Home design data management system and method based on cloud platform

Abstract

The invention discloses a home design data management system and method based on a cloud platform, and relates to the technical field of data management. The system comprises a historical project data processing module, a design template construction module, a real-time demand matching module and a design adjustment and optimization module. The historical project data processing module analyzes historical project data and constructs a design resource database; the design template construction module creates design templates based on the design resource database, and generates label feature vectors for each design template; the real-time demand matching module constructs a demand feature vector according to the real-time project demand data, and matches the demand feature vector with a tag feature vector of a design template to obtain a reference design template; and the design adjustment and optimization module analyzes the deviation between the real-time project demand and the reference design template, generates optimization prompt information, assists related personnel in adjusting the design template, and generates a target design scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a home design data management system and method based on a cloud platform. Background Art

[0002] With the increasing diversification of modern home design needs, home designers are faced with a large number of design tasks and workloads at work, especially in the design process of large-scale residential, residential, commercial space and other projects, which often require designers to repeatedly create a large number of similar design elements. Traditional home design methods mainly rely on designers to manually create each design element, model or layout, which not only increases the duplication of design work, but also leads to inefficiency and waste of resources in the design process.

[0003] At present, many home design companies and individual designers still use traditional design tools and methods. Although these tools can complete basic design functions, they generally lack flexible resource sharing and reuse mechanisms. For example, in multiple projects, designers need to design living rooms of different sizes or use similar furniture and accessories in multiple room layouts; the traditional design process requires starting from scratch every time. Even if there are ready-made furniture models or space templates, designers still need to judge which ready-made furniture models or space templates are closest to the current project, and which elements of the selected furniture models or space templates need to be adjusted. Since the design requirements and details of each project are different, this practice leads to a lot of time and energy waste. In the case of multiple projects such as large-scale residential, community and commercial spaces being carried out alternately, the problems of inefficiency and duplication of work are more prominent. Summary of the invention

[0004] The purpose of the present invention is to provide a home design data management system and method based on a cloud platform to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: A home design data management method based on a cloud platform comprises the following steps: Step S100. Acquire historical project data of the home design platform, divide the historical project data according to preset minimum elements, and thus obtain an element set corresponding to the historical project data; classify the elements corresponding to the historical project data according to the corresponding element set, and thus construct a design resource database; Step S200. Based on the design resource database, a number of design templates are created on the home design platform, and a tag set corresponding to the design template is obtained by analyzing the preset minimum elements of each design template, and keywords are extracted from the tag set to construct a corresponding tag feature vector; Step S300. Acquire real-time project demand data, analyze the real-time project demand data, and thus construct a real-time project demand feature vector; analyze the real-time project demand feature vector and the label feature vector corresponding to each design template, match the design template corresponding to the real-time project demand data according to the analysis result, and use the design template corresponding to the real-time project demand data as a reference design template; Step S400. Analyze the deviation value between the real-time project requirement feature vector and the label feature vector corresponding to the reference design template, and generate corresponding prompt information based on the deviation value. Relevant personnel adjust the reference design template based on the prompt information to generate a target design solution.

[0006] Furthermore, step S100 includes: S101. Obtain a number of historical project data from the home design platform, divide each historical project data according to a preset minimum element, and each historical project data is decomposed into a set of element sets Ai, and Ai={ai1,ai2,...,ain}, wherein Ai represents the element set corresponding to the i-th historical project data, i represents the data number of the historical project data, which is a positive integer; ai1 represents the first element of the i-th historical project data, ai2 represents the second element of the i-th historical project data, and so on, ain represents the n-th element of the i-th historical project data; the preset minimum element refers to the most basic unit in the home design project that can be independently identified, analyzed and processed; for example, sofas, beds, tables, chairs, lamps, walls, windows, doors, floors, etc.; S102. Summarize the element set corresponding to all historical project data, and extract the element attributes corresponding to each element in the element set, where the element attribute refers to the feature description corresponding to the preset minimum element; construct the corresponding element attribute feature vector V1 based on the element attributes, and the element attribute feature vector V1=[v11,v12,...,v1u], where u represents the dimension of the element attribute feature vector V1, v11 represents the first dimension feature of the element attribute feature vector V1, v12 represents the second dimension feature of the element attribute feature vector V1, and so on, v1u represents the u-th dimension feature of the element attribute feature vector V1; calculate the similarity between all element attribute feature vectors V1, and use a clustering algorithm to divide all elements into several categories, and each category corresponds to a category label, so as to construct a design resource database.

