) to ensure that maintenance personnel can pass smoothly, and export the updated layout plan as a DWG file. Based on the maintenance tool compatibility list in the maintenance feasibility evaluation results, select a suitable six-degree-of-freedom quick-release base (such as the Kupo six-degree-of-freedom quick-release base), and create a BIM model of the quick-release base in Revit, and set the XYZ-axis slide rail and ball joint structure parameters of the base (slide rail stroke 100 mm, ball joint rotation angle ±30°). After deploying the quick-release base to the lighting fixture installation node, use Revit's "parametric family editor" to record the installation position and angle of each base, and then export the lighting fixture module quick-release unit mapping table as an Excel file. Use DELMIA simulation software to import the revised lighting fixture layout plan (DWG file) and the lighting fixture module quick-release unit mapping table (Excel file), create a digital twin model of the maintenance personnel, set the access paths and operation actions, run the simulation to simulate the access and operation process of the maintenance personnel, and generate a maintenance accessibility dynamic simulation report. According to the potential problems identified in the simulation report, use a Python script to read the report data, identify the conflict points between the maintenance access nodes and the six-degree-of-freedom quick-release base, and use the "node adjustment" tool in Revit to dynamically adjust the positions and dimensions of the maintenance access nodes. After adjustment, run the simulation again in DELMIA to verify whether the optimized maintenance operations meet the design requirements, and export the final optimized layout plan as DWG and IFC format files.

[0164] Preferably, step S4 includes the following steps:

[0165] Step S41: Extract the spatial position data of the lighting fixtures from the feasible lighting fixture layout plan, and obtain the component surface reflectivity distribution map;

[0166] Step S42: Establish a virtual scene of ray tracing in the target area based on the spatial position data of the lighting fixtures and the component surface reflectivity distribution map;

[0167] Step S43: Perform Monte Carlo photon tracing on the virtual scene of ray tracing in the target area to obtain the original photon mapping data;

[0168] Step S44: Perform spatial clustering on the original photon mapping data to obtain a photon density distribution map;

[0169] Step S45: Reconstruct the radiance field based on the photon density distribution map to obtain surface brightness distribution data; perform ray tracing based on the surface brightness distribution data to obtain initial illuminance distribution data;

[0170] Step S46: Perform bilinear interpolation on the initial illuminance distribution data to obtain continuous illuminance distribution data; calculate the illuminance gradient field based on the continuous illuminance distribution data to obtain an illuminance change rate distribution map;

[0171] Step S47: Determine the lamp layout optimization parameter set based on the illuminance change rate distribution map.

[0172] Particularly importantly, step S47 further includes the following steps:

[0173] Step S471: Perform threshold screening on the illuminance change rate distribution map to obtain key area markers; construct a multi-resolution sampling scheme based on the key area markers;

[0174] Step S472: Perform hierarchical directional ray tracing based on the multi-resolution sampling scheme to obtain multi-level illuminance sampling data; perform interpolation and fusion on the multi-level illuminance sampling data to obtain a mixed-resolution illuminance distribution map;

[0175] Step S473: Perform heat map rendering based on the mixed-resolution illuminance distribution map to obtain a heat map of the operating room illuminance distribution;

[0176] Step S474: Extract lighting quality parameters according to the heat map of the operating room illuminance distribution to obtain a lighting quality evaluation index set;

[0177] Step S475: Perform multi-constraint parameter reverse derivation on the lighting quality evaluation index set to obtain the lamp layout optimization parameter set.

