CN116702298A

CN116702298A - Model construction method and system for interior decoration design

Info

Publication number: CN116702298A
Application number: CN202310952402.0A
Authority: CN
Inventors: 周志胜
Original assignee: All House Premium Technology Shenzhen Co ltd
Current assignee: All House Premium Technology Shenzhen Co ltd
Priority date: 2023-08-01
Filing date: 2023-08-01
Publication date: 2023-09-05
Anticipated expiration: 2043-08-01
Also published as: CN116702298B

Abstract

The invention relates to the technical field of model design, and discloses a model construction method and a system for indoor decoration design, which are used for realizing on-line processing of the indoor decoration design and improving the efficiency of the indoor decoration design. Comprising the following steps: acquiring building parameter information and classifying data to obtain parameter identification data and identifying key points to obtain a structure key point set; performing topology structure analysis on the target house, generating a geometric topology structure of the target house, and performing geometric structure generation to obtain a target geometric structure; constructing an initial model to obtain an initial house model, and acquiring indoor space information; performing decoration element matching on the indoor space information to generate a decoration element set; performing decoration scheme matching to generate a target decoration scheme; performing element screening on the decoration element set to generate a target element set, and performing spatial position matching on the target element set to generate a spatial position set; and carrying out parameter adjustment on the initial house model to generate a target house model.

Description

Model construction method and system for interior decoration design

Technical Field

The invention relates to the technical field of model design, in particular to a model construction method and system for interior decoration design.

Background

In the field of interior decoration design, it has become an emerging trend to realize decoration of a target house by applying a related technique. By combining technical means such as building parameter information, house structure key point identification, geometric topological structure analysis, decoration element matching and the like, a more intelligent, efficient and personalized indoor decoration design scheme can be realized.

However, there are still some disadvantages in the prior art. Obtaining building parameter information for a target house may require detailed measurements and investigation involving a number of parameters such as dimensions, materials, structures, etc. Efficient data classification and storage of these parameters is a complex task that requires comprehensive consideration of data structure and standardization issues. The identification of building structure keypoints is an important step in achieving geometric topology analysis. However, existing identification methods may suffer from certain errors or limitations, particularly in the case of complex building structures or irregular shapes, where accuracy is to be improved.

Disclosure of Invention

The invention provides a model construction method and a system for indoor decoration design, which are used for realizing on-line processing of the indoor decoration design and improving the efficiency of the indoor decoration design.

The first aspect of the invention provides a model construction method for interior decoration design, which comprises the following steps:

building parameter information of a target house is obtained, and the building parameter information is subjected to data classification storage to obtain various types of parameter identification data;

performing house structure key point identification on the multiple types of parameter identification data to obtain a structure key point set of the target house;

performing geometric topological structure analysis on the target house through the structure key point set to generate a geometric topological structure corresponding to the target house, and performing geometric structure generation on the target house through the geometric topological structure to obtain a target geometric structure corresponding to the target house;

constructing an initial model of the target house through the target geometric structure to obtain a corresponding initial house model, importing the initial house model to a preset platform, and simultaneously, acquiring indoor space information of the initial house model in the platform;

performing decoration element matching on the indoor space information to generate a decoration element set in the platform;

collecting target decoration parameters and target decoration types of target users, and performing decoration scheme matching on the target decoration parameters and the target decoration types to generate a target decoration scheme;

Performing element screening on the decoration element set through the target decoration scheme to generate a target element set, and performing spatial position matching on the target element set to generate a corresponding spatial position set;

and carrying out parameter adjustment on the initial house model through the space position set based on the space position set and the indoor space information to generate a target house model.

With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the acquiring building parameter information of the target building, and performing data classification storage on the building parameter information to obtain multiple types of parameter identification data includes:

building parameter information of the target house is obtained, size parameter extraction is carried out on the building parameter information, and size parameter data are generated;

extracting material parameters from the building parameter information to generate material parameter data;

extracting structural parameters from the building parameter information to generate structural parameter data;

and respectively carrying out parameter identification generation on the size parameter data, the material parameter data and the structure parameter data, generating size parameter identification data, material parameter identification data and structure parameter identification data, and taking the size parameter identification data, the material parameter identification data and the structure parameter identification data as the parameter identification data of various types.

With reference to the first implementation manner of the first aspect, in a second implementation manner of the first aspect of the present invention, the performing house structure key point recognition on the multiple types of parameter identification data to obtain a structure key point set of the target house includes:

performing data comparison on the size parameter identification data based on standard size information, and determining corresponding critical size data;

performing key material position analysis on the material parameter identification data through the key size data to determine the key material position;

and extracting house structure key points from the structure parameter identification data through the key material positions to generate a structure key point set of the target house.

With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, the performing, by using the structure key point set, geometric topology analysis on the target building, to generate a geometric topology corresponding to the target building, and performing, by using the geometric topology, geometric structure generation on the target building, to obtain a target geometric structure corresponding to the target building, includes:

extracting connection relations of each structure key point in the structure key point set to generate a connection relation set;

Analyzing the position data of each structural key point to generate corresponding key point position data;

performing geometric topology analysis on the key point position data through the connection relation set to generate a geometric topology structure corresponding to the target house;

generating house profile data of the target house through the geometric topological structure, and determining house profile information;

and generating the geometric structure of the target house through the house contour information to obtain a target geometric structure corresponding to the target house.

With reference to the first aspect, in a fourth implementation manner of the first aspect of the present invention, the collecting the target decoration parameter and the target decoration type of the target user, and performing decoration scheme matching on the target decoration parameter and the target decoration type, to generate a target decoration scheme includes:

collecting target decoration parameters and target decoration types of the target users;

performing vector conversion on the target decoration parameters to generate a corresponding parameter vector set, and simultaneously performing feature extraction on the target decoration types to generate corresponding decoration type feature vectors;

vector fusion is carried out on the parameter vector set and the decoration type feature vector, and a corresponding target fusion vector is generated;

And inputting the target fusion vector into a preset decoration scheme analysis model to carry out decoration scheme analysis, and generating a corresponding target decoration scheme.

With reference to the first aspect, in a fifth implementation manner of the first aspect of the present invention, the performing element screening on the decoration element set by using the target decoration scheme to generate a target element set, and performing spatial location matching on the target element set to generate a corresponding spatial location set, includes:

extracting keywords from the target decoration scheme to generate a plurality of decoration scheme keywords;

generating word vectors for the plurality of decoration scheme keywords to obtain a plurality of target word vectors;

performing data encoding on each decoration element in the decoration element set to generate decoration element encoding data;

performing similarity calculation on the decorative element coding data through each target word vector to obtain a plurality of similarity data;

based on a preset similarity threshold value, element screening is carried out on the decoration element set through a plurality of similarity data, and a target element set is generated;

and performing spatial position matching on the target element set to generate a corresponding spatial position set.

With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, the generating, based on the set of spatial positions and the indoor space information, the target house model by performing parameter adjustment on the initial house model by using the set of spatial positions includes:

performing element rotation angle analysis on the target element set through the space position set, and determining rotation angle data corresponding to each target element;

carrying out positive direction calibration on the indoor space information through rotation angle data corresponding to each target element to generate a corresponding target positive direction;

and based on the target positive direction, carrying out parameter adjustment on the initial house model through the indoor space information and the space position set to generate a target house model.

