Indoor component intelligent configuration method and device, equipment and storage medium

Abstract

This invention provides a method, device, equipment, and storage medium for intelligent configuration of indoor components. The method includes: calculating initial position parameters of components through matrix transformation in a pre-constructed three-dimensional coordinate system; constructing a tree-shaped data structure based on component combination relationships and performing attribute inheritance propagation to obtain inherited attribute parameters; establishing a parameter-dependent directed acyclic graph based on the initial position parameters and inherited attribute parameters, and performing parameter linkage propagation calculation to obtain linkage update parameters; performing bounding box collision detection and multi-objective optimization calculation on the linkage update parameters to obtain optimal component configuration parameters that satisfy the constraints. This invention achieves intelligent and precise configuration of indoor components through parameterized matrix transformation, tree-structured attribute inheritance, directed acyclic graph parameter linkage, and automated conflict optimization.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, device, and storage medium for intelligent configuration of indoor components. Background Technology

[0002] Interior design is a complex engineering field involving the precise configuration of various components, requiring comprehensive consideration of factors such as spatial layout, component dimensions, material properties, and cost control. With the rapid development of the decoration industry and the increasing demand for personalized customization, traditional manual design methods can no longer meet the requirements for high-efficiency and high-precision configuration.

[0003] Currently, static template-based component configuration systems are widely used in interior design. These systems require manual, item-by-item configuration of components. Designers must manually calculate the position parameters of each component, manually set component attributes, and manually adjust the relationships between components. When a component's parameters need to be modified, related components cannot be automatically updated; designers must check and adjust all associated components one by one. This manual operation makes the configuration process cumbersome, time-consuming, and prone to errors, severely restricting design efficiency and configuration accuracy. Summary of the Invention

[0004] The main objective of this invention is to solve the technical problem that existing indoor component configuration methods lack intelligent parameter calculation and automatic linkage update mechanisms, resulting in low configuration efficiency and high error rate. This invention provides a method for intelligent configuration of indoor components, the method comprising: In a pre-constructed three-dimensional coordinate system, the initial position parameters of the component are calculated through matrix transformation; A tree-like data structure is constructed based on the component combination relationship, and attribute inheritance propagation is performed based on the tree-like data structure to obtain the inherited attribute parameters of the components; Based on the initial position parameters and the inherited attribute parameters, a directed acyclic graph of parameter dependencies between components is established to perform parameter linkage propagation calculations and obtain the linkage update parameters of the components. The linked update parameters are subjected to bounding box collision detection and multi-objective optimization calculation to obtain the optimal component configuration parameters that satisfy the constraints.

[0005] The present invention also provides an intelligent configuration device for indoor components, the intelligent configuration device for indoor components comprising: The position calculation unit is used to calculate the initial position parameters of the component in a pre-constructed three-dimensional coordinate system through matrix transformation; The attribute propagation unit is used to construct a tree data structure based on the component combination relationship, and to perform attribute inheritance propagation based on the tree data structure to obtain the inherited attribute parameters of the components. The parameter linkage unit is used to establish a directed acyclic graph of parameter dependencies between components based on the initial position parameters and the inherited attribute parameters, so as to perform parameter linkage propagation calculation and obtain the linkage update parameters of the components. The optimization calculation unit is used to perform bounding box collision detection and multi-objective optimization calculation on the linked update parameters to obtain the optimal component configuration parameters that satisfy the constraints.

[0006] The present invention also provides an intelligent configuration device for indoor components, comprising: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a line; the at least one processor invokes the instructions in the memory to cause the intelligent configuration device for indoor components to perform the steps of the above-described intelligent configuration method for indoor components.

[0007] The present invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the steps of the above-described intelligent configuration method for indoor components.

[0008] The aforementioned intelligent configuration method, device, equipment, and storage medium for indoor components calculate the initial position parameters of the components through matrix transformation in a pre-constructed three-dimensional coordinate system; construct a tree-shaped data structure based on the component combination relationship, and obtain inherited attribute parameters through attribute inheritance propagation; establish a parameter-dependent directed acyclic graph based on the initial position parameters and inherited attribute parameters, and obtain linked update parameters through parameter linkage propagation calculation; perform bounding box collision detection and multi-objective optimization calculation on the linked update parameters to obtain the optimal component configuration parameters that satisfy the constraints. This invention achieves intelligent and precise configuration of indoor components through parameterized matrix transformation, tree-structured attribute inheritance, directed acyclic graph parameter linkage, and automated conflict optimization.

[0009] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0010] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the first embodiment of the intelligent configuration method for indoor components in this invention; Figure 2 This is a schematic diagram of a second embodiment of the intelligent configuration method for indoor components in this invention; Figure 3 This is a schematic diagram of the third embodiment of the intelligent configuration method for indoor components in this invention; Figure 4 This is a schematic diagram of one embodiment of the intelligent configuration device for indoor components in this invention; Figure 5 This is a schematic diagram of one embodiment of the intelligent configuration device for indoor components in this invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0014] To facilitate understanding of this embodiment, a detailed description of an intelligent configuration method for indoor components disclosed in this invention will be provided first. For example... Figure 1 As shown, this method includes the following steps: 101. Calculate the initial position parameters of the component in a pre-constructed three-dimensional coordinate system through matrix transformation; In this embodiment, the three-dimensional coordinate system is an LGBT space reference coordinate system; the calculation of the initial position parameters of the component in the pre-constructed three-dimensional coordinate system by matrix transformation includes: in the pre-constructed LGBT space reference coordinate system, determining boundary parameters including the left boundary, right boundary, bottom boundary, and top boundary based on the room boundary dimensions; setting the relative position coordinates and boundary offset of the component relative to the boundary parameters based on the input space dimension parameters; converting the relative position coordinates and boundary offset into absolute position coordinates through a matrix transformation formula; and determining the initial position parameters of the component in three-dimensional space based on the absolute position coordinates.