[0007] Further, step S200 includes: S201. Based on the design resource database, create several design templates on the home design platform, and each design template represents a complete home design plan; obtain the design element set Dj corresponding to each design template, expressed as: Dj={ej1,ej2,...,ejm}, where Dj represents the design element set corresponding to the j-th design template, j represents the data number of the design template; ej1 represents the first design element of the j-th design template, ej2 represents the second design element of the j-th design template, and so on, ejm represents the m-th design element of the j-th design template; the design elements in the design element set correspond to the preset minimum elements in the design resource database; S202. For each design element set Dj corresponding to each design template, obtain the category labels of the design elements in the design resource database, and summarize the category labels of all design elements to form a label set Lj corresponding to the design element set Dj; perform word segmentation and keyword extraction on each category label in the label set Lj, and calculate the weight coefficient W of each category label in the corresponding design template, and the specific calculation formula of the weight coefficient W is: W(kc)=[TF(kc,Lj) / ∑kd∈Lj,TF(kd,Lj)]×IDF(kc); Among them, W(kc) represents the weight coefficient of the category label kc, c represents the data number of the category label in the design resource database; TF(kc,Lj) represents the frequency of the category label kc in the label set Lj; ∑kd∈Lj,TF(kd,Lj) represents the sum of the frequencies of all category labels in the label set Lj; IDF(kc) is the inverse document frequency, which represents the scarcity of the category label kc in all design templates, and the calculation formula is: IDF(kc)=log[N / DF(kc)]; Where N represents the total number of design templates, and DF(kc) represents the number of design templates containing category label kc; S203. According to the weight coefficient of each category label kc in the label set Lj and in combination with the design resource database, a label feature vector V_Lj corresponding to the design template Dj is constructed, and V_Lj=[W(k1),W(k2),...,W(kp)], where W(k1) represents the weight coefficient of category label k1, W(k2) represents the weight coefficient of category label k2, and so on, W(kp) represents the weight coefficient of category label kp, p is equal to the total number of category labels in the design resource database, and p is greater than or equal to m; the order of the category label weight coefficients in the label feature vector V_Lj is according to the order of the category labels in the design resource database. If the label set Lj does not contain a category label in the design resource database, the weight coefficient of this category label is assigned to zero.

[0008] Furthermore, step S300 includes: S301. Acquire real-time project demand data, and extract the preset minimum element and the element attribute corresponding to the preset minimum element from the real-time project demand data according to the analysis method of historical project data, so as to obtain the real-time demand element set B and the real-time demand element attribute feature vector V2 corresponding to the real-time project demand data; perform similarity calculation on the real-time demand element attribute feature vector V2 corresponding to each element in the real-time demand element set B and the element attribute feature vector V1 of each category in the design resource database, and calculate the average similarity of the corresponding category, and select the category label with the largest average similarity as the category label of the corresponding element in the real-time demand element set B; summarize all element category labels in the real-time demand element set B, and refer to the analysis process of the label feature vector corresponding to the design template for all element category labels in the real-time demand element set B, so as to construct the real-time project demand feature vector V_req, and the dimension of the real-time project demand feature vector V_req is equal to p; S302. The real-time project requirement feature vector and the label feature vector corresponding to each design template are represented by a radar chart. The specific analysis process is as follows: Take any point in the two-dimensional plane as the origin of the coordinates, and take the dimension p of the real-time project demand feature vector V_req as the number of number axes. Each number axis starts from the origin of the coordinates, and the angles between each number axis are equal. Mark the eigenvalues ​​corresponding to the real-time project demand feature vector V_req on the number axis in a clockwise order to obtain p data points, and connect the data points of adjacent number axes in turn to form a closed polygon, and calculate the area of ​​the closed polygon S_req. According to the analysis method of the real-time project demand feature vector V_req, the label feature vector V_Lj corresponding to each design template is represented on a two-dimensional plane, and the corresponding closed polygon area S_Lj is calculated; according to the closed polygon areas S_req and S_Lj, the matching index R corresponding to the real-time project demand feature vector and the label feature vector corresponding to each design template is calculated in turn, and the specific calculation formula is: R={(S_req·S_Lj) / [S_req+S_Lj-|S_req-S_Lj|]}·α; Where α represents the shape similarity of the closed polygon corresponding to the real-time project requirement feature vector and the label feature vector corresponding to the design template, and its value ranges from 0 to 1. The matching index R corresponding to each design template is arranged in order from large to small, and the design template with the largest matching index R is selected as the reference design template.

[0009] Furthermore, step S400 includes: The difference between the eigenvalues ​​of the real-time project requirement feature vector V_req and the label feature vector V_Lj corresponding to the reference design template in each dimension is calculated, and the absolute value of the difference calculation result is obtained to obtain the corresponding deviation value Δf; the deviation values ​​Δf corresponding to each dimension are sorted in order from large to small, and a deviation sorting list is generated, and the deviation sorting list is output to relevant personnel as prompt information, and the relevant personnel adjust the reference design template based on the prompt information, and use the adjusted reference design template as the target design solution.