A second aspect of the present invention provides a model building system of an interior decoration design, the model building system of an interior decoration design comprising:

the acquisition module is used for acquiring building parameter information of a target house, and carrying out data classification storage on the building parameter information to obtain various types of parameter identification data;

the identification module is used for identifying the house structure key points of the plurality of types of parameter identification data to obtain a structure key point set of the target house;

The analysis module is used for carrying out geometric topological structure analysis on the target house through the structure key point set, generating a geometric topological structure corresponding to the target house, and carrying out geometric structure generation on the target house through the geometric topological structure, so as to obtain a target geometric structure corresponding to the target house;

the importing module is used for constructing an initial model of the target house through the target geometric structure to obtain a corresponding initial house model, importing the initial house model to a preset platform, and acquiring indoor space information of the initial house model in the platform;

the matching module is used for matching the decoration elements of the indoor space information to generate a decoration element set in the platform;

the generating module is used for collecting target decoration parameters and target decoration types of target users, and carrying out decoration scheme matching on the target decoration parameters and the target decoration types to generate a target decoration scheme;

the screening module is used for carrying out element screening on the decoration element set through the target decoration scheme to generate a target element set, and carrying out space position matching on the target element set to generate a corresponding space position set;

And the adjusting module is used for carrying out parameter adjustment on the initial house model through the space position set based on the space position set and the indoor space information to generate a target house model.

A third aspect of the present invention provides a model building apparatus for interior decoration design, comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the model building device of the interior decoration design to perform the model building method of the interior decoration design described above.

A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein, which when run on a computer, cause the computer to perform the model building method of interior decoration design described above.

In the technical scheme provided by the invention, building parameter information of a target house is obtained, and the building parameter information is subjected to data classification storage to obtain various types of parameter identification data; carrying out house structure key point identification on the parameter identification data of various types to obtain a structure key point set of the target house; performing geometric topological structure analysis on the target house through the structure key point set to generate a geometric topological structure corresponding to the target house, and performing geometric structure generation on the target house through the geometric topological structure to obtain a target geometric structure corresponding to the target house; constructing an initial model of a target house through a target geometric structure to obtain a corresponding initial house model, guiding the initial house model into a preset platform, and simultaneously obtaining indoor space information of the initial house model in the platform; performing decoration element matching on the indoor space information to generate a decoration element set in the platform; collecting target decoration parameters and target decoration types of target users, and performing decoration scheme matching on the target decoration parameters and the target decoration types to generate a target decoration scheme; performing element screening on the decoration element set through a target decoration scheme to generate a target element set, and performing spatial position matching on the target element set to generate a corresponding spatial position set; and carrying out parameter adjustment on the initial house model through the space position set based on the space position set and the indoor space information, and generating the target house model. By carrying out data classification storage on the building parameter information of the target house, effective management and retrieval of various types of parameter identification data can be realized. The organization and accessibility of the data can be improved, and the time cost of data processing and inquiry can be reduced. By performing house structure key point identification on various types of parameter identification data, a structure key point set of a target house can be accurately determined. The method is beneficial to accurately describing the geometric structure characteristics of the house and provides an accurate basis for the subsequent analysis of geometric topological structures. By performing a geometric topology analysis on the set of structural keypoints, the geometric topology of the target house can be generated. The method is beneficial to accurately describing the spatial layout and the relation of the house and provides accurate basis for the subsequent geometric structure generation. By means of decoration element matching and target decoration scheme generation, a personalized decoration scheme can be generated according to decoration parameters and decoration types of target users. Helping to meet the specific needs and preferences of users and providing a customized interior decoration design experience.

Drawings

FIG. 1 is a schematic diagram of an embodiment of a model construction method of an interior decoration design according to an embodiment of the present invention;

FIG. 2 is a flowchart of performing house structure key point identification on multiple types of parameter identification data in an embodiment of the present invention;

FIG. 3 is a flow chart of a geometric topology analysis of a target house through a set of structural keypoints in an embodiment of the invention;

FIG. 4 is a flowchart of matching a decoration scheme for a target decoration parameter and a target decoration type according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of one embodiment of a model building system for interior decoration design in an embodiment of the present invention;

FIG. 6 is a schematic diagram of an embodiment of a model building apparatus for interior decoration design in an embodiment of the present invention.

Detailed Description

The embodiment of the invention provides a model construction method and a system for indoor decoration design, which are used for realizing intelligent battery state monitoring and improving the accuracy of battery life prediction. The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.

For ease of understanding, a specific flow of an embodiment of the present invention is described below with reference to fig. 1, and an embodiment of a method for constructing a model of an interior decoration design according to the embodiment of the present invention includes:

s101, acquiring building parameter information of a target house, and performing data classification storage on the building parameter information to obtain multiple types of parameter identification data;

it will be appreciated that the execution subject of the present invention may be a model building system designed for interior decoration, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.

Specifically, the server first obtains the building parameter information of the target house through measurement, drawing or user input and other modes. Such information includes data on the size, material, and structure of the house. For example, the server obtains building parameter information of a house, including a length of 10 meters, a width of 8 meters, a height of 3 meters, walls are made of brick materials, floors are made of wood floors, ceilings are made of gypsum boards, the house is a two-story building, and stairs are connected. Next, the size parameter is extracted from the acquired construction parameter information. The server extracts data relating to the dimensions of the length, width, height of the house, the area of each room, the thickness of the wall, etc. For example, the house has a length of 10 meters, a width of 8 meters, a height of 3 meters, an area of room A of 20 square meters, an area of room B of 15 square meters, and a wall thickness of 20 centimeters. And then, extracting the material parameters of the building parameter information. The server identifies descriptive words related to materials, such as bricks, wood floors, plasterboards and the like, from the information, and extracts corresponding material parameter data. For example, the wall is made of brick materials, the floor is made of wood floor materials, and the ceiling is made of gypsum board materials. Then, the construction parameter information is subjected to structural parameter extraction. The server analyzes the structure type, floor layout, connection mode and the like of the house and extracts the structure parameter data of the house. For example, the house is a two-storey building, with stairs connected, and a partition wall between the room A and the room B. Then, corresponding parameter identification data is generated for the size parameter data, the material parameter data and the structure parameter data. A unique identifier is generated for each parameter for subsequent data classification and management. For example, an abbreviation of House ID plus parameter type is used as an identification, such as "hous001_length" represents the Length of House001, and "hous001_material_wall" represents the Wall Material of House 001. And finally, storing the size parameter identification data, the material parameter identification data and the structure parameter identification data as various types of parameter identification data. These data can be used in a subsequent interior decoration design process for rapid and accurate data matching and processing.

