Yacht interior design optimization method and system based on virtual reality

Abstract

The invention relates to the technical field of yacht design, and discloses a yacht interior design optimization method and system based on virtual reality, and the method comprises the steps: carrying out the interaction with a user through a virtual reality system, collecting the preference information of the user in the aspects of spatial layout, material selection and function demands, and constructing a user preference model in combination with the interaction behavior data; a building information modeling model of the target yacht is obtained, and parametric modeling is carried out on a cabin structure, furniture arrangement and illumination arrangement; in combination with the user preference model and the parameterized model, constructing a design optimization objective function, and introducing an intelligent optimization algorithm to generate a plurality of interior design schemes; loading the interior design scheme into a virtual reality environment, carrying out interactive evaluation by a user through a voice, gesture or sight line control mode, and carrying out feedback marking on an unsatisfied part; and updating the user preference model according to the interactive behavior data of the user in the virtual reality environment and the feedback marking result. The method has the advantage of improving the design efficiency and quality.

Description

Technical Field

[0001] The present invention relates to the technical field of yacht design, and in particular to a method and system for optimizing the interior design of yachts based on virtual reality. Background Technique

[0002] In the process of yacht interior design, factors such as spatial layout and material selection have important impacts on overall comfort, aesthetics, and practicality. Traditional design methods mainly rely on the experience of designers, manually adjusting the layout and materials through two-dimensional drawings or three-dimensional modeling software. However, this method has the following limitations: The rationality of the spatial layout depends on the intuition and experience of designers, lacking data support, and easily leading to problems such as low space utilization and unreasonable functional area division. For example, in the limited interior space of a yacht, how to optimize the placement of furniture, the width of passages, and the allocation of storage space is a complex multi-objective optimization problem. Material selection is usually based on subjective judgment, without fully considering the physical properties, durability, and environmental adaptability of different materials. Yacht interior materials need to meet special requirements such as lightweight, moisture-proof, and fire-proof, and the existing systems lack an intelligent recommendation mechanism, resulting in a time-consuming material matching process and prone to unreasonable selection. The existing VR systems mainly rely on manual adjustment in design optimization, lacking an intelligent optimization function based on algorithms. Currently, some high-end design software supports parametric modeling, but still requires designers to manually adjust parameters, making it difficult to automatically generate the optimal layout plan. Therefore, it is necessary to design a method and system for optimizing the interior design of yachts based on virtual reality to improve design efficiency and quality. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the present invention provides a method and system for optimizing the interior design of yachts based on virtual reality, which has the advantages of automatically recommending the optimal design plan, reducing the complexity of manual adjustment, and improving design efficiency and quality, and solves the problems in the above background technique.

[0004] To achieve the above purpose of automatically recommending the optimal design plan, reducing the complexity of manual adjustment, and improving design efficiency and quality, the present invention provides the following technical solution: A method for optimizing the interior design of yachts based on virtual reality, including the following steps: S1: Interact with the user through a virtual reality system, collect the user's preference information in terms of spatial layout, material selection, and functional requirements, and combine the interaction behavior data to construct a user preference model.

[0005] Preferably, the S1 further includes supporting multi-modal feedback such as voice control, gesture recognition, and gaze tracking through virtual reality devices, collecting the user's behavior data in real time, analyzing these data through deep learning algorithms, identifying the user's potential preferences and needs, and based on the deep neural network algorithm, the system constructs a personalized user preference model.

[0006] S2: Obtain the building information modeling model of the target yacht, and perform parametric modeling on its cabin structure, furniture layout, and lighting layout.

[0007] Preferably, the S2 further includes obtaining the building information modeling model of the target yacht through various methods, supporting mainstream BIM file formats. Based on the BIM model, the system automatically identifies the cabin structure and generates a parametric space model, allowing real-time adjustment of cabin dimensions, functional partitions, furniture, and equipment, supporting lighting simulation, evaluating the impact of different lighting schemes on the spatial effect. The user can adjust the layout and lighting design in real time in the virtual reality environment. Combining the user's needs and optimization algorithms, the system automatically generates a design scheme that meets the space utilization efficiency and user preferences.

[0008] S3: Combine the user preference model and the parametric model to construct an optimization objective function for the design, and introduce intelligent optimization algorithms to generate multiple interior design schemes.

