An interior design method and an interior design system

Abstract

The embodiment of the application provides an indoor design method, which is applied to the technical field of smart home and comprises the following steps: acquiring sunlight and indoor environment data; establishing a prediction model based on the sunlight data to generate a sunlight change track, and determining a furniture target placement coordinate set by means of an optimization algorithm in combination with the environment data; establishing a projection coordinate system, mapping the track and the coordinate set to generate a synthesized visual graph containing the sunlight track and the furniture contour, and projecting the synthesized visual graph to an indoor surface to form a visual reference datum; planning a path based on the coordinate set and the obstacle distribution, and controlling the furniture to move; in the moving process, periodically collecting the furniture position and comparing the furniture position with the projected contour to generate a displacement error signal; jointly analyzing the signal and the sunlight track, dynamically correcting the driving instruction and the path parameter, and until the furniture reaches the target position. The system comprises corresponding function modules, realizes automatic optimization placement of the furniture along with the change of the sunlight, and improves the indoor light environment comfort, the furniture protection effect and the system operation reliability.

Description

Technical Field

[0001] This application relates to the field of smart home technology, and in particular to an interior design method and an interior design system. Background Technology

[0002] With the development of smart home technology, more and more indoor furniture has the ability to automatically adjust its position and posture to adapt to spatial layout, ambient lighting, and user needs. Existing indoor furniture arrangements usually rely on manual moving or simple electric drive to achieve position adjustment, lacking comprehensive consideration of changes in outdoor sunlight, indoor lighting distribution, and the interaction between furniture and the light environment, making it difficult to achieve synergistic optimization between furniture and natural lighting conditions.

[0003] Existing automated furniture movement solutions mainly rely on fixed paths or single environmental parameters for position control, lacking a mechanism to predict future changes in sunlight. They cannot provide dynamic placement coordinates for furniture to adapt to different sunlight conditions at different times. Traditional furniture movement control systems typically lack the ability to link with sunlight prediction results, resulting in lag in furniture adjustments. While existing interior design assistance systems can display furniture placement diagrams or interior layouts through projection, they only provide static visual references and fail to achieve overlay display with sunlight prediction data. They also fail to establish a correspondence between the projected area and the target furniture placement area, thus failing to provide direct visual guidance for the automatic movement of furniture.

[0004] Therefore, there is a need for a method and system that can comprehensively utilize solar radiation forecast data, indoor environmental information, and the current position of furniture, and achieve automatic movement and dynamic placement of furniture in indoor spaces through visualization projection, path planning, and closed-loop correction, so as to improve the intelligence level and environmental adaptability of furniture placement. Summary of the Invention

[0005] The embodiments of this application provide an interior design method and system that enable automatic movement and dynamic placement of furniture in an interior space, improving the intelligence level and environmental adaptability of furniture placement. To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application provides an interior design method applied to an interior space equipped with movable furniture, environmental sensing devices, and projection display devices, comprising: Acquire sunshine data, including outdoor sunshine intensity, incident angle, and solar trajectory; Collect indoor environmental data, including indoor lighting distribution, current position and posture information of furniture, and spatial distribution of obstacles; A time series prediction model is established based on the sunshine data to generate the sunshine change trajectory for the target period, and the coordinate set of the target furniture placement is determined by combining the indoor environmental data. Establish a coordinate system for the projection area corresponding to the interior space; The solar radiation change trajectory is mapped to the coordinate system of the projection area to generate a solar radiation movement trajectory graphic for the target time period; Map the furniture target placement coordinate set to the projection area coordinate system to generate a target placement contour image for each piece of furniture; The image of the sun's movement trajectory is superimposed and fused with the image of the target placement outline to form a composite visual reference image; Based on different furniture target placement coordinate sets, the entire projection area is divided into multiple independent projection blocks, each projection block corresponding to a furniture target placement area; Generate an independent block coordinate index for each of the projected blocks; Based on the block coordinate index, the projection images of different furniture categories are independently adjusted in terms of light intensity; The projection images after light intensity modulation are projected onto their corresponding indoor surfaces to obtain the visual placement outlines corresponding to the visual graphics. Based on the target furniture placement coordinate set and the spatial distribution of obstacles, a movement path is planned, a drive command set is generated, and the furniture is controlled to move along the planned path based on the drive command set; During the furniture movement, the current position of the furniture is periodically collected, and the collected current position of the furniture is compared with the visible placement outline to generate a displacement error signal; The drive command set is corrected based on the displacement error signal and the sunlight change trajectory until the furniture moves to the target position.

