Indoor home design platform management system and method based on intelligent model

Abstract

The invention discloses an indoor home design platform management system and method based on an intelligent model, and belongs to the technical field of intelligent home design. The system comprises a retrieval database, a data acquisition module, an intelligent analysis module, an automatic generation module and an indoor home design platform, the retrieval database is used for collecting different home design schemes; the data acquisition module is used for acquiring user behavior data of different users in the indoor home design platform; collecting browsing data of browsing the home design scheme by a user; the intelligent analysis module is used for analyzing the relevance between labels in the home design scheme browsed and selected by the user and analyzing the support degree of each label among the labels in other design elements; the automatic generation module is used for automatically generating a home design scheme fitting a user; and the indoor home design platform is used for the user to log in and browse the home design scheme after authorization.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home design, and specifically to an indoor home design platform management system and method based on an intelligent model. Background Art

[0002] With the continuous improvement of people's living standards, the requirements for the personalization and comfort of the living environment are increasing day by day. However, the traditional indoor home design process is difficult to accurately grasp the user's needs. This process often takes a long time and is prone to design solutions not meeting the customer's expectations due to misunderstandings. With the gradual maturity and development of cutting-edge technologies such as artificial intelligence and big data, in the field of indoor home design, using generative AI technology, the indoor home design platform can quickly generate multiple design solutions, significantly improving the design efficiency and user personalization settings. However, the quality of resource management for home design solutions in the prior art is uneven, lacking effective classification and screening mechanisms, resulting in a reduction in the overall efficiency of home design solutions; and the ability to analyze the data generated during the user's browsing of home design solutions is insufficient, making it difficult to effectively meet the user's needs and generate furniture design solutions that meet the user. Summary of the Invention

[0003] The purpose of the present invention is to provide an indoor home design platform management system and method based on an intelligent model to solve the problems raised in the above background art.

[0004] To solve the above technical problems, the present invention provides the following technical solution: An indoor home design platform management method based on an intelligent model, the method comprising the following steps: Step S1, establish a retrieval database and collect different home design solutions; the home design solutions contain several design elements; each design element corresponds to several tags; determine the tags according to the design elements, determine the feature vectors according to the tags, and store the collected home design solutions divided into different feature vectors in the retrieval database; Step S2, obtain user permissions and collect user behavior data of different users in the indoor home design platform; the user behavior data contains the home design solutions browsed by the user and the home design solutions selected by the user; Step S3, analyze the user behavior data collected in Step S2, according to the design elements and tags, analyze the relevance between the tags in the home design solutions browsed and selected by the user, and according to the home design solutions selected by different users, analyze the support degree between the tags in other design elements; Step S4: After the user authorizes and logs in to the indoor home design platform, different home design solutions are presented on the indoor home design platform. When the user browses the home design solutions on the indoor home design platform, the browsing data of the user browsing the home design solutions is collected; the indoor home design platform includes a one-click generation function; when the user clicks the one-click generation function, based on the collected browsing data, the retrieval database established in Step S1, the relevance between the tags in the browsed and selected home design solutions analyzed in Step S3, and the support degree between the tags of each tag in other design elements, an intelligent model is established to automatically generate a home design solution that suits the user.