S102, carrying out house structure key point identification on various types of parameter identification data to obtain a structure key point set of a target house;

specifically, the server performs data comparison on the size parameter identification data based on the standard size information, and determines critical size data. The standard size information can be preset and can be flexibly adjusted according to design requirements. The server identifies data associated with the critical dimension by comparing with the standard dimension information. For example, if the total Length is defined as 10 meters and the total Width is 8 meters in the standard size information, the server finds the size parameter identification data corresponding to these standard sizes, such as "Hous001_Length" and "Hous001_Width", by comparison. And carrying out key material position analysis on the material parameter identification data through the key size data, and determining the key material position. Using the critical dimension data, the server determines a critical location associated with the texture parameter. For example, assuming that there is an important Wall at the central location of the House Length, the server determines the Material parameter identification data corresponding to the Wall, such as "Hous001_Material_wall", by identifying the critical dimension data "Hous001_Length". And extracting house structure key points from the structure parameter identification data through the key material positions to generate a structure key point set of the target house. And according to the determined key material position, the server extracts parameter identification data related to the house structure. If the key material position determines an important Wall, the server obtains the structural key point corresponding to the Wall by identifying structural parameter identification data corresponding to the material position, such as 'Hous001_Structure_wall'. For example, assume that the server has building parameter information for a house. The dimension parameters comprise length, width and height, the material parameters comprise wall materials and floor materials, and the structural parameters comprise wall structures, beam column structures and the like. The standard size information set by the server is 12 meters in total length and 10 meters in total width. By comparing the size parameter identification data with the standard size information, the server determines the critical size data "Hous001_Length" and "Hous001_Width". Next, the texture parameter identification data is analyzed by the critical dimension data server, assuming that the server determines the critical texture location as a Wall located in the center of the House length, i.e., "Hous001_Material_wall". Finally, based on the key texture location, the server extracts structural parameter identification data, such as "Hous001_Structure_Wall". And identifying and extracting structural key points of the house by comparing standard size information and analyzing key material positions. The collection of these key points will provide valuable information for the subsequent design process, helping the server to do interior decoration design, space layout, etc.

S103, performing geometric topological structure analysis on the target house through the structure key point set to generate a geometric topological structure corresponding to the target house, and performing geometric structure generation on the target house through the geometric topological structure to obtain a target geometric structure corresponding to the target house;

specifically, the server first has a set of structural keypoints for the target house, where each structural keypoint represents a significant location or feature in the house. These key points may be corner points, intersection points, etc. of the house. The server will perform connection relation extraction and location data analysis on these key points. And extracting the connection relation of each structure key point in the structure key point set to generate a connection relation set. The server determines the connection relationship between the structural key points by analyzing the relative positions and the connection modes between the structural key points. These connections may be straight line segments, curved line segments, or other geometric connections. For example, if there are three key points A, B and C, the server determines that there is a straight line connection between a and B, and a curved line connection between B and C. And analyzing the position data of each structural key point to generate key point position data. The server obtains specific location data of the keypoints by analyzing the coordinates or relative location information of each structural keypoint. These location data may be two-dimensional or three-dimensional coordinates representing the location of the structural key points in the room space. For example, for the key point a, the server determines its coordinates on the two-dimensional plane as (x 1, y 1). And carrying out geometric topological structure analysis through the connection relation set and the key point position data to generate the geometric topological structure of the target house. And constructing a geometric topological structure of the house by using the connection relation set and the key point position data. This may represent geometric elements of the boundary, wall, roof, etc. of the house by line segments or curve segments connecting the keypoints. By determining the connection relationship and the key point position, the server creates a geometric figure model reflecting the structure and shape of the house. And generating the geometric structure of the target house through house profile information. Using the geometric topology, the server determines profile information for the house. The house profile information may include boundary lines of walls, positions and shapes of doors and windows, etc. This information can be used for further geometry generation to obtain a specific geometry of the target house. For example, assume that the server has a set of structural keypoints for the target house, including four keypoints A, B, C and D. Through the connection relation extraction, the server determines a straight line connection relation between A and B, a curve connection relation between B and C, and a straight line connection relation between C and D. Through the position data analysis, the server determines the coordinates of each key point, such as a for (x 1, y 1), B for (x 2, y 2), and so on. The server generates a geometric topology of the target house including boundary lines and shapes represented by the connection relationships using the connection relationships and the position data. Further, by generating the house profile information, the server obtains the geometry of the target house, such as walls, doors and windows, and the like. And the server acquires specific geometric shape and structure information of the target house through geometric topological structure analysis and geometric structure generation of the target house. The information has important significance in the aspects of building design, model display, space planning and the like, can provide accurate reference and foundation, and ensures the accuracy and feasibility of design and planning.

S104, constructing an initial model of a target house through a target geometric structure to obtain a corresponding initial house model, importing the initial house model to a preset platform, and simultaneously, acquiring indoor space information of the initial house model in the platform;

specifically, the server first builds an initial house model using a Computer Aided Design (CAD) tool or three-dimensional modeling software, based on the target geometry. This can be achieved by creating the basic geometry of the house from the size and shape information of the target geometry. For example, if the target house is a two-storey building with one rectangular plane, the server uses CAD tools to draw the corresponding rectangular plane and add the connection structure between floors. By adjusting the size, shape and scale, the server creates an initial house model that conforms to the target geometry. Next, the initial house model is imported into a preset platform. The platform generally provides the function of importing an external model, and can support a common three-dimensional model format, such as OBJ, FBX, GLTF. The server imports the initial house model into the platform by selecting the appropriate importation options and file formats. Once the initial house model is successfully imported into the platform, the server uses the functionality provided by the platform to obtain indoor space information. The platform typically provides rich editing and customization options for the user to further manipulate the initial house model. Through the visual editing tool of the platform, a user can add and adjust the positions of the dividing walls, windows and doors of a room, arrange furniture and decorations, set light sources and materials and the like. These operations not only allow the user to personalize the interior space of the house, but also achieve a similar indoor layout and style to the actual house. For example, assume that the server has a target house geometry that includes an open living room and kitchen, two bedrooms, and two bathrooms. The server creates a corresponding initial house model, including the main room and the connection channels, from this geometry using CAD tools. The server then imports this model into the platform, sets the room dividing wall, window and door positions using the platform's editing tools, and places furniture and selects the appropriate materials. By operating and adjusting in the platform, the server implements a real initial house model and obtains its indoor space information in the platform, such as the layout of the room, the placement of furniture, and lighting effects.

