Customized furniture intelligent manufacturing platform

Abstract

The invention relates to the technical field of intelligent control, in particular to a customized furniture intelligent manufacturing platform which comprises a data-driven behavior analysis module, a personalized space modeling module, an intelligent design generation module and a component interaction optimization module. According to the furniture design method and system, through real-time monitoring and accurate analysis of the behavior data of the user, the individuation degree and accuracy of furniture design are improved, it can be ensured that the design scheme is optimized completely according to the actual use habit and comfort requirement of the user, the user satisfaction degree and the practical value of a product are greatly improved, and the user experience is improved. A three-dimensional model is used for dynamically simulating a living space and optimizing furniture layout, the visualization and interactivity of design are remarkably enhanced, the rework rate in the design and production process is reduced, meanwhile, the selection and matching process of components is effectively simplified through intelligent recommendation, material waste is reduced, the production cost is reduced, and the production efficiency is improved. Meanwhile, the environmental protection property and the economic efficiency of the production process are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent manufacturing platform for customized furniture. Background Art

[0002] The field of intelligent control technology involves the use of modern computer technology, sensor technology, artificial intelligence, automatic control, data communication and other technologies to achieve efficient, accurate and intelligent management of various equipment, systems, processes and environments. Its core goal is to achieve autonomous decision-making and control in different systems through real-time data acquisition and analysis to improve efficiency, reduce human intervention and reduce costs. Intelligent control technology is widely used in many industries such as industrial automation, home intelligence, robotics, intelligent transportation, medical automation, etc. Through precise control of system behavior, intelligent control can cope with complex and changing environments and improve the adaptability, stability and safety of the system.

[0003] Among them, the customized furniture intelligent manufacturing platform is an intelligent production system for the furniture industry. It realizes customized production of furniture products by integrating automated equipment, robotics technology, data collection and analysis, and artificial intelligence control algorithms. It manages the production process digitally and automatically optimizes the design and manufacturing processes. It can meet personalized needs while greatly improving production efficiency and product quality. Its main purpose is to provide efficient and flexible customized furniture manufacturing solutions, support personalized design based on the specific needs of consumers, and complete customized furniture manufacturing tasks quickly and efficiently through precise control and optimization of the production process.

[0004] Although intelligent control technology has been widely used in automation and process control, in the furniture industry, its application is mostly focused on improving production efficiency, but less on in-depth user behavior analysis and design personalization, resulting in the limited ability of existing technologies to meet personalized and diversified market demands and to effectively adapt to rapidly changing consumer needs. The inadequacy of traditional platforms in addressing specific user needs leads to product designs that are inconsistent with user expectations, affecting user experience and brand image. In addition, existing technologies fail to fully utilize user data for optimized design, limiting the ability to innovate and improve market competitiveness, and also leading to inaccurate resource utilization, unnecessary production costs and time expenditures. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a customized furniture intelligent manufacturing platform.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A customized furniture intelligent manufacturing platform comprises:

[0007] The data-driven behavior analysis module collects user behavior data through sensors, records the frequency of furniture use and spatial activity trajectories, performs time series analysis on the data, identifies user usage patterns and activity habits based on the time series analysis results, analyzes user preferences to determine comfort needs, and obtains user behavior insights;

[0008] The personalized space modeling module constructs a three-dimensional model of the user's living space based on the user behavior insight results, simulates different layout plans, adjusts the size and shape of furniture based on the simulation results of the different layout plans, optimizes and iterates the model layout, matches the user behavior and usage habits, and obtains a space configuration plan;

[0009] The intelligent design generation module selects the decoration style, color tone and material matching the user behavior and the space configuration based on the space configuration scheme, constructs a multi-design option table, makes design adjustments based on the construction results of the multi-design option table and user feedback, determines the design style and material selection, and obtains a customized design scheme;

[0010] The component interaction optimization module analyzes the functional requirements of the furniture components in the customized design solution, automatically recommends matching component configurations, verifies the actual matching effects of the components through simulated assembly, and obtains the simulated manufacturing results of customized furniture.

[0011] As a further solution of the present invention, the step of analyzing the data in time series is:

[0012] Install sensors in the user's home environment to capture and record the user's interaction frequency with furniture in real time, record the spatial activity trajectory, and generate an original user behavior data set;

[0013] Based on the original user behavior data set, outliers are deleted, data errors are corrected, and data formats are unified to obtain a standardized user behavior data set;

[0014] Based on the standardized user behavior data set, time series analysis is performed using the formula:

[0015]

[0016] Calculate the user behavior intensity value Y(t) at time t and obtain the time series analysis results, where X(t) and X(t-1) represent the number of times furniture is used at the current and previous moments, respectively, and E(t) represents the standardized value of the environmental variable.