[0005] An indoor home design platform management system based on an intelligent model, which includes a retrieval database, a data collection module, an intelligent analysis module, an automatic generation module, and an indoor home design platform; The retrieval database is used to collect different home design solutions, store them after splitting the collected home design solutions into different feature vectors; the home design solutions include several design elements; each design element corresponds to several tags; The data collection module is used to obtain user permissions and collect user behavior data of different users on the indoor home design platform; the user behavior data includes the home design solutions browsed by the user and the home design solutions selected by the user; collect the browsing data of the user browsing the home design solutions; send the collected user behavior data to the intelligent analysis module; send the collected browsing data to the automatic generation module; The intelligent analysis module is used to analyze the user behavior data sent by the data collection module, analyze the relevance between the tags in the browsed and selected home design solutions of the user according to the design elements and tags, and analyze the support degree between the tags of each tag in other design elements according to the home design solutions selected by different users; send the relevance between the tags in the browsed and selected home design solutions of the user and the support degree between the tags of each tag in other design elements to the automatic generation module; The automatic generation module is used to, when the user clicks the one-click generation function, based on the browsing data sent by the data collection module, different home design solutions collected by the retrieval database, the relevance between the tags in the browsed and selected home design solutions of the user sent by the intelligent analysis module, and the support degree between the tags of each tag in other design elements, establish an intelligent model, automatically generate a home design solution that suits the user, and send the generated home design solution that suits the user to the indoor home design platform; The indoor home design platform is used for the user to log in after authorization and browse the home design solutions; the indoor home design platform includes a one-click generation function; it is used to push a home design solution that suits the user.

[0006] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: A management system and method for an indoor home design platform based on an intelligent model are provided. By establishing a retrieval database, home design schemes are classified and stored for management. By analyzing the correlation between tags in the home design schemes browsed and selected by different users, the home design schemes preferred by users can be better predicted based on the home design scheme currently browsed by the user. By analyzing the support degree between tags in other design elements, it helps to make the automatically generated home design scheme more in line with user needs, optimizes the generated home design scheme, makes the prediction result more accurate, recombines the retrieval database into a new home design scheme, provides personalized needs for users, and improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 FIG. is a schematic structural diagram of a management system for an indoor home design platform based on an intelligent model of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0008] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0009] Please refer to Figure 1 , the present invention provides a technical solution: In the first embodiment: A management method for an indoor home design platform based on an intelligent model is provided. The method includes the following steps: Step S1, establish a retrieval database and collect different home design schemes; the home design schemes include several design elements; each design element corresponds to several tags; according to the design elements and tags, the collected home design schemes are segmented into different feature vectors and stored in the retrieval database.

[0010] Further, the method for segmenting the collected home design schemes into different feature vectors according to the design elements and tags is: determine several design elements of the home design scheme; according to the collected home design schemes, determine the tags of each home design scheme under different design elements, and use the tags of each home design scheme under different design elements as a feature vector to segment the collected home design schemes to obtain different feature vectors; wherein, the design element represents each unit constituting the home design scheme; the tag represents the specific product features describing the design element.

[0011] It should be noted that by establishing a retrieval database, the existing home design solutions are segmented into tags under different design elements, and each different tag corresponds to a feature vector. The home design solutions are classified according to the design elements and tags, which facilitates the subsequent automatic generation of home design solutions that suit users by the indoor home design platform.

[0012] In this embodiment, the design elements of the home design solution include but are not limited to each unit that constitutes the home design solution, such as selecting floors, walls, ceilings, furniture, lighting design, etc.; each design element corresponds to several tags, which are used to describe the specific product features of the design element. For example, floors include but are not limited to solid wood floors, composite floors, and PVC floors with specific product features; furniture includes but is not limited to fabric sofas, round dining tables, and sliding door wardrobes with specific product features.

[0013] Step S2: Obtain user permissions and collect user behavior data of different users in the indoor home design platform; the user behavior data includes the home design solutions browsed by users and the home design solutions selected by users.

[0014] It should be noted that the user behavior data collected in step S2 is historical data generated by different users in the indoor home design platform; when a user logs in, according to the home design solution push methods such as public preferences and view counts, different home design solutions are pushed through a waterfall layout on the interface of the indoor home design platform. Users can perform sliding operations on the interface of the home design platform and click to enter and view the corresponding furniture design solutions; among them, the home design solutions browsed by users are all collected in the retrieval database; the home design solutions selected by users are one or more home design solutions finally selected by users, which can be the preferences, collections, and finally confirmed home design solutions set by users.