[0010] A home design data management system based on a cloud platform, comprising: a historical project data processing module, a design template building module, a real-time demand matching module, and a design adjustment and optimization module; The historical project data processing module obtains the historical project data of the home design platform, divides the historical project data according to the preset minimum elements, thereby obtaining the element set corresponding to the historical project data; classifies the elements corresponding to the historical project data according to the corresponding element set, thereby constructing a design resource database; The design template construction module creates several design templates on the home design platform based on the design resource database, obtains the label set corresponding to the design template by analyzing the preset minimum elements of each design template, and extracts keywords from the label set to construct the corresponding label feature vector; The real-time demand matching module obtains the real-time project demand data, analyzes the real-time project demand data, and thus constructs a real-time project demand feature vector; analyzes the real-time project demand feature vector with the label feature vector corresponding to each design template, matches the design template corresponding to the real-time project demand data according to the analysis result, and uses the design template corresponding to the real-time project demand data as a reference design template; The design adjustment and optimization module analyzes the deviation value between the real-time project requirement feature vector and the label feature vector corresponding to the reference design template, and generates corresponding prompt information based on the deviation value. Relevant personnel adjust the reference design template based on the prompt information to generate a target design solution.

[0011] Further, the historical project data processing module includes a historical project data acquisition and division unit, an element attribute extraction and analysis unit, and a design resource database construction unit; The historical project data acquisition and division unit acquires multiple historical project data from the home design platform, divides them according to preset minimum elements, and extracts the element set of each historical project data; the element attribute extraction and analysis unit extracts the attributes of each element, and constructs an element attribute feature vector based on these attributes; the design resource database construction unit divides all elements into several categories based on the element attribute feature vector, and each category corresponds to a category label, thereby constructing a design resource database.

[0012] Further, the design template construction module includes a design template creation unit, a label extraction and weight calculation unit, and a label feature vector construction unit; The design template creation unit creates multiple design templates on the platform based on the design resource database, and each design template represents a complete home design plan; the label extraction and weight calculation unit extracts the category labels related to each design template, and calculates the weight coefficient of each category label through keyword extraction and word segmentation processing; the label feature vector construction unit constructs the label feature vector of the design template according to the weight coefficient of each category label.

[0013] Further, the real-time demand matching module includes a real-time demand data processing unit, an element attribute and label matching unit, and a matching design template unit; The real-time demand data processing unit acquires and analyzes the real-time project demand data, extracts the preset minimum elements and their attributes, and constructs a real-time demand feature vector; the element attribute and label matching unit obtains the category label of each element in the real-time demand data by calculating the similarity between the real-time demand element attributes and the element attribute feature vector in the design resource database, and constructs a real-time project demand feature vector based on the category label of the real-time demand element; the matching design template unit compares the real-time project demand feature vector with the label feature vectors of all design templates, calculates the matching index, and selects the design template with the largest matching index as the reference design template.

[0014] Furthermore, the design adjustment and optimization module includes a deviation analysis and prompt generation unit and a target design solution generation unit; The deviation analysis and prompt generation unit analyzes the deviation between the real-time demand feature vector and the label feature vector of the reference design template, generates a deviation sorting list based on the deviation value, and outputs the deviation sorting list as prompt information to relevant personnel; the target design scheme generation unit generates a target design scheme based on the prompt information and the relevant personnel adjust the reference design template.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: by constructing a design resource database and a design template library, the design elements, furniture, accessories, etc. in historical projects can be classified and sorted, and designers do not need to start from scratch every time, and can quickly retrieve and reuse existing design templates and resources; the matching process of real-time project demand data and design templates can automatically provide designers with the most suitable reference design templates, thereby significantly reducing the initial preparation work of the design. In the traditional design process, designers often need to manually judge which ready-made furniture models or space templates best meet the needs of the current project, and also consider the adjustment of the model; in the present invention, through the calculation and analysis of label feature vectors, designers can quickly obtain design templates that match the project requirements, and the system automatically provides matching scores and optimization suggestions, reducing errors and subjective judgments of manual intervention. The present invention makes the matching between real-time project requirements and historical design templates more accurate through a label feature vector construction mechanism based on an element set; through the radar chart analysis between the label feature vector of the design template and the real-time demand feature vector, not only the global matching of multi-dimensional design features is achieved, but also the optimal reference can be provided to designers according to the matching index, avoiding the limitations of relying on the subjective experience of designers in traditional methods. The present invention can generate prompt information based on the deviation value, and guide the designer to accurately adjust the reference design template according to the project requirements; this data-driven adjustment method can provide designers with specific optimization directions, ensure that the design solution can better meet the actual needs, and the design results are more reasonable and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a schematic diagram of a module of a home design data management system based on a cloud platform of the present invention. DETAILED DESCRIPTION

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

[0018] See also Figure 1 , the present invention provides a technical solution: A home design data management system based on a cloud platform, comprising: a historical project data processing module, a design template building module, a real-time demand matching module, and a design adjustment and optimization module; The historical project data processing module obtains the historical project data of the home design platform, divides the historical project data according to the preset minimum elements, thereby obtaining the element set corresponding to the historical project data; classifies the elements corresponding to the historical project data according to the corresponding element set, thereby constructing a design resource database; The design template construction module creates several design templates on the home design platform based on the design resource database, obtains the label set corresponding to the design template by analyzing the preset minimum elements of each design template, and extracts keywords from the label set to construct the corresponding label feature vector; The real-time demand matching module obtains the real-time project demand data, analyzes the real-time project demand data, and thus constructs a real-time project demand feature vector; analyzes the real-time project demand feature vector with the label feature vector corresponding to each design template, matches the design template corresponding to the real-time project demand data according to the analysis result, and uses the design template corresponding to the real-time project demand data as a reference design template; The design adjustment and optimization module analyzes the deviation value between the real-time project requirement feature vector and the label feature vector corresponding to the reference design template, and generates corresponding prompt information based on the deviation value. Relevant personnel adjust the reference design template based on the prompt information to generate a target design solution.