[0015] Specifically, the LGBT spatial reference coordinate system in this embodiment adopts a relative positioning method based on the room boundaries, which differs from traditional absolute coordinate devices. The device first determines four boundary parameters in the LGBT coordinate system based on the spatial dimension parameters such as room length L, width W, and height H input by the user: Left, Right, Bottom, and Top. These boundary parameters constitute the reference framework for component positioning.

[0016] After determining the boundary parameters, the device needs to set the relative position coordinates of the components with respect to each boundary. It's important to note that relative position coordinates represent the relative distance between the component and the room boundaries, while boundary offsets are used to adjust the safe distance between the component and the boundaries, ensuring that the component does not adhere tightly to the wall or other boundary structures. For example, for wall-mounted cabinet components, the device will set their horizontal offset relative to the left and right boundaries and their vertical offset relative to the top boundary. The setting of boundary offsets takes into account practical engineering requirements such as construction errors, thermal expansion and contraction, and subsequent maintenance space.

[0017] In this embodiment, the specific form of the matrix transformation formula is as follows: ; in, , This is the boundary offset. , Using relative coordinates, this matrix transformation allows the device to convert the relative position coordinates and boundary offsets of components into absolute position coordinates Xabs, Yabs, and Zabs in three-dimensional space.

[0018] During matrix transformation calculations, the relative position coordinate vector [Xrel, Yrel, 1]T is first multiplied by the transformation matrix to obtain the basic transformation result. The transformation matrix is ​​designed to fully consider the characteristics of the LGBT coordinate system, maintaining the orthogonality of the coordinate system through an identity matrix while introducing offset parameters to achieve flexible position adjustments. The device then adds the basic transformation result to the component size offset vector to ultimately determine the precise position of the component in three-dimensional space. This matrix transformation method based on the LGBT coordinate system not only ensures the accuracy and consistency of component position calculations but also adapts to variations in room sizes and component types, providing reliable positional data for subsequent attribute inheritance and parameter linkage calculations.

[0019] 102. Construct a tree data structure based on the component combination relationship, and perform attribute inheritance propagation based on the tree data structure to obtain the inherited attribute parameters of the components; In this embodiment, the step of constructing a tree data structure based on the component combination relationship and performing attribute inheritance propagation based on the tree data structure to obtain the inherited attribute parameters of the components includes: establishing a tree data structure based on the component combination relationship, wherein the tree data structure includes a root node, child nodes, and connection relationships between nodes; determining whether each child node satisfies the attribute inheritance condition; if it satisfies the attribute inheritance condition, setting the corresponding attribute inheritance coefficient; if it does not satisfy the condition, using an explicit setting value; determining the attribute propagation path based on the connection relationship; for child nodes that satisfy the attribute inheritance condition, calculating and propagating the attribute parameters of the root node according to the attribute inheritance coefficient; for child nodes that do not satisfy the attribute inheritance condition, calculating according to the corresponding explicit setting value to obtain the inherited attribute parameters of each component.

[0020] Specifically, the tree-like data structure in this embodiment is represented by the mathematical model T=(V,E,r), where V is the set of component nodes, E is the set of parent-child relationship edges, and r is the root node. The device decomposes complex decoration scenarios into hierarchical component structures based on the component combination relationships set by the user. For example, in cabinet assembly design, the root node can be the overall cabinet assembly, child nodes include main components such as base cabinet units and wall cabinet units, while accessories such as corner connectors and handles serve as deeper-level child nodes.

[0021] After establishing the tree-like data structure, the device needs to determine the connections between nodes. These connections not only represent the physical assembly relationships of components but, more importantly, define the paths for attribute propagation. It should be noted that determining the attribute inheritance condition is a crucial step in the entire propagation process. The device determines the Boolean value of `inherit(j)` by analyzing the material compatibility, functional relevance, and process requirements of the components. When `inherit(j) = true`, it indicates that child node `j` can inherit attributes from its parent node; when `inherit(j) = false`, a preset explicit setting value is used.

[0022] The attribute inheritance propagation in this embodiment follows the following mathematical model: ; Where Aj is the inheritance coefficient, Aparent(j) is the parent node attribute, λj is the attribute inheritance coefficient, and Aexplicit(j) is the explicit setting value. The attribute inheritance coefficient λj typically ranges from [0,1] and is used to control the strength of attribute propagation. When λj = 1.0, it indicates complete inheritance of the parent node attribute; when λj < 1.0, it indicates partial inheritance or adjusted inheritance.

[0023] During attribute propagation calculations, a depth-first traversal algorithm can be used to propagate attribute parameters layer by layer downwards along the connection relationships, starting from the root node. For child nodes that meet the attribute inheritance conditions, the device multiplies the attribute parameter of the parent node by the corresponding inheritance coefficient to obtain the inherited attribute value. For example, when the root node's material attribute is "solid wood particleboard" and λj = 1.0, the child node's material attribute is also "solid wood particleboard". For child nodes that do not meet the attribute inheritance conditions, the device directly uses preset explicit settings, such as setting corner connectors to "metal connector" material to meet specific structural strength requirements.

[0024] An attribute conflict detection mechanism is also considered during attribute propagation. When a child node receives attribute propagation from multiple parent nodes simultaneously, the device resolves the conflict based on node priority and attribute importance. Furthermore, the device supports attribute overriding, allowing users to manually set attribute values ​​at specific nodes, overriding inherited attributes. The time complexity of the entire attribute inheritance propagation process is O(n), where n is the total number of nodes in the tree structure, ensuring the device's computational efficiency when handling large-scale component combinations.