[0009] Preferably, the S3 further includes collecting the user's interaction data and behavior data through virtual reality to construct a user preference model to describe the user's preferences for design elements. Based on the parametric design elements generated from the BIM model, the system converts the design elements into adjustable variables, combines the user preference model with the objective function, sets the optimization objective, and the system uses a variety of intelligent optimization algorithms for optimization and solution, automatically generating multiple design schemes, and evaluating and ranking the schemes based on the objective function.

[0010] S4: Load the interior design scheme into the virtual reality environment, and the user conducts interactive evaluation through voice, gesture, or eye control methods, and marks the dissatisfied parts for feedback.

[0011] Preferably, the S4 further includes that after the system generates the finally optimized interior design scheme, it loads it into the virtual reality environment to create a highly immersive virtual space, supporting panoramic rendering and real-time rendering technologies. The user interacts with the system through voice commands, gesture recognition, and eye tracking technologies to adjust the layout, furniture configuration, and lighting effects in real time. The system embeds voice recognition, gesture operation, and eye tracking functions. The user directly feedbacks the dissatisfied design parts, and the system automatically records the feedback and performs adaptive optimization according to the user preference model. The system collects the user's interaction data in real time and optimizes the design scheme through data analysis.

[0012] S5: Update the user preference model according to the user's behavior data and feedback marking results in the virtual reality environment.

[0013] Preferably, S5 further includes automatically collecting the user's behavior data through real-time interaction in the virtual reality environment, extracting the user's design preference features in terms of spatial layout, material selection, function configuration, and lighting requirements by analyzing the user's behavior data and feedback marks in the virtual reality environment, and constructing a dynamic user preference model using machine learning algorithms. The system automatically updates the model parameters according to the feedback collected after each interaction.

[0014] A virtual reality-based yacht interior design optimization system includes: User preference collection module: Collect the user's preference information in terms of spatial layout, material selection, and function requirements, and construct a user preference model by combining the interaction behavior data; Building information modeling module: Obtain the building information modeling model of the yacht, and perform parametric modeling on the cabin structure, furniture layout, and lighting configuration; Design optimization module: Combine the user preference model and the parametric model to construct a design optimization objective function, and introduce intelligent optimization algorithms to generate multiple interior design schemes; Virtual reality interaction module: Load the design scheme into the virtual reality environment, allow the user to perform interactive evaluation through voice, gesture, or gaze control, and mark the dissatisfied parts; User preference model update module: Real-time update the user preference model according to the user's behavior data and feedback mark results in the virtual reality environment.

[0015] Compared with the prior art, the present invention provides a virtual reality-based yacht interior design optimization method and system, which has the following beneficial effects: The present invention collects the user's active selection and implicit behavior data in terms of spatial layout, material selection, and function configuration through virtual reality technology, constructs a dynamically updatable user preference model using machine learning algorithms, and accurately identifies and understands the user's deep needs; Based on the parametric design framework constructed by the BIM model, the cabin structure, furniture layout, and lighting configuration can be flexibly adjusted, and multiple design schemes can be efficiently generated through multi-objective optimization algorithms. The user can naturally interact in the virtual reality environment in multiple modalities such as voice, gesture, and gaze, and immerse themselves in evaluating each scheme. The system can optimize the recommendation results in real time according to the user's feedback marks and behavior data. This method effectively shortens the design cycle, improves the design accuracy, significantly enhances the user's sense of participation and satisfaction, and has high practical value and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the method of the present invention; Figure 2 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1: Please refer to Figure 1 As shown, the yacht interior design optimization method based on virtual reality according to the embodiment of the present invention comprises the following steps: S1: Interact with users through the virtual reality system to collect user preference information in terms of spatial layout, material selection and functional requirements, and build a user preference model based on the interaction behavior data.