[0019] The historical project data processing module includes a historical project data acquisition and division unit, an element attribute extraction and analysis unit, and a design resource database construction unit; The historical project data acquisition and division unit acquires multiple historical project data from the home design platform, divides them according to preset minimum elements, and extracts the element set of each historical project data; the element attribute extraction and analysis unit extracts the attributes of each element, and constructs an element attribute feature vector based on these attributes; the design resource database construction unit divides all elements into several categories based on the element attribute feature vector, and each category corresponds to a category label, thereby constructing a design resource database.

[0020] The design template construction module includes a design template creation unit, a label extraction and weight calculation unit, and a label feature vector construction unit; The design template creation unit creates multiple design templates on the platform based on the design resource database, and each design template represents a complete home design plan; the label extraction and weight calculation unit extracts the category labels related to each design template, and calculates the weight coefficient of each category label through keyword extraction and word segmentation processing; the label feature vector construction unit constructs the label feature vector of the design template according to the weight coefficient of each category label.

[0021] The real-time demand matching module includes a real-time demand data processing unit, an element attribute and label matching unit, and a matching design template unit; The real-time demand data processing unit acquires and analyzes the real-time project demand data, extracts the preset minimum elements and their attributes, and constructs a real-time demand feature vector; the element attribute and label matching unit obtains the category label of each element in the real-time demand data by calculating the similarity between the real-time demand element attributes and the element attribute feature vector in the design resource database, and constructs a real-time project demand feature vector based on the category label of the real-time demand element; the matching design template unit compares the real-time project demand feature vector with the label feature vectors of all design templates, calculates the matching index, and selects the design template with the largest matching index as the reference design template.

[0022] The design adjustment and optimization module includes a deviation analysis and prompt generation unit and a target design solution generation unit; The deviation analysis and prompt generation unit analyzes the deviation between the real-time demand feature vector and the label feature vector of the reference design template, generates a deviation sorting list based on the deviation value, and outputs the deviation sorting list as prompt information to relevant personnel; the target design scheme generation unit generates a target design scheme based on the prompt information and the relevant personnel adjust the reference design template.

[0023] A home design data management method based on a cloud platform comprises the following steps: Step S100. Acquire historical project data of the home design platform, divide the historical project data according to preset minimum elements, and thus obtain an element set corresponding to the historical project data; classify the elements corresponding to the historical project data according to the corresponding element set, and thus construct a design resource database; Step S200. Based on the design resource database, a number of design templates are created on the home design platform, and a tag set corresponding to the design template is obtained by analyzing the preset minimum elements of each design template, and keywords are extracted from the tag set to construct a corresponding tag feature vector; Step S300. Acquire real-time project demand data, analyze the real-time project demand data, and thus construct a real-time project demand feature vector; analyze the real-time project demand feature vector and the label feature vector corresponding to each design template, match the design template corresponding to the real-time project demand data according to the analysis result, and use the design template corresponding to the real-time project demand data as a reference design template; Step S400. Analyze the deviation value between the real-time project requirement feature vector and the label feature vector corresponding to the reference design template, and generate corresponding prompt information based on the deviation value. Relevant personnel adjust the reference design template based on the prompt information to generate a target design solution.

[0024] Step S100 includes: S101. Obtain a number of historical project data from the home design platform, divide each historical project data according to a preset minimum element, and each historical project data is decomposed into a set of element sets Ai, and Ai={ai1,ai2,...,ain}, wherein Ai represents the element set corresponding to the i-th historical project data, i represents the data number of the historical project data, which is a positive integer; ai1 represents the first element of the i-th historical project data, ai2 represents the second element of the i-th historical project data, and so on, ain represents the n-th element of the i-th historical project data; the preset minimum element refers to the most basic unit in the home design project that can be independently identified, analyzed and processed; for example, sofas, beds, tables, chairs, lamps, walls, windows, doors, floors, etc.; S102. Summarize the element set corresponding to all historical project data, and extract the element attributes corresponding to each element in the element set, where the element attribute refers to the feature description corresponding to the preset minimum element; construct the corresponding element attribute feature vector V1 based on the element attributes, and the element attribute feature vector V1=[v11,v12,...,v1u], where u represents the dimension of the element attribute feature vector V1, v11 represents the first dimension feature of the element attribute feature vector V1, v12 represents the second dimension feature of the element attribute feature vector V1, and so on, v1u represents the u-th dimension feature of the element attribute feature vector V1; calculate the similarity between all element attribute feature vectors V1, and use a clustering algorithm to divide all elements into several categories, and each category corresponds to a category label, so as to construct a design resource database.