[0025] 103. Based on the initial position parameters and the inherited attribute parameters, establish a directed acyclic graph of parameter dependencies between components to perform parameter linkage propagation calculations and obtain the linkage update parameters of the components; In this embodiment, the step of establishing a directed acyclic graph of parameter dependencies between components based on the initial position parameters and the inherited attribute parameters to perform parameter linkage propagation calculations and obtain the linkage update parameters of the components includes: determining the parameter dependency relationship between components based on the initial position parameters and the inherited attribute parameters, and establishing a directed acyclic graph of parameter dependencies; receiving user input to modify component parameters, determining the associated component parameters affected by the modified input based on the directed acyclic graph of parameter dependencies; calculating the change value of the associated component parameters according to the parameter dependency relationship, and performing linkage update on the corresponding component parameters to obtain the linkage update parameters of the components.

[0026] Specifically, in this embodiment, the parameter-dependent directed acyclic graph is represented by a graph theory model G=(V,E), where V is the set of parameter nodes and E is the set of dependency edges. Based on the initial position parameters and inherited attribute parameters obtained in the preceding steps, the device analyzes the geometric constraints, functional coupling relationships, and process dependencies between components to construct a complete parameter dependency network. For example, the width parameter of a wall cabinet may depend on the net width of the room, while the number of cabinet doors depends on the width of the cabinet, forming a multi-level parameter dependency chain.

[0027] When constructing a directed acyclic graph (DAG) with parameter dependencies, the device needs to ensure the graph's acyclicity to avoid computational deadlock caused by cyclic dependencies between parameters. The device verifies the graph's validity using a topology sorting algorithm and automatically resolves potential loops or prompts the user to adjust the design. It should be noted that parameter dependencies include not only direct numerical dependencies but also conditional and functional dependencies, enabling the device to handle complex design constraints.

[0028] When a user modifies a component parameter, the device first identifies the parameter's position in the directed acyclic graph (DAG), and then traverses all affected downstream parameter nodes using a breadth-first search algorithm. The device employs a parameter linkage propagation model to calculate the changes in related parameters; the mathematical expression of this model is: ; Where path(i,j) represents the dependency path from parameter Pi to parameter Pj. Pk / Pk-1 represents the dependency coefficient between adjacent parameters.

[0029] During the parameter propagation calculation process, the device employs an incremental update strategy, recalculating only the affected parameters to avoid performance overhead from global recalculation. The device maintains a parameter change queue and processes parameter updates sequentially according to the topological order of dependencies. For each parameter requiring an update, the device selects the appropriate calculation method based on its dependency type: linear dependencies use proportional calculations, while non-linear dependencies invoke predefined function models.

[0030] The device also implements a constraint checking mechanism for parameter changes, ensuring that the updated parameter values ​​still meet design constraints. When the updated result of a parameter exceeds the constraint range, the device will automatically adjust the relevant parameters or prompt the user to modify the design scheme. For example, if the user increases the width of the wall cabinet, causing the number of door panels to exceed the structural load-bearing capacity, the device may automatically adjust the door panel thickness or suggest using a stronger support structure.

[0031] The entire parameter linkage propagation process supports batch operations and undo / redo functionality. The device records the historical trajectory of each parameter modification, allowing users to revert to any historical state. Simultaneously, the device provides parameter impact analysis, enabling users to preview the potential impact of a parameter modification on the entire design scheme, helping them make more informed design decisions.

[0032] 104. Perform bounding box collision detection and multi-objective optimization calculations on the linked update parameters to obtain the optimal component configuration parameters that satisfy the constraints.

[0033] In this embodiment, the bounding box collision detection and multi-objective optimization calculation of the linkage update parameters to obtain the optimal component configuration parameters that meet the constraint conditions include: generating bounding boxes for each component according to the linkage update parameters, and determining whether there is a spatial conflict between components through a collision detection algorithm; converting the spatial conflict detection result into a spatial constraint condition, and forming a complete set of constraint conditions together with the preset size constraint, load-bearing constraint and volume constraint; extracting the length, width and height of the components in the linkage update parameters as optimization variables, and substituting them into a multi-objective optimization function including material cost, processing cost and space utilization rate; determining the value range of each optimization variable according to the set of constraint conditions, establishing the feasible region of the optimization problem, and using a multi-objective optimization algorithm to perform iterative calculations within the feasible region to search for the parameter combination that minimizes the objective function value, so as to obtain the optimal component configuration parameters that meet all constraint conditions and have the minimum objective function value.

[0034] Specifically, in this embodiment, the bounding box collision detection adopts the AABB (axis-aligned bounding box) algorithm to generate the minimum circumscribed rectangle bounding box for each component. According to the component position and size information calculated by the device based on the linkage update parameters, for components and , their bounding boxes are respectively . The determination condition for collision detection is: ; where k represents the coordinate axis, k ∈ {x, y, z} corresponding to the X-axis, Y-axis and Z-axis respectively; ki1 and ki2 represent the minimum and maximum values of component Ci on the k-axis, and kj1 and kj2 represent the minimum and maximum values of component Cj on the k-axis. This formula adopts a simplified representation method, which is equivalent to simultaneously satisfying the overlapping conditions on three coordinate axes: when k = x, xi1 < xj2 and xj1 < xi2; when k = y, yi1 < yj2 and yj1 < yi2; when k = z, zi1 < zj2 and zj1 < zi2.