[0015] Step S3: Analyze the user behavior data collected in step S2. According to the design elements and tags, analyze the correlation between the tags in the home design solutions browsed and selected by users, and analyze the support degree between the tags in other design elements according to the home design solutions selected by different users.

[0016] Specifically, the method steps are as follows: Step S31: Analyze the user behavior data collected in step S2, segment the home design solutions browsed by different users, and determine the set of feature vectors of the tags corresponding to each design element in the home design solutions browsed by different users ; ; where represents the number of users in the user behavior data analyzed; ; represents the number of types of design elements in the home design solution; Denote the set of eigenvector features corresponding to each design element's label in the home design solutions browsed by the th user; Denote the eigenvector of the label corresponding to each design element in the home design solution browsed by the th user; Segment the home design solutions selected by different users, and determine the set of eigenvector features corresponding to each design element's label in the home design solutions selected by different users ; ; Among them, Denote the set of eigenvector features corresponding to each design element's label in the home design solution selected by the th user; Denote the eigenvector of the label corresponding to each design element in the home design solution selected by the th user; Step S32: According to the sets of eigenvector features corresponding to each design element's label in the home design solutions browsed and selected by different users, determine the number of occurrences of the same eigenvector labels among different users, obtain the number of occurrences of each design element's label corresponding to different users, and determine the correlation between the labels in the browsed and selected home design solutions based on the number of occurrences of each design element's label when different users browse the home design solutions and the number of occurrences of each design element's label when they select the home design solutions; Step S33: According to the sets of eigenvector features corresponding to each design element's label in the home design solutions selected by different users, determine the frequency of selecting the labels of other design elements when selecting the labels of the corresponding design element for different users, and use the frequency of the labels of other design elements as the support degree between the labels in other design elements for each label.

[0017] It should be noted that based on the number of occurrences of each design element's label when users browse the home design solutions and the number of occurrences of each design element's label when they select the home design solutions, determine the correlation between the labels in the browsed and selected home design solutions. If, when a user browses a certain design element of the design solution, a total of 100 labels are browsed, and the number of occurrences of label P1 is 10 times and the number of occurrences of label P2 is 5 times, while the number of occurrences of both label P1 and label P2 in the home design solution selected by the user is 1, then the correlation of label P1 in the browsed and selected home design solutions is 0.1, and the correlation of label P2 is 0.2; determine the frequency of selecting the labels of other design elements when selecting the labels of the corresponding design element for different users, and use the frequency of the labels of other design elements as the support degree between the labels in other design elements for each label. If the label corresponding to the design element selected by the user is solid wood floor and the frequency of the fabric sofa is 20%, then the support degree of the fabric sofa for the fabric sofa is 0.2.

[0018] It should be noted that by analyzing the correlation between tags in the home design solutions browsed and selected by the user, the home design solutions preferred by the user can be better predicted based on the current home design solution browsed by the user; by analyzing the support degree between tags in other design elements, the process of automatically generating home design solutions can be made more in line with the user's needs and the generated home design solutions can be optimized; in this embodiment, by analyzing the correlation between tags in the home design solutions browsed and selected by different users and the support degree between tags in other design elements, the standard deviation method is used to obtain the correlation between tags in the browsed and selected home design solutions and the support degree between tags in other design elements.

[0019] Step S4: After the user authorizes and logs in to the indoor home design platform, different home design solutions are presented on the indoor home design platform. When the user browses the home design solutions on the indoor home design platform, the browsing data of the user browsing the home design solutions is collected; the indoor home design platform includes a one-click generation function; when the user clicks the one-click generation function, based on the collected browsing data, the retrieval database established in step S1, the correlation between tags in the browsed and selected home design solutions analyzed in step S3, and the support degree between tags in other design elements, an intelligent model is established to automatically generate a home design solution that suits the user.