[0025] In this embodiment, a clustering algorithm is used to divide all elements into several categories, and each category corresponds to a category label. The process can be concretely described by the following steps: In order to cluster elements based on the similarity between them, it is necessary to calculate the similarity between all element attribute feature vectors V1. Usually, Euclidean distance, cosine similarity or Manhattan distance can be used to measure the similarity between feature vectors.

[0026] After calculating the similarity of all elements, you can select a suitable clustering algorithm to divide the elements into several categories. Common clustering algorithms include: K-means clustering: By selecting K initial cluster centers, the cluster centers are iteratively updated until the clustering results converge. It is suitable for scenarios where the number of clusters is known.

[0027] Hierarchical clustering: Clusters are continuously merged or split by building a cluster tree (hierarchical structure) until the stopping condition is met. It is suitable when the number of clusters is uncertain.

[0028] DBSCAN (density-based spatial clustering algorithm): divides clusters according to density, which is suitable for situations where the shape of clusters is irregular and the number of clusters is not fixed.

[0029] When choosing a clustering algorithm, you need to consider the following factors: Size of the dataset: If the amount of data is very large, K-means may be a good choice.

[0030] Knowing the number of clusters: If you know the number of clusters you want to divide, you can choose K-means. If you are not sure, you can consider hierarchical clustering or DBSCAN.

[0031] Data distribution: If the data is densely distributed and has obvious clustering patterns, DBSCAN may perform better.

[0032] After clustering is completed, each cluster represents a category; for each cluster, a category label can be assigned (for example, category 1, category 2, ...). This label can be defined by the characteristics of the elements in the cluster, or it can be set according to actual needs.

[0033] For example, if the K-means algorithm is used and K categories are obtained, all elements belonging to the same category will be given the same label; each category label can be named by the characteristics of the cluster or the center of the cluster, or named according to the actual usage scenario (such as the functional module or style of home design).

[0034] Step S200 includes: S201. Based on the design resource database, create several design templates on the home design platform, and each design template represents a complete home design plan; obtain the design element set Dj corresponding to each design template, expressed as: Dj={ej1,ej2,...,ejm}, where Dj represents the design element set corresponding to the j-th design template, j represents the data number of the design template; ej1 represents the first design element of the j-th design template, ej2 represents the second design element of the j-th design template, and so on, ejm represents the m-th design element of the j-th design template; the design elements in the design element set correspond to the preset minimum elements in the design resource database; S202. For each design element set Dj corresponding to each design template, obtain the category labels of the design elements in the design resource database, and summarize the category labels of all design elements to form a label set Lj corresponding to the design element set Dj; perform word segmentation and keyword extraction on each category label in the label set Lj, and calculate the weight coefficient W of each category label in the corresponding design template, and the specific calculation formula of the weight coefficient W is: W(kc)=[TF(kc,Lj) / ∑kd∈Lj,TF(kd,Lj)]×IDF(kc); Among them, W(kc) represents the weight coefficient of the category label kc, c represents the data number of the category label in the design resource database; TF(kc,Lj) represents the frequency of the category label kc in the label set Lj; ∑kd∈Lj,TF(kd,Lj) represents the sum of the frequencies of all category labels in the label set Lj; IDF(kc) is the inverse document frequency, which represents the scarcity of the category label kc in all design templates, and the calculation formula is: IDF(kc)=log[N / DF(kc)]; Where N represents the total number of design templates, and DF(kc) represents the number of design templates containing category label kc; S203. According to the weight coefficient of each category label kc in the label set Lj and in combination with the design resource database, a label feature vector V_Lj corresponding to the design template Dj is constructed, and V_Lj=[W(k1),W(k2),...,W(kp)], where W(k1) represents the weight coefficient of category label k1, W(k2) represents the weight coefficient of category label k2, and so on, W(kp) represents the weight coefficient of category label kp, p is equal to the total number of category labels in the design resource database, and p is greater than or equal to m; the order of the category label weight coefficients in the label feature vector V_Lj is according to the order of the category labels in the design resource database. If the label set Lj does not contain a category label in the design resource database, the weight coefficient of this category label is assigned to zero.

[0035] In this embodiment, it is assumed that the design resource database has three category labels: k1, k2, k3, and the corresponding weight coefficients are W(k1), W(k2), and W(k3). If the label set Lj contains k1 and k3, but does not contain k2, then the constructed label feature vector VLj is: VLj=[W(k1),0,W(k3)], that is, the weight coefficient corresponding to k2 is assigned to zero.