[0035] When it is detected that there is a spatial conflict between components, the device converts the conflict information into a spatial constraint condition, requiring that the minimum distance between component bounding boxes is greater than the preset safety distance. These spatial constraint conditions and the size constraint, load-bearing constraint and volume constraint preset by the device together constitute a complete set of constraint conditions.

[0036] In the multi-objective optimization calculation, the device establishes the following optimization function: ; where α and β are weight coefficients, and Cmaterial and Cprocess are the material cost and processing cost respectively. The constraint conditions include: ; The length L, width W, and height H of the component are extracted from the linked update parameters as optimization variables. These variables directly affect material usage and processing complexity. The material cost Cmaterial is usually proportional to the component volume, while the processing cost Cprocess is related to the component's surface area and the complexity of the processing technology. The weighting coefficients α and β need to be adjusted according to the specific project's cost structure and design requirements.

[0037] When establishing the feasible region of the optimization problem, the device comprehensively considers the intersection of all constraints. Dimensional constraints ensure that the component dimensions are within a reasonable range, load-bearing constraints guarantee structural safety, volume constraints meet functional requirements, and spatial constraints avoid component conflicts. The device employs a constraint propagation algorithm to reduce the feasible region and improve the efficiency of optimization computation.

[0038] This embodiment employs a multi-objective genetic algorithm for optimization, which can handle multiple conflicting objective functions. The algorithm maintains a solution population and searches for the optimal solution within the feasible region through operations such as selection, crossover, and mutation. The convergence condition is set as follows: the fitness change of the population is less than a preset threshold for several consecutive generations, or the maximum number of iterations is reached.

[0039] During the optimization process, the device also implements a constraint violation repair mechanism. When a candidate solution violates a constraint, the device uses a constraint repair strategy to adjust it to the feasible region. For example, when the component size exceeds the limit, the device automatically trims it to the constraint boundary; when a spatial conflict is detected, the device eliminates the conflict by fine-tuning the component position.

[0040] Ultimately, the device outputs a Pareto optimal solution set that satisfies all constraints and minimizes the objective function value. Users can then select the most suitable configuration from this set based on their specific needs. The device also provides sensitivity analysis capabilities to help users understand the degree of influence of each parameter on the objective function.

[0041] The optimal component configuration parameters obtained through the above optimization calculations are the core output of the entire intelligent configuration device. These parameters directly guide the actual decoration construction and component manufacturing process. Specifically, the optimal component configuration parameters include the precise dimensions, material properties, location coordinates, and assembly relationships of each component, providing a complete technical basis for subsequent production, logistics, and on-site installation.

[0042] It's important to note that these optimized parameters not only ensure the technical feasibility of the design but also achieve an optimal balance between cost control and space utilization. For example, in kitchen cabinet configuration, the optimal parameters output by the device might specify a base cabinet length of 2400mm, a height of 850mm, and a depth of 600mm, made of particleboard, while also determining the precise dimensions for matching with wall cabinets and countertops. These parameters comprehensively consider user needs, space constraints, structural safety, and cost limitations.

[0043] In practical applications, optimal component configuration parameters can be directly imported into CAD devices to generate detailed construction drawings, or transmitted to intelligent manufacturing devices to drive automated production lines. For example, sheet metal cutting equipment can automatically adjust its cutting program based on optimized dimensional parameters, reducing material waste and improving processing accuracy. Simultaneously, these parameters can also be used to generate accurate bills of materials and cost budgets, helping users make more informed renovation decisions.

[0044] In this embodiment, the initial position parameters of the components are calculated through matrix transformation in a pre-constructed three-dimensional coordinate system; a tree-shaped data structure is constructed based on the component combination relationship, and inherited attribute parameters are obtained through attribute inheritance propagation; a parameter-dependent directed acyclic graph is established based on the initial position parameters and inherited attribute parameters, and linked update parameters are obtained through parameter linkage propagation calculation; bounding box collision detection and multi-objective optimization calculations are performed on the linked update parameters to obtain the optimal component configuration parameters that satisfy the constraints. This invention achieves intelligent and precise configuration of indoor components through parameterized matrix transformation, tree-structured attribute inheritance, directed acyclic graph parameter linkage, and automated conflict optimization.

[0045] Please see Figure 2 The second embodiment of the intelligent configuration method for indoor components in this application includes: 201. Calculate the initial position parameters of the component in a pre-constructed three-dimensional coordinate system through matrix transformation; 202. Construct a tree data structure based on the component combination relationship, and perform attribute inheritance propagation based on the tree data structure to obtain the inherited attribute parameters of the components; 203. Based on the initial position parameters and the inherited attribute parameters, establish a directed acyclic graph of parameter dependencies between components to perform parameter linkage propagation calculations and obtain the linkage update parameters of the components; 204. Perform bounding box collision detection and multi-objective optimization calculations on the linked update parameters to obtain the optimal component configuration parameters that satisfy the constraints; In this embodiment, steps 201-204 are similar to steps 101-104 in the first embodiment, and will not be described again here.

[0046] 205. Determine whether the optimal component configuration parameters meet user needs or performance requirements. If not, trigger the component replacement process. In this embodiment, the device performs a comprehensive requirement matching verification of the optimal component configuration parameters. This verification process covers multiple dimensions, including functional requirements, performance indicators, and user preferences. The device first establishes a user requirement assessment model, quantifying the user's functional requirements, aesthetic preferences, cost budget, and special performance requirements into comparable indicator parameters. For example, in cabinet configuration, users may have specific requirements regarding storage capacity, material quality, color scheme, and hardware brand.