S105, performing decoration element matching on the indoor space information to generate a decoration element set in the platform;

specifically, first, to achieve matching of decoration elements, the server establishes a library of decoration elements. This library may contain a wide variety of decorative elements such as furniture, lighting, wallpaper, flooring, curtains, etc. Each decorative element should contain information about its associated properties, such as size, color, texture, style, etc. The library of decorative elements may create custom decorative elements by gathering existing decorative element data on the market, or by 3D modeling techniques. Next, the server analyzes and matches the indoor space information. For the indoor space of the target house, the server acquires key information such as the size, layout, functions, etc. of the room. This may be achieved by a measurement tool or user input provided by the platform. By analyzing the structure and characteristics of the indoor space, the server determines the area to be decorated and the corresponding decoration requirements. Based on the decoration element library and the indoor space information, the server performs matching of the decoration elements. This can be achieved by comparing the properties of the decorative element with the requirements of the indoor space. For example, for a living room area, the server selects the appropriate elements such as sofas, coffee tables, carpets, etc., according to its size and style requirements. By matching the size, color, material, etc. of the decorative element, the server ensures that the decorative element is coordinated with the indoor space. Once the matching of the decoration elements is completed, the server generates a collection of decoration elements in the platform. This set contains a 3D model or other representation of decorative elements that match the indoor space. These decorative elements can be arranged, adjusted and previewed in the platform to achieve a simulation of the decorative effect of the interior of the house. For example, assume that the server has room space information for a target house, which includes a restaurant area for dining. The server selects proper dining table, chair, lamp and other elements from the decoration element library, and matches the dining table, chair, lamp and other elements according to the size and style requirements of the restaurant area. For example, the server selects a modern style of dining table and chair that is sized and colored to fit the restaurant area. By matching the decorative elements, the server generates a collection containing the decorative elements, which can be arranged and adjusted in the platform to simulate the decorative effect of the restaurant area.

S106, collecting target decoration parameters and target decoration types of target users, and performing decoration scheme matching on the target decoration parameters and the target decoration types to generate a target decoration scheme;

specifically, the server may first perform data collection by means of user investigation, questionnaire investigation, user feedback, etc. in order to obtain the decoration parameters and types of the target user. The user may provide information on decorative style preferences, color preferences, furniture type requirements, budget constraints, etc. Then, vector conversion is carried out on the collected target decoration parameters. This may be by a specific data processing method converting the user provided parameters into a vector representation. For example, for color preference, different colors may be mapped to the RGB or HSV color space and a color vector constructed. For the size parameters, the size data can be normalized or normalized according to specific requirements to form a size vector. And simultaneously, extracting the characteristics of the target decoration type. This involves converting user-provided type information into feature vectors with differentiation and presentation capabilities. For example, for furniture types, different types of furniture may be represented by one-hot coding or embedded vectors, forming furniture type feature vectors. For decoration style preferences, different styles may be mapped to different style categories and style feature vectors constructed. Then, the parameter vector set and the decoration type feature vector are subjected to vector fusion. This step aims to fuse different types of decoration parameters and types of features into one comprehensive target fusion vector to better represent the decoration needs and preferences of the user. The fusion method can adopt simple splicing, weighted average and other modes, and can also use more complex characteristic fusion models, such as a neural network or a clustering method. And finally, inputting the target fusion vector into a preset decoration scheme analysis model to carry out decoration scheme analysis. This model may be a machine learning based classifier, regressor or generative model, or may be a rule based expert system. The model analyzes the relation between the target fusion vector and different decoration schemes, and generates corresponding target decoration schemes according to the requirements and preferences of users. For example, suppose there is an interior decoration design that a target user wishes to personalize. He provided the following target decoration parameters and target decoration types: warm, comfortable, natural, modern, green plants and wooden furniture. In order to achieve the generation of the target decoration scheme, the server performs the following steps: first, a vector conversion is performed on decoration parameters provided by a target user. Descriptive parameters such as "warm", "comfortable" and "natural" are converted into corresponding vector representations. For example, warmth may be converted to a value representing the degree of warmth, comfort converted to a comfort score, and natural converted to a binary value of the presence or absence of natural elements. And secondly, extracting the characteristics of the target decoration type. For two decoration types, green plants and wooden furniture, the server extracts features related to the two decoration types. For example, characteristics such as plant type, plant number, plant placement position, etc. can be extracted for green plants; the characteristics of furniture type, wood type, furniture placement mode and the like can be extracted for the wooden furniture. Next, the decoration parameter vector and the decoration type feature vector are vector-fused. And combining the converted parameter vector and the extracted feature vector to form a target fusion vector. This vector will comprehensively represent the decoration parameters and type features of the target user for subsequent decoration scheme analysis. And finally, inputting the target fusion vector into a preset decoration scheme analysis model to carry out decoration scheme analysis. This model may be a machine learning based classifier or regressor or may be a rule based expert system. The model analyzes the relation between the target fusion vector and different decoration schemes, and generates a corresponding target decoration scheme according to the requirements and preferences of the user.

S107, performing element screening on the decoration element set through a target decoration scheme to generate a target element set, and performing spatial position matching on the target element set to generate a corresponding spatial position set;

specifically, the server first performs keyword extraction on the target decoration scheme. And extracting keywords or key phrases from the target decoration scheme by text analysis and processing. These keywords may be words describing aspects of decoration style, material, color, theme, etc. Next, word vector generation is performed on a plurality of the decoration scheme keywords. The extracted keywords are converted into the form of Word vectors, which can be mapped into vector space using Word embedding models (e.g., word2Vec, gloVe, etc.). Each decorative element in the set of decorative elements is data encoded. Representing the decorative elements as numerical values or vectors, each decorative element may be converted to corresponding encoded data using feature extraction, image processing, text representation, and the like. And calculating the similarity of the decorative element coding data through each target word vector. And comparing the coded data of each decoration element with the target word vector by using a similarity calculation method (such as cosine similarity and Euclidean distance) to obtain a plurality of similarity data which represent the similarity degree of each decoration element and the target scheme keyword. And based on a preset similarity threshold value, carrying out element screening on the decoration element set through a plurality of similarity data. And selecting decorative elements with similarity higher than the threshold value as target elements according to the set threshold value to form a target element set. These elements have a high degree of matching with the target decoration scheme keywords. And finally, performing spatial position matching on the target element set to generate a corresponding spatial position set. And according to the spatial position information (such as coordinates, size, shape and the like) of the elements, performing spatial position matching on the target element set, and determining the layout and placement positions of the target element set in the indoor space. This may be accomplished by Computer Aided Design (CAD) software, in-house design tools, or virtual reality techniques. For example, assuming that the target decoration scheme is a modern conclusive style, the extracted keywords include "conclusive", "modern", "clear", and the like. The set of decorative elements includes various furniture, wall colors, lights, and the like. After converting the keywords into word vectors, the server obtains a vector representation of each keyword. At the same time, each element in the decorative element set is data-encoded, represented as a corresponding vector or feature. And then, calculating the similarity between the target word vector and the decoration element coding data to obtain the matching degree of each decoration element and the target scheme keyword. And screening out decoration elements highly matched with the target scheme keywords according to a preset similarity threshold value to form a target element set. And finally, carrying out space position matching on the target element sets, and determining the layout and placement positions of the target element sets in the indoor space so as to realize the design requirement of the target decoration scheme.

S108, performing parameter adjustment on the initial house model through the space position set based on the space position set and the indoor space information, and generating a target house model.