[0019] The system interacts with users immersively through virtual reality devices and supports users to provide feedback in a variety of ways, including multimodal interactions such as voice control, gesture recognition, and eye tracking. For example, users adjust the layout of furniture through gestures, or change the material selection through voice commands, and the system records the user's choices and preferences in real time. Through the virtual reality system, the user's behavior data is collected in real time during the user interaction, including gaze time, operation frequency, interaction path, and stay area. The system analyzes these interactive behaviors through deep learning algorithms to identify the user's potential preferences and needs, so as to more accurately understand the user's specific preferences in terms of spatial layout, material selection, functional requirements, etc. Based on the collected interaction data, a personalized user preference model is constructed using algorithms such as machine learning or deep neural networks. The preference model not only considers the user's explicit choices during the design process, but also combines user behavior. For example, a longer stay in a specific space indicates that the area or layout is more important to the user, generating more accurate preference predictions. The system can adaptively update the preference model based on the user's new interaction behavior data. As user interaction continues, the preference model will be continuously optimized to improve the personalization of the design solution. For example, when a user changes a spatial layout or material combination in the system, the preference model automatically adjusts its design recommendations based on the new selection.

[0020] Through the analysis of various interaction methods and behavioral data, the system can accurately capture user preferences and recommend personalized design solutions. The interior design requirements of each user can be precisely identified, thus avoiding the errors caused by relying on manual speculation in traditional designs. The system's multimodal interaction methods, such as voice, gesture, and eye tracking, enable users to express their design requirements more naturally, no longer limited to traditional click-based operations, and significantly enhance the immersive feeling of user-system interaction. The real-time feedback mechanism based on virtual reality can quickly display the design effects, allowing users to immediately evaluate and adjust the design without waiting for a long iterative process. By combining user preference data, the system can provide optimization suggestions and automatically generate design solutions that meet user needs, significantly improving design efficiency. By accurately modeling user preferences and behavioral data, the system can deeply analyze user needs, avoid subjective biases or information loss in traditional design methods, and improve the accuracy of the final design solution and user satisfaction. As users continuously interact with the virtual reality environment, the system can dynamically adjust the design solution, making the design solutions recommended after each user interaction closer to user needs and achieving the effect of "continuous optimization".

[0021] S2: Obtain the building information modeling model of the target yacht and perform parametric modeling on its cabin structure, furniture layout, and lighting layout.

[0022] The system obtains the building information modeling (BIM) model of the target yacht through various methods, including: obtaining the standard BIM model from the yacht manufacturer's database. Using methods such as laser scanning technology or drone photography to perform three-dimensional scanning on the existing structure of the yacht to generate a high-precision digital model. The system supports importing mainstream BIM file formats, such as IFC, RVT, DWG, OBJ, etc., to ensure compatibility with various design tools.

[0023] Based on the yacht cabin structure in the BIM model, the system can automatically identify the boundaries and functional partitions of each cabin and transform them into a parametric spatial model with flexible adjustment capabilities. The size and functions of each cabin, such as the sleeping cabin, dining room, living room, etc., can be updated in real time by adjusting the parametric model. Users can adjust the size, proportion, and position of the space according to their needs, thereby optimizing the space utilization rate. Based on the BIM model, the system supports parametric modeling of furniture, equipment, decorations, etc. Each furniture unit has flexible size and style adjustment functions, and users can adjust according to their personal needs and the size of the space. The system provides a variety of furniture styles, material selections, and sizes to ensure the individuality and efficiency of the design. Based on virtual reality technology, users can perform real-time interactions for furniture layout in the virtual space and feedback on the layout effect. Based on the BIM model, the system can perform lighting simulation to evaluate the impact of different lighting schemes on the spatial effect. The system can identify and simulate the distribution effects of natural light and artificial lighting, considering factors such as the position, intensity, and color temperature of the light sources. The lighting layout parameters can be adjusted in real time through the virtual reality environment, enabling users to see the visual effects of the space under different lighting conditions and optimize the design scheme. Combining user input, the system can automatically adjust the space layout, furniture arrangement, and lighting design to achieve the best design effect. The system makes intelligent adjustments according to the optimization algorithm based on the space utilization efficiency and user preferences and automatically generates a compliant solution.