[0006] Based on historical sunshine data and collected sunshine data, a time series prediction model is trained to predict the trajectory of changes in sunshine intensity and incident angle within a target period. The change trajectory is taken as the solar radiation change trajectory and integrated with the indoor light distribution, space size and obstacle spatial distribution in the indoor environment data to generate an integrated result; The integrated results are input into the light environment adaptability optimization calculation model, in which an optimization function is constructed with the optimization objective of improving indoor light uniformity and reducing the exposed area of ​​furniture; Based on the optimization function, an iterative optimization algorithm is used to solve the problem, and the output is a set of furniture target placement coordinates that satisfy the optimization objective and are collision-free.

[0007] Within the feasible area defined by the interior space dimensions, generate an initial population of placement coordinate sets representing the possible locations of furniture; Based on the spatial distribution of obstacles, collision detection is performed on each set of coordinates in the population, and a penalty term is applied to the coordinate sets that cause collisions. For the set of furniture placement coordinates that pass collision detection, calculate the indoor light uniformity and furniture exposure area under the influence of the solar radiation change trajectory. Substitute the calculated indoor light uniformity and furniture exposure area into the optimization function to obtain the fitness value; Based on the fitness values, a new generation of placement coordinate set population is generated through selection, crossover, and mutation operations; Collision detection and fitness assessment were performed on the population duplication of the new generation of positioning coordinate set; When the preset number of iterations or fitness convergence condition is reached, the set of placement coordinates whose fitness values ​​meet the preset threshold requirements is output as the final target placement coordinate set.

[0008] Collision detection is performed based on the spatial distribution of obstacles, and an initial path is planned from the current position of the furniture to the coordinates of the target placement position. When the initial path cannot avoid obstacles, the movement path is replanned by combining the spatial and obstacle information based on the synthetic visualization reference graphics.

[0009] The drive instruction set is parsed into coordinated drive signals for controlling multiple walking motors; The speed and direction of each walking motor are adjusted based on the coordinated drive signal, which controls the direction and speed of the furniture so that the furniture moves along the planned path. During movement, the furniture's posture is monitored by attitude sensors; When the deviation between the attitude and the expected attitude exceeds the safety threshold, a braking command is generated and the driving signal is cut off.

[0010] During the movement of the furniture, the position coordinates and posture information of the furniture are periodically collected; The collected location coordinates are compared with the spatial position of the projected furniture target placement outline; The positional deviation is calculated based on the comparison results, and a displacement error signal is generated.

[0011] The preset time threshold for the furniture to reach the target location is determined based on the trajectory of sunlight changes; Based on the distance between the furniture's current position and the target position, its current moving speed, and the displacement error signal, predict its arrival time; When the predicted arrival time is later than the preset time threshold, increase the movement speed parameter in the drive instruction set, or replan the movement path based on the path curvature optimization algorithm.

[0012] Secondly, this application provides an interior design system, comprising: The data acquisition module is used to acquire sunshine data and indoor environment data. The sunshine data includes outdoor sunshine intensity, incident angle and sun movement trajectory. The indoor environment data includes indoor light distribution, furniture current position and posture information and obstacle spatial distribution. The processing and calculation module is used to build a time series prediction model based on sunshine data to generate the sunshine change trajectory for the target period, and to determine the target placement coordinate set of furniture by combining indoor environmental data and using optimization algorithms. The projection control module is used to establish the coordinate system of the indoor projection area, map the trajectory of sunlight change and the coordinate set of furniture target placement to generate a composite visual reference graphic, and control the projection display device to project it onto the indoor surface to form a visible placement outline. The path planning module is used to plan movement paths based on the furniture target placement coordinate set and the spatial distribution of obstacles, and generate a set of driving instructions. The drive execution module is used to convert drive instruction sets into drive signals to control the electric moving parts of the furniture to move along the planned path; The status monitoring module is used to periodically collect the current position and posture information of the furniture during the furniture movement process, and compare it with the visible placement outline to generate a displacement error signal; The decision control module is used to jointly analyze the displacement error signal and the trajectory of changes in sunlight, and correct the drive command set and movement path based on the analysis results until the furniture reaches the target position.