[0020] Specifically, the method steps for automatically generating a home design solution that suits the user are as follows: Step S41: Analyze the browsing data of the user browsing the home design solutions on the indoor home design platform, segment the home design solutions browsed by the user, and determine the feature vectors of the tags corresponding to each design element in the home design solutions browsed by the user. Step S42: Establish an intelligent model. According to the feature vectors of the tags corresponding to each design element obtained in step S41, use the Pearson correlation coefficient to perform retrieval analysis in the retrieval database. According to the calculation formula: ; where represents the correlation coefficient retrieved in the retrieval database for the th tag under the th design element; represents the feature vector of the th tag under the th design element in the home design solutions browsed by the user; represents the average value of the feature vectors of the th tag under the th design element in the home design solutions browsed by the user. The eigenvector of the th label under the th design element in the retrieved data; The average value of the eigenvectors of the th label under the th design element in the retrieved data; ; Indicates the number of label types under the th design element.

[0021] It should be noted that ranges from -1 to 1. When is closer to 1, the positive correlation is stronger. By retrieving the eigenvectors associated with the home design solutions browsed by the user in the retrieved data, the L eigenvectors with the strongest positive correlation are selected and substituted into step S43 to calculate the index values of the home design solutions; L represents the number of selected eigenvectors; Step S43: According to the retrieval analysis in step S42, determine the correlation coefficients between different eigenvectors in the retrieval database and the eigenvectors corresponding to the labels in the home design solutions browsed by the user. According to the relevance between the labels in the browsed and selected home design solutions analyzed in step S3 and the support degree between the labels in other design elements, predict the eigenvectors corresponding to the labels of different design elements in the home design solutions that fit the user , so that satisfies the conditional formula: ; Among them, represents the index value for evaluating the predicted home design solution; represents the influence weight of the relevance between the labels in the browsed and selected home design solutions on the prediction of the home design solution; represents the influence weight of the support degree between the labels in other design elements on the prediction of the home design solution; represents the number of types of design elements in the home design solution; represents the eigenvector corresponding to the label of the th design element in the home design solution predicted to fit the user; ; represents the eigenvector in the relevance between the labels in the browsed and selected home design solutions; represents the correlation coefficient of the label eigenvector retrieved from the retrieval database; represents the eigenvector The support between the corresponding label and the corresponding labels of other design elements in the predicted home design solution; Step S44, according to , obtain the labels of the design elements corresponding to each feature vector in the retrieved data, combine the obtained labels of the design elements, and obtain an automatically generated home design solution that fits the user.

[0022] In this embodiment, in order to better fit the user's current preferences and predict the home design solution that the user likes, when automatically generating a home design solution that fits the user, the 10 most recently viewed home design solutions of the user are used as browsing data for analysis.

[0023] In this embodiment, the indoor home design platform pushes the automatically generated home design solution that fits the user to the user, and provides an interested button and a not interested button; the user can collect the automatically generated home design solution through the interested button for convenient subsequent viewing; the user can determine the label under the design element that is not interested in the automatically generated home design solution through the not interested button, or re - automatically generate a new home design solution; if the user determines the label under the design element that is not interested, at this time, cancel the feature vectors corresponding to the labels under the design element that is not interested in the calculation in steps S41 - S44, keep the labels under other design elements unchanged, execute steps S41 - S44, obtain new feature vectors again, and determine new labels according to the new feature vectors; if re - automatically generate a new home design solution, at this time, cancel the feature vectors corresponding to the labels of all design elements in the pushed home design solution in steps S41 - S44, execute steps S41 - S44, and obtain a new home design solution again.

[0024] It should be noted that by establishing an intelligent model, through step S42 for retrieval and analysis in the retrieval database, according to the Pearson correlation coefficient, the feature vectors that meet the user's browsed home design solutions are screened out in the retrieval database. According to the relevance between the labels in the browsed and selected home design solutions and the support between the labels of each label in other design elements, through step S43, the screened - out feature vectors are further screened. Finally, a home design solution that fits the user is determined. In this method, the home design solution is segmented into labels under different design elements, the relevance and support between each label are analyzed, so that the prediction result is more accurate. By reorganizing the retrieved database into a new home design solution, it provides personalized needs for users and improves the user experience.