[0024] Through parametric modeling, the system can automatically adapt to the user's spatial requirements and the structural characteristics of the yacht, optimize the space layout, and achieve more efficient space utilization. Users can adjust the space size and functional partitions in real time to ensure the maximization of the use efficiency of each cabin while maintaining the balance between aesthetics and functionality. Users can flexibly select and adjust the types, sizes, and layouts of furniture, equipment, and decorations according to their personal preferences and needs. Through parametric modeling, each furniture element can be customized individually, avoiding the limitations of fixed styles and sizes in traditional designs and meeting diverse design requirements. Through the parametric modeling and simulation of lighting layout, the system can accurately evaluate and optimize the natural light and artificial lighting effects in the space. Users can view the spatial effects under different lighting conditions during the day and at night according to different light source settings, helping to design a more comfortable and beautiful environment. Through the combination of the BIM model and virtual reality technology, users can view the design effect in real time in the virtual environment, adjust the layout, and obtain immediate feedback, greatly improving the design efficiency. The automatically generated optimization solution can help designers produce high-quality design solutions that meet user requirements in a short time. Users can perform immersive interactions through the virtual reality system and can more intuitively feel the actual effect of the space design, enhancing the experience of the design solution. Through the real-time interaction and feedback mechanism, the system can make design adjustments according to the user's real-time operations and feedback, further improving the satisfaction of the personalized design solution.

[0025] S3: Combine the user preference model and the parametric model to construct a design optimization objective function, and introduce an intelligent optimization algorithm to generate multiple interior design solutions.

[0026] The process of constructing a design optimization objective function by combining the user preference model and the parametric model is as follows: Take the key parameters affecting the design as optimization variables, denoted as: , where each variable corresponds to the parameters of a design element, such as: furniture position, size; cabin space size; light intensity, angle; Normalize the original scoring data, and the formula is:

[0027] In the formula, is the normalized value of the i-th solution on the j-th variable, is the preference score of the i-th design solution on the j-th design variable, and m is the design solution; Calculate the information entropy of the i-th design variable:

[0028] Among them, ; Design variable difference degree: ; Preference weight is expressed as the proportion of the difference degree of the i-th design variable in the overall:

[0029] The user preference model represents the preference intensity of the user for different design elements, and is expressed as a preference score function by a weighted function, and the formula is:

[0030] In the formula, is the preference weight of the user for the -th variable, is the preference score of the design variable under the current configuration.

[0031] The preference score is obtained in the following ways: Expert modeling: Set the scoring range based on experience or design specifications; Machine learning modeling: Use a regression model or a neural network to train a preference function according to user feedback samples; VR interactive sampling: The user scores different configurations in a virtual scenario, and the system automatically fits a preference function.

[0032] The process of generating multiple interior design schemes is as follows: Based on BIM, Rhino-Grasshopper, Revit-Dynamo or a 3D modeling engine, construct an adjustable parameter interior model, with design elements and variables Establish a one-to-one correspondence to form a searchable design space; Construct a preference scoring function , integrating entropy weight and preference score to reflect individualized aesthetic and functional requirements; Render the candidate design schemes in real time in the VR engine, allowing users to have an immersive experience, comparison and selection. Provide feedback learning based on user selection to further optimize the scheme generation process.

[0033] The user preference model is constructed by collecting user interaction data such as spatial layout, material selection, functional requirements, etc. and user behavior data such as visual stay time and operation frequency through the virtual reality system. The model describes in detail the user's preferences for different design elements, such as material, color, layout, functional area, etc. Based on the parametric design elements generated in the BIM model, such as cabin size, furniture layout, lighting arrangement, etc., each design element is converted into a variable with adjustment parameters. Each design element, such as furniture size, position, color, material, etc., can be adjusted to meet different design requirements. The objective function sets the goal of design optimization by combining the user preference model with the parametric model. For example, the objective function can contain multiple optimization goals: space utilization efficiency, design aesthetics, user satisfaction, energy efficiency, etc. In order to optimize multiple design factors at the same time, such as spatial layout, functional configuration and aesthetics, the objective function adopts weighted summation or Pareto optimization method to ensure that user preferences and design effects are maximized under the premise of meeting all constraints. The constraints of the design are set in the objective function, such as the minimum size of each cabin, the reasonable spacing of furniture, the appropriate range of lighting, etc., to ensure that the generated design scheme meets both user needs and actual operation and safety standards. According to the characteristics of the objective function, a variety of intelligent optimization algorithms are introduced for optimization and solution. By simulating the natural selection process, multiple possible design schemes are automatically evolved, and the best design is selected through the fitness function. Using the concept of swarm intelligence, the design space is quickly explored and the optimal solution is found by simulating the foraging behavior of bird flocks. The nonlinear relationship between user feedback and design elements is learned through neural networks to automatically generate design schemes that meet user needs. By simulating the physical annealing process, the global optimal solution is found to avoid falling into the local optimal solution. The algorithm continuously generates multiple design schemes according to the objective function, and gradually converges to the optimal or near-optimal design scheme through evaluation and selection. During the optimization process, the algorithm will be dynamically adjusted according to user feedback to ensure that the design scheme is more in line with the actual needs of users. The intelligent optimization algorithm automatically generates multiple design schemes based on user preferences and parameterized models. Each design scheme may be different in terms of space layout, furniture arrangement, lighting configuration, etc. to meet different user needs. The system automatically evaluates the generated design solutions and sorts them based on the scoring criteria of the objective function, such as space utilization, material aesthetics, and functional requirements. Users can view the generated design solutions in real time and make adjustments. The system adjusts the optimization process based on user feedback to ensure that each design solution better meets the user's actual needs.