Specifically, the server first performs element rotation angle analysis on the target element set through the spatial position set. And analyzing and calculating the element rotation angle according to the layout and the placement position of each element in the target element set in the space position set. And determining rotation angle data corresponding to each target element by considering the relative positions among the elements, the geometric shape of the indoor space and other factors. And then, calibrating the indoor space information in the positive direction through rotation angle data corresponding to each target element. And (3) calibrating the space in the positive direction by utilizing the rotation angle data of the target element and combining the characteristics of the indoor space. The positive orientation calibration may determine the orientation of the room or some reference direction and ensure that subsequent parameter adjustments and layout operations are based on a consistent coordinate system. And based on the target positive direction, carrying out parameter adjustment on the initial house model through the indoor space information and the space position set. And (3) carrying out parameter adjustment on the initial house model by utilizing information such as the forward direction of the target, the size and layout of the indoor space and the like and combining the element layout and the placement requirements in the space position set. This includes fine-tuning the target house by adjusting the size of the room. By adjusting the parameters, the server ensures that the decorative elements are matched with the size, proportion and layout of the indoor space, so that the decorative elements are more vivid and natural. For example, assume that the server has an indoor space in which a table and chairs are contained as target elements. The server has determined their placement and placement locations in the set of spatial locations. Through the element rotation angle analysis, the server calculates the angle that each target element should be rotated so as to make it look more reasonable in the indoor space. Next, by positive orientation calibration, the server determines a standard orientation of the indoor space, such as north. Thus, the server ensures that all decorative elements are placed according to the same orientation, and the consistency of the overall layout is ensured. Finally, the server performs parameter adjustment on the initial house model based on the target forward direction and the indoor space information. The server adjusts the size, shape and position of the room according to the layout requirement of the target elements, so that the room is consistent with the target decoration scheme and the indoor space information. For example, if the target element requires a large bookcase to be placed on one side of the indoor space, the server adjusts the wall position and size of the room to accommodate this bookcase and maintain overall balance.

In the embodiment of the invention, building parameter information of a target house is acquired, and the building parameter information is subjected to data classification storage to obtain various types of parameter identification data; carrying out house structure key point identification on the parameter identification data of various types to obtain a structure key point set of the target house; performing geometric topological structure analysis on the target house through the structure key point set to generate a geometric topological structure corresponding to the target house, and performing geometric structure generation on the target house through the geometric topological structure to obtain a target geometric structure corresponding to the target house; constructing an initial model of a target house through a target geometric structure to obtain a corresponding initial house model, guiding the initial house model into a preset platform, and simultaneously obtaining indoor space information of the initial house model in the platform; performing decoration element matching on the indoor space information to generate a decoration element set in the platform; collecting target decoration parameters and target decoration types of target users, and performing decoration scheme matching on the target decoration parameters and the target decoration types to generate a target decoration scheme; performing element screening on the decoration element set through a target decoration scheme to generate a target element set, and performing spatial position matching on the target element set to generate a corresponding spatial position set; and carrying out parameter adjustment on the initial house model through the space position set based on the space position set and the indoor space information, and generating the target house model. By carrying out data classification storage on the building parameter information of the target house, effective management and retrieval of various types of parameter identification data can be realized. The organization and accessibility of the data can be improved, and the time cost of data processing and inquiry can be reduced. By performing house structure key point identification on various types of parameter identification data, a structure key point set of a target house can be accurately determined. The method is beneficial to accurately describing the geometric structure characteristics of the house and provides an accurate basis for the subsequent analysis of geometric topological structures. By performing a geometric topology analysis on the set of structural keypoints, the geometric topology of the target house can be generated. The method is beneficial to accurately describing the spatial layout and the relation of the house and provides accurate basis for the subsequent geometric structure generation. By means of decoration element matching and target decoration scheme generation, a personalized decoration scheme can be generated according to decoration parameters and decoration types of target users. Helping to meet the specific needs and preferences of users and providing a customized interior decoration design experience.

In a specific embodiment, the process of executing step S101 may specifically include the following steps:

(1) Building parameter information of a target house is obtained, size parameter extraction is carried out on the building parameter information, and size parameter data are generated;

(2) Extracting material parameters of the building parameter information to generate material parameter data;

(3) Extracting structural parameters from the building parameter information to generate structural parameter data;

(4) And respectively carrying out parameter identification generation on the size parameter data, the material parameter data and the structure parameter data, generating size parameter identification data, material parameter identification data and structure parameter identification data, and taking the size parameter identification data, the material parameter identification data and the structure parameter identification data as various types of parameter identification data.

Specifically, the server first acquires building parameter information of the target house. This can be done in a variety of ways, such as measuring the size of an existing building, consulting building drawings, using Building Information Model (BIM) data, and so forth. The building parameter information includes data on the size, material, structure, etc. of the building. Next, size parameter extraction is performed on the construction parameter information. Size-related data such as the length, width, height, thickness of walls, etc. of the room are extracted from the construction parameter information. These data may represent spatial dimensional characteristics of the building. And then, extracting the material parameters of the building parameter information. And extracting data related to the materials from the construction parameter information, such as the materials of floors, paint colors of walls, the materials of windows and the like. These data may represent the material characteristics of the building. Then, the construction parameter information is subjected to structural parameter extraction. Data relating to the structure, such as the number of floors, the locations of the columns, the spans of the beams, etc., are extracted from the building parameter information. These data may represent structural features of the building. And generating parameter identification aiming at the size parameter data, the material parameter data and the structure parameter data. Size parameter identification data, texture parameter identification data, and structure parameter identification data are generated by associating each parameter with a unique identifier. These identifiers may be used to identify and reference parameters during subsequent processing. And finally, taking the dimension parameter identification data, the material parameter identification data and the structure parameter identification data as various types of parameter identification data. These parameter identification data may be used for work at various stages of building design, construction, simulation, analysis, etc. For example, assume that a server is to perform extraction of building parameter information and parameter identification generation for a house. The server first measures the dimensions of the house, including the length, width, height of the room, the size of the doors and windows, etc. Size parameters are extracted from these Size data, and Size parameter identification data such as "room1_length", "window1_size", and the like are generated. The server then looks at the material information of the house, including the material of the floor, the paint color of the wall, the material of the ceiling, etc. Texture parameters are extracted from these texture data, and texture parameter identification data such as "floor_Material", "Wall1_color", and the like are generated. Finally, the server checks the building's structural information including the number of floors, the locations of the columns, the span of the beams, etc. Structural parameters are extracted from these structural data, and structural parameter identification data such as "number_of_flows", "Column1_position", and the like are generated. In this embodiment, the server obtains the building parameter information and extracts the size parameter, the material parameter and the structure parameter. Then, through parameter identification generation, the server generates a unique identifier for each parameter for use in subsequent architectural design and analysis processes. In this way, the server better manages and processes the building parameter data and ensures the accuracy and consistency of the data.

In a specific embodiment, as shown in fig. 2, the process of executing step S102 may specifically include the following steps:

s201, carrying out data comparison on the size parameter identification data based on standard size information, and determining corresponding critical size data;

s202, analyzing the key material positions of the material parameter identification data through the key size data, and determining the key material positions;

s203, extracting house structure key points from the structure parameter identification data through the key material positions, and generating a structure key point set of the target house.

