An indoor home design platform management system and method based on intelligent model

Abstract

The present invention discloses an intelligent model-based interior home design platform management system and method, belonging to the field of intelligent home design technology. The system includes a retrieval database, a data acquisition module, an intelligent analysis module, an automatic generation module, and an interior home design platform. The retrieval database is used to collect different home design solutions. The data acquisition module is used to collect user behavior data of different users on the interior home design platform and collect browsing data of users browsing home design solutions. The intelligent analysis module is used to analyze the correlation between tags in home design solutions browsed and selected by users, and analyze the support of each tag among other design elements. The automatic generation module is used to automatically generate a home design solution that suits the user. The interior home design platform is used for users to log in and browse home design solutions after authorization.

Description

Technical Field

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

[0002] As people's living standards continue to improve, the requirements for personalization and comfort of living environments are increasing. However, the traditional interior home design process is difficult to accurately grasp user needs. This process is often time-consuming and prone to misunderstandings, resulting in design solutions that do not meet customer expectations. With the gradual maturity and development of cutting-edge technologies such as artificial intelligence and big data, in the field of interior home design, using generative AI technology, interior home design platforms can quickly generate a variety of design solutions, significantly improving design efficiency and user personalization settings. However, the quality of resource management of home design solutions in existing technologies is uneven, and there is a lack of effective classification and screening mechanisms, which leads to a reduction in the overall efficiency of home design solutions. In addition, the ability to analyze data generated during the user's browsing of home design solutions is insufficient, making it difficult to effectively meet user needs and generate furniture design solutions that meet user needs. Summary of the Invention

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

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for managing an interior home design platform based on an intelligent model, the method comprising the following steps:

[0005] Step S1: Establish a search database to collect different home design solutions; the home design solutions include several design elements; each design element corresponds to several tags; determine tags based on the design elements, determine feature vectors based on the tags, divide the collected home design solutions into different feature vectors, and store them in the search database;

[0006] Step S2: obtaining user permissions and collecting user behavior data of different users in the interior home design platform; the user behavior data includes the home design plans browsed by the user and the home design plans selected by the user;

[0007] Step S3: Analyze the user behavior data collected in step S2, and analyze the correlation between the tags in the home design plans browsed and selected by users based on the design elements and tags. Analyze the support of each tag among the tags in other design elements based on the home design plans selected by different users.

[0008] Step S4: After authorization, the user logs in to the interior home design platform, and different home design plans are presented on the interior home design platform. When the user browses the home design plans on the interior home design platform, browsing data of the user browsing the home design plans is collected; the interior home design platform includes a one-click generation function; when the user clicks the one-click generation function, an intelligent model is established based on the collected browsing data, the retrieval database established in step S1, the correlation between the tags in the browsed and selected home design plans analyzed in step S3, and the support between the tags in other design elements, to automatically generate a home design plan that suits the user.

[0009] An interior home design platform management system based on an intelligent model, the system includes a retrieval database, a data acquisition module, an intelligent analysis module, an automatic generation module and an interior home design platform;

[0010] The search database is used to collect different home design plans, divide the collected home design plans into different feature vectors and store them; the home design plans include several design elements; each design element corresponds to several tags;

[0011] The data collection module is used to obtain user permissions and collect user behavior data of different users in the interior home design platform; the user behavior data includes the home design plans browsed by the user and the home design plans selected by the user; collect browsing data of the home design plans browsed by the user; send the collected user behavior data to the intelligent analysis module; and send the collected browsing data to the automatic generation module;

[0012] The intelligent analysis module is used to analyze the user behavior data sent by the data collection module, analyze the correlation between the tags in the home design plans browsed and selected by the users based on the design elements and tags, and analyze the support between the tags of each tag in other design elements based on the home design plans selected by different users; and send the correlation between the tags in the home design plans browsed and selected by the users and the support between the tags of each tag in other design elements to the automatic generation module;

[0013] The automatic generation module is used to establish an intelligent model when the user clicks the one-click generation function, based on the browsing data sent by the data acquisition module, the different home design plans collected by the search database, the correlation between the tags in the home design plans browsed and selected by the user sent by the intelligent analysis module, and the support between the tags of each tag in other design elements, to automatically generate a home design plan that fits the user, and send the generated home design plan that fits the user to the interior home design platform;

[0014] The interior home design platform is used for users to log in after authorization and browse home design plans; the interior home design platform includes a one-click generation function; and is used to push home design plans that suit the user.