[0034] By combining the user preference model and the parametric model, the system can automatically generate multiple interior design solutions, greatly improving the automation of the design process and reducing the need for manual intervention. Users can select the solution that best meets their individual needs from the multiple generated design solutions, avoiding the inefficiency of repeated manual modifications in the traditional design process. Multi-objective optimization enables the system to consider multiple factors simultaneously, such as space utilization, material aesthetics, functional requirements, etc., ensuring that the generated design solutions achieve a good balance in multiple aspects. Through the dynamic adjustment of the objective function, the system can effectively solve the conflicts between multiple objectives and generate the best design solution that meets user preferences. By combining intelligent optimization algorithms and user preference models, the system can accurately identify and meet the personalized design needs of users, improving the personalization and accuracy of the design. Users can not only adjust the solution through design feedback but also obtain a customized design that better meets their own needs through the optimization process of the algorithm, greatly improving user satisfaction. The traditional design process often relies on the experience of designers and manual adjustment, with a long design cycle and it is difficult to ensure the best design effect. Through intelligent optimization algorithms, the system can generate multiple design solutions in a short time and perform precise optimization, greatly improving the design efficiency. The optimization ability of the system ensures the design quality, ensuring a reasonable space layout, efficient and aesthetic material selection, and full satisfaction of functional requirements. The system can adjust the design solution in real time according to user feedback and interaction behavior and update the optimization model. As users continuously interact with the system, the system will continuously learn and optimize the design solution to achieve adaptive adjustment to better meet user needs. During the process of users selecting design solutions, they can clearly see the differences, advantages and disadvantages between different solutions, enhancing the transparency and controllability of the design process. Through the optimization suggestions and feedback mechanism of the algorithm, users can make more reasonable and need-compliant design decisions.

[0035] S4: Load the interior design solution into the virtual reality environment, and the user conducts interactive evaluation through voice, gesture or eye control methods and marks the dissatisfied parts for feedback.

[0036] After the design plan is generated, the system loads the finally optimized interior design plan into the virtual reality environment to create a highly immersive virtual space. This space includes design elements such as yacht cabins, furniture, materials, lighting, etc. with a high degree of realism, allowing users to interact and experience in the virtual environment. To ensure the authenticity and smoothness of the virtual environment, the system adopts panoramic rendering technology and real-time rendering technology, supporting users to freely move in the virtual environment, view different angles and details of the design plan, and ensuring that the design effects in the virtual environment are highly consistent with the actual design. The virtual reality system can accurately display the texture of materials, lighting effects, spatial dimensions, etc., helping users to comprehensively evaluate the design plan. Users can interact with the system through voice commands, such as asking the system to adjust the layout of a certain area, select a specific furniture style, modify the lighting intensity, etc. The system is embedded with voice recognition technology, which can accurately recognize the user's voice commands and respond in real time according to the user's needs. With the gesture recognition technology in the virtual reality device, users can make simple gesture operations, such as waving, pointing, grabbing, etc., to adjust the spatial layout and furniture configuration, and even directly drag design elements in the virtual environment. Through eye movement tracking technology, the system can capture the user's line of sight focus. Users can select, mark or adjust a design element, such as furniture, wall, lamp, etc., just by looking at it, improving the naturalness and smoothness of the interaction. Users can directly give feedback on the unsatisfactory parts of the design in the virtual reality environment. Through gestures or eye tracking, users can mark the unsatisfactory areas in the virtual space, such as wall color, furniture arrangement, lighting effect, etc., and these marks will be automatically recorded in the system. Users can use voice commands to tell the system the unsatisfactory parts and ask the system to make adjustments. The system will perform adaptive optimization according to the user feedback marks. The system will combine the user's feedback mark information with the user preference model to help the design system perform precise optimization according to the user's needs. During the interaction between the user and the virtual reality environment, the system collects the user's interaction data in real time, including voice commands, gesture actions, line of sight focus, feedback marks and other data. Through data analysis, the system can understand the user's behavior patterns, preference changes and potential demand changes during the design evaluation process. These data will be further used to optimize the user preference model and enhance the accuracy of the design plan. According to the user's real-time feedback and interaction data, the system can adjust the design and provide a design plan that better meets the user's needs in the next round of evaluation.