[0047] Specifically, the device employs a multi-level demand matching algorithm to evaluate the optimal configuration parameters. Functional requirement verification includes basic functional indicators such as whether the storage space meets minimum requirements, whether the operating area conforms to ergonomic standards, and whether ventilation and heat dissipation meet safety standards. Performance requirement verification involves technical indicators such as load-bearing capacity, durability, moisture resistance, and environmental protection level. User preference matching calculates the matching score between the current configuration scheme and the user's expectations using a preset preference weight matrix.

[0048] It should be noted that the mechanism for triggering component replacement employs a threshold control strategy. The device sets corresponding satisfaction thresholds for different types of requirements. When the matching degree of a key requirement falls below a preset threshold, the device automatically triggers the component replacement process. For example, when the material grade matching degree is below 80%, or the cost exceeds the budget by more than 15%, the device will initiate a replacement program to find a more suitable component solution. Furthermore, the device also supports user-initiated replacement processes. Users can specify the type of component to be replaced or performance requirements through the interactive interface, and the device will then enter an intelligent search and recommendation mode.

[0049] 206. Calculate the feature vector of the component based on the optimal component configuration parameters, and search for compatible components in the component library using a similarity matching algorithm; In this embodiment, the device establishes a component feature vector extraction model, converting multi-dimensional component attribute information into standardized numerical vector representations. The feature vectors contain parameters across multiple dimensions, including geometric features, material features, functional features, and manufacturing process features. Geometric features encompass spatial attributes such as the component's length, width, height, volume, and key node coordinates; material features include physical attributes such as material type, density, strength, and surface treatment processes; functional features describe performance parameters such as the component's load-bearing capacity, service life, and applicable environment; and manufacturing process features reflect manufacturing and usage-related attributes such as processing difficulty, installation complexity, and maintenance requirements.

[0050] Specifically, the feature vectors are constructed using a weighted normalization method to ensure that parameters with different dimensions can be effectively compared within the same vector space. The device assigns a corresponding weight coefficient to each feature dimension, with important features receiving higher weight values. For example, when replacing door panel components, size compatibility and material matching may receive higher weights, while aesthetic attributes such as color and texture have relatively lower weights.

[0051] During the component library search process, the device employs a similarity matching algorithm to calculate the degree of matching between the target component and candidate components in the library. The search results are ranked based on the similarity distance value, with priority given to recommending compatible components with the highest similarity. Simultaneously, the device implements a multi-constraint filtering mechanism to ensure that the recommended components are not only similar in features but also meet practical application requirements such as spatial constraints, cost constraints, and process constraints.

[0052] 207. Verify the version compatibility of the searched compatible components. If the verification is successful, map the optimal component configuration parameters to the updated component parameters of the compatible components using an affine transformation matrix. In this embodiment, the version compatibility verification process is based on the similarity distance value calculated in step 206. The device compares the similarity distance value of each candidate component with a preset compatibility threshold θ. The judgment criterion is: if d(v1,v2) ≤ θ, the candidate component is considered compatible with the original component version and can be replaced; if the similarity distance value exceeds the threshold, it is determined that the version is incompatible and the component is not suitable as a replacement. The setting of the compatibility threshold θ needs to comprehensively consider factors such as component type, application scenario, and quality requirements, and is usually determined through historical data analysis and expert experience.

[0053] After the component passes version compatibility verification, the device initiates the affine transformation parameter mapping process. The mathematical model of the affine transformation matrix is: ; Where A is the transformation matrix, b is the offset vector, Pold is the original component parameter vector, and Pnew is the new component parameter vector after mapping. The device solves for the optimal transformation parameters using the least squares method: ; Specifically, the process of solving the transformation matrix A and the offset vector b considers the different characteristics of the component parameters. For geometric parameters, the device uses linear transformations to maintain proportional relationships; for material and functional parameters, the device establishes an attribute mapping table to ensure the correct correspondence of characteristics. The affine transformation process also includes a constraint verification step, where the device checks whether the mapped parameters meet the design constraints. If a constraint violation is found, the device automatically adjusts the transformation parameters or prompts the user to modify the design scheme. After the transformation is completed, the device generates a complete set of updated component parameters, providing accurate input data for subsequent linkage propagation calculations.

[0054] 208. Substitute the updated component parameters back into the parameter-dependent directed acyclic graph for linkage propagation calculation, update the configuration parameters of the relevant components, and obtain new optimal component configuration parameters.

[0055] In this embodiment, the linkage propagation calculation for updating component parameters adopts an incremental update strategy based on a directed acyclic graph. The device first locates the parameter node corresponding to the replaced component in the parameter dependency graph G=(V,E), and analyzes the dependency network of this node using a depth-first search algorithm to identify all downstream nodes that may be affected by parameter changes. The device establishes an impact propagation path graph to determine the specific order and scope of the linkage calculation, avoiding any omission of relevant parameter updates.

[0056] Each parameter node requiring updating is processed sequentially according to the topological sorting order of parameter dependencies. For direct dependencies, the device uses a linear propagation model to quickly calculate parameter changes; for complex indirect dependencies, the device calculates step by step through a multi-level propagation chain to ensure the accuracy and completeness of propagation.

[0057] During the propagation process, the device implements an intelligent conflict detection mechanism. When parameter changes lead to spatial conflicts, dimensional mismatches, or performance failures between components, the device automatically initiates a conflict resolution procedure. Resolution strategies include fine-tuning relevant parameters, suggesting alternative solutions, or triggering further component replacements. After the propagation calculation is completed, the device re-executes bounding box collision detection and multi-objective optimization calculations to ensure that the updated overall solution still meets all design constraints and optimization objectives, ultimately generating new optimal component configuration parameters.