[0015] Compared with the prior art, the beneficial effects achieved by the present invention are: providing an interior home design platform management system and method based on an intelligent model, classifying and storing home design plans by establishing a retrieval database; analyzing the correlation between tags in home design plans browsed and selected by different users, thereby better predicting the home design plans preferred by the user through the home design plans currently browsed by the user; by analyzing the support between tags of each tag in other design elements, helping the home design plans to be more in line with user needs during the automatic generation process, optimizing the generated home design plans, making the prediction results more accurate, and reorganizing new home design plans through the retrieval database to provide users with personalized needs and improve user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a structural diagram of an interior home design platform management system based on an intelligent model of the present invention. DETAILED DESCRIPTION

[0017] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] See also Figure 1 , the present invention provides a technical solution:

[0019] In the first embodiment, a method for managing an interior home design platform based on an intelligent model is provided, the method comprising the following steps:

[0020] Step S1: Establish a search database to collect different home design solutions; the home design solutions include several design elements; each design element corresponds to several labels; based on the design elements and labels, the collected home design solutions are divided into different feature vectors and stored in the search database.

[0021] Furthermore, the method of segmenting the collected home design plans into different feature vectors according to design elements and labels is as follows: determining several design elements of the home design plan; determining the labels of each home design plan under different design elements according to the collected home design plans, and taking the labels of each home design plan under different design elements as a feature vector, segmenting the collected home design plans to obtain different feature vectors; wherein, the design elements represent the various units that constitute the home design plan; and the labels represent the specific product features that describe the design elements.

[0022] It should be noted that by establishing a retrieval database, the existing home design plans are divided into labels under different design elements. Each different label corresponds to a feature vector. The home design plans are classified according to the design elements and labels, which facilitates the subsequent interior home design platform to automatically generate home design plans that suit the user.

[0023] In this embodiment, the design elements of the home design scheme include but are not limited to the selection of floors, walls, ceilings, furniture, lighting design and other units that constitute the home design scheme; each design element corresponds to several labels, which are used to describe the specific product characteristics of the design elements, for example: floors include but are not limited to solid wood floors, composite floors and PVC floors with specific product characteristics; furniture includes but is not limited to fabric sofas, round dining tables and sliding door wardrobes with specific product characteristics.

[0024] Step S2: Obtain user permissions and collect user behavior data of different users in the interior home design platform; the user behavior data includes the home design plans browsed by the user and the home design plans selected by the user.

[0025] It should be noted that the user behavior data collected in step S2 is the historical data generated by different users in the interior home design platform; when the user logs in, different home design plans are pushed on the interior home design platform interface through a waterfall flow layout based on the home design plan push method such as public preferences and page views. The user can slide on the home design platform interface and click to enter and browse the corresponding furniture design plan; among them, the home design plans browsed by the user are collected in the retrieval database; the home design plan selected by the user is the home design plan finally selected by one or more users, which can be the favorites, favorites and finally confirmed home design plans set by the user.

[0026] Step S3: Analyze the user behavior data collected in step S2. Based on the design elements and tags, analyze the correlation between the tags in the home design plans browsed and selected by users. Based on the home design plans selected by different users, analyze the support of each tag among the tags in other design elements.

[0027] Specifically, the method steps are:

[0028] Step S31: Analyze the user behavior data collected in step S2, segment the home design plans browsed by different users, and determine the feature vector set of labels corresponding to each design element in the home design plans browsed by different users. ; ;in, Indicates the number of users in the analyzed user behavior data; ; Indicates the number of types of design elements in the home design plan; Indicates the The feature vector set of labels corresponding to each design element in the home design schemes browsed by each user; Indicates the The feature vector of the label corresponding to each design element in the home design schemes browsed by each user; the home design schemes selected by different users are divided, and the feature vector set of the label corresponding to each design element in the home design schemes selected by different users is determined ; ;in, Indicates the A set of feature vectors corresponding to the labels of each design element in the home design scheme selected by the user; Indicates the The feature vector of the label corresponding to each design element in the home design plan selected by the user;

[0029] Step S32: Determine the number of times the same feature vector label appears for different users based on the feature vector sets of labels corresponding to each design element in the home design plans browsed and selected by different users, and obtain the number of times each label corresponding to each design element appears for different users. Determine the correlation between the labels in the browsed and selected home design plans based on the number of times each label corresponding to each design element appears when the different users browse the home design plans and the number of times each label corresponding to each design element appears when the different users select the home design plans.