[0013] As can be seen from the above technical solution, this application has the following beneficial effects: 1. This invention establishes a time series prediction model to accurately predict the trajectory of solar radiation changes. Combined with indoor environmental data, it automatically determines the optimal furniture placement coordinate set with the optimization goals of improving light uniformity and reducing furniture exposure area. This can significantly improve the uniformity of indoor light distribution, avoid local areas that are too dark or too bright, and effectively reduce the aging and fading damage of furniture materials caused by ultraviolet rays and strong light by actively avoiding prolonged direct sunlight. Thus, it improves visual comfort and extends the service life of furniture.

[0014] 2. This invention establishes an intuitive visual reference benchmark by projecting the target placement contour as a synthesized visual reference graphic. Based on this benchmark, a closed-loop control system integrating displacement error detection, solar radiation change joint analysis, and drive command correction is constructed. This improves the positioning accuracy and anti-interference capability of furniture during movement, ensuring that it can accurately reach the target position. By monitoring the furniture posture and conducting safety assessments, braking and replanning can be triggered in a timely manner when abnormal posture or path conflict occurs, effectively avoiding collisions during furniture movement and ensuring the smoothness of the entire placement process. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 A first flowchart of the system provided in the embodiments of this application; Figure 2 A second flowchart of the system provided in this application embodiment; Figure 3 A third flowchart of the system provided in this application embodiment; Figure 4 The fourth flowchart of the system provided in this application embodiment. Detailed Implementation

[0017] The terms "first," "second," and "third," etc., used in this application specification, claims, and drawings are used to distinguish different objects, not to limit a specific order.

[0018] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0019] Research has revealed that existing automated furniture movement solutions primarily rely on fixed paths or single environmental parameters for position control, lacking mechanisms to predict future changes in sunlight. This prevents the provision of furniture placement coordinates that adapt to varying sunlight conditions at different times, resulting in lag in furniture adjustments. While existing interior design support systems can project furniture placement diagrams or interior layouts, they only provide static visual references and fail to overlay them with sunlight prediction data. Furthermore, they do not establish a correspondence between the projected areas and the target furniture placement areas, thus failing to provide direct visual guidance for the automatic movement of furniture.

[0020] To address the aforementioned problems, this application provides an interior design method and system: Example 1 To solve the above problems, such as Figure 1 As shown, in this embodiment, the scenario is a smart living room with south-facing floor-to-ceiling windows, movable furniture, environmental sensors, and a projection display. This living room is equipped with a central control unit for processing sunlight data and indoor environmental data.

[0021] The system acquires external sunlight intensity, incident angle, and solar trajectory via an external sunlight acquisition module and stores this data in a historical database. Then, an indoor lighting sensor network collects light distribution data at various locations, along with information on the current position and posture of furniture and the spatial distribution of obstacles. Based on historical and current sunlight data, the central control unit establishes a time-series prediction model to predict future sunlight patterns. The system integrates the predicted sunlight patterns with the current indoor lighting distribution to form a sunlight illumination model. This model is then matched with the light reflectance coefficient of furniture materials, surface exposure limits, and comfortable lighting standards for human activity areas to construct a comprehensive optimization function. The goal is to improve indoor lighting uniformity and reduce the area of ​​furniture exposed to direct sunlight.

[0022] In the central control unit, an iterative optimization algorithm is used to solve the problem based on an optimization function. Within the feasible area defined by the indoor space dimensions, an initial population of furniture placement coordinate sets is generated, representing the possible positions of the furniture. Based on the spatial distribution of obstacles, collision detection is performed on each placement coordinate set in the population, and a penalty term is applied to the coordinate sets that cause collisions. For the placement coordinate sets that pass the collision detection, the indoor light uniformity and furniture exposure area under the influence of the solar radiation change trajectory are calculated. The calculated indoor light uniformity and furniture exposure area are substituted into the optimization function to obtain the fitness value. Based on the fitness value, a new generation of placement coordinate set population is generated through selection, crossover, and mutation operations. Collision detection and fitness evaluation are repeatedly performed on the new generation of placement coordinate set population. When the preset number of iterations or fitness convergence condition is reached, the placement coordinate set whose fitness value meets the preset threshold requirement is output as the final target placement coordinate set.