Specifically, the server first creates a standard size information base containing standard size data of various building elements, such as standard window sizes, standard door sizes, and standard room sizes. These standard size data may be collected and consolidated according to industry standards or design specifications. Next, the size parameter identification data is data-compared with a standard size information base. By matching the elements in the dimensional parameter identification data with the data in the standard size information base, the critical dimension data corresponding to each element can be determined. The process of alignment may be matched by element name, identifier or other unique identifying information. For example, assume that one element in the Size parameter identification data is identified as "window1_size" indicating the Size of a certain Window. By comparing the Size parameter identification data with the Standard Size information base, standard Size data matching "window1_size" such as "standard_window_size" is found. Thus, it is determined that the critical dimension data corresponding to "window1_size" is "standard_window_size". After the critical dimension data is determined, the critical texture location analysis may be performed on the texture parameter identification data via the critical dimension data. And analyzing the material information related to the critical dimension in the material parameter identification data according to the critical dimension data. For example, if the critical dimension data specifies a room width of 5 meters, then in the texture parameter identification data, the texture parameter identification associated with the room width may represent the texture of the room walls. By correlation analysis between critical dimension data and texture parameter identification data, the location of the critical texture, i.e., which texture is applied at which locations, can be determined. And finally, extracting house structure key points from the structure parameter identification data through the key material positions to generate a structure key point set of the target house. And analyzing structural information related to the key material position in the structural parameter identification data according to the key material position. For example, if the key material location determines the wall material of a room, key point information associated with the wall, such as a start point, an end point, or a connection point of the wall, etc., in the structural parameter identification data of the room may be extracted. By extracting these keypoints, a set of structural keypoints for the target house can be generated for further analysis and application.

In a specific embodiment, as shown in fig. 3, the process of executing step S103 may specifically include the following steps:

s301, extracting connection relations of each structure key point in the structure key point set to generate a connection relation set;

s302, analyzing position data of each structural key point to generate corresponding key point position data;

s303, analyzing the geometric topological structure of the key point position data through the connection relation set to generate a geometric topological structure corresponding to the target house;

s304, generating house profile data of the target house through the geometric topological structure, and determining house profile information;

s305, generating a geometric structure of the target house through house contour information to obtain a target geometric structure corresponding to the target house.

Specifically, the server first performs connection relation extraction on each structure key point in the structure key point set, and generates a connection relation set. The connection mode and the relation between the structural key points are determined by analyzing the interrelationship between the structural key points. The connection relationship may include different connection forms such as straight line segments, curved line segments, arc line segments, etc. For example, for a building structure, the connection between elements such as walls, stairs, doors and windows of the building can be described by a set of connection relations. And then, carrying out position data analysis on each structural key point to generate corresponding key point position data. By analyzing the position and coordinate information of the structural key points in space, the specific position data of the structural key points are determined. These position data may be two-dimensional coordinates (e.g., positions on a plan view) or three-dimensional coordinates (e.g., positions in space). The keypoint data may be used for subsequent geometric topology analysis and geometry generation. And carrying out geometric topological structure analysis on the key point position data through the connection relation set to generate a geometric topological structure corresponding to the target house. The geometric topology describes the topological relation between the house elements, i.e. how they are connected to each other and combined to form an overall structure. By analyzing the connection relation set and the key point position data, the whole topological structure of the house can be determined, including the main structure, the auxiliary structure, the layout of the internal space and the like of the house. The geometric topology may be represented in the form of a graph or data structure that facilitates subsequent generation of house profile data and geometry generation. And generating house profile data of the target house through the geometric topological structure, and determining house profile information. According to the geometric topological structure and the key point position data, the outline shape and the boundary line of the house can be calculated. The house profile data may be represented in the form of a series of line segments, curves or polygons, etc. that describe the shape and boundaries of the house. And finally, generating a geometric structure of the target house through house contour information to obtain a geometric structure corresponding to the target house. From the house profile data and the geometric topology, specific geometry and structure information of the house can be generated. This includes geometric features of the floor plan, facade form, roof shape, etc. The geometric structure can be used in the fields of building design, engineering planning, visual display and the like, and provides basic data and references for the construction and use of actual buildings. For example, assume that there is a set of structural key points of the target house, including corner points of walls, position points of doors and windows, and start and end points of stairs. And (3) determining the connection relation between the wall and the corner, the connection relation between the door and the wall and the connection relation between the stairs and the wall through connection relation extraction. And then, carrying out position data analysis on each structural key point to acquire two-dimensional coordinates of each structural key point on a plan view. And carrying out geometric topological structure analysis through the connection relation set and the key point position data to obtain the whole topological structure of the house, wherein the whole topological structure comprises the layout of walls, the positions of doors and windows and the relative positions of stairs and the walls. And generating outline data of the house according to the topological structure and the key point position data, and determining the outline boundary of the house. Finally, based on the profile information, the geometry of the target house is generated, including the height of the wall, the shape of the stairs, the size of the doors and windows, etc.

In a specific embodiment, as shown in fig. 4, the process of executing step S106 may specifically include the following steps:

s401, collecting target decoration parameters and target decoration types of target users;

s402, carrying out vector conversion on the target decoration parameters to generate a corresponding parameter vector set, and simultaneously, carrying out feature extraction on the target decoration types to generate corresponding decoration type feature vectors;

s403, vector fusion is carried out on the parameter vector set and the decoration type feature vector, and a corresponding target fusion vector is generated;

s404, inputting the target fusion vector into a preset decoration scheme analysis model to analyze the decoration scheme, and generating a corresponding target decoration scheme.

Specifically, the server first collects a target decoration parameter and a target decoration type of a target user. The target decoration parameters may include specific parameter information such as user preferences for decoration elements, size requirements, color preferences, material requirements, and the like. The target decoration type may include user selection of different decoration styles, themes, or specific elements. Such information may be obtained by means of user surveys, questionnaires, interviews, and the like. Then, vector conversion is carried out on the target decoration parameters, and a corresponding parameter vector set is generated. The collected target decoration parameters are converted into a numerical vector representation so as to facilitate subsequent calculation and analysis. For example, converting color parameters into a vector representation of RGB values, converting size parameters into a vector representation of specific length, width, height, converting texture parameters into a vector representation of texture attributes, and so forth. And simultaneously, extracting the characteristics of the target decoration type to generate a corresponding decoration type characteristic vector. And extracting the characteristics related to the decoration type according to the description of the target decoration type or the label selected by the user. This may include features of a decorative style, features of a theme element, features of a particular pattern or shape, etc. These features are converted into a vector representation to represent a feature vector of the decoration type. And carrying out vector fusion on the parameter vector set and the decoration type feature vector to generate a corresponding target fusion vector. And obtaining a fusion vector representing the decoration requirement of the target user by carrying out fusion or splicing operation on the parameter vector set and the decoration type feature vector. The fusion vector integrates the information of the target decoration parameters and the decoration types, and can be used as the input of the analysis of the subsequent decoration scheme. And finally, inputting the target fusion vector into a preset decoration scheme analysis model to carry out decoration scheme analysis, and generating a corresponding target decoration scheme. According to a preset decoration scheme analysis model, the model can be a model constructed based on machine learning, artificial intelligence and other technologies, and is used for analyzing and matching an input target fusion vector and recommending a proper decoration scheme. The decoration scheme can comprise specific decoration element selection, layout suggestion, color collocation, material recommendation and the like so as to meet the decoration requirements of target users. By way of example, suppose that the target user provides the following decoration parameters and decoration types: the color preference is blue, the size requirement is that a sofa with the length of 3 meters is made of leather; the decoration types are modern style, marble elements and compact lines. First, the color parameters are converted into a vector representation of RGB values, e.g., [0,0,255] representing blue; converting the dimensional parameter into a vector representation of length 3, for example [300,0,0] representing a sofa of 3 meters length; the texture parameters are converted into a vector representation of texture properties, e.g., [1, 0] representing leather texture. Meanwhile, feature extraction is performed on the decoration type, for example, the feature vector of the modern style is [1, 0], the feature vector of the marble element is [0,1,0], and the feature vector of the simple line is [0, 1]. And carrying out vector fusion on the parameter vector set and the decoration type feature vector to obtain a target fusion vector [0,0,255,300,0,0,1,0,0,0,1,0,0,0,1]. And finally, inputting the target fusion vector into a decoration scheme analysis model, and analyzing the target fusion vector and recommending a proper decoration scheme according to the existing decoration scheme database and corresponding rules by the model. According to the output of the model, a living room decoration scheme with a current style can be obtained, including blue-colored furniture, marble decoration elements and simple line designs. This decoration scheme meets the decoration needs and preferences of the target user.