[0025] Please refer to Figure 1, in the second embodiment: A management system for an indoor home design platform based on an intelligent model is provided. The system includes a retrieval database, a data collection module, an intelligent analysis module, an automatic generation module, and an indoor home design platform; The retrieval database is used to collect different home design schemes, segment the collected home design schemes into different feature vectors and then store them; the home design schemes contain several design elements; each design element corresponds to several labels; The data collection module is used to obtain user permissions and collect user behavior data of different users in the indoor home design platform; the user behavior data includes the home design schemes browsed by the user and the home design schemes selected by the user; collect the browsing data of the home design schemes browsed by the user; send the collected user behavior data to the intelligent analysis module; send the collected browsing data to the automatic generation module; The intelligent analysis module is used to analyze the user behavior data sent by the data collection module, analyze the correlation between the labels in the home design schemes browsed and selected by the user according to the design elements and labels, and analyze the support degree between the labels in other design elements according to the home design schemes selected by different users; send the correlation between the labels in the home design schemes browsed and selected by the user and the support degree between the labels in other design elements to the automatic generation module; The automatic generation module is used to, when the user clicks the one-key generation function, establish an intelligent model according to the browsing data sent by the data collection module, different home design schemes collected by the retrieval database, the correlation between the labels in the home design schemes browsed and selected by the user sent by the intelligent analysis module, and the support degree between the labels in other design elements, and automatically generate a home design scheme that suits the user, and send the generated home design scheme that suits the user to the indoor home design platform; The indoor home design platform is used for users to log in after authorization and browse home design schemes; the indoor home design platform includes a one-key generation function; it is used to push home design schemes that suit the user.

[0026] Further, the intelligent analysis module includes a user behavior data analysis unit, a correlation analysis unit, and a support degree analysis unit; The user behavior data analysis unit is used to analyze the collected user behavior data, segment the home design schemes browsed by different users, and determine the set of feature vectors of the labels corresponding to each design element in the home design schemes browsed by different users; segment the home design schemes selected by different users, and determine the set of feature vectors of the labels corresponding to each design element in the home design schemes selected by different users; send the user behavior data analysis results to the correlation analysis unit and the support degree analysis unit; The relevance analysis unit is used to determine the number of occurrences of the same feature vector tags of different users according to the set of feature vectors of each design element corresponding tags in the home design solutions browsed and selected by different users, obtain the number of occurrences of each design element corresponding tags of different users, and determine the relevance between the tags in the browsed and selected home design solutions according to the number of occurrences of each design element corresponding tags when different users browse the home design solutions and the number of occurrences of each design element corresponding tags when selecting the home design solutions; The support analysis unit is used to determine the frequency of selecting tags of other design elements when selecting tags of each design element corresponding to the home design solutions selected by different users according to the set of feature vectors of each design element corresponding tags in the home design solutions selected by different users, and use the frequency of tags of other design elements as the support between the tags in other design elements for each tag.

[0027] Further, the automatic generation module includes a browsing data analysis unit, a model management unit, and a solution automatic generation unit; The browsing data analysis unit is used to analyze the browsing data of the home design solutions browsed by users on the indoor home design platform, segment the home design solutions browsed by users, and determine the feature vectors of each design element corresponding tags in the home design solutions browsed by users; send the browsing data analysis result to the model management unit; The model management unit is used to establish an intelligent model, and perform retrieval analysis in the retrieval database using the Pearson correlation coefficient according to the browsing data analysis result sent by the model management unit; send the retrieval analysis result to the solution automatic generation unit; The solution automatic generation unit is used to determine the correlation coefficient between different feature vectors in the retrieval database and the feature vectors corresponding to the tags in the home design solutions browsed by users according to the retrieval analysis result sent by the model management unit, and predict the feature vectors of each design element corresponding tags in the home design solution that fits the user according to the relevance between the tags in the browsed and selected home design solutions and the support between the tags in other design elements for each tag, obtain the tags of each design element corresponding to the feature vectors in the retrieval data, and combine the obtained tags of the design elements to obtain the automatically generated home design solution that fits the user.