[0023] To assist furniture movement and user supervision, the system establishes an indoor projection area coordinate system, mapping the trajectory of sunlight changes and the coordinate set of the furniture target placement to generate a composite visual reference graphic, which is then projected onto the indoor floor via a projection device. The projected furniture target placement outline will serve as a visual reference benchmark for the system to compare posture errors during subsequent movement.

[0024] If, during the furniture movement, a displacement error caused by ground friction or external force interference is detected that affects the furniture's timely arrival at the target placement position, the system will perform joint analysis based on the displacement error signal and the trajectory of sunlight changes, and correct the drive instruction set and update the path parameters. This correction and update will be performed multiple times depending on the error situation to ensure that the furniture is adjusted before the expected sunlight conditions are met.

[0025] Example 2 like Figure 2Specifically, the system involves installing high dynamic range cameras and 3D laser scanning devices indoors to acquire data on the distribution of obstacles and the reflection characteristics of walls. This data is then combined with viewing angle data from frequently used user activity areas, such as sofa reading areas and TV viewing areas. The system uses eye-tracking and glare index calculation models to predict potential glare hotspots that may form during future periods.

[0026] The central control unit incorporates visual comfort constraints into the comprehensive optimization model. This means the optimization objectives include not only illumination uniformity and furniture exposure area, but also reducing the glare index. An improved ant colony algorithm is used for global solution, and the evaluation function includes a penalty term for glare, ensuring the system prioritizes furniture layouts that block or reduce glare.

[0027] In the planned furniture placement scheme, some large pieces of furniture, such as high-backed chairs or bookshelves, can be systematically placed in specific positions near windows to block strong reflections or direct sunlight from entering the user's line of sight. The projection device divides the entire projection area into multiple independent projection blocks based on different furniture target placement coordinate sets, such as sofa areas and bookcase areas, with each block corresponding to a target furniture placement area. The system generates an independent coordinate index for each block and, based on the index and indoor ambient lighting, performs independent light intensity adjustment on the projected images of different blocks. Finally, the optimized images are projected onto their corresponding floor or wall areas. The projected content includes the sunlight trajectory, glare hotspots, and recommended furniture placement outlines for the next few hours, allowing users to intuitively see the effect of layout adjustments on glare control.

[0028] Once the furniture begins to move, if the initial path planned based on the spatial distribution of obstacles cannot avoid temporarily appearing obstacles, such as bags placed by the user, the system will use the aforementioned projected composite visual reference graphics. Areas in the graphics that are not occupied by the furniture outline and are not marked as glare hotspots are identified by the system as "feasible spaces," and a new movement path will be planned based on these feasible spaces.

[0029] The central control unit periodically collects the actual position and posture of the furniture and compares them with the projected outline. If a displacement error is detected, the system will generate instructions to accelerate movement or replan the path by jointly analyzing the trajectory of sunlight changes and the displacement error signal, so as to ensure effective shading at critical moments.

[0030] Example 3 like Figure 3Specifically, the drive control system adopts a distributed architecture. Each movable furniture piece is equipped with an independent motion control unit. The drive command set issued by the central control unit is first parsed into a set of coordinated drive signals for the collaborative control of multiple walking motors. The motion control unit receives these signals and, based on the coordinated drive signals, independently and synchronously adjusts the speed and direction of each walking motor, thereby precisely controlling the furniture's direction and speed of travel, allowing it to move along the planned path. It is responsible for receiving instructions from the central system and executing local control. The motion control unit includes a motor drive module, a power management module, and a communication module. The motor drive module uses vector control technology to precisely adjust the speed and torque of the walking motors. The power management module monitors the battery status in real time and optimizes energy consumption distribution. The communication module maintains real-time data exchange with the central system via a wireless network.

[0031] The displacement error detection system achieves this through multi-sensor data fusion. The visual positioning system uses a wide-angle camera mounted on the ceiling to capture marker points on furniture in real time and calculate their position coordinates. The inertial navigation system uses gyroscopes and accelerometers to measure relative displacement and attitude changes. A wheel encoder records the walking distance and turning angle. The system fuses multi-source data using a Kalman filter algorithm to obtain a high-precision position estimate.