[0036] Step S300 includes: S301. Acquire real-time project demand data, and extract the preset minimum element and the element attribute corresponding to the preset minimum element from the real-time project demand data according to the analysis method of historical project data, so as to obtain the real-time demand element set B and the real-time demand element attribute feature vector V2 corresponding to the real-time project demand data; perform similarity calculation on the real-time demand element attribute feature vector V2 corresponding to each element in the real-time demand element set B and the element attribute feature vector V1 of each category in the design resource database, and calculate the average similarity of the corresponding category, and select the category label with the largest average similarity as the category label of the corresponding element in the real-time demand element set B; summarize all element category labels in the real-time demand element set B, and refer to the analysis process of the label feature vector corresponding to the design template for all element category labels in the real-time demand element set B, so as to construct the real-time project demand feature vector V_req, and the dimension of the real-time project demand feature vector V_req is equal to p; S302. The real-time project requirement feature vector and the label feature vector corresponding to each design template are represented by a radar chart. The specific analysis process is as follows: Take any point in the two-dimensional plane as the origin of the coordinates, and take the dimension p of the real-time project demand feature vector V_req as the number of number axes. Each number axis starts from the origin of the coordinates, and the angles between each number axis are equal. Mark the eigenvalues ​​corresponding to the real-time project demand feature vector V_req on the number axis in a clockwise order to obtain p data points, and connect the data points of adjacent number axes in turn to form a closed polygon, and calculate the area of ​​the closed polygon S_req. According to the analysis method of the real-time project demand feature vector V_req, the label feature vector V_Lj corresponding to each design template is represented on a two-dimensional plane, and the corresponding closed polygon area S_Lj is calculated; according to the closed polygon areas S_req and S_Lj, the matching index R corresponding to the real-time project demand feature vector and the label feature vector corresponding to each design template is calculated in turn, and the specific calculation formula is: R={(S_req·S_Lj) / [S_req+S_Lj-|S_req-S_Lj|]}·α; Where α represents the shape similarity of the closed polygon corresponding to the real-time project requirement feature vector and the label feature vector corresponding to the design template, and its value ranges from 0 to 1. The matching index R corresponding to each design template is arranged in order from large to small, and the design template with the largest matching index R is selected as the reference design template.

[0037] In this embodiment, there are the following data: Real-time project requirement feature vector: V_req=(3,5,4,6), Design template label feature vector 1: V_L1=(4,5,4,5), Design template label feature vector 2: V_L2=(6,5,3,7), Design template label feature vector 3: V_L3=(2,6,5,6); Calculate the area of ​​a closed polygon Assume the following areas are calculated by the cross product method (example values ​​are given here): S_req=16 (polygon area corresponding to the real-time project demand feature vector), S_L1=15 (label feature vector area of ​​design template 1), S_L2=18 (label feature vector area of ​​design template 2), S_L3=17 (label feature vector area of ​​design template 3); The matching index R is calculated according to the formula R={(S_req·S_Lj) / [S_req+S_Lj-|S_req-S_Lj|]}·α. Assuming that the calculated matching indexes R are in the order of R1, R2 and R3 from large to small, then the design template 1 is selected as the reference design template.

[0038] Step S400 includes: The difference between the eigenvalues ​​of the real-time project requirement feature vector V_req and the label feature vector V_Lj corresponding to the reference design template in each dimension is calculated, and the absolute value of the difference calculation result is obtained to obtain the corresponding deviation value Δf; the deviation values ​​Δf corresponding to each dimension are sorted in order from large to small, and a deviation sorting list is generated, and the deviation sorting list is output to relevant personnel as prompt information, and the relevant personnel adjust the reference design template based on the prompt information, and use the adjusted reference design template as the target design solution.

[0039] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0040] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A home design data management method based on a cloud platform, characterized in that: The method comprises the following steps: Step S100. Acquire historical project data of the home design platform, divide the historical project data according to preset minimum elements, and thus obtain an element set corresponding to the historical project data; classify the elements corresponding to the historical project data according to the corresponding element set, and thus construct a design resource database; Step S200. Based on the design resource database, a number of design templates are created on the home design platform, and a tag set corresponding to the design template is obtained by analyzing the preset minimum elements of each design template, and keywords are extracted from the tag set to construct a corresponding tag feature vector; Step S300. Acquire real-time project demand data, analyze the real-time project demand data, and thus construct a real-time project demand feature vector; analyze the real-time project demand feature vector and the label feature vector corresponding to each design template, match the design template corresponding to the real-time project demand data according to the analysis result, and use the design template corresponding to the real-time project demand data as a reference design template; Step S400. Analyze the deviation value between the real-time project requirement feature vector and the label feature vector corresponding to the reference design template, and generate corresponding prompt information based on the deviation value. Relevant personnel adjust the reference design template based on the prompt information to generate a target design solution.