[0017] As a further solution of the present invention, the steps of obtaining the user behavior insight results are:

[0018] According to the time series analysis results, extract the frequency and movement path of furniture use by users in different time periods, identify normal behavior patterns, reveal the daily activity habits of users, and generate a comprehensive report on user behavior patterns;

[0019] Based on the comprehensive report of user behavior patterns, evaluate the user's preference for furniture type and space usage, combine the user's response under different environmental conditions, determine the user's comfort needs, and form a user preference analysis report;

[0020] Combine the user behavior pattern comprehensive report with the user preference analysis report to analyze the key factors affecting user needs and generate user behavior insight results.

[0021] As a further solution of the present invention, the simulation steps of the difference layout solution are:

[0022] Based on the user behavior insight results, analyze the user's activity areas and frequencies in all time periods, determine the preliminary layout of room functional areas and furniture configurations, and generate functional area distribution data for living spaces;

[0023] Based on the functional area distribution data of the living space, a three-dimensional living space model is constructed by adjusting the size and position of furniture to match user habits;

[0024] The three-dimensional living space model is simulated in multiple scenarios, and the furniture layout of the functional areas is adjusted using the formula:

[0025]

[0026] Calculate the space utilization S of the i-th solution i , generate simulation results of different layout schemes; where a ij represents the area contribution of the j-th furniture in the i-th solution, u j is the frequency of use of the corresponding furniture, A i is the total area of ​​the ith solution, and n represents the number of furniture.

[0027] As a further solution of the present invention, the steps of obtaining the space configuration solution are:

[0028] Based on the simulation results of the different layout schemes, each scheme is analyzed, and the size and shape of furniture are designed according to the user's stay time and activity frequency in the different areas and the size of the corresponding areas to obtain preliminary furniture design parameters;

[0029] Applying the preliminary furniture design parameters, updating the three-dimensional living space model, adjusting the furniture layout through iterative cycles, evaluating the impact of the furniture on the functionality of the space and the comfort of the user, and generating an optimized iterative three-dimensional model layout;

[0030] The optimized iterative 3D model layout is evaluated to compare the consistency between the model layout and the actual user behavior, using the formula:

[0031]

[0032] Calculate the efficiency E of the space configuration and get the space configuration plan, where v k represents the usage frequency of the kth region, d k It represents the degree of design optimization of the target area, T is the total investigated area, and m is the total number of investigated areas.

[0033] As a further solution of the present invention, the steps of constructing the multivariate design option table are:

[0034] Based on the space configuration plan, analyze the user's daily behavior and activity range, identify the space function requirements, and generate user behavior and space usage analysis results;

[0035] Evaluate the user behavior and space usage analysis results, select matching decorative styles, tones and materials, and generate a preliminary list of design options;

[0036] Based on the preliminary design options list, the formula is used:

[0037]

[0038] Calculate the design option score and get a multivariate design option table, where a represents the design aesthetics score, b represents the design practicality score, c represents the cost-effectiveness data, and d represents the environmental adaptability score.

[0039] As a further solution of the present invention, the steps of obtaining the customized design solution are:

[0040] Collect user feedback through online surveys and field interviews, record preferences for each design option, functional requirements, and improvement suggestions, and generate comprehensive analysis results of user feedback;

[0041] According to the comprehensive analysis results of the user feedback and the multi-dimensional design option table, the design options are evaluated, the design scheme is adjusted to match the actual needs of the user, the style preference and material selection are determined, and the adjusted design scheme list is generated in combination with the cost and implementation feasibility;

[0042] Based on the adjusted design plan list, combined with market trends and sustainable development standards, and integrating user personalized needs and design practicality, a customized design plan is generated.