In a specific embodiment, the process of executing step S107 may specifically include the following steps:

(1) Extracting keywords from the target decoration scheme to generate a plurality of decoration scheme keywords;

(2) Generating word vectors for the plurality of decoration scheme keywords to obtain a plurality of target word vectors;

(3) Performing data encoding on each decoration element in the decoration element set to generate decoration element encoding data;

(4) Performing similarity calculation on the decoration element coding data through each target word vector to obtain a plurality of similarity data;

(5) Performing element screening on the decoration element set through a plurality of similarity data based on a preset similarity threshold value to generate a target element set;

(6) And performing spatial position matching on the target element set to generate a corresponding spatial position set.

Specifically, first, keywords are extracted from a target decoration scheme. By applying natural language processing technology, text processing and analysis can be performed on the target decoration scheme, and keywords in the target decoration scheme can be extracted. These keywords may reflect the topic, style, or characteristics of the scheme. Then, a word vector is generated for the extracted keywords. The keyword is converted into a numerical representation using a Word vector model, such as Word2Vec or GloVe, to generate a corresponding Word vector. Each keyword may be represented as a vector for capturing its semantics and relevance. Next, each decorative element in the set of decorative elements is data encoded. Each element in the set of decorative elements is data encoded and converted into a form of feature vector. These feature vectors may contain attributes, features, or descriptive information related to the decorative element. And performing similarity calculation on the decoration element coding data by using the generated target word vector. By calculating the similarity between the target word vector and the decoration element encoded data, the correlation therebetween can be evaluated. Thus, a plurality of similarity data can be obtained, and the matching degree between the decoration elements and the target decoration scheme is reflected. And screening elements of the decoration element set according to a preset similarity threshold. And comparing the similarity data with a preset threshold value, screening out decoration elements matched with the target decoration scheme, and forming a target element set. This set contains decorative elements that are consistent with the target decorative scheme. And finally, performing spatial position matching on the target element set. And matching the target element set with the layout and position requirements of the indoor space, and determining the placement positions of the target element set in the space. This may generate a corresponding set of spatial locations defining a specific location of each decorative element in the indoor space. For example, suppose that there is a target user who wishes to decorate his office with a modern, reduced style. First, the server extracts keywords, such as "modern" and "conciseness," from the target decoration scheme. Next, the server converts these keywords into corresponding word vector representations. For example, "modern" may be converted to a vector containing modern style related features, while "reduced" may be converted to a vector containing features that are reduced, refreshed, etc. Meanwhile, the server prepares a set of decorative elements including various furniture, artwork, lights, etc. The server encodes each decoration element with data, converting its attributes and features into a vector representation. Next, the server performs similarity calculation with the decoration element encoded data using the generated target word vector. For example, the server calculates the similarity between the "modern" word vector and the encoded data for each decorative element, and then obtains a set of similarity data. Based on a preset similarity threshold, the server screens out decoration elements matched with the target decoration scheme. For example, if a certain decorative element has a similarity to a "modern" keyword above a threshold, it is included in the set of target elements. Finally, the server determines the specific location of each decorative element in the office space based on the location requirements of the target element set. This allows for spatial location matching based on office layout, spatial dimensions and functional requirements.

In a specific embodiment, the process of executing step S108 may specifically include the following steps:

(1) Performing element rotation angle analysis on the target element set through the space position set, and determining rotation angle data corresponding to each target element;

(2) Carrying out forward direction calibration on indoor space information through rotation angle data corresponding to each target element to generate a corresponding target forward direction;

(3) And based on the target positive direction, carrying out parameter adjustment on the initial house model through the indoor space information and the space position set to generate a target house model.

Specifically, first, the server has a target element set containing decorative elements or furniture to be disposed in the indoor space. At the same time, the server also needs a set of spatial locations that record the location information of each target element in the indoor space. Next, the server uses the set of spatial locations to perform element rotation angle analysis. The server analyzes the rotation angle of each target element in the indoor space by considering the layout and placement position of the target element in the set of spatial positions. This angle may be determined based on factors such as the relative position between the elements, the geometry of the indoor space, etc. Once the rotation angle data for each target element is determined, the server applies these angles to the indoor space information for positive direction calibration. And the server adjusts the positive direction of the indoor space through the rotation angle data corresponding to each target element, so that the follow-up parameter adjustment and layout operation are ensured to be based on a consistent coordinate system. Based on the target forward direction and the indoor space information, the server performs parameter adjustment on the initial house model by using the space position set to generate a target house model. The process includes adjusting parameters such as position, size, and rotation angle of the house element according to the target positive direction to achieve the final target effect. For example, assume that the server has a target space that includes several decoration elements, such as A, B and C. The server has a set of spatial locations that record the locations of these elements in the target space. First, the server determines rotation angle data corresponding to each decoration element by analyzing the set of spatial positions. Based on the relative positions between the elements and the geometry of the indoor space, the server calculates the rotation angle that positions each element at the desired position. For example, the server finds that element A needs to be rotated 30 degrees counter-clockwise, element B needs to be rotated 45 degrees clockwise, and element C does not need to be rotated. Next, the server performs a forward direction calibration for the indoor space using the rotation angle data. By considering the rotation angle of each element, the server determines the positive direction of the target space. For example, the server determines that the positive direction is the north direction. Based on the forward direction and the spatial position set, the server performs parameter adjustment on the initial spatial model to generate a target spatial model. The server adjusts the position, size and rotation angle of each element so that they meet the target effect. For example, element A may be moved to the east side of the target space, element B placed directly in front of element A, and element C placed to the west side of the target space. In this embodiment, the server performs rotation angle analysis on the target element set through the spatial position set, further performs forward calibration on indoor space information, and finally performs parameter adjustment on the initial space model through the indoor space information and the spatial position set, so as to generate the target space model. This process can be applied to different decoration elements and target spaces, helping the server to implement a personalized decoration scheme.