[0028] In this embodiment: The retrieval database collects different home design solutions, segments the collected home design solutions into different feature vectors and then stores them; The data acquisition module obtains user permissions, collects the user behavior data of different users on the indoor home design platform; collects the browsing data of the home design solutions browsed by users; sends the collected user behavior data to the intelligent analysis module; sends the collected browsing data to the automatic generation module; The user behavior data analysis unit in the intelligent analysis module analyzes the collected user behavior data and sends the analysis results of the user behavior data to the relevance analysis unit and the support degree analysis unit; the relevance analysis unit determines the relevance between the labels in the browsed and selected home design solutions and sends it to the automatic generation module; the support degree analysis unit determines the support degree between the labels of each label in other design elements and sends it to the automatic generation module; The browsing data analysis unit in the automatic generation module analyzes the browsing data of the user browsing the home design solutions on the indoor home design platform and sends the analysis results of the browsing data to the model management unit; the model management unit establishes an intelligent model, conducts a retrieval analysis in the retrieval database, and sends the retrieval analysis results to the solution automatic generation unit; the solution automatic generation unit predicts the feature vectors corresponding to the labels of different design elements in the home design solution that fits the user, obtains the labels of the design elements corresponding to each feature vector in the retrieval data, combines the obtained labels of the design elements, obtains the automatically generated home design solution that fits the user, and sends the generated home design solution that fits the user to the indoor home design platform; After the user authorizes and logs in to the indoor home design platform and browses the home design solutions, when the user clicks the one-key generation function, a signal is sent to the push automatic generation module, and the automatic generation module generates a home design solution that fits the user and displays it through the indoor home design platform.

[0029] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A management method for an indoor home design platform based on an intelligent model, characterized in that: The method includes the following steps: Step S1: Establish a retrieval database and collect different home design solutions; the home design solutions contain several design elements; each design element corresponds to several tags; determine the tags according to the design elements, determine the feature vectors according to the tags, and store the collected home design solutions in the retrieval database after splitting them into different feature vectors; Step S2: Obtain user permissions and collect user behavior data of different users in the indoor home design platform; the user behavior data includes the home design solutions browsed by the users and the home design solutions selected by the users; Step S3: Analyze the user behavior data collected in Step S2. According to the design elements and tags, analyze the correlation between the tags in the home design solutions browsed and selected by the users. According to the home design solutions selected by different users, analyze the support degree between the tags of each tag in other design elements; Step S4: After the user authorizes, log in to the indoor home design platform. Present different home design solutions in the indoor home design platform. When the user browses the home design solutions in the indoor home design platform, collect the browsing data of the user browsing the home design solutions; the indoor home design platform includes a one-click generation function; when the user clicks the one-click generation function, establish an intelligent model according to the collected browsing data, the retrieval database established in Step S1, the correlation between the tags in the browsed and selected home design solutions analyzed in Step S3, and the support degree between the tags of each tag in other design elements, and automatically generate a home design solution that suits the user.

2. The management method of an indoor home design platform based on an intelligent model according to claim 1, characterized in that: The method of splitting the collected home design solutions into different feature vectors according to the design elements and tags is as follows: determine several design elements of the home design solution; according to the collected home design solutions, determine the tags of each home design solution under different design elements, and use the tags of each home design solution under different design elements as a feature vector to split the collected home design solutions to obtain different feature vectors; wherein, the design element represents each unit that makes up the home design solution; the tag represents the specific product features that describe the design element.