[0043] As a further solution of the present invention, the steps of obtaining the customized furniture simulation manufacturing result are:

[0044] Based on the customized design solution, combined with the user's behavior patterns and preferences, and integrated component design parameters, a furniture functional demand analysis report is generated;

[0045] Using the furniture functional demand analysis report, combined with component performance and cost-effectiveness, the formula is adopted:

[0046]

[0047] Calculate the fitness score F for each component match , generate a list of matching component configurations, where p i is the performance score, D i is the design score, C i represents the cost, and N represents the number of components;

[0048] The matching component configuration list is simulated for assembly, the compatibility and matching effects of the components are tested, the practicality and feasibility of the furniture design are tested, and the customized furniture simulation manufacturing results are output.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are:

[0050] In the present invention, by real-time monitoring and precise analysis of user behavior data, the personalization and accuracy of furniture design are improved, and it can be ensured that the design plan is completely optimized according to the user's actual usage habits and comfort needs, which greatly improves user satisfaction and the practical value of the product. The three-dimensional model is used to dynamically simulate the living space and optimize the furniture layout, which significantly enhances the visualization and interactivity of the design and reduces the rework rate in the design and production process. At the same time, intelligent recommendation effectively simplifies the component selection and matching process, reduces material waste, reduces production costs, and improves the environmental protection and economic efficiency of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a system flow chart of the present invention;

[0052] Figure 2 The present invention is a flow chart of the time series analysis of data;

[0053] Figure 3 A flowchart for obtaining user behavior insight results of the present invention;

[0054] Figure 4 It is a simulation flow chart of the difference layout scheme of the present invention;

[0055] Figure 5 A flowchart for obtaining the spatial configuration scheme of the present invention;

[0056] Figure 6A flowchart for constructing a multivariate design option table of the present invention;

[0057] Figure 7 A flow chart for obtaining a customized design solution for the present invention;

[0058] Figure 8 A flow chart for obtaining the simulation manufacturing results of customized furniture according to the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0061] See also Figure 1 , a customized furniture intelligent manufacturing platform includes:

[0062] The data-driven behavior analysis module collects user behavior data through sensors, records the frequency of furniture use and spatial activity trajectories, performs time series analysis on the data, identifies user usage patterns and activity habits based on the time series analysis results, analyzes user preferences to determine comfort needs, and obtains user behavior insights;

[0063] The personalized space modeling module builds a three-dimensional model of the user's living space based on the user behavior insights, simulates different layout plans, adjusts the size and shape of furniture based on the simulation results of the different layout plans, optimizes and iterates the model layout, matches user behavior and usage habits, and obtains a space configuration plan;

[0064] Based on the space configuration plan, the intelligent design generation module selects the decoration style, color tone and material that matches the user behavior and space configuration, builds a multi-design option table, makes design adjustments based on the results of the multi-design option table construction and user feedback, determines the design style and material selection, and obtains a customized design plan;

[0065] The component interaction optimization module analyzes the functional requirements of furniture components in the customized design plan, automatically recommends matching component configurations, verifies the actual matching effects of components through simulated assembly, and obtains the simulated manufacturing results of customized furniture.

[0066] User behavior insight results include usage patterns, habit preference records, and comfort needs analysis results; space configuration plans include layout plans, furniture size and shape adjustment records, and furniture placement records; customized design plans include style determination results, tone matching records, and material selection results; customized furniture simulation manufacturing results specifically refer to function matching results, component compatibility analysis results, and actual assembly effects.

[0067] See also Figure 2 , the steps for analyzing the time series of data are:

[0068] Install sensors in the user's home environment to capture and record the user's interaction frequency with furniture in real time, record the spatial activity trajectory, and generate an original user behavior data set;

[0069] After deploying sensors in a home environment, it is necessary to ensure that the activity data of the residents can be captured seamlessly. The sensor installation locations should cover key areas such as the living room, kitchen and bedroom. They can comprehensively record the frequency of users' activities in different spaces, as well as the interaction data between users and furniture. After installation, the functions of the sensors should be tested to ensure the real-time transmission and accuracy of the data. Subsequently, the collected data should be preliminarily classified to distinguish between furniture usage data and space activity data, laying the foundation for subsequent data processing and analysis.

[0070] Based on the original user behavior data set, outliers are deleted, data errors are corrected, and the data format is unified to obtain a standardized user behavior data set;

[0071] When processing the original user behavior data set, the algorithm is first used to automatically identify and remove abnormal data points caused by sensor errors, and then the remaining data is timestamped to ensure that the time information of all data points is accurate. The process involves multiple iterative processing of the data set to ensure data consistency and reliability. Finally, the data is formatted in a unified manner, such as unifying all time formats into ISO standard formats, and spatial coordinates into relative coordinates, etc., which can make the data set more standardized and convenient for subsequent advanced analysis.