[0030] Step S33: Based on the feature vector set of the label corresponding to each design element in the home design solutions selected by different users, determine the frequency of different users selecting labels under other design elements when selecting labels under the corresponding design elements, and use the frequency of labels under other design elements as the support between each label in the labels of other design elements.

[0031] It should be noted that the correlation between the labels in the browsed and selected home design plans is determined based on the number of times the corresponding label of each design element appears when the user browses the home design plans and the number of times the corresponding label of each design element appears when the user selects the home design plan. If the user browses a certain design element of the design plan and browses a total of 100 labels, and the number of times label P1 appears is 10 times, and the number of times label P2 appears is 5 times, and the number of times label P1 and label P2 appear in the home design plan selected by the user is 1, then the correlation between label P1 in the browsed and selected home design plans is 0.1, and the correlation between label P2 is 0.2; determine the frequency of different users selecting labels under other design elements when selecting labels under corresponding design elements, and use the frequency of labels under other design elements as the support between labels in other design elements. If the user selects solid wood flooring as the label under the corresponding design element, and the frequency of fabric sofa appearance is 20%, then the support of fabric sofa to fabric sofa is 0.2.

[0032] It should be noted that, by analyzing the correlation between tags in the home design plans browsed and selected by users, the user's preferred home design plans can be better predicted based on the home design plans currently browsed by the user; by analyzing the support between tags in other design elements, the process of automatically generating home design plans can be more in line with user needs and the generated home design plans can be optimized; in this implementation, by analyzing the correlation between tags in home design plans browsed and selected by different users, and analyzing the support 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 plans and the support between tags in other design elements.

[0033] Step S4: After authorization, the user logs in to the interior home design platform, and different home design plans are presented on the interior home design platform. When the user browses the home design plans on the interior home design platform, browsing data of the user browsing the home design plans is collected; the interior home design platform includes a one-click generation function; when the user clicks the one-click generation function, an intelligent model is established based on the collected browsing data, the retrieval database established in step S1, the correlation between the tags in the browsed and selected home design plans analyzed in step S3, and the support between the tags in other design elements, to automatically generate a home design plan that suits the user.

[0034] Specifically, the method steps for automatically generating a home design plan that suits the user are as follows:

[0035] Step S41: analyzing browsing data of home design plans browsed by users on the interior home design platform, segmenting the home design plans browsed by users, and determining feature vectors of labels corresponding to each design element in the home design plans browsed by users;

[0036] Step S42: Establish an intelligent model. Based on the characteristic vector of the label corresponding to each design element obtained in step S41, use the Pearson correlation coefficient to perform search analysis in the search database. According to the calculation formula:

[0037] ;

[0038] in, Indicates the The first design element The correlation coefficient of the tags in the search database; Indicates the home design plan that the user browses. Design elements under Feature vector of the label; Indicates the home design plan that the user browses. The first design element The average value of the label feature vector; Indicates the first The first design element Feature vector of the label; Indicates the first The first design element The average value of the label feature vector; ; Indicates the The number of types of labels under a design element.

[0039] It should be noted that The value range is between -1 and 1. The closer it is to 1, the stronger the positive correlation is. By searching the search data for feature vectors associated with the home design solutions browsed by the user, L feature vectors with the strongest positive correlation are selected and substituted into step S43 to calculate the index value of the home design solution. L represents the number of feature vectors selected.

[0040] Step S43: Based on the search analysis in step S42, determine the correlation coefficients between different feature vectors in the search database and feature vectors corresponding to labels in the home design solutions browsed by the user; based on the correlation between labels in the browsed and selected home design solutions analyzed in step S3 and the support between labels of each label in other design elements, predict the feature vectors corresponding to labels of different design elements in the home design solutions that fit the user. ,make The formula to meet the conditions is:

[0041] ;

[0042] in, represents the index value for evaluating the predicted home design solution; The weight of the impact 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 influence of the support between labels in other design elements on the prediction of home design solutions; 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;

[0043] Step S44: , obtain the label of the design element corresponding to each feature vector in the search data, combine the obtained labels of the design elements, and obtain an automatically generated home design plan that suits the user.

[0044] In this implementation, in order to better fit the user's current preferences and predict the user's favorite home design plans, when automatically generating a home design plan that fits the user, the 10 home design plans that the user has browsed most recently are analyzed as browsing data.