[0058] In this embodiment, the initial position parameters of the components are calculated through matrix transformation in a pre-constructed three-dimensional coordinate system; a tree-shaped data structure is constructed based on the component combination relationship, and inherited attribute parameters are obtained through attribute inheritance propagation; a parameter-dependent directed acyclic graph is established based on the initial position parameters and inherited attribute parameters, and linked update parameters are obtained through parameter linkage propagation calculation; bounding box collision detection and multi-objective optimization calculations are performed on the linked update parameters to obtain the optimal component configuration parameters that satisfy the constraints. This invention achieves intelligent and precise configuration of indoor components through parameterized matrix transformation, tree-structured attribute inheritance, directed acyclic graph parameter linkage, and automated conflict optimization.

[0059] Please see Figure 3 The third embodiment of the intelligent configuration method for indoor components in this application includes: 301. Calculate the initial position parameters of the component in a pre-constructed three-dimensional coordinate system through matrix transformation; 302. Construct a tree-shaped data structure based on the component combination relationship, and perform attribute inheritance propagation based on the tree-shaped data structure to obtain the inherited attribute parameters of the components; 303. Based on the initial position parameters and the inherited attribute parameters, establish a directed acyclic graph of parameter dependencies between components to perform parameter linkage propagation calculations and obtain the linkage update parameters of the components; 304. Perform bounding box collision detection and multi-objective optimization calculations on the linked update parameters to obtain the optimal component configuration parameters that satisfy the constraints; In this embodiment, steps 301-304 are similar to steps 101-104 in the first embodiment, and will not be described again here.

[0060] 305. Set the perspective projection transformation matrix and orthographic projection transformation matrix according to the optimal component configuration parameters; In this embodiment, the mathematical foundation for multi-view display is established based on a projection transformation mathematical model. Accurate mapping from three-dimensional space to a two-dimensional screen is achieved by constructing different types of projection transformation matrices. The perspective projection transformation matrix is ​​used to generate realistic three-dimensional stereoscopic views, and its mathematical expression is: ; Where n is the near clipping plane distance, f is the far clipping plane distance, and l, r, t, and b are the left, right, top, and bottom boundary parameters of the view frustum, respectively. Based on the spatial dimensions and component distribution in the optimal component configuration parameters, appropriate view frustum parameters are automatically calculated to ensure that all components are correctly displayed in the view.

[0061] Orthogonal projection transformation matrices are used to generate two-dimensional plan views, including engineering drawings such as plan views and elevation views. The form of an orthogonal projection transformation matrix is: ; Specifically, the projection parameters are dynamically adjusted based on the spatial range of the component configuration and the user's observation needs. For perspective projection, the optimal frustum setting is calculated considering the user's viewpoint position and viewing direction; for orthographic projection, an appropriate projection range is set based on the maximum bounding box size of the components to ensure that all components are fully displayed in the view. It also supports multiple preset standard view modes, such as front view, side view, and top view, allowing users to quickly switch to different viewing angles. The projection matrix setting process also considers the resolution and aspect ratio of the display device to ensure optimal visual effects on different display devices.

[0062] 306. Generate a three-dimensional solid view using the perspective projection transformation matrix, generate a two-dimensional planar view using the orthographic projection transformation matrix, and construct an event propagation diagram between the views; In this embodiment, the view generation process employs graphics rendering pipeline technology. It takes the geometric information, material properties, and position data from the optimal component configuration parameters as input, and calculates the screen coordinates and display attributes of the components in each view using a projection transformation matrix. The three-dimensional solid view generated by perspective projection transformation can realistically reflect the spatial relationships and perspective effects of the components, allowing users to intuitively understand the spatial layout of the overall design scheme. The two-dimensional planar view generated by orthographic projection transformation provides accurate dimensional information and displays according to engineering drawing standards, facilitating precise design adjustments and dimensional verification by users.

[0063] The event propagation graph is constructed using a directed graph model G=(V,E) to represent the linkage relationships between views. Here, V is the set of view nodes, including different view nodes such as the 3D main view, plan view, and elevation view; E is the set of propagation edges, representing the parameter synchronization relationships between views. A corresponding propagation delay time is assigned to each propagation edge, and the propagation delay calculation formula is: ; Where tij is the propagation time from view vi to view vj. The basic propagation delay is set according to the computational complexity and rendering requirements of different views. For example, the propagation delay from a 3D view to a floor plan may be 5ms, and the propagation delay from a floor plan to an elevation view may be 8ms.

[0064] Specifically, the event propagation graph also includes a view synchronization transformation model to handle coordinate system transformations and parameter mappings between different views. It establishes transformation matrices between views, such as the rotation transformation matrix from the front view to the side view, and the projection transformation matrix from 3D space to a 2D plane. The event propagation graph supports parallel processing mechanisms; when a view changes, update events can be propagated to multiple related views simultaneously, improving overall response efficiency.

[0065] 307. Receive user input on interactive operations of components in any view, calculate the linkage changes and synchronization update delays of corresponding components in other views through event propagation graph, and synchronously update the display content of all views within a preset response time threshold to achieve real-time interactive linkage between multiple views.

[0066] In this embodiment, the interactive input processing adopts an event-driven mechanism, supporting various types of user interaction operations, including component selection, position adjustment, size modification, and material change. When a user performs an interactive operation in a view, the operation object and operation type are first identified, and then the operation is converted into corresponding parameter changes. For example, when a user drags a component in a 3D view, the change in the component's position is calculated and converted into a modification of coordinate parameters.

[0067] The event propagation graph is used to calculate the linked changes in other views. The propagation process follows the shortest path algorithm to find the optimal propagation path from the operated view to all other views. For each view that needs to be updated, the corresponding parameter transformations and display updates are calculated. The calculation of linked changes takes into account the characteristics of different views. For example, a rotation operation in a 3D view may correspond to a position movement in a plan view, and a size adjustment in a plan view may correspond to a height change in an elevation view.