[0072] Based on the standardized user behavior data set, time series analysis is performed using the formula:

[0073]

[0074] Calculate the user behavior intensity value Y(t) at time t to obtain the time series analysis results, where X(t) and X(t-1) represent the number of furniture uses at the current and previous moments, respectively, and E(t) represents the standardized value of the environmental variable;

[0075] The number of times furniture is used at a specific moment is X(t) = 4 and X(t-1) = 9, and the standardized value of environmental variables (including temperature, light intensity, noise level, etc.) is E(t) = 16. The calculation process is:

[0076]

[0077] The results show that, considering the influence of furniture usage data before and after and environmental variables, the obtained behavior intensity value is 1.5, which shows the combined influence of furniture usage frequency and spatial activity trajectory at a specific moment.

[0078] See also Figure 3 , the steps to obtain user behavior insight results are:

[0079] Based on the results of time series analysis, extract the frequency and movement paths of furniture used by users in different time periods, identify normal behavior patterns, reveal the daily activity habits of users, and generate a comprehensive report on user behavior patterns;

[0080] Based on the results of time series analysis, we extract data on the frequency of furniture use and spatial activity paths of users. Behavioral patterns are identified mainly by observing the frequency of users' activities in various areas of the home and their changes over time. The process not only includes simple data collection, but also involves the identification of data characteristics, such as how to distinguish behavioral patterns on weekdays and weekends, and how to understand the differences between holidays and ordinary days. This needs to be achieved through data clustering and pattern recognition technology. Based on the pattern recognition results, we predict users' future behavior and provide decision support for subsequent services. Finally, we generate a comprehensive report on user behavior patterns, which summarizes users' activity habits and possible changes in preferences.

[0081] Based on the comprehensive report of user behavior patterns, evaluate users' preferences for furniture types and space usage, combine user responses under different environmental conditions, determine user comfort needs, and form a user preference analysis report;

[0082] Based on the comprehensive report on user behavior patterns, we further evaluate and analyze users' specific preferences, including the frequency of use of different furniture and responses to home environmental factors. For example, under which specific environmental conditions (lighting, temperature) do users show more frequent activities? The analysis process involves statistical analysis of a large number of data points, such as analyzing users' sensitivity to lighting changes through scatter plots, and determining the sensitivity of temperature changes to user activities through regression analysis. Ultimately, we form an analysis report that includes multi-dimensional user preferences, which will be directly related to subsequent living space design and service improvement suggestions.

[0083] Combine the comprehensive report on user behavior patterns with the user preference analysis report to analyze the key factors that affect user needs and generate user behavior insights;

[0084] Integrate the analysis results of the first two steps, and through comprehensive consideration of user behavior patterns and preference analysis, gain insight into the deep-seated needs and potential behavior drivers behind them. For example, if it is found that users frequently use furniture near heaters in winter, it indicates that relevant warm furniture needs to be designed. The process is not only data analysis, but also an in-depth interpretation of the meaning behind the data, which will help form specific improvement suggestions, such as designing the shape, size, materials, etc. of warm furniture, so as to compile the final user behavior insight results and provide a basis for improving users' living experience.

[0085] See also Figure 4 , the simulation steps of the difference layout scheme are:

[0086] Based on the user behavior insights, analyze the user's activity areas and frequencies at all time periods, determine the preliminary layout of room functional areas and furniture configurations, and generate functional area distribution data for living spaces;

[0087] Based on the detailed data of user behavior analysis, the frequency and time period of the activity area in the room are first determined. This is completed through long-term monitoring of user activities and data collection at the corresponding time, so that the user's activity pattern can be accurately predicted and the functional area layout of the room can be determined accordingly. This process involves a lot of data processing and pattern recognition technology, rather than simple data collection, ensuring that the layout of each functional area can meet the actual needs of the user to the greatest extent. The practicality and comfort of different layout schemes are verified through model simulation, and finally highly personalized functional area distribution data of the living space is obtained.

[0088] Based on the distribution data of functional areas of living space, a three-dimensional living space model is constructed by adjusting the size and position of furniture to match user habits;

[0089] The distribution data of functional areas of living space is used to guide the use of 3D modeling software. The size and position of furniture in the model are precisely adjusted to match the analyzed user behavior data. In this process, detailed calculations and parameter settings ensure that the 3D model accurately reflects user habits. Each position adjustment of furniture is optimized based on user behavior data to ensure that the practicality and comfort of the model are maximized. Advanced rendering technology is used to preview the living space effects under different configurations, which helps to evaluate and select the final living space design, and is conducive to determining the design size and shape requirements of subsequent customized furniture.