[0032] The error correction mechanism employs a predictive control strategy. The system establishes kinematic and dynamic models of the furniture, enabling it to predict the motion trajectory under different control parameters. When a displacement error is detected, the control algorithm calculates the optimal correction strategy, including velocity adjustment, path curvature correction, and attitude compensation. For persistent system errors, the control parameters undergo online self-tuning to gradually improve control accuracy.

[0033] The safety monitoring system implements a multi-level protection mechanism. The lowest level of protection is achieved through hardware limit switches and emergency stop circuits, the middle level through software logic judgment, and the highest level through manual monitoring. When abnormal vibration, unexpected resistance, or communication interruption is detected, the system will immediately activate the corresponding safety plan to ensure safe and reliable movement.

[0034] The system also features self-optimization capabilities. By recording historical operating data, the system automatically analyzes the control effect and gradually optimizes the control parameters. For frequently traveled paths, the system learns the optimal trajectory and control parameters to improve operating efficiency and stability.

[0035] Example 4 like Figure 4As shown, specifically: the data acquisition module adopts a multi-channel acquisition architecture. The module includes a sensor interface unit, a data preprocessing unit, and a quality assessment unit. The sensor interface unit supports multiple communication protocols and can connect to different types of environmental sensors. The data preprocessing unit is responsible for signal filtering, data alignment, and outlier handling. The quality assessment unit monitors data reliability in real time and marks and compensates for abnormal data.

[0036] The processing and computing module is deployed on edge computing nodes using a containerized architecture. The module comprises multiple dedicated processing units: a solar radiation prediction unit runs time-series models, an environmental analysis unit processes spatial data, and an optimization computing unit executes layout algorithms. These units exchange data via message queues to achieve parallel processing. The system also includes a computing resource scheduler that dynamically allocates computing resources based on task priority.

[0037] The projection control module employs real-time rendering technology. The module includes a scene management unit, a graphics rendering unit, and a projection calibration unit. The scene management unit maintains the indoor 3D model and projection content data; the graphics rendering unit generates visual graphics based on GPU acceleration; and the projection calibration unit ensures accurate matching between the projected image and the physical space. The system supports multiple projection modes, including full-space projection, area-enhanced projection, and dynamic guidance projection. The path planning module uses a multi-algorithm fusion scheme. The module includes a global planning unit, a local planning unit, and a collaborative planning unit. The global planning unit uses a sampling-based motion planning algorithm, the local planning unit uses a model predictive control algorithm, and the collaborative planning unit is responsible for coordinating the movement sequence and path allocation of multiple pieces of furniture. The planning results are only sent to the execution system after feasibility verification.

[0038] The drive execution module adopts a closed-loop control architecture. The module includes an instruction parsing unit, a motion control unit, and a status feedback unit. The instruction parsing unit converts path instructions into control sequences, the motion control unit adjusts drive parameters in real time, and the status feedback unit collects execution data and feeds it back to the control system. The module supports multiple control modes, including position control, speed control, and torque control. The status monitoring module implements full-process monitoring. The module includes a data acquisition unit, an anomaly detection unit, and an early warning management unit. The data acquisition unit collects system status data at fixed intervals, the anomaly detection unit identifies anomaly patterns using machine learning algorithms, and the early warning management unit initiates corresponding processing procedures based on the anomaly level.

[0039] The decision control module employs a hierarchical decision-making mechanism. The module comprises a strategy generation unit, an evaluation and optimization unit, and a decision execution unit. The strategy generation unit generates candidate solutions based on the current state and target requirements; the evaluation and optimization unit performs multi-dimensional evaluations of the solutions; and the decision execution unit selects the optimal solution and monitors the execution process. The entire decision-making process supports manual intervention and parameter adjustment.

[0040] Example 5 like Figures 1 to 4 As shown, specifically, the system adopts a cloud-edge-device collaborative architecture. The cloud is responsible for long-term data storage, model training, and system management; edge nodes are responsible for real-time data processing and decision control; and terminal devices are responsible for data acquisition and command execution. Data exchange between each layer is conducted through a secure communication protocol to ensure the real-time performance and reliability of the system's response.

[0041] In terms of network architecture, the system adopts a multi-network convergence solution. Environmental sensors are connected to the network, video data is transmitted via the network, and control commands are issued via the network. The network management system monitors communication quality in real time and dynamically adjusts transmission strategies to ensure reliable transmission of critical data.