[0045] In this embodiment, the interior home design platform pushes the automatically generated home design plan that suits the user to the user, and provides an interested button and a not interested button; the user can collect the automatically generated home design plan through the interested button for easy subsequent viewing; the user can use the not interested button to determine the labels under the design elements that are not of interest in the automatically generated home design plan, or automatically generate a new home design plan; if the user determines the labels under the design elements that are not of interest, the feature vectors corresponding to the labels under the design elements that are not of interest are cancelled from participating in the calculation in steps S41-step S44, and the labels under other design elements remain unchanged, and steps S41-step S44 are executed to obtain new feature vectors and determine new labels based on the new feature vectors; if a new home design plan is automatically generated, the feature vectors corresponding to the labels under all design elements in the pushed home design plan are cancelled from participating in the calculation in steps S41-step S44, and steps S41-step S44 are executed to obtain a new home design plan.

[0046] It should be noted that, by establishing an intelligent model, a search analysis is performed in the retrieval database through step S42, and based on the Pearson correlation coefficient, feature vectors that meet the home design plans browsed by the user are screened out in the retrieval database. Based on the correlation between the labels in the browsed and selected home design plans and the support between the labels of each label in other design elements, the screened feature vectors are further screened through step S43 to finally determine the home design plan that suits the user. In this method, the home design plan is divided into labels under different design elements, and the correlation and support between the labels are analyzed to make the prediction result more accurate. The new home design plan is reorganized by searching the database to provide users with personalized needs and improve the user experience.

[0047] See also Figure 1 ,In this embodiment 2: ,provides an interior home design platform management system based on an intelligent model, ,which includes a retrieval database, a data acquisition module, an intelligent ,analysis module, an automatic generation module and an interior home design platform;

[0048] The search database is used to collect different home design plans, divide the collected home design plans into different feature vectors and store them; the home design plans include several design elements; each design element corresponds to several tags;

[0049] The data collection module is used to obtain user permissions and collect user behavior data of different users in the interior home design platform; the user behavior data includes the home design plans browsed by the user and the home design plans selected by the user; collect browsing data of the home design plans browsed by the user; send the collected user behavior data to the intelligent analysis module; and send the collected browsing data to the automatic generation module;

[0050] The intelligent analysis module is used to analyze the user behavior data sent by the data collection module, analyze the correlation between the tags in the home design plans browsed and selected by the users based on the design elements and tags, and analyze the support between the tags of each tag in other design elements based on the home design plans selected by different users; and send the correlation between the tags in the home design plans browsed and selected by the users and the support between the tags of each tag in other design elements to the automatic generation module;

[0051] The automatic generation module is used to establish an intelligent model when the user clicks the one-click generation function, based on the browsing data sent by the data acquisition module, the different home design plans collected by the search database, the correlation between the tags in the home design plans browsed and selected by the user sent by the intelligent analysis module, and the support between the tags of each tag in other design elements, to automatically generate a home design plan that fits the user, and send the generated home design plan that fits the user to the interior home design platform;

[0052] The interior home design platform is used for users to log in after authorization and browse home design plans; the interior home design platform includes a one-click generation function; and is used to push home design plans that suit the user.

[0053] Furthermore, the intelligent analysis module includes a user behavior data analysis unit, a correlation analysis unit and a support analysis unit;

[0054] The user behavior data analysis unit is used to analyze the collected user behavior data, segment the home design plans browsed by different users, and determine the feature vector set of labels corresponding to each design element in the home design plans browsed by different users; segment the home design plans selected by different users, and determine the feature vector set of labels corresponding to each design element in the home design plans selected by different users; and send the user behavior data analysis results to the correlation analysis unit and the support analysis unit;

[0055] The correlation analysis unit is used to determine the number of times the same feature vector label appears for different users based on the feature vector set of the label corresponding to each design element in the home design plans browsed and selected by different users, obtain the number of times the label corresponding to each design element appears for different users, and determine the correlation between the labels in the browsed and selected home design plans based on the number of times the label corresponding to each design element appears when the different users browse the home design plans and the number of times the label corresponding to each design element appears when the different users select the home design plans;

[0056] The support analysis unit is used to determine the frequency of different users selecting labels under other design elements when selecting labels under corresponding design elements based on the feature vector set of labels corresponding to each design element in the home design plans selected by different users, and use the frequency of labels under other design elements as the support of each label among the labels in other design elements.