[0090] Perform multiple scheme simulations on the three-dimensional living space model and adjust the furniture layout of the functional areas using the formula:

[0091]

[0092] Calculate the space utilization S of the i-th solution i , generate simulation results of different layout schemes; where a ij represents the area contribution of the j-th furniture in the i-th solution, u j is the frequency of use of the corresponding furniture, A i is the total area of ​​the ith solution, n represents the number of furniture;

[0093] a ij =2m 2 Indicates the floor space of a piece of furniture, u j =0.8 indicates the frequency of furniture use, A i =50m 2 Indicates the total area of ​​the plan. If only one target furniture is calculated, the calculation process is as follows:

[0094]

[0095] S i =0.032

[0096] The results show that, considering the frequency of furniture use and area distribution, the space utilization rate of this layout scheme is 3.2%, indicating that the furniture layout under this configuration has a low utilization rate of the effective space and the furniture needs to be customized, which will guide further optimization of the furniture layout and scheme selection.

[0097] See also Figure 5 , the steps to obtain the space configuration plan are:

[0098] Based on the simulation results of different layout schemes, each scheme is analyzed, and the size and shape of furniture are designed according to the user's stay time and activity frequency in different areas and the size of the corresponding area to obtain preliminary furniture design parameters;

[0099] When adjusting furniture design parameters, we first analyzed user behavior data to determine which furniture needed to be redesigned to better suit the living space, including analyzing user residence time and activity frequency data in various areas. Based on these data, we used statistical methods and data analysis techniques to design and determine the optimal sizes and shapes of various furniture. The process included a detailed analysis of user activity data to ensure the rationality and scientific nature of the furniture design. Then, based on the design results, we formulated a specific furniture design parameter adjustment plan. This plan took into account the actual needs of users and the maximum utilization of living space, ensuring the practicality and aesthetics of the design, and ultimately obtained the recommended furniture design parameters for adjustment.

[0100] Apply preliminary furniture design parameters to update the 3D living space model, adjust the furniture layout through iterative cycles, evaluate the impact of furniture on space functionality and user comfort, and generate an optimized iterative 3D model layout;

[0101] After updating the furniture design parameters, in order to ensure the optimization of the furniture layout, these parameters are loaded and tested. The process includes precise adjustments to the size and position of each piece of furniture. After each adjustment, the effect of the interaction between the furniture and the space and how the user's possible behavior patterns interact with these layouts are simulated. Through this iterative approach, the impact of various layout configurations on the functionality of the living space and the user's comfort is continuously tested and evaluated. Each iteration process records in detail the simulated response of user behavior and the improvement of the efficiency of space use, and the design can be gradually optimized based on feedback data. In addition, during the iteration process, virtual reality technology is used to allow designers and customers to experience different layout plans in a preset environment to ensure that the final selected layout plan is not only effective in theory, but also can achieve the expected comfort and functionality in actual use, allowing for rapid response to design changes and real-time viewing of adjustment effects, generating an optimized iterative 3D model layout.

[0102] Evaluate the optimized and iterated 3D model layout and compare the consistency between the model layout and the actual user behavior using the formula:

[0103]

[0104] Calculate the efficiency E of the space configuration and get the space configuration plan, where v k represents the usage frequency of the kth region, d k It indicates the degree of design optimization of the target area, T is the total investigated area, and m is the total number of investigated areas;

[0105] If v k =0.3,d k=2.5 (based on a comprehensive analysis of spatial indicators such as rationality of movement lines, space utilization, user satisfaction, visual permeability and environmental comfort, the score range is between 1-5, where 2.5 represents a "medium optimization" level), T = 100, m = 5 (for ease of calculation, assuming that the data of the five areas are the same), then:

[0106]

[0107] The results show that, considering the usage frequency and design optimization degree of each area, the overall space configuration efficiency is about 0.274, and the efficiency is greater than 0, which means that the spatial layout optimizes the user's living experience and the efficiency of space utilization to a certain extent.

[0108] See also Figure 6 , the steps to construct the multivariate design option table are:

[0109] Based on the space configuration plan, analyze the user's daily behavior and activity range, identify the space function requirements, and generate user behavior and space usage analysis results;

[0110] A detailed user behavior analysis was conducted based on the space configuration plan. First, by observing and recording the frequently used areas of the living space, the hot spots of users' activities in daily life were identified. The data was collected through sensors over a period of one month, with timestamps and location information as the main parameters, recording the frequency and duration of use of each area. Next, the data was analyzed to determine the specific areas where users carry out activities at home, such as work, rest, and entertainment. A functional assessment was conducted on each activity area. The assessment criteria included but were not limited to space size, lighting conditions, and ventilation conditions. Based on these evaluation results, a comprehensive analysis of user behavior and space usage was formed, providing data support and decision-making basis for subsequent design options.