[0042] The data management system implements full lifecycle management. Raw sensor data, after cleaning and labeling, is stored in a time-series database; processing results and system status data are stored in a relational database; and unstructured data such as video and point clouds are stored in an object storage system. The data service platform provides a unified data access interface to support the data needs of various applications.

[0043] The computing resource scheduling system employs a dynamic allocation strategy. The system allocates appropriate computing resources based on task type: high-priority resources are allocated to tasks requiring real-time control, elastic resources are allocated to batch processing tasks, and GPU-accelerated resources are allocated to AI tasks. The resource manager monitors system load in real time and automatically adjusts resources and balances the load.

[0044] System reliability is ensured through multiple safeguards. At the hardware level, redundant design is employed, with backup devices for critical sensors and controllers. At the software level, health monitoring is implemented, with regular system self-checks and performance evaluations. At the operations and maintenance level, a preventative maintenance mechanism is established, with maintenance plans scheduled based on equipment uptime and usage intensity.

[0045] The performance optimization system continuously monitors operational metrics. By analyzing key indicators such as system response time, control accuracy, and energy efficiency, it continuously optimizes algorithm parameters and system configuration. The system also establishes a knowledge base to record solutions to typical problems, enhancing its autonomy.

[0046] User experience optimization covers multiple aspects. The system offers various interactive interfaces, including a web console, mobile applications, and a voice assistant. Users can choose the appropriate operation method based on their usage habits.

[0047] The foregoing has shown and described the basic principles, main features, and advantages of this application. Those skilled in the art should understand that this application is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this application. Various changes and modifications can be made to this application without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of this application as claimed. The scope of protection of this application is defined by the appended claims and their equivalents.

Claims

1. An interior design method, characterized in that, The method is applied to indoor spaces equipped with movable furniture, environmental sensing devices, and projection display devices. The method includes: Acquire sunshine data, including outdoor sunshine intensity, incident angle, and solar trajectory; Collect indoor environmental data, including indoor lighting distribution, current position and posture information of furniture, and spatial distribution of obstacles; A time series prediction model is established based on the sunshine data to generate the sunshine change trajectory for the target period, and the coordinate set of the target furniture placement is determined by combining the indoor environmental data. Establish a coordinate system for the projection area corresponding to the interior space; The solar radiation change trajectory is mapped to the coordinate system of the projection area to generate a solar radiation movement trajectory graphic for the target time period; Map the furniture target placement coordinate set to the projection area coordinate system to generate a target placement contour image for each piece of furniture; The image of the sun's movement trajectory is superimposed and fused with the image of the target placement outline to form a composite visual reference image; Based on different furniture target placement coordinate sets, the entire projection area is divided into multiple independent projection blocks, each projection block corresponding to a furniture target placement area; Generate an independent block coordinate index for each of the projected blocks; Based on the block coordinate index, the projection images of different furniture categories are independently adjusted in terms of light intensity; The projection images after light intensity modulation are projected onto their corresponding indoor surfaces to obtain the visual placement outlines corresponding to the visual graphics. Based on the target furniture placement coordinate set and the spatial distribution of obstacles, a movement path is planned, a drive command set is generated, and the furniture is controlled to move along the planned path based on the drive command set; During the furniture movement, the current position of the furniture is periodically collected, and the collected current position of the furniture is compared with the visible placement outline to generate a displacement error signal; The drive command set is corrected based on the displacement error signal and the sunlight change trajectory until the furniture moves to the target position.

2. The method according to claim 1, characterized in that, The step of establishing a time series prediction model based on the sunshine data to generate the sunshine change trajectory for the target period, and determining the target furniture placement coordinate set by combining the indoor environmental data, includes: Based on historical sunshine data and collected sunshine data, a time series prediction model is trained to predict the trajectory of changes in sunshine intensity and incident angle within a target period. The change trajectory is taken as the solar radiation change trajectory and integrated with the indoor lighting distribution, space size and obstacle space distribution in the indoor environment data to generate an integrated result; The integrated results are input into the light environment adaptability optimization calculation model, wherein the optimization function is constructed with the optimization objective of improving indoor light uniformity and reducing the exposed area of ​​furniture; Based on the optimization function, an iterative optimization algorithm is used to solve the problem, and the output is a set of furniture target placement coordinates that satisfy the optimization objective and are collision-free.