[0068] The synchronization update latency is calculated using a queuing theory model for performance analysis. The arrival rate is set to λ = 10 times / second (user operation frequency), the service rate to μ = 60 times / second (60fps rendering), and the utilization rate to ρ = λ / μ = 10 / 60 = 0.167. The average response time T = 1 / (μ-λ) = 1 / (60-10) = 1 / 50 = 0.02 seconds = 20ms. The queuing waiting time Wq = ρ² / (1-ρ)·1 / μ = 0.167² / (1-0.167)·1 / 60 = 0.00056 seconds ≈ 0.56ms.

[0069] Specifically, a preset response time threshold of 100ms is set to ensure a smooth user experience. When the calculated total response time Ttotal=T+Wq=20+0.56=20.56ms is less than the threshold, the normal synchronous update process is executed. A priority scheduling mechanism is also implemented, giving the highest update priority to the main view currently being operated by the user to ensure immediate response to the main interaction, and then updating other views in sequence according to the event propagation graph.

[0070] In this embodiment, the initial position parameters of the components are calculated through matrix transformation in a pre-constructed three-dimensional coordinate system; a tree-like data structure is constructed based on the component combination relationship, and inherited attribute parameters are obtained through attribute inheritance propagation; a parameter-dependent directed acyclic graph is established based on the initial position parameters and inherited attribute parameters, and linked update parameters are obtained through parameter linkage propagation calculation; bounding box collision detection and multi-objective optimization calculations are performed on the linked update parameters to obtain the optimal component configuration parameters that satisfy the constraints. This invention achieves intelligent and precise configuration of indoor components through parameterized matrix transformation, tree-like attribute inheritance, directed acyclic graph parameter linkage, and automated conflict optimization.

[0071] The above describes the intelligent configuration method for indoor components in the embodiments of the present invention. The following describes the intelligent configuration device for indoor components in the embodiments of the present invention. Please refer to [link to relevant documentation] for details on this intelligent configuration device. Figure 3 One embodiment of the intelligent configuration device for indoor components in this invention includes: The position calculation unit 401 is used to calculate the initial position parameters of the component in a pre-constructed three-dimensional coordinate system through matrix transformation; The attribute propagation unit 402 is used to construct a tree data structure based on the component combination relationship, and to perform attribute inheritance propagation based on the tree data structure to obtain the inherited attribute parameters of the component. The parameter linkage unit 403 is used to establish a directed acyclic graph of parameter dependencies between components based on the initial position parameters and the inherited attribute parameters, so as to perform parameter linkage propagation calculation and obtain the linkage update parameters of the components. The optimization calculation unit 404 is used to perform bounding box collision detection and multi-objective optimization calculation on the linked update parameters to obtain the optimal component configuration parameters that meet the constraints.

[0072] In this embodiment of the invention, the intelligent configuration device for indoor components operates the aforementioned intelligent configuration method for indoor components. The device calculates the initial position parameters of the components through matrix transformation in a pre-constructed three-dimensional coordinate system; constructs a tree-like data structure based on the component combination relationship, and performs attribute inheritance propagation to obtain inherited attribute parameters; establishes a parameter-dependent directed acyclic graph based on the initial position parameters and inherited attribute parameters, and performs parameter linkage propagation calculation to obtain linkage update parameters; and performs bounding box collision detection and multi-objective optimization calculation on the linkage update parameters to obtain the optimal component configuration parameters that satisfy the constraints. This invention achieves intelligent and precise configuration of indoor components through parameterized matrix transformation, tree-like attribute inheritance, directed acyclic graph parameter linkage, and automated conflict optimization.

[0073] above Figure 4The intelligent configuration device for indoor components in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The intelligent configuration device for indoor components in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0074] Figure 5 This is a schematic diagram of the structure of an intelligent configuration device for indoor components provided in an embodiment of the present invention. The intelligent configuration device 500 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) for storing application programs 533 or data 532. The memory 520 and storage media 530 can be temporary or persistent storage. The program stored in the storage media 530 may include one or more units (not shown in the diagram), each unit may include a series of instruction operations on the intelligent configuration device 500. Furthermore, the processor 510 may be configured to communicate with the storage media 530 and execute the series of instruction operations in the storage media 530 on the intelligent configuration device 500 to implement the steps of the aforementioned intelligent configuration method for indoor components.

[0075] The indoor component intelligent configuration device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input / output interfaces 560, and / or one or more operating systems 531, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 5 The illustrated intelligent configuration device structure for indoor components does not constitute a limitation on the intelligent configuration device for indoor components provided by this invention. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0076] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the intelligent configuration method for indoor components.

[0077] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0078] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent configuration of indoor components, characterized in that, The intelligent configuration method for indoor components includes: In a pre-constructed three-dimensional coordinate system, the initial position parameters of the component are calculated through matrix transformation; A tree-like data structure is constructed based on the component combination relationship, and attribute inheritance propagation is performed based on the tree-like data structure to obtain the inherited attribute parameters of the components; Based on the initial position parameters and the inherited attribute parameters, a directed acyclic graph of parameter dependencies between components is established to perform parameter linkage propagation calculations and obtain the linkage update parameters of the components. The linked update parameters are subjected to bounding box collision detection and multi-objective optimization calculation to obtain the optimal component configuration parameters that satisfy the constraints.