[0111] Evaluate user behavior and space usage analysis results, select matching decorative styles, tones and materials, and generate a preliminary list of design options;

[0112] Through detailed comparison of user behavior and space usage analysis results, a series of matching tests were conducted on the selected decorative styles, tones and materials. First, based on the results of user behavior analysis, materials and tones that meet the usage frequency and activity characteristics were selected. For example, in areas with frequent activities, highly wear-resistant materials and warmer colors were selected to increase the comfort and aesthetics of the space. For different space functions, such as sleeping areas or work areas, different styles were selected to match the main purpose of the space. In this process, existing home design software was used for simulation, and the input data included but was not limited to space size, main activity type and user preferences. Multiple design schemes were provided based on the data, and finally a preliminary list of design options was generated, which provided a scientific basis for selecting the most suitable space design.

[0113] Based on the preliminary design options list, the formula is used:

[0114]

[0115] Calculate the design option score and get a multivariate design option table, where a represents the design aesthetics score, b represents the design practicality score, c represents the cost-effectiveness data, and d represents the environmental adaptability score;

[0116] a represents the design aesthetics analysis result. By comparing the visual feedback scores of different design cases, the average aesthetics score obtained through user surveys is 7.5. b represents the design practicality analysis result. The practicality score obtained from the user's space usage frequency and functional demand analysis is 9.0. c represents the cost-effectiveness data, which is 4.0 calculated by the budget and expected benefit ratio. d represents the environmental adaptability score. According to the matching evaluation of the space environment and the design plan, the score is 8.0. The calculation process is as follows: First, calculate the score part:

[0117]

[0118] Then do the sum:

[0119]

[0120] Apply absolute value and square root operations:

[0121]

[0122] The final design option score was 1.155, which is greater than 1. The results show that the calculated score is high, indicating that the selected design option has achieved a good balance in aesthetics, practicality, cost-effectiveness and environmental adaptability. This numerical result will be used to further screen and optimize the design scheme.

[0123] See also Figure 7, the steps to obtain a customized design solution are:

[0124] Collect user feedback through online surveys and field interviews, record preferences for each design option, functional requirements, and improvement suggestions, and generate comprehensive analysis results of user feedback;

[0125] A large amount of user feedback was collected through online survey tools and face-to-face interviews. The specially designed questionnaire covered design preferences, functional requirements and improvement suggestions. After the data was collected, it was processed using advanced data analysis software SPSS and Excel. The analysis tools helped identify key user preference trends and problem points, and clustered and regressed the data to present key trends in graphical and tabular form. These analysis results directly supported the formulation of subsequent design decisions, provided the design team with clear user preference guidance, and ensured the consistency of the design direction with user expectations. The detailed data processing and trend analysis results of this step are the basis for creating a design solution with high user satisfaction.

[0126] Based on the comprehensive analysis results of user feedback and the multi-design option table, we evaluate the design options, adjust the design scheme to match the actual needs of users, determine the style preferences and material selection, and generate a list of adjusted design schemes based on cost and feasibility;

[0127] After obtaining detailed user feedback, the design team held several working meetings, during which multimedia presentations and physical samples were used to demonstrate the comparison between the existing design and user feedback. Through intuitive comparison, team members were able to deeply understand the specific needs of users and fine-tune each element of the design, including material selection, color scheme and layout design. In particular, in the selection of materials for high-frequency use areas, the team selected more durable and easy-to-clean materials to extend the service life, and adjusted the color scheme to adapt to the emotional feedback of users. This process ensured that the design was not only beautiful, but also highly practical and user-personalized. Ultimately, a series of optimized design solutions were generated, which are expected to significantly improve user satisfaction and quality of life.

[0128] Generate customized design solutions based on the adjusted design solution list, combined with market trends and sustainable development standards, comprehensive user personalized needs and design practicality;

[0129] After integrating all user feedback and market data, the project management team conducted a detailed feasibility analysis and cost assessment, simulating the cost and time frame for implementing each design solution to ensure the economic benefits and feasibility of the solution. The team also considered environmental impact and material sustainability, and selected environmentally friendly materials and energy-efficient design solutions. These decisions went through multiple rounds of review and adjustment to ultimately determine a design solution that can be implemented within budget and has minimal impact on the environment. The rigor of this process ensures the market competitiveness and long-term sustainable development of the design solution, thereby generating a customized design solution that comprehensively considers user needs, cost-effectiveness and environmental sustainability, which is expected to effectively enhance the overall value of the project and user satisfaction.