[0037] By loading the design scheme into a virtual reality environment, users can evaluate the design effect immersive and obtain an evaluation experience close to the real space. Users can view details from different angles and at different viewing distances, greatly improving the accuracy and reliability of the evaluation. The panoramic virtual experience helps users intuitively feel the spatial layout, furniture configuration, lighting effects, etc., avoiding two-dimensional drawings or static models in traditional designs and increasing the perceptual depth of design evaluation. The combination of various interaction methods such as voice, gesture, and gaze control enables users to interact with the virtual environment in a more natural and intuitive way. This highly free interaction method improves the user's operation comfort and reduces the limitations of traditional input devices such as mice and keyboards. Multimodal interaction enables users to select appropriate interaction methods according to actual needs, enhancing the flexibility and adaptability of the interaction experience. Users can clearly point out the dissatisfied design parts through the instant feedback marking function, helping the system quickly identify problems in the design and reducing the information transfer time between designers and users. The feedback marking can not only improve the accuracy of design adjustment but also provide data support for subsequent optimization processes, enhancing the response speed and adaptability of the design system. Based on the user interaction data collected in real time, the system can analyze the user's behavior and feedback, automatically adjust the design scheme, meet personalized needs, and improve user satisfaction. The real-time learning and analysis of interaction data enable the system to continuously improve the user preference model and optimize the design effect in each evaluation to ensure that the final generated design scheme better meets the user's expectations. Users can evaluate and adjust the design scheme in real time in the virtual environment, reducing the time waste in traditional design processes such as repeated modifications and on-site measurements. Each user interaction directly affects the design adjustment, making the design process more efficient. The instant feedback and quick adjustment mechanism in the virtual environment greatly accelerate the design iteration process, making the final design scheme more refined and accurate. Users can switch design schemes multiple times and conduct detailed evaluations for each scheme, quickly adjusting design elements through different interaction methods. The system can automatically adjust according to the feedback of each interaction, providing users with multiple optimized design options to ensure that the finally selected design scheme best meets the requirements. The characteristics of multi-round interaction optimization enable the design scheme to be quickly adjusted and tend to be perfect, enhancing the flexibility and adaptability of the design.

[0038] S5: Update the user preference model according to the user's behavior data and feedback marking results in the virtual reality environment.

[0039] The system automatically collects the user's behavior data through real-time interaction in the virtual reality environment. These data include but are not limited to: Gaze tracking data: The target elements that the user gazes at in the virtual environment, such as furniture, walls, lighting, etc., reflect the design details with high user attention.

[0040] Gesture operation data: Operations performed by the user through gesture control, such as moving, rotating, and adjusting design elements, can provide intuitive feedback from the user on the design solution.

[0041] Voice command data: Records of the user's interaction with the system through voice commands, reflecting the user's direct opinions and requirements for the design solution.

[0042] Feedback marker data: Parts of the design that the user marks as unsatisfactory through gestures or voice, such as wall color, furniture layout, lighting, etc., as feedback information for subsequent design optimization.