3. The method according to claim 2, characterized in that, Based on the aforementioned optimization function, an iterative optimization algorithm is used to solve the problem, outputting a set of furniture target placement coordinates that satisfy the optimization objective and are collision-free, including: Within the feasible area defined by the interior space dimensions, generate an initial population of placement coordinate sets representing the possible locations of furniture; Based on the spatial distribution of the obstacles, collision detection is performed on each set of coordinates in the population, and a penalty term is applied to the coordinate sets that produce collisions. For the set of placement coordinates that pass the collision detection, calculate the indoor light uniformity and furniture exposure area under the influence of the solar radiation change trajectory. Substitute the calculated indoor light uniformity and furniture exposure area into the optimization function to obtain the fitness value; Based on the fitness values, a new generation of placement coordinate set population is generated through selection, crossover, and mutation operations; Collision detection and fitness assessment were performed on the population of the new generation of positioning coordinate set. When the preset number of iterations or fitness convergence condition is reached, the set of placement coordinates whose fitness values ​​meet the preset threshold requirements is output as the final target placement coordinate set.

4. The method according to claim 1, characterized in that, The process of planning a movement path based on the furniture target placement coordinate set and the spatial distribution of obstacles includes: Collision detection is performed based on the spatial distribution of obstacles, and an initial path is planned from the current position of the furniture to the target placement coordinates; When the initial path cannot avoid obstacles, the movement path is replanned by combining the spatial and obstacle information based on the synthetic visualization reference graphics.

5. The method according to claim 1, characterized in that, The control of furniture to move along the planned path based on the drive instruction set includes: The drive instruction set is parsed into coordinated drive signals for controlling multiple walking motors; Based on the coordinated drive signal, the speed and direction of each walking motor are adjusted to control the direction and speed of the furniture, so that the furniture moves along the planned path. During movement, the furniture's posture is monitored by attitude sensors; When the deviation between the stated posture and the expected posture exceeds a safety threshold, a braking command is generated and the driving signal is cut off.

6. The method according to claim 1, characterized in that, During the furniture movement process, the current position of the furniture is periodically collected, and the collected current position of the furniture is compared with the visible placement outline to generate a displacement error signal, including: During the movement of the furniture, the position coordinates and posture information of the furniture are periodically collected; The collected position coordinates are compared with the spatial position of the projected furniture target placement outline; The positional deviation is calculated based on the comparison results, and the displacement error signal is generated.

7. The method according to claim 1, characterized in that, The method of correcting the drive instruction set based on the displacement error signal and the solar radiation change trajectory includes: The preset time threshold for the furniture to reach the target location is determined based on the sunlight change trajectory. Based on the distance between the furniture's current position and the target position, its current moving speed, and the displacement error signal, its arrival time is predicted; When the predicted arrival time is later than the preset time threshold, the movement speed parameter in the drive instruction set is increased, or the movement path is replanned based on the path curvature optimization algorithm.

8. An interior design system for implementing the method of claim 1, characterized in that, include: The data acquisition module is used to acquire sunshine data and indoor environment data. The sunshine data includes outdoor sunshine intensity, incident angle and sun movement trajectory. The indoor environment data includes indoor light distribution, current position and posture information of furniture and spatial distribution of obstacles. The processing and calculation module is used to establish a time series prediction model based on the sunshine data to generate the sunshine change trajectory for the target period, and to determine the target placement coordinate set of furniture by combining the indoor environment data and using an optimization algorithm. The projection control module is used to establish an indoor projection area coordinate system, map the solar radiation change trajectory and furniture target placement coordinate set to generate a composite visual reference graphic, and control the projection display device to project it onto the indoor surface to form a visual placement outline. The path planning module is used to plan a movement path based on the furniture target placement coordinate set and the spatial distribution of obstacles, and generate a set of driving instructions; The drive execution module is used to convert the drive instruction set into drive signals to control the electric walking parts of the furniture to move along the planned path; The status monitoring module is used to periodically collect the current position and posture information of the furniture during the furniture movement process, and compare it with the visible placement outline to generate a displacement error signal; The decision control module is used to jointly analyze the displacement error signal and the solar radiation change trajectory, and correct the drive command set and movement path based on the analysis results until the furniture reaches the target position.

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