[0130] See also Figure 8 , the steps to obtain the simulation manufacturing results of customized furniture are:

[0131] Based on customized design solutions, combined with user behavior patterns and preferences, and integrated component design parameters, a furniture functional demand analysis report is generated;

[0132] First, behavioral data is extracted from the user database, which includes but is not limited to the user's browsing history, purchase history, and interactive behaviors (such as clicks, swipes, dwell time, etc.); second, data mining techniques, such as cluster analysis and association rule learning, are used to conduct in-depth analysis of this data to identify user preference patterns for different design elements; then, the patterns are associated with the physical properties of the furniture (such as size, material, function, etc.) to determine which design parameters best meet user needs; then, the information is used to compile a detailed report that details the impact of various design parameters on user satisfaction and provides specific suggestions for future product design improvements, thereby helping the design team and production department better understand market trends and user expectations.

[0133] Using the furniture functional demand analysis report, combined with component performance and cost-effectiveness, the formula is adopted:

[0134]

[0135] Calculate the fitness score F for each component match , generate a list of matching component configurations, where p i is the performance score, D i is the design score, C i represents the cost, and N represents the number of components;

[0136] There are three components, with performance scores of p i =[15, 20, 25], the design score is D i = [10, 15, 20], cost is C i =[5, 10, 15].

[0137] Apply the formula to each component to get the fitness score of the three components:

[0138]

[0139] Compute the average of these scores:

[0140]

[0141] This indicates that the model comprehensively evaluates the component's adaptation score based on its performance, design, and cost factors. This numerical result (6.62, which is relatively high) means that a relatively balanced configuration score is obtained, which helps to optimize the subsequent component configuration strategy.

[0142] Simulate the assembly of matching component configuration lists, test the compatibility and matching effects of components, test the practicality and feasibility of furniture design solutions, and output customized furniture simulation manufacturing results;

[0143] During the simulated assembly and testing of the component configuration list, virtual simulation software is used to build a three-dimensional model of the components and set the position and orientation of each component according to the configuration list parameters; then, the software's built-in physics engine is used to simulate the physical response of each component under actual use conditions, such as bearing force, torque, and vibration; in addition, the software will also perform stress tests on the connection strength between components to ensure that all components will not structurally fail under the specified safety load; based on the results of these tests, the software will generate a detailed test report, which not only evaluates the performance and compatibility of each component, but also indicates any possible design defects or room for improvement; finally, it will be used for the assembly of customized furniture for use by the design and engineering teams to optimize the performance and reliability of the final furniture.

[0144] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A customized furniture intelligent manufacturing platform, characterized in that: The platform includes: The data-driven behavior analysis module collects user behavior data through sensors, records the frequency of furniture use and spatial activity trajectories, performs time series analysis on the data, identifies user usage patterns and activity habits based on the time series analysis results, analyzes user preferences to determine comfort needs, and obtains user behavior insights; The personalized space modeling module constructs a three-dimensional model of the user's living space based on the user behavior insight results, simulates different layout plans, adjusts the size and shape of furniture based on the simulation results of the different layout plans, optimizes and iterates the model layout, matches the user behavior and usage habits, and obtains a space configuration plan; The intelligent design generation module selects the decoration style, color tone and material matching the user behavior and the space configuration based on the space configuration scheme, constructs a multi-design option table, makes design adjustments based on the construction results of the multi-design option table and user feedback, determines the design style and material selection, and obtains a customized design scheme; The component interaction optimization module analyzes the functional requirements of the furniture components in the customized design solution, automatically recommends matching component configurations, verifies the actual matching effects of the components through simulated assembly, and obtains the simulated manufacturing results of customized furniture.

2. The customized furniture intelligent manufacturing platform according to claim 1 is characterized in that: The steps of analyzing the time series of data are as follows: Install sensors in the user's home environment to capture and record the user's interaction frequency with furniture in real time, record the spatial activity trajectory, and generate an original user behavior data set; Based on the original user behavior data set, outliers are deleted, data errors are corrected, and data formats are unified to obtain a standardized user behavior data set; Based on the standardized user behavior data set, time series analysis is performed using the formula: Calculate the user behavior intensity value Y(t) at time t and obtain the time series analysis results, where X(t) and X(t-1) represent the number of times furniture is used at the current and previous moments, respectively, and E(t) represents the standardized value of the environmental variable.