[0178] In this embodiment, in the lighting design of the operating room, it is necessary to extract the spatial position data of the luminaires from the feasible luminaire layout schemes. The specific operations are as follows: Open the luminaire layout scheme using BIM modeling software (such as Revit). Use the "Data Extraction" tool in Revit to export the spatial position data of the luminaires (including X, Y, and Z coordinates) as a CSV file. At the same time, use Revit's "Material Property Manager" to extract the reflectivity data of the component surfaces and export it as an Excel file. Import the luminaire position data and reflectivity data into the lighting design software (such as DIALux). Use DIALux to import the spatial position data of the luminaires (CSV file) and the component surface reflectivity distribution map (Excel file). Set the scene parameters in DIALux, including the room dimensions (such as 10 meters long, 8 meters wide, and 3 meters high) and the luminous intensity distribution curve of the luminaires (imported from an IES file). Run the scene construction function of DIALux to generate a ray tracing virtual scene. Import the ray tracing virtual scene using optical simulation software (such as TracePro). Set the Monte Carlo photon tracing parameters in TracePro, including the number of photons (10 6) and tracking depth (5 reflections). Run the photon tracking simulation to generate the original photon mapping data and export it as a binary file. Use a Python script to read the original photon mapping data (binary file). Use the NumPy library to convert the data to a point cloud format and use DBSCAN from the SciPy library for spatial clustering. Set the clustering parameters (such as a radius of 0.5 meters and a minimum sample number of 10) to generate a photon density distribution map. Export the photon density distribution map as a PNG file. Import the photon density distribution map using TracePro. Set the radiance field reconstruction parameters in TracePro, including the grid density (0.2 meters) and the radiance calculation accuracy (error ≤ 5%). Run the radiance field reconstruction function to generate the surface radiance distribution data and export it as a CSV file. Import the surface radiance distribution data using DIALux, perform ray tracing, and generate the initial illuminance distribution data. Use a Python script to read the initial illuminance distribution data (CSV file). Use the bilinear interpolation function from the SciPy library to interpolate the data to a higher density grid (such as 0.1 meters). Calculate the interpolated illuminance gradient field to generate an illuminance change rate distribution map. Export the illuminance change rate distribution map as a PNG file. Use a Python script to read the illuminance change rate distribution map (PNG file). Use the OpenCV library to perform thresholding on the image, setting the threshold range (such as areas with a change rate ≥ 10% as the key areas of concern). Generate a key area of concern marker map and export it as a PNG file. Based on the key area of concern marker map, use a Python script to construct a multi-resolution sampling scheme, setting the sampling density (such as 0.1 meters in the key areas of concern and 0.5 meters in non-key areas). Import the multi-resolution sampling scheme using TracePro. Set the hierarchical directional ray tracing parameters in TracePro, including the number of photons (10 in the key areas of concern 6 , 10 in non-key areas 5) and tracking directions (such as vertical and horizontal directions). Run ray tracing simulation to generate multi-level illuminance sampling data and export it as a CSV file. Use a Python script to interpolate and fuse the multi-level data to generate a mixed-resolution illuminance distribution map. Use a Python script to read the mixed-resolution illuminance distribution map (CSV file). Use the Matplotlib library to generate a heat map of the illuminance distribution in the operating room, and set the color mapping (e.g., low illuminance is blue and high illuminance is red). Export the heat map as a PNG file. Open the heat map of the illuminance distribution (PNG file) using DIALux. Run the lighting quality assessment function in DIALux to extract parameters including uniformity, glare index, and color rendering index. Export the extracted parameters as a PDF report, detailing the numerical values and assessment results of each parameter. Use a Python script to read the lighting quality assessment metric set (PDF report). Use the optimization function of the SciPy library to perform multi-constraint parameter reverse derivation based on the assessment metrics, set the constraint conditions (e.g., uniformity ≥ 0.7, glare index ≤ 19), run the optimization function, generate a set of optimized lamp layout parameters, and export it as a JSON file.

[0179] Preferably, step S5 includes the following steps:

[0180] Step S51: Standardize the data format of the set of optimized lamp layout parameters to obtain a standard lamp layout parameter table;

[0181] Step S52: Perform three-dimensional coordinate conversion on the lamp installation positions in the feasible lamp layout scheme based on the standard lamp layout parameter table to obtain the lamp positioning data under the building construction reference system;

[0182] Step S53: Calculate the installation and fixing points for the lamp positioning data to obtain a set of coordinates for the lamp hoisting anchor points; generate lamp installation measurement reference data based on the set of coordinates for the lamp hoisting anchor points;

[0183] Step S54: Plan the electrical connection path for the lamp installation measurement reference data to obtain a layout diagram of the lamp power supply lines;

[0184] Step S55: Generate a mapping table of lamp control parameters based on the layout diagram of the lamp power supply lines; integrate the mapping table of lamp control parameters with the lamp installation measurement reference data to obtain a comprehensive construction guidance document;

[0185] Step S56: Predict the lighting impact on the target area based on the comprehensive construction guidance document to obtain illuminance impact prediction data;

[0186] Step S57: Generate a fault tolerance interval for the lamp installation guidance data based on the illuminance impact prediction data to obtain a complete construction plan for the lighting system.