3. The method for managing an interior home design platform based on an intelligent model according to claim 1, characterized in that: The specific method steps of Step S3 are as follows: Step S31: Analyze the user behavior data collected in Step S2, split the home design solutions browsed by different users, and determine the set of feature vectors of the tags corresponding to each design element in the home design solutions browsed by different users; split the home design solutions selected by different users, and determine the set of feature vectors of the tags corresponding to each design element in the home design solutions selected by different users; Step S32: According to the set of feature vectors of the tags corresponding to each design element in the home design solutions browsed and selected by different users, determine the number of times the same feature vector tags appear for different users, obtain the number of times the tags corresponding to each design element appear for different users, and determine the correlation between the tags in the browsed and selected home design solutions according to the number of times the tags corresponding to each design element appear when different users browse the home design solutions and the number of times the tags corresponding to each design element appear when different users select the home design solutions; Step S33: According to the set of feature vectors of the labels corresponding to each design element in the home design solutions selected by different users, determine the frequency of the labels under other design elements when different users select the labels under the corresponding design elements, and use the frequency of the labels under other design elements as the support degree between the labels in other design elements.

4. A management method for an indoor home design platform based on an intelligent model according to claim 3, characterized in that: The method steps for automatically generating a home design solution that fits the user in step S4 are as follows: Step S41: Analyze the browsing data of the home design solutions browsed by the user on the indoor home design platform, segment the home design solutions browsed by the user, and determine the feature vectors of the labels corresponding to each design element in the home design solutions browsed by the user. Step S42: Establish an intelligent model, and use the Pearson correlation coefficient to perform retrieval and analysis in the retrieval database according to the feature vectors of the labels corresponding to each design element obtained in step S41. Step S43: According to the retrieval analysis in step S42, determine the correlation coefficients between different feature vectors in the retrieval database and the feature vectors corresponding to the labels in the home design scheme browsed by the user. According to the relevance between the labels in the browsed and selected home design schemes analyzed in step S3 and the support degrees between the labels of each label in other design elements, predict the feature vectors corresponding to the labels of different design elements in the home design scheme that fits the user , so that satisfies the conditional formula: ； in, represents the index value for evaluating the predicted home design solution; The weight of the influence of the correlation between the tags in the browsed and selected home design plans on the prediction of home design plans; Indicates the weight of the support between labels of each label in other design elements on the prediction of home design schemes; Indicates the number of types of design elements in the home design plan; Indicates the first home design solution that is predicted to be suitable for the user. The feature vector of the label corresponding to each design element; ; Represents the feature vector the correlation between tags in browsing and selecting home design solutions; Represents the tag feature vector retrieved in the retrieval database Correlation coefficient of Represents the feature vector The support between the corresponding label and the corresponding labels of other design elements in the predicted home design plan; Step S44. According to , obtain the labels of the design elements corresponding to each feature vector from the retrieved data, combine the obtained labels of the design elements, and obtain an automatically generated home design solution that fits the user.

5. The method for managing an interior home design platform based on an intelligent model according to claim 4, characterized in that: The calculation formula for performing retrieval and analysis in the retrieval database is: ； Among them, represents the correlation coefficient retrieved for the th label under the th design element in the retrieval database; represents the feature vector of the th label under the th design element in the home design solution browsed by the user; represents the average value of the feature vectors of the th label under the th design element in the home design solution browsed by the user; represents the feature vector of the th label under the th design element for retrieval analysis in the retrieval data; represents the average value of the feature vectors of the th label under the th design element for retrieval analysis in the retrieval data; ; represents the number of label types under the th design element.