3. The customized furniture intelligent manufacturing platform according to claim 2 is characterized in that: The steps for obtaining the user behavior insight results are: According to the time series analysis results, extract the frequency and movement path of furniture use by users in different time periods, identify normal behavior patterns, reveal the daily activity habits of users, and generate a comprehensive report on user behavior patterns; Based on the comprehensive report of user behavior patterns, evaluate the user's preference for furniture type and space usage, combine the user's response under different environmental conditions, determine the user's comfort needs, and form a user preference analysis report; Combine the user behavior pattern comprehensive report with the user preference analysis report to analyze the key factors affecting user needs and generate user behavior insight results.

4. The customized furniture intelligent manufacturing platform according to claim 3 is characterized in that: The simulation steps of the difference layout scheme are: Based on the user behavior insight results, analyze the user's activity areas and frequencies in all time periods, determine the preliminary layout of room functional areas and furniture configurations, and generate functional area distribution data for living spaces; Based on the functional area distribution data of the living space, a three-dimensional living space model is constructed by adjusting the size and position of furniture to match user habits; The three-dimensional living space model is simulated in multiple scenarios, and the furniture layout of the functional areas is adjusted using the formula: Calculate the space utilization S of the i-th solution i , generate simulation results of different layout schemes; where a ij represents the area contribution of the j-th furniture in the i-th solution, u j is the frequency of use of the corresponding furniture, A i is the total area of ​​the ith solution, and n represents the number of furniture.

5. The customized furniture intelligent manufacturing platform according to claim 4 is characterized in that: The steps of obtaining the space configuration scheme are as follows: Based on the simulation results of the different layout schemes, each scheme is analyzed, and the size and shape of furniture are designed according to the user's stay time and activity frequency in the different areas and the size of the corresponding areas to obtain preliminary furniture design parameters; Applying the preliminary furniture design parameters, updating the three-dimensional living space model, adjusting the furniture layout through iterative cycles, evaluating the impact of the furniture on the functionality of the space and the comfort of the user, and generating an optimized iterative three-dimensional model layout; The optimized iterative 3D model layout is evaluated to compare the consistency between the model layout and the actual user behavior, using the formula: Calculate the efficiency E of the space configuration and get the space configuration plan, where v k represents the usage frequency of the kth region, d k It represents the degree of design optimization of the target area, T is the total investigated area, and m is the total number of investigated areas.

6. The customized furniture intelligent manufacturing platform according to claim 5 is characterized in that: The steps for constructing the multivariate design option table are: Based on the space configuration plan, analyze the user's daily behavior and activity range, identify the space function requirements, and generate user behavior and space usage analysis results; Evaluate the user behavior and space usage analysis results, select matching decorative styles, tones and materials, and generate a preliminary list of design options; Based on the preliminary design options list, the formula is used: Calculate the design option score and get a multivariate design option table, where a represents the design aesthetics score, b represents the design practicality score, c represents the cost-effectiveness data, and d represents the environmental adaptability score.

7. The customized furniture intelligent manufacturing platform according to claim 6 is characterized in that: The steps for obtaining the customized design solution are: Collect user feedback through online surveys and field interviews, record preferences for each design option, functional requirements, and improvement suggestions, and generate comprehensive analysis results of user feedback; According to the comprehensive analysis results of the user feedback and the multi-dimensional design option table, the design options are evaluated, the design scheme is adjusted to match the actual needs of the user, the style preference and material selection are determined, and the adjusted design scheme list is generated in combination with the cost and implementation feasibility; Based on the adjusted design plan list, combined with market trends and sustainable development standards, and integrating user personalized needs and design practicality, a customized design plan is generated.

8. The customized furniture intelligent manufacturing platform according to claim 7 is characterized in that: The steps for obtaining the customized furniture simulation manufacturing results are as follows: Based on the customized design solution, combined with the user's behavior patterns and preferences, and integrated component design parameters, a furniture functional demand analysis report is generated; Using the furniture functional demand analysis report, combined with component performance and cost-effectiveness, the formula is adopted: Calculate the fitness score F for each component match , generate a list of matching component configurations, where p i is the performance score, D i is the design score, C i represents the cost, and N represents the number of components; The matching component configuration list is simulated for assembly, the compatibility and matching effects of the components are tested, the practicality and feasibility of the furniture design are tested, and the customized furniture simulation manufacturing results are output.

Images