2. A cloud platform-based home design data management method according to claim 1, characterized in that: The step S100 includes: S101. Obtain a number of historical project data from the home design platform, divide each historical project data according to a preset minimum element, and each historical project data is decomposed into a set of element sets Ai, and Ai={ai1,ai2,...,ain}, wherein Ai represents the element set corresponding to the i-th historical project data, i represents the data number of the historical project data, which is a positive integer; ai1 represents the first element of the i-th historical project data, ai2 represents the second element of the i-th historical project data, and so on, ain represents the n-th element of the i-th historical project data; the preset minimum element refers to the most basic unit in the home design project that can be independently identified, analyzed and processed; S102. Summarize the element set corresponding to all historical project data, and extract the element attributes corresponding to each element in the element set, where the element attribute refers to the feature description corresponding to the preset minimum element; construct the corresponding element attribute feature vector V1 based on the element attributes, and the element attribute feature vector V1=[v11,v12,...,v1u], where u represents the dimension of the element attribute feature vector V1, v11 represents the first dimension feature of the element attribute feature vector V1, v12 represents the second dimension feature of the element attribute feature vector V1, and so on, v1u represents the u-th dimension feature of the element attribute feature vector V1; calculate the similarity between all element attribute feature vectors V1, and use a clustering algorithm to divide all elements into several categories, and each category corresponds to a category label, so as to construct a design resource database.

3. A cloud platform-based home design data management method according to claim 2, characterized in that: The step S200 includes: S201. Based on the design resource database, create several design templates on the home design platform, and each design template represents a complete home design plan; obtain the design element set Dj corresponding to each design template, expressed as: Dj={ej1,ej2,...,ejm}, where Dj represents the design element set corresponding to the j-th design template, j represents the data number of the design template; ej1 represents the first design element of the j-th design template, ej2 represents the second design element of the j-th design template, and so on, ejm represents the m-th design element of the j-th design template; the design elements in the design element set correspond to the preset minimum elements in the design resource database; S202. For each design element set Dj corresponding to each design template, obtain the category labels of the design elements in the design resource database, and summarize the category labels of all design elements to form a label set Lj corresponding to the design element set Dj; perform word segmentation and keyword extraction on each category label in the label set Lj, and calculate the weight coefficient W of each category label in the corresponding design template, and the specific calculation formula of the weight coefficient W is: W(kc)=[TF(kc,Lj) / ∑kd∈Lj,TF(kd,Lj)]×IDF(kc); Among them, W(kc) represents the weight coefficient of the category label kc, c represents the data number of the category label in the design resource database; TF(kc,Lj) represents the frequency of the category label kc in the label set Lj; ∑kd∈Lj,TF(kd,Lj) represents the sum of the frequencies of all category labels in the label set Lj; IDF(kc) is the inverse document frequency, which represents the scarcity of the category label kc in all design templates, and the calculation formula is: IDF(kc)=log[N / DF(kc)]; Where N represents the total number of design templates, and DF(kc) represents the number of design templates containing category label kc; S203. According to the weight coefficient of each category label kc in the label set Lj and in combination with the design resource database, a label feature vector V_Lj corresponding to the design template Dj is constructed, and V_Lj=[W(k1),W(k2),...,W(kp)], where W(k1) represents the weight coefficient of category label k1, W(k2) represents the weight coefficient of category label k2, and so on, W(kp) represents the weight coefficient of category label kp, p is equal to the total number of category labels in the design resource database, and p is greater than or equal to m; the order of the category label weight coefficients in the label feature vector V_Lj is according to the order of the category labels in the design resource database. If the label set Lj does not contain a category label in the design resource database, the weight coefficient of this category label is assigned to zero.

4. A cloud platform-based home design data management method according to claim 3, characterized in that: The step S300 includes: S301. Acquire real-time project demand data, and extract the preset minimum element and the element attribute corresponding to the preset minimum element from the real-time project demand data according to the analysis method of historical project data, so as to obtain the real-time demand element set B and the real-time demand element attribute feature vector V2 corresponding to the real-time project demand data; perform similarity calculation on the real-time demand element attribute feature vector V2 corresponding to each element in the real-time demand element set B and the element attribute feature vector V1 of each category in the design resource database, and calculate the average similarity of the corresponding category, and select the category label with the largest average similarity as the category label of the corresponding element in the real-time demand element set B; summarize all element category labels in the real-time demand element set B, and refer to the analysis process of the label feature vector corresponding to the design template for all element category labels in the real-time demand element set B, so as to construct the real-time project demand feature vector V_req, and the dimension of the real-time project demand feature vector V_req is equal to p; S302. The real-time project requirement feature vector and the label feature vector corresponding to each design template are represented by a radar chart. The specific analysis process is as follows: Take any point in the two-dimensional plane as the origin of the coordinates, and take the dimension p of the real-time project demand feature vector V_req as the number of number axes. Each number axis starts from the origin of the coordinates, and the angles between each number axis are equal. Mark the eigenvalues ​​corresponding to the real-time project demand feature vector V_req on the number axis in a clockwise order to obtain p data points, and connect the data points of adjacent number axes in turn to form a closed polygon, and calculate the area of ​​the closed polygon S_req. According to the analysis method of the real-time project demand feature vector V_req, the label feature vector V_Lj corresponding to each design template is represented on a two-dimensional plane, and the corresponding closed polygon area S_Lj is calculated; according to the closed polygon areas S_req and S_Lj, the matching index R corresponding to the real-time project demand feature vector and the label feature vector corresponding to each design template is calculated in turn, and the specific calculation formula is: R={(S_req·S_Lj) / [S_req+S_Lj-|S_req-S_Lj|]}·α; Where α represents the shape similarity of the closed polygon corresponding to the real-time project requirement feature vector and the label feature vector corresponding to the design template, and its value ranges from 0 to 1. The matching index R corresponding to each design template is arranged in order from large to small, and the design template with the largest matching index R is selected as the reference design template.