[0043] The system extracts the user's design preference features by analyzing the user's behavior data and feedback markers. These preference features include tendencies in aspects such as spatial layout, material selection, function configuration, lighting requirements, etc. Machine learning algorithms are used to model the user's preferences. The model can dynamically adjust weights and preference features based on each interaction behavior, such as the eye gaze duration, operation frequency, etc., and feedback markers, such as the marks of unsatisfactory parts, to more accurately reflect the user's true needs. According to the real-time user behavior data, the system can adaptively adjust the user preference model to continuously adapt to the changes in the user's preferences. Each update after user interaction can improve the accuracy and personalization of the model. The system combines historical data and current data to continuously optimize the user preference model. For example, if the user repeatedly marks that they dislike a certain material or layout style, the system will adjust the model according to this trend and provide more design solutions that meet their needs in subsequent designs. Every feedback from the user in the virtual reality environment, whether through gestures, voice, or visual markers, can be immediately collected and processed, and the system will update the corresponding parameters in the user preference model based on these real-time feedbacks. The user preference model of the system is not static but a dynamically updated process. After each interaction, the system adjusts the design solution according to the changes in the user's preferences, such as automatically adjusting the parameters of design elements, such as furniture placement, color matching, etc., thereby gradually improving the accuracy of the design solution.

[0044] Through real-time analysis of the user's behavioral data in the virtual reality environment, such as line of sight, gestures, voice, etc., and feedback markers, the system can capture the user's design preferences more precisely and improve the accuracy of the preference model. This means that the design scheme can better meet the user's personalized needs and avoid design errors caused by differences in manual understanding in traditional design. Through the continuously updated user preference model, the system can generate personalized design schemes based on the user's historical behavior and real-time feedback. With each interaction, the system adjusts the design elements to make them more in line with the user's needs and tastes, providing increasingly precise customized designs. Personalized optimization makes each user's design experience closer to their personal preferences, improving user satisfaction and design experience. The real-time updated preference model enables the system to continuously adapt to changes in user preferences. As the user interacts more with the system, the system can automatically adjust the optimization strategy to make the design scheme more in line with the user's needs in each evaluation. The characteristic of adaptive learning enables the system to not only make adjustments based on the current user's feedback but also accumulate experience in long-term interactions, optimize the user preference model, and improve the long-term accuracy of the design scheme. Users can participate in the design process through direct feedback and interaction and provide instant feedback to the system by marking the dissatisfied parts. This active participation in the design process not only allows users to express their needs more clearly but also enhances the interactivity and sense of participation in the design process. Enhancing the interaction experience enables users to adjust the design more actively and feel their influence on the design results, improving the attractiveness and immersion of the virtual design environment. The user's design preferences may vary over time and in different environments. The system can continuously update the preference model based on the information feedback in each interaction to ensure that the design scheme is always in a state that meets the user's needs. This dynamic adaptability enables the design scheme to be flexibly adjusted and optimized after each interaction evaluation. After each user interaction and feedback, the design scheme will be further improved and optimized, gradually approaching the optimal scheme. Through precise updating of the user preference model, the system can capture the user's real needs for the design scheme more quickly, thereby reducing the number of repeated modifications and adjustments in the design process and saving design time and costs. Automated adjustment enables the design scheme to be quickly adjusted after user feedback without relying on manual modification, improving design efficiency.

[0045] Example 2: Please refer to Figure 2 As shown, the virtual reality-based yacht interior design optimization system described in the embodiment of the present invention includes: User preference collection module: Collect the user's preference information in terms of spatial layout, material selection, and functional requirements, and construct a user preference model in combination with interaction behavior data; Building information modeling module: Obtain the building information modeling model of the yacht and perform parametric modeling on the cabin structure, furniture layout, and lighting configuration; Design Optimization Module: Combine the user preference model and the parametric model to construct a design optimization objective function, and introduce intelligent optimization algorithms to generate multiple interior design solutions; Virtual Reality Interaction Module: Load the design solutions into a virtual reality environment, allowing users to conduct interactive evaluations through voice, gesture, or eye control, and mark the dissatisfied parts; User Preference Model Update Module: According to the user's behavioral data and feedback marking results in the virtual reality environment, update the user preference model in real time.

[0046] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0047] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the interior design of a yacht based on virtual reality, characterized in that, It includes the following steps: S1: Interact with the user through a virtual reality system, collect the user's preference information regarding spatial layout, material selection, and functional requirements, and combine the interaction behavior data to construct a user preference model; S2: Obtain the building information modeling model of the target yacht, and perform parametric modeling on the cabin structure, furniture arrangement, and lighting layout; S3: Combine the user preference model and the parametric model to construct an objective function for design optimization, and introduce an intelligent optimization algorithm to generate multiple interior design schemes; S4: Load the interior design scheme into the virtual reality environment, and the user conducts interactive evaluation through voice, gesture, or eye gaze control methods, and marks the dissatisfied parts for feedback; S5: Update the user preference model according to the interaction behavior data and feedback marking results of the user in the virtual reality environment.