The method for constructing a model of an interior decoration design in the embodiment of the present invention is described above, and the system for constructing a model of an interior decoration design in the embodiment of the present invention is described below, referring to fig. 5, and one embodiment of the system for constructing a model of an interior decoration design in the embodiment of the present invention includes:

the acquiring module 501 is configured to acquire building parameter information of a target building, and perform data classification storage on the building parameter information to obtain multiple types of parameter identification data;

the identifying module 502 is configured to identify the house structure key points of the multiple types of parameter identification data, so as to obtain a structure key point set of the target house;

the analysis module 503 is configured to perform geometric topology analysis on the target house through the structure key point set, generate a geometric topology structure corresponding to the target house, and perform geometric structure generation on the target house through the geometric topology structure, so as to obtain a target geometric structure corresponding to the target house;

an importing module 504, configured to perform initial model construction on the target house through the target geometry structure, obtain a corresponding initial house model, import the initial house model to a preset platform, and obtain indoor space information of the initial house model in the platform;

The matching module 505 is configured to match the indoor space information with a decoration element, and generate a decoration element set in the platform;

the generating module 506 is configured to collect a target decoration parameter and a target decoration type of a target user, and match a decoration scheme with the target decoration parameter and the target decoration type to generate a target decoration scheme;

the screening module 507 is configured to perform element screening on the decoration element set through the target decoration scheme to generate a target element set, and perform spatial position matching on the target element set to generate a corresponding spatial position set;

and the adjustment module 508 is configured to perform parameter adjustment on the initial house model through the spatial location set based on the spatial location set and the indoor space information, and generate a target house model.

Acquiring building parameter information of a target house through cooperation of the components, and performing data classification storage on the building parameter information to obtain multiple types of parameter identification data; carrying out house structure key point identification on the parameter identification data of various types to obtain a structure key point set of the target house; performing geometric topological structure analysis on the target house through the structure key point set to generate a geometric topological structure corresponding to the target house, and performing geometric structure generation on the target house through the geometric topological structure to obtain a target geometric structure corresponding to the target house; constructing an initial model of a target house through a target geometric structure to obtain a corresponding initial house model, guiding the initial house model into a preset platform, and simultaneously obtaining indoor space information of the initial house model in the platform; performing decoration element matching on the indoor space information to generate a decoration element set in the platform; collecting target decoration parameters and target decoration types of target users, and performing decoration scheme matching on the target decoration parameters and the target decoration types to generate a target decoration scheme; performing element screening on the decoration element set through a target decoration scheme to generate a target element set, and performing spatial position matching on the target element set to generate a corresponding spatial position set; and carrying out parameter adjustment on the initial house model through the space position set based on the space position set and the indoor space information, and generating the target house model. By carrying out data classification storage on the building parameter information of the target house, effective management and retrieval of various types of parameter identification data can be realized. The organization and accessibility of the data can be improved, and the time cost of data processing and inquiry can be reduced. By performing house structure key point identification on various types of parameter identification data, a structure key point set of a target house can be accurately determined. The method is beneficial to accurately describing the geometric structure characteristics of the house and provides an accurate basis for the subsequent analysis of geometric topological structures. By performing a geometric topology analysis on the set of structural keypoints, the geometric topology of the target house can be generated. The method is beneficial to accurately describing the spatial layout and the relation of the house and provides accurate basis for the subsequent geometric structure generation. By means of decoration element matching and target decoration scheme generation, a personalized decoration scheme can be generated according to decoration parameters and decoration types of target users. Helping to meet the specific needs and preferences of users and providing a customized interior decoration design experience.

The model building system of the interior decoration design in the embodiment of the present invention is described in detail above in fig. 5 from the point of view of the modularized functional entity, and the model building apparatus of the interior decoration design in the embodiment of the present invention is described in detail below from the point of view of the hardware processing.

Fig. 6 is a schematic structural diagram of an indoor decoration design model building device 600 according to an embodiment of the present invention, where the indoor decoration design model building device 600 may have a relatively large difference according to a configuration or performance, and may include one or more processors (central processing units, CPU) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) storing application programs 633 or data 632. Wherein the memory 620 and the storage medium 630 may be transitory or persistent storage. The program stored in the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations in the model building apparatus 600 for interior decoration design. Still further, the processor 610 may be configured to communicate with the storage medium 630 to execute a series of instruction operations in the storage medium 630 on the model building device 600 of the upholstery design.

The model building device 600 for upholstery design may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input/output interfaces 660, and/or one or more operating systems 631, such as Windows Server, mac OS X, unix, linux, freeBSD, and the like. It will be appreciated by those skilled in the art that the model building apparatus structure of the upholstery design shown in fig. 6 does not constitute a limitation of the model building apparatus of the upholstery design, and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.

The present invention also provides an indoor decoration design model construction apparatus, which includes a memory and a processor, wherein the memory stores computer readable instructions that, when executed by the processor, cause the processor to execute the steps of the indoor decoration design model construction method in the above embodiments.

The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and may also be a volatile computer readable storage medium, where instructions are stored in the computer readable storage medium, when the instructions are executed on a computer, cause the computer to perform the steps of the method for constructing a model of an interior decoration design.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on the understanding that the technical solution of the present invention may be embodied in essence or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a storage medium, comprising instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random acceS memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of modeling an interior decoration design, the method comprising:

2. The method for constructing an interior decoration design model according to claim 1, wherein the steps of obtaining the building parameter information of the target house, and performing data classification storage on the building parameter information to obtain a plurality of types of parameter identification data, include:

3. The method for constructing an interior decoration design model according to claim 2, wherein the step of performing house structure key point recognition on the plurality of types of parameter identification data to obtain the structure key point set of the target house comprises:

4. The method for constructing an interior decoration model according to claim 1, wherein the performing geometric topology analysis on the target house through the structure key point set to generate a geometric topology corresponding to the target house, and performing geometric structure generation on the target house through the geometric topology to obtain a target geometric structure corresponding to the target house includes:

5. The method for constructing an interior decoration design model according to claim 1, wherein the steps of collecting a target decoration parameter and a target decoration type of a target user, and performing decoration scheme matching on the target decoration parameter and the target decoration type, and generating a target decoration scheme include:

6. The method for constructing an interior decoration design model according to claim 1, wherein the performing element screening on the decoration element set through the target decoration scheme to generate a target element set, and performing spatial position matching on the target element set to generate a corresponding spatial position set includes:

7. The method for constructing an interior decoration design model according to claim 1, wherein the generating a target house model by performing parameter adjustment on the initial house model through the set of spatial positions based on the set of spatial positions and the interior space information comprises:

8. A model building system of an interior decoration design, the model building system of an interior decoration design comprising:

9. A model construction apparatus of an interior decoration design, characterized in that the model construction apparatus of an interior decoration design comprises: a memory and at least one processor, the memory having instructions stored therein;

The at least one processor invokes the instructions in the memory to cause the model building device of the interior decoration design to perform the model building method of the interior decoration design of any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, which when executed by a processor, implement the model building method of an interior decoration design according to any one of claims 1-7.