[0057] Furthermore, the automatic generation module includes a browsing data analysis unit, a model management unit and a solution automatic generation unit;

[0058] The browsing data analysis unit is used to analyze the browsing data of home design plans browsed by users on the interior home design platform, segment the home design plans browsed by users, determine the feature vector of the label corresponding to each design element in the home design plans browsed by users; and send the browsing data analysis results to the model management unit;

[0059] The model management unit is used to establish an intelligent model, perform retrieval analysis in the retrieval database using the Pearson correlation coefficient based on the browsing data analysis results sent by the model management unit, and send the retrieval analysis results to the automatic solution generation unit;

[0060] The automatic scheme generation unit is used to determine the correlation coefficients between different feature vectors in the retrieval analysis results sent by the model management unit and the feature vectors corresponding to the labels in the home design schemes browsed by the user, and predict the feature vectors corresponding to the labels of different design elements in the home design scheme that fits the user based on the correlation between the labels in the analyzed browsed and selected home design schemes and the support between the labels of each label 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 an automatically generated home design scheme that fits the user.

[0061] In this embodiment:

[0062] Search the database to collect different home design plans, divide the collected home design plans into different feature vectors and store them;

[0063] The data collection module obtains user permissions and collects user behavior data of different users in the interior home design platform; collects browsing data of users browsing home design plans; sends the collected user behavior data to the intelligent analysis module; and sends the collected browsing data to the automatic generation module;

[0064] The user behavior data analysis unit in the intelligent analysis module analyzes the collected user behavior data and sends the user behavior data analysis results to the correlation analysis unit and the support analysis unit; the correlation analysis unit determines the correlation between the tags in the browsed and selected home design plans and sends it to the automatic generation module; the support analysis unit determines the support between each tag in other design elements and sends it to the automatic generation module;

[0065] The browsing data analysis unit in the automatic generation module analyzes the browsing data of the home design plans browsed by the user in the interior home design platform, and sends the browsing data analysis results to the model management unit; the model management unit establishes an intelligent model, performs 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 plan 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 plan that fits the user, and sends the generated home design plan that fits the user to the interior home design platform;

[0066] After authorization, the user logs in to the interior home design platform and browses home design plans. When the user clicks the one-click generation function, a signal is sent to the push automatic generation module, and the automatic generation module generates a home design plan that suits the user and displays it through the interior home design platform.

[0067] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are 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 will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for managing an interior home design platform based on an intelligent model, characterized by: The method comprises the following steps: Step S1: Establish a search database to collect different home design solutions; the home design solutions include several design elements; each design element corresponds to several tags; determine tags based on the design elements, determine feature vectors based on the tags, divide the collected home design solutions into different feature vectors, and store them in the search database; Step S2: obtaining user permissions and collecting user behavior data of different users in the interior home design platform; the user behavior data includes the home design plans browsed by the user and the home design plans selected by the user; Step S3: Analyze the user behavior data collected in step S2. Analyze the correlation between tags in the home design solutions browsed and selected by users based on design elements and tags. Analyze the support of each tag in other design elements based on the home design solutions selected by different users. The specific steps are as follows: Step S31: Analyze the user behavior data collected in step S2, segment the home design plans browsed by different users, and determine a set of feature vectors of labels corresponding to each design element in the home design plans browsed by different users; segment the home design plans selected by different users, and determine a set of feature vectors of labels corresponding to each design element in the home design plans selected by different users; Step S32: Determine the number of times the same feature vector label appears for different users based on the feature vector sets of labels corresponding to each design element in the home design plans browsed and selected by different users, and obtain the number of times each label corresponding to each design element appears for different users. Determine the correlation between the labels in the browsed and selected home design plans based on the number of times each label corresponding to each design element appears when the different users browse the home design plans and the number of times each label corresponding to each design element appears when the different users select the home design plans. Step S33: Based on the feature vector sets of labels corresponding to each design element in the home design solutions selected by different users, determine the frequency of different users selecting labels under other design elements when selecting labels under the corresponding design elements, and use the frequency of labels under other design elements as the support degree of each label among the labels in other design elements; The method for segmenting the collected home design plans into different feature vectors according to design elements and labels is as follows: determining several design elements of the home design plan; determining the labels of each home design plan under different design elements according to the collected home design plans, and taking the labels of each home design plan under different design elements as a feature vector, segmenting the collected home design plans to obtain different feature vectors; wherein, the design elements represent the various units constituting the home design plan; the labels represent the specific product features describing the design elements. Step S4: After authorization, the user logs in to the interior home design platform, and different home design plans are presented on the interior home design platform. When the user browses the home design plans on the interior home design platform, browsing data of the user browsing the home design plans is collected; the interior home design platform includes a one-click generation function; when the user clicks the one-click generation function, an intelligent model is established based on the collected browsing data, the search database established in step S1, the correlation between tags in the browsed and selected home design plans analyzed in step S3, and the support between tags of each tag in other design elements, to automatically generate a home design plan that fits the user; The method steps for automatically generating a home design plan that suits the user are as follows: Step S41: analyzing browsing data of home design plans browsed by users on the interior home design platform, segmenting the home design plans browsed by users, and determining feature vectors of labels corresponding to each design element in the home design plans browsed by users; Step S42: Establish an intelligent model and perform search analysis in a search database using the Pearson correlation coefficient based on the feature vector of the label corresponding to each design element obtained in step S41; Step S43: Based on the search analysis in step S42, the correlation coefficients between different feature vectors in the search database and feature vectors corresponding to labels in the home design solutions browsed by the user are determined. Based on the correlation between labels in the browsed and selected home design solutions analyzed in step S3 and the support between labels of each label in other design elements, feature vectors N1, N2, ..., N corresponding to labels of different design elements in the home design solutions that fit the user are predicted. x , so that N1, N2, ..., N x The formula to meet the conditions is: Among them, Z represents the index value of the predicted home design scheme; w1 represents the influence weight of the correlation between the tags in the browsed and selected home design schemes on the prediction of the home design scheme; w2 represents the influence weight of the support between the tags in other design elements on the prediction of the home design scheme; x represents the number of design elements in the home design scheme; N u The feature vector representing the label corresponding to the u-th design element in the predicted home design plan that best fits the user; u = {1, 2, ... x}; Represents the eigenvector N u the correlation between tags in browsing and selecting home design solutions; Represents the tag feature vector N retrieved in the retrieval database u Correlation coefficient of Represents the eigenvector N u The support between the corresponding label and the corresponding labels of other design elements in the predicted home design plan; Step S44: According to N1, N2, ..., N x , obtain the label of the design element corresponding to each feature vector in the retrieval database, combine the obtained labels of the design elements, and obtain an automatically generated home design plan that suits the user.

2. The method for managing an interior home design platform based on an intelligent model according to claim 1, characterized in that: The calculation formula for searching and analyzing in the search database is: Among them, r i×j It represents the correlation coefficient of the jth tag under the i-th design element in the search database; The feature vector representing the jth label under the i-th design element in the home design plan browsed by the user; Indicates the i-th design element in the home design scheme browsed by the user under x i The average value of the label feature vector; Represents the feature vector of the jth label under the i-th design element for retrieval analysis in the retrieval database; Indicates that the retrieval analysis is performed on the retrieval database under the i-th design element in x i The average value of the label feature vector; j = {1,2,...,x i };x i Indicates the number of types of labels under the i-th design element.

3. An interior home design platform management system based on an intelligent model, characterized by: The system includes a retrieval database, a data acquisition module, an intelligent analysis module, an automatic generation module and an interior furniture design platform; The search database is used to collect different home design plans, divide the collected home design plans into different feature vectors and store them; the home design plans 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 in the interior home design platform; the user behavior data includes the home design plans browsed by the user and the home design plans selected by the user; and collect browsing data of the home design plans 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 tags in the home design plans browsed and selected by the users based on the design elements and tags, and analyze the support between the tags of each tag in other design elements based on the home design plans selected by different users; and send the correlation between the tags in the home design plans browsed and selected by the users and the support between the tags of each tag in other design elements to the automatic generation module; The intelligent analysis module includes a user behavior data analysis unit, a correlation analysis unit and a support analysis unit; The user behavior data analysis unit is used to analyze the collected user behavior data, segment the home design plans browsed by different users, and determine the feature vector set of labels corresponding to each design element in the home design plans browsed by different users; segment the home design plans selected by different users, and determine the feature vector set of labels corresponding to each design element in the home design plans selected by different users; and send the user behavior data analysis results to the correlation analysis unit and the support analysis unit; The correlation analysis unit is used to determine the number of times the same feature vector label appears for different users based on the feature vector set of the label corresponding to each design element in the home design plans browsed and selected by different users, obtain the number of times the label corresponding to each design element appears for different users, and determine the correlation between the labels in the