2. The intelligent configuration method for indoor components according to claim 1, characterized in that, The three-dimensional coordinate system is the LGBT space reference coordinate system; The calculation of the initial position parameters of the component in the pre-constructed three-dimensional coordinate system through matrix transformation includes: In a pre-built LGBT space reference coordinate system, boundary parameters including the left, right, bottom, and top boundaries are determined based on the room boundary dimensions. The relative position coordinates and boundary offset of the component with respect to the boundary parameters are set according to the input spatial dimension parameters; The relative position coordinates and boundary offsets are converted into absolute position coordinates using a matrix transformation formula; The initial position parameters of the component in three-dimensional space are determined based on the absolute position coordinates.

3. The intelligent configuration method for indoor components according to claim 1, characterized in that, The step of constructing a tree-like data structure based on the component combination relationship, and performing attribute inheritance propagation based on the tree-like data structure to obtain the inherited attribute parameters of the components includes: A tree-like data structure is established based on the component combination relationship. The tree-like data structure includes a root node, child nodes, and the connection relationship between nodes. Determine whether each child node meets the attribute inheritance conditions. If the conditions are met, set the corresponding attribute inheritance coefficient; otherwise, use an explicit value. Based on the connection relationship, the attribute propagation path is determined. For child nodes that meet the attribute inheritance conditions, the attribute parameters of the root node are calculated and propagated according to the attribute inheritance coefficient. For child nodes that do not meet the attribute inheritance conditions, the inherited attribute parameters of each component are calculated according to the corresponding explicit set value.

4. The intelligent configuration method for indoor components according to claim 1, characterized in that, The method of establishing a directed acyclic graph of parameter dependencies between components based on the initial position parameters and the inherited attribute parameters, and performing parameter linkage propagation calculations to obtain the linkage update parameters of the components, includes: The parameter dependencies between components are determined based on the initial position parameters and the inherited attribute parameters, and a directed acyclic graph of parameter dependencies is established. Receive user input to modify component parameters, and determine the associated component parameters affected by the modified input based on the parameter-dependent directed acyclic graph; The change values ​​of the associated component parameters are calculated based on the parameter dependency relationship, and the corresponding component parameters are updated in a coordinated manner to obtain the coordinated update parameters of the components.

5. The intelligent configuration method for indoor components according to claim 1, characterized in that, The process of performing bounding box collision detection and multi-objective optimization calculations on the linked update parameters to obtain the optimal component configuration parameters that satisfy the constraints includes: Based on the aforementioned linkage update parameters, bounding boxes are generated for each component, and a collision detection algorithm is used to determine whether there are spatial conflicts between components. The spatial conflict detection results are converted into spatial constraints, which, together with preset size constraints, load-bearing constraints, and volume constraints, form a complete set of constraints. Extract the component length, width, and height from the linked update parameters as optimization variables, and substitute them into a multi-objective optimization function that includes material cost, processing cost, and space utilization. The range of values ​​for each optimization variable is determined based on the set of constraints, the feasible region of the optimization problem is established, and a multi-objective optimization algorithm is used to perform iterative calculations within the feasible region to search for the parameter combination that minimizes the objective function value, thereby obtaining the optimal component configuration parameters that satisfy all constraints and minimize the objective function value.

6. The intelligent configuration method for indoor components according to claim 5, characterized in that, After performing bounding box collision detection and multi-objective optimization calculations on the linked update parameters to obtain the optimal component configuration parameters that satisfy the constraints, the method further includes: Determine whether the optimal component configuration parameters meet user needs or performance requirements; if not, trigger the component replacement process. The feature vector of the component is calculated based on the optimal component configuration parameters, and a similarity matching algorithm is used to search for compatible components in the component library; The searched compatible components are verified for version compatibility. If the verification is successful, the optimal component configuration parameters are mapped to the updated component parameters of the compatible components using an affine transformation matrix. The updated component parameters are then substituted back into the parameter-dependent directed acyclic graph for linkage propagation calculation, and the configuration parameters of the relevant components are updated to obtain new optimal component configuration parameters.

7. The intelligent configuration method for indoor components according to claim 1, characterized in that, After performing bounding box collision detection and multi-objective optimization calculations on the linked update parameters to obtain the optimal component configuration parameters that satisfy the constraints, the method further includes: Set the perspective projection transformation matrix and orthographic projection transformation matrix according to the optimal component configuration parameters; A three-dimensional solid view is generated using the perspective projection transformation matrix, and a two-dimensional planar view is generated using the orthographic projection transformation matrix. An event propagation graph is then constructed between the views. It receives user input on interactive operations of components in any view, calculates the linkage changes and synchronization update delays of corresponding components in other views through an event propagation graph, and synchronously updates the display content of all views within a preset response time threshold, thereby realizing real-time interactive linkage between multiple views.

8. An intelligent configuration device for indoor components, characterized in that, The intelligent configuration device for indoor components includes: The position calculation unit is used to calculate the initial position parameters of the component in a pre-constructed three-dimensional coordinate system through matrix transformation; The attribute propagation unit is used to construct a tree data structure based on the component combination relationship, and to perform attribute inheritance propagation based on the tree data structure to obtain the inherited attribute parameters of the components. The parameter linkage unit is used to establish a directed acyclic graph of parameter dependencies between components based on the initial position parameters and the inherited attribute parameters, so as to perform parameter linkage propagation calculation and obtain the linkage update parameters of the components. The optimization calculation unit is used to perform bounding box collision detection and multi-objective optimization calculation on the linked update parameters to obtain the optimal component configuration parameters that satisfy the constraints.

9. An intelligent configuration device for indoor components, characterized in that, The intelligent configuration device for indoor components includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the intelligent configuration device for indoor components to perform the steps of the intelligent configuration method for indoor components as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instruction is executed by the processor, it implements the steps of the intelligent configuration method for indoor components as described in any one of claims 1-7.

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