5. A cloud platform-based home design data management method according to claim 4, characterized in that: The step S400 includes: The difference between the eigenvalues ​​of the real-time project requirement feature vector V_req and the label feature vector V_Lj corresponding to the reference design template in each dimension is calculated, and the absolute value of the difference calculation result is obtained to obtain the corresponding deviation value Δf; the deviation values ​​Δf corresponding to each dimension are sorted in order from large to small, and a deviation sorting list is generated, and the deviation sorting list is output to relevant personnel as prompt information, and the relevant personnel adjust the reference design template based on the prompt information, and use the adjusted reference design template as the target design solution.

6. A home design data management system based on a cloud platform, applied to a home design data management method based on a cloud platform as claimed in any one of claims 1 to 5, characterized in that: The system includes: a historical project data processing module, a design template building module, a real-time demand matching module, and a design adjustment and optimization module; The historical project data processing module obtains the historical project data of the home design platform, divides the historical project data according to the preset minimum elements, thereby obtaining an element set corresponding to the historical project data; and classifies the elements corresponding to the historical project data according to the corresponding element set, thereby constructing a design resource database; The design template construction module creates several design templates on the home design platform based on the design resource database, obtains a tag set corresponding to the design template by analyzing the preset minimum elements of each design template, and extracts keywords from the tag set to construct a corresponding tag feature vector; The real-time demand matching module acquires real-time project demand data, analyzes the real-time project demand data, and thereby constructs a real-time project demand feature vector; analyzes the real-time project demand feature vector and the label feature vector corresponding to each design template, matches the design template corresponding to the real-time project demand data according to the analysis result, and uses the design template corresponding to the real-time project demand data as a reference design template; The design adjustment and optimization module analyzes the deviation value between the real-time project requirement feature vector and the label feature vector corresponding to the reference design template, and generates corresponding prompt information based on the deviation value. Relevant personnel adjust the reference design template based on the prompt information to generate a target design solution.

7. A cloud platform-based home design data management system according to claim 6, characterized in that: The historical project data processing module includes a historical project data acquisition and division unit, an element attribute extraction and analysis unit, and a design resource database construction unit; The historical project data acquisition and division unit acquires multiple historical project data from the home design platform, divides them according to preset minimum elements, and extracts an element set of each historical project data; the element attribute extraction and analysis unit extracts the attributes of each element, and constructs an element attribute feature vector based on these attributes; the design resource database construction unit divides all elements into several categories based on the element attribute feature vector, and each category corresponds to a category label, thereby constructing a design resource database.

8. The cloud platform-based home design data management system according to claim 6, characterized in that: The design template construction module includes a design template creation unit, a label extraction and weight calculation unit, and a label feature vector construction unit; The design template creation unit creates multiple design templates on the platform based on the design resource database, and each design template represents a complete home design plan; the label extraction and weight calculation unit extracts the category label related to each design template, and calculates the weight coefficient of each category label through keyword extraction and word segmentation processing; the label feature vector construction unit constructs the label feature vector of the design template according to the weight coefficient of each category label.

9. The cloud platform-based home design data management system according to claim 6, characterized in that: The real-time demand matching module includes a real-time demand data processing unit, an element attribute and label matching unit, and a matching design template unit; The real-time demand data processing unit acquires and analyzes the real-time project demand data, extracts the preset minimum element and its attributes, and constructs a real-time demand feature vector; the element attribute and label matching unit obtains the category label of each element in the real-time demand data by calculating the similarity between the real-time demand element attributes and the element attribute feature vector in the design resource database, and constructs the real-time project demand feature vector based on the category label of the real-time demand element; the matching design template unit compares the real-time project demand feature vector with the label feature vectors of all design templates, calculates the matching index, and selects the design template with the largest matching index as the reference design template.

10. The home design data management system based on cloud platform according to claim 6, characterized in that: The design adjustment and optimization module includes a deviation analysis and prompt generation unit and a target design solution generation unit; The deviation analysis and prompt generation unit analyzes the deviation between the real-time demand feature vector and the label feature vector of the reference design template, generates a deviation sorting list based on the deviation value, and outputs the deviation sorting list as prompt information to relevant personnel; the target design scheme generation unit generates a target design scheme by allowing relevant personnel to adjust the reference design template based on the prompt information.

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