2. The method for optimizing the interior design of a yacht based on virtual reality according to claim 1, wherein The S1 further includes using virtual reality devices to support multimodal feedback, collecting the user's interaction behavior data in real time, analyzing the interaction behavior data through deep learning algorithms to identify the user's potential preferences and needs, and based on the deep neural network algorithm, the virtual reality system constructs a personalized user preference model.

3. The method for optimizing the interior design of a yacht based on virtual reality according to claim 1, characterized in that, The S2 further includes obtaining the building information modeling model of the target yacht through various methods, supporting the BIM file format. Based on the BIM model, the virtual reality system automatically identifies the cabin structure and generates a parametric space model, allowing real-time adjustment of cabin dimensions, functional partitions, furniture, and equipment, supporting lighting simulation, evaluating the impact of different lighting schemes on the spatial effect, and the user can adjust the layout and lighting design in real time in the virtual reality environment, and automatically generate a design scheme that meets the spatial utilization efficiency and user preferences.

4. The virtual reality-based yacht interior design optimization method according to claim 1, characterized in that The user preference model is used to describe the user's preferences for design elements. The S3 further includes parametric design elements generated based on the BIM model, converting the design elements into adjustable variables, combining the user preference model and the objective function, setting optimization objectives, using multiple intelligent optimization algorithms for optimization solution, automatically generating multiple design schemes, and evaluating and ranking the schemes based on the objective function.

5. The method for optimizing the interior decoration design of a yacht based on virtual reality according to claim 1, wherein The S4 further includes that after the virtual reality system generates the finally optimized interior design scheme, the virtual reality system loads the design scheme into the virtual reality environment, creates a virtual space, supports panoramic rendering and real-time rendering technologies, the user interacts with the system through voice commands, gesture recognition, and eye movement tracking technologies, adjusts the layout, furniture configuration, and lighting effects in real time, the virtual reality system can perform voice recognition, gesture operation, and eye movement tracking, the user directly feedbacks the dissatisfied design parts, the virtual reality system automatically records the feedback and performs adaptive optimization according to the user preference model, collects the user's interaction data in real time, and optimizes the design scheme through data analysis.

6. The method for optimizing the interior design of a yacht based on virtual reality according to claim 1, characterized in that, The S5 further includes automatically collecting the user's interaction behavior data through real-time interaction in the virtual reality environment, extracting the design preference features of the user in terms of spatial layout, material selection, function configuration, and lighting requirements by analyzing the interaction behavior data and feedback marks of the user in the virtual reality environment, and constructing a dynamic user preference model using machine learning algorithms. The virtual reality system automatically updates the model parameters according to the feedback collected after each interaction.

7. The optimized method for yacht interior design based on virtual reality according to claim 1, wherein After the virtual reality system completes the generation of the optimized design plan, it supports exporting the design plan as a standardized format file, including IFC, OBJ, FBX, or VR format. The virtual reality system supports the design version management and historical tracking functions, compares and traces the design plans at different stages, and assists the user in screening and decision-making of the plans.

8. A virtual reality system for yacht interior design based on virtual reality, which is applied to the method for optimizing yacht interior design based on virtual reality according to any one of claims 1-6, characterized in that, Including: User preference collection module: Collect the preference information of the user in terms of spatial layout, material selection, and function requirements, and construct a user preference model in combination with the interaction behavior data; Building information modeling module: Obtain the building information modeling model of the yacht, and perform parametric modeling on the cabin structure, furniture layout, and lighting configuration; Design optimization module: Combine the user preference model and the parametric model, construct a design optimization objective function, and introduce an intelligent optimization algorithm to generate multiple interior design plans; Virtual reality interaction module: Load the design plan into the virtual reality environment, allow the user to perform interactive evaluation through voice, gesture, or gaze control, and mark the dissatisfied parts; User preference model update module: Real-time update the user preference model according to the interaction behavior data and feedback mark results of the user in the virtual reality environment.

Images