[0187] Particularly importantly, step S57 further includes the following steps:

[0188] Step S571: Generate process-level installation process data according to the comprehensive construction guidance document; extract construction quality control points from the process-level installation process data to obtain the construction quality inspection form for the lighting system;

[0189] Step S572: Conduct simulation of construction parameter disturbances on the construction quality inspection form for the lighting system to obtain a construction parameter fluctuation range table; construct parameter deviation-illuminance impact mapping data based on the construction parameter fluctuation range table;

[0190] Step S573: Calculate the construction fault tolerance threshold according to the parameter deviation-illuminance impact mapping data to obtain a construction parameter allowable deviation table;

[0191] Step S574: Generate a construction auxiliary tool specification according to the construction parameter allowable deviation table; formulate a distribution map of lighting effect acceptance test points according to the construction auxiliary tool specification;

[0192] Step S575: Conduct optical environment simulation verification on the distribution map of lighting effect acceptance test points to obtain expected illuminance distribution prediction data;

[0193] Step S576: Conduct a quantitative assessment of the compliance of the expected illuminance distribution prediction data with lighting standards according to the illuminance impact prediction data to obtain a lighting system acceptance score table;

[0194] Step S577: Integrate the full life cycle parameters of the lighting system acceptance score table and the construction parameter allowable deviation table for traceability to obtain a complete lighting system construction plan.

[0195] In this embodiment, in the lighting design project of the operating room, the data format of the optimized parameter set for the luminaire layout is standardized. The specific operations are as follows: Use a Python script to read the optimized parameter set (such as a JSON file), use the Pandas library for data cleaning and format conversion, generate a standard luminaire layout parameter table, and export it as an Excel file. The standard luminaire layout parameter table includes parameters such as the type of luminaire, location (X, Y, Z coordinates), luminous intensity distribution curve (imported from an IES file), and installation angle. For the standard luminaire layout parameter table, use the "Coordinate System Conversion" tool in Revit to convert the luminaire installation location from the virtual scene coordinate system to the three-dimensional coordinates under the building construction reference system, and export it as a CSV file. Calculate the installation fixed points for the luminaire positioning data. The specific operations are as follows: Use the "Structural Analysis" plugin in Revit to calculate the hoisting anchor points based on the three-dimensional coordinates of the luminaires, generate a set of anchor point coordinates, and export it as an Excel file. The set of anchor point coordinates includes the exact location and required bearing capacity (such as ≥100N) of each anchor point. Using these coordinate sets, generate the luminaire installation measurement reference data in Revit to ensure the accuracy of the installation process. Import the luminaire installation measurement reference data using DIALux, combine it with the BIM model for electrical wiring path planning, generate a luminaire power supply line layout diagram, and export it as a DWG file. Generate a luminaire control parameter mapping table based on the luminaire power supply line layout diagram. The specific operations are as follows: In Excel, combine the power supply line layout diagram and the luminaire layout parameter table to generate a control parameter mapping table, recording the power supply line (such as circuit number, wire diameter) and control parameters (such as switch position, dimming range) of each luminaire. Integrate the mapping table with the luminaire installation measurement reference data to generate a comprehensive construction guidance document and export it as a PDF file. Based on the comprehensive construction guidance document, predict the lighting impact on the target area. The specific operations are as follows: Import the comprehensive construction guidance document using DIALux, run the lighting simulation function, generate illuminance impact prediction data, and export it as a CSV file. The prediction data includes the illuminance value and uniformity of each measurement point. According to the prediction data, use a Python script combined with the NumPy library to calculate the tolerance interval (such as the allowable illuminance deviation range of ±10%), generate the luminaire installation guidance data, and export it as a JSON file. Generate the process-level installation flow data according to the comprehensive construction guidance document. The specific operations are as follows: In Excel, list in detail the steps and requirements of each installation process, including the installation sequence, fixing method, and electrical connection method of the luminaires. Extract the construction quality control points (such as the bearing capacity test of the anchor points, the insulation test of the electrical connections) to generate a lighting system construction quality inspection form and export it as a PDF file. Use Simulink to perform perturbation simulation on the construction parameters in the quality inspection form, generate a construction parameter fluctuation range table (such as the anchor point position deviation of ±5 mm), and construct the parameter deviation - illuminance impact mapping data. According to the mapping data, calculate the construction tolerance threshold.The specific operations are as follows: Use a Python script in combination with the SciPy library to calculate the construction fault tolerance threshold based on the mapping data (such as the allowable range of deviation in the position of the anchoring point is ±5 mm, and the allowable range of deviation in illuminance is ±10%). Generate a table of allowable deviations for construction parameters and export it as an Excel file. Based on the table of allowable deviations, generate a specification for construction auxiliary tools (such as laser levels, torque wrenches), and formulate a distribution map of acceptance test points for lighting effects. Conduct a light environment simulation verification on the distribution map of acceptance test points for lighting effects. The specific operations are as follows: Use DIALux to import the distribution map of test points, run the light environment simulation function, generate prediction data for the expected illuminance distribution, and export it as a PNG file. According to the prediction data of illuminance influence, use a Python script to quantitatively evaluate the compliance of the prediction data with lighting standards (such as illuminance uniformity ≥ 0.7, glare index ≤ 19), generate an acceptance scoring table for the lighting system, and export it as a PDF file. By integrating the full-life cycle parameter traceability of the acceptance scoring table and the table of allowable deviations for construction parameters, finally generate a complete construction plan for the lighting system.

[0196] Preferably, the present invention further provides a lighting device automatic layout system based on a BIM model for performing the lighting device automatic layout method based on a BIM model as described above. The lighting device automatic layout system based on a BIM model includes:

[0197] A model initialization module for obtaining a BIM model of a target area and constructing a light distribution surface grid model of a lighting fixture; constructing a lighting characteristic model of the target area based on the BIM model of the target area and the light distribution surface grid model of the lighting fixture;

[0198] A requirement analysis module for identifying the functional area space in the BIM model of the target area to obtain a functional area space relationship network; determining a lighting requirement distribution map of the target area based on the functional area space relationship network;

[0199] A layout planning module for constructing an initial lighting fixture layout plan based on the lighting characteristic model of the target area according to the lighting requirement distribution map of the target area; adjusting the structural conflicts of the initial lighting fixture layout plan to obtain a modified lighting fixture layout plan; adjusting the installation path conflicts of the modified lighting fixture layout plan to obtain a feasible lighting fixture layout plan;

[0200] A layout optimization module for performing environmental photon mapping simulation on the target area based on the feasible lighting fixture layout plan to obtain an illuminance distribution heat map; identifying the light gradient change gradient field in the illuminance distribution heat map to generate an illuminance change rate distribution map; determining a set of lighting fixture layout optimization parameters based on the illuminance change rate distribution map;

[0201] The layout integration module is used to translate construction parameters for the lamp layout optimization parameter set to obtain lamp installation guidance data; perform light influence prediction based on the lamp installation guidance data to obtain illuminance influence prediction data; generate a fault tolerance interval for the lamp installation guidance data according to the illuminance influence prediction data to obtain a complete lighting system construction plan.

[0202] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0203] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. An automatic layout method for lighting equipment based on a BIM model, characterized in that, It includes the following steps: Step S1: Obtain the BIM model of the target area and construct a lighting distribution surface grid model for the luminaires; Construct a lighting characteristic model for the target area based on the BIM model of the target area and the lighting distribution surface grid model for the luminaires; Step S2: Identify the functional area spaces in the BIM model of the target area to obtain a functional area spatial relationship network; Determine the lighting demand distribution map for the target area based on the functional area spatial relationship network; Step S3: Based on the lighting characteristic model of the target area, construct an initial luminaire layout plan according to the lighting demand distribution map for the target area; Adjust the structural conflicts of the initial luminaire layout plan to obtain a revised luminaire layout plan; Adjust the installation path conflicts of the revised luminaire layout plan to obtain a feasible luminaire layout plan; Step S4: Conduct environmental photon mapping simulation on the target area based on the feasible luminaire layout plan to obtain an illuminance distribution heat map; Identify the light gradient change gradient field in the illuminance distribution heat map to generate an illuminance change rate distribution map; Determine the luminaire layout optimization parameter set based on the illuminance change rate distribution map; Step S5: Translate the construction parameters of the luminaire layout optimization parameter set to obtain luminaire installation guidance data; Conduct a lighting impact prediction based on the luminaire installation guidance data to obtain illuminance impact prediction data; Generate a fault tolerance interval for the luminaire installation guidance data according to the illuminance impact prediction data to obtain a complete lighting system construction plan.

2. The automatic layout method of lighting equipment based on the BIM model according to claim 1, characterized in that In step S1, constructing the lighting distribution surface grid model for the luminaires includes: Obtain the IES lighting distribution file of the luminaires; Verify the data integrity of the IES lighting distribution file of the luminaires to obtain a valid IES lighting distribution file of the luminaires; Conduct a geometric structure analysis on the BIM model of the target area to obtain a spatial component topological relationship diagram; Extract and standardize the parameters of the valid IES lighting distribution file of the luminaires to obtain the standard light distribution data of the luminaires; Convert the C-γ polar coordinate data in the standard light distribution data of the luminaires into Cartesian coordinates to obtain a point cloud of the three-dimensional spatial light intensity distribution of the luminaires; Conduct triangular meshing on the point cloud of the three-dimensional spatial light intensity distribution of the luminaires to obtain the lighting distribution surface grid model for the luminaires.

3. The automatic layout method of lighting equipment based on the BIM model according to claim 1, characterized in that In step S1, constructing the lighting characteristic model for the target area based on the BIM model of the target area and the lighting distribution surface grid model for the luminaires includes: Fit a mathematical expression to the lighting distribution surface grid model for the luminaires to obtain a continuous lighting distribution function for the luminaires; Extract the material optical characteristics of the spatial component topological relationship diagram to obtain a component surface reflectivity distribution map; Construct a fast illuminance calculation unit based on the continuous lighting distribution function for the luminaires and the component surface reflectivity distribution map; Conduct a parameter sensitivity assessment on the fast illuminance calculation unit to obtain a lighting parameter influence factor table; Construct a digital twin model of the luminaires according to the lighting parameter influence factor table; Conduct spatial positioning matching on the digital twin model of the luminaires and the BIM model of the target area to obtain an initial integrated model; Conduct geometric-optical coupling conflict detection and optimization on the initial integrated model to obtain the lighting characteristic model for the target area.

4. The automatic layout method of lighting equipment based on BIM model according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Identify the functional area spaces in the BIM model of the target area to obtain a functional area spatial relationship network; Step S22: Perform workflow path simulation and optimization on the functional area spatial relationship network to obtain the optimal functional area configuration plan; generate a three-dimensional boundary surface according to the optimal functional area configuration plan to obtain the functional area demarcation surface; Step S23: Obtain the illuminance requirement parameters of different functional areas in the functional area spatial relationship network from the preset medical lighting standard library to generate a standard illuminance parameter set; Step S24: Generate spatial sampling points according to the functional area demarcation surface to obtain the target area illuminance calculation grid; Step S25: Perform parameter mapping on the target area illuminance calculation grid based on the standard illuminance parameter set to obtain the initial illuminance requirement distribution data; Step S26: Perform boundary constraints on the initial illuminance requirement distribution data to obtain a boundary-coordinated illuminance requirement map; perform lighting quality parameter expansion on the boundary-coordinated illuminance requirement map to obtain the target area lighting requirement distribution map.

5. The automatic layout method of lighting equipment based on the BIM model according to claim 1, characterized in that, In step S3, construct an initial lighting fixture layout plan based on the target area lighting characteristic model according to the target area lighting requirement distribution map, including: Perform grid discretization on the target area lighting requirement distribution map to obtain lighting requirement dot matrix data; extract the light distribution intensity distribution curve of the lighting fixture according to the target area lighting characteristic model; Construct an illuminance optimization target expression based on the light distribution intensity distribution curve of the lighting fixture and the lighting requirement dot matrix data; Perform parametric modeling on the ceiling structure of the target area to obtain the lighting fixture installation constraint surface; Obtain the physical size and installation spacing requirements of the lighting fixture; screen the lighting fixture installation points on the lighting fixture installation constraint surface according to the physical size and installation spacing requirements of the lighting fixture to obtain a lighting fixture layout candidate point set; Construct a lighting layout optimization model based on the illuminance optimization target expression and the lighting fixture layout candidate point set; Implement a hybrid optimization strategy on the lighting layout optimization model to obtain an initial lighting fixture layout plan, where the implementation of the hybrid optimization strategy includes collaborative optimization of the genetic algorithm and the simulated annealing algorithm.

6. The automatic layout method of lighting equipment based on BIM model according to claim 1, characterized in that, In step S3, perform structural conflict adjustment on the initial lighting fixture layout plan, including: Perform lighting effect simulation according to the initial lighting fixture layout plan to obtain lighting effect prediction data; perform glare quantification evaluation on the lighting effect prediction data to obtain the lighting fixture glare evaluation result; Obtain the ceiling installation conditions; perform spatial conflict detection based on the initial lighting fixture layout plan and the ceiling installation conditions to obtain an installation feasibility evaluation report; Perform constraint adjustment on the initial lighting fixture layout plan according to the installation feasibility evaluation report to obtain a revised lighting fixture layout plan, where the constraint adjustment is specifically: Extract the ceiling keel topology data based on the target area BIM model; Generate a ceiling structure avoidance buffer zone according to the ceiling keel topology data, and offset the lighting fixture layout points in the initial lighting fixture layout plan to the non-keel area, where the offset distance ≥ 50 mm, and the non-keel area is the extended area of the ceiling structure avoidance buffer zone; Use a laser three-dimensional scanner to scan the ceiling installation surface to generate a ceiling installation surface topology point cloud; Determine a universal hinge type installation bracket according to the installation space conflict area in the installation feasibility evaluation report; Deploy a universal hinge type mounting bracket at the lamp installation nodes in the initial lamp layout plan to generate a set of mounting angle adaptation parameters for the bracket, where the deviation between the normal direction of the universal hinge type mounting bracket and the ceiling surface is ≤2°; Perform a layout space remapping on the initial lamp layout plan based on the topological point cloud of the ceiling installation surface to generate a three-dimensional space remapped layout diagram; Insert redundant installation nodes into the three-dimensional space remapped layout diagram to obtain redundant installation node distribution data, where the spacing between redundant installation nodes is ≤1.5 m; Construct a digital twin scene for ceiling installation, import the three-dimensional space remapped layout diagram and the mounting angle adaptation parameter set of the bracket into the digital twin scene for ceiling installation to simulate the installation process, thereby generating an installation conflict detection report; Dynamically eliminate nodes from the redundant installation node distribution data according to the installation conflict detection report to generate an optimized set of installation nodes; Deploy a magnetic adsorption type mounting base in the optimized set of installation nodes to generate a magnetic force distribution table for the base, where the magnetic adsorption strength in the magnetic force distribution table for the base is ≥80 N and the disassembly torque is ≤3 N·m; Modify the initial lamp layout plan according to the ceiling structure avoidance buffer zone, the mounting angle adaptation parameter set of the bracket, the optimized set of installation nodes, and the magnetic force distribution table for the base to obtain a corrected lamp layout plan.

7. The automatic layout method of lighting equipment based on BIM model according to claim 1, characterized in that In step S3, perform an adjustment on the installation path conflict of the corrected lamp layout plan, including: Verify the maintainability reachability of the corrected lamp layout plan to obtain a maintainability feasibility evaluation result; Perform constraint-driven fine-tuning on the corrected lamp layout plan according to the maintainability feasibility evaluation result to obtain a feasible lamp layout plan, where the constraint-driven fine-tuning is specifically: Generate a maintenance operation path conflict area according to the maintainability feasibility evaluation result; Insert maintenance channel nodes into the corrected lamp layout plan according to the maintenance operation path conflict area to generate maintenance channel three-dimensional space occupancy data; Deploy a six-degree-of-freedom quick-release base at the lamp installation nodes in the corrected lamp layout plan according to the maintenance tool compatibility list in the maintainability feasibility evaluation result, where the six-degree-of-freedom quick-release base includes XYZ-axis slide rails and a ball joint structure, and replace the traditional bolt fixing points with magnetic quick-release bases to generate a lamp module quick-release unit mapping table; Based on the maintenance channel three-dimensional space occupancy data and the lamp module quick-release unit mapping table, construct a maintenance operation digital twin scene in the DELMIA simulation platform to simulate the movement and operation process of maintenance personnel, thereby generating a maintenance reachability dynamic simulation report; Iteratively optimize the maintenance channel nodes and the six-degree-of-freedom quick-release base according to the maintenance reachability dynamic simulation report, and finally generate a feasible lamp layout plan.

8. The automatic layout method of lighting equipment based on BIM model according to claim 1, characterized in that Step S4 includes the following steps: Step S41: Extract the lamp spatial position data from the feasible lamp layout plan and obtain the component surface reflectivity distribution map; Step S42: Establish a virtual scene for ray tracing in the target area based on the lamp spatial position data and the component surface reflectivity distribution map; Step S43: Perform Monte Carlo photon tracing on the virtual scene for ray tracing in the target area to obtain the original photon mapping data; Step S44: Perform spatial clustering on the original photon mapping data to obtain the photon density distribution map; Step S45: Reconstruct the radiance field based on the photon density distribution map to obtain surface brightness distribution data; perform ray tracing based on the surface brightness distribution data to obtain initial illuminance distribution data; Step S46: Perform bilinear interpolation on the initial illuminance distribution data to obtain continuous illuminance distribution data; calculate the illuminance gradient field based on the continuous illuminance distribution data to obtain an illuminance change rate distribution map; Step S47: Determine the lamp layout optimization parameter set based on the illuminance change rate distribution map.

9. The automatic layout method of lighting equipment based on the BIM model according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Standardize the data format of the lamp layout optimization parameter set to obtain a standard lamp layout parameter table; Step S52: Perform three-dimensional coordinate conversion on the lamp installation positions in the feasible lamp layout scheme based on the standard lamp layout parameter table to obtain lamp positioning data in the building construction reference system; Step S53: Calculate the installation fixed points for the lamp positioning data to obtain a set of lamp hoisting anchor point coordinates; generate lamp installation measurement reference data based on the set of lamp hoisting anchor point coordinates; Step S54: Plan the electrical connection path for the lamp installation measurement reference data to obtain a lamp power supply line layout diagram; Step S55: Generate a lamp control parameter mapping table based on the lamp power supply line layout diagram; integrate the lamp control parameter mapping table with the lamp installation measurement reference data to obtain a comprehensive construction guidance document; Step S56: Predict the lighting impact on the target area based on the comprehensive construction guidance document to obtain illuminance impact prediction data; Step S57: Generate a fault tolerance interval for the lamp installation guidance data based on the illuminance impact prediction data to obtain a complete lighting system construction plan.

10. An automatic lighting equipment layout system based on a BIM model, characterized in that, For implementing the automatic lamp layout method based on the BIM model as described in claim 1, the automatic lamp layout system based on the BIM model includes: A model initialization module, configured to obtain the BIM model of the target area and construct a lamp light distribution surface grid model; construct a target area lighting characteristic model based on the BIM model of the target area and the lamp light distribution surface grid model; A requirement analysis module, configured to identify the functional area spaces in the BIM model of the target area to obtain a functional area space relationship network; determine the target area lighting requirement distribution map based on the functional area space relationship network; A layout planning module, configured to construct an initial lamp layout scheme based on the target area lighting characteristic model according to the target area lighting requirement distribution map; adjust the structural conflicts of the initial lamp layout scheme to obtain a revised lamp layout scheme; adjust the installation path conflicts of the revised lamp layout scheme to obtain a feasible lamp layout scheme; A layout optimization module, configured to perform environmental photon mapping simulation on the target area based on the feasible lamp layout scheme to obtain an illuminance distribution heat map; identify the light gradient change gradient field in the illuminance distribution heat map to generate an illuminance change rate distribution map; determine the lamp layout optimization parameter set based on the illuminance change rate distribution map; A layout integration module is used to translate construction parameters for an optimized parameter set of lamp layout to obtain lamp installation guidance data; perform illumination impact prediction based on the lamp installation guidance data to obtain illuminance impact prediction data; and generate a fault tolerance interval for the lamp installation guidance data according to the illuminance impact prediction data to obtain a construction plan for a complete lighting system.

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