6. An indoor home design platform management system based on an intelligent model, characterized in that: The system includes a retrieval database, a data collection module, an intelligent analysis module, an automatic generation module, and an indoor home design platform. The retrieval database is used to collect different home design solutions, segment the collected home design solutions into different feature vectors and then store them; the home design solutions contain several design elements; each design element corresponds to several labels. The data collection module is used to obtain user permissions and collect the user behavior data of different users on the indoor home design platform; the user behavior data includes the home design solutions browsed by the user and the home design solutions selected by the user; collect the browsing data of the home design solutions browsed by the user. Send the collected user behavior data to the intelligent analysis module; send the collected browsing data to the automatic generation module. The intelligent analysis module is used to analyze the user behavior data sent by the data collection module, analyze the correlation between the labels in the home design solutions browsed and selected by the user according to the design elements and labels, and analyze the support degree between the labels in other design elements according to the home design solutions selected by different users; send the correlation between the labels in the home design solutions browsed and selected by the user and the support degree between the labels in other design elements to the automatic generation module. The automatic generation module is used to, when the user clicks the one-key generation function, establish an intelligent model according to the browsing data sent by the data collection module, the different home design solutions collected by the retrieval database, the correlation between the labels in the home design solutions browsed and selected by the user sent by the intelligent analysis module, and the support degree between the labels in other design elements, automatically generate a home design solution that fits the user, and send the generated home design solution that fits the user to the indoor home design platform. The indoor home design platform is used for the user to log in after authorization and browse the home design solutions; the indoor home design platform includes a one-key generation function; it is used to push the home design solutions that fit the user.

7. The intelligent model-based interior design platform management system according to claim 6, characterized in that: The intelligent analysis module includes a user behavior data analysis unit, a correlation analysis unit, and a support degree analysis unit. The user behavior data analysis unit is used to analyze the collected user behavior data, segment the home design solutions browsed by different users, and determine the set of feature vectors corresponding to the labels of each design element in the home design solutions browsed by different users; segment the home design solutions selected by different users, and determine the set of feature vectors corresponding to the labels of each design element in the home design solutions selected by different users; send the user behavior data analysis results to the relevance analysis unit and the support analysis unit; The relevance analysis unit is used to determine the number of occurrences of the same feature vector labels of different users according to the set of feature vectors corresponding to the labels of each design element in the home design solutions browsed and selected by different users, obtain the number of occurrences of the labels corresponding to each design element of different users, and determine the relevance between the labels in the browsed and selected home design solutions according to the number of occurrences of the labels corresponding to each design element when different users browse the home design solutions and the number of occurrences of the labels corresponding to each design element when they select the home design solutions; The support analysis unit is used to determine the frequency of selecting the labels under other design elements when selecting the labels under the corresponding design elements of different users according to the set of feature vectors corresponding to the labels of each design element in the home design solutions selected by different users, and use the frequency of the labels under other design elements as the support between the labels in other design elements.

8. The intelligent model-based interior design platform management system according to claim 7, characterized in that: The automatic generation module includes a browsing data analysis unit, a model management unit, and a solution automatic generation unit; The browsing data analysis unit is used to analyze the browsing data of the home design solutions browsed by the user on the indoor home design platform, segment the home design solutions browsed by the user, and determine the feature vectors corresponding to the labels of each design element in the home design solutions browsed by the user; send the browsing data analysis results to the model management unit; The model management unit is used to establish an intelligent model, and perform retrieval analysis in the retrieval database using the Pearson correlation coefficient according to the browsing data analysis results sent by the model management unit; send the retrieval analysis results to the solution automatic generation unit; The solution automatic generation unit is used to determine the correlation coefficient between the different feature vectors in the retrieval database and the feature vectors corresponding to the labels in the home design solutions browsed by the user according to the retrieval analysis results sent by the model management unit, predict the feature vectors corresponding to the labels of different design elements in the home design solutions that fit the user according to the relevance between the labels in the analyzed browsed and selected home design solutions and the support between the labels in other design elements, obtain the labels of the design elements corresponding to each feature vector in the retrieval data, and combine the obtained labels of the design elements to obtain the automatically generated home design solutions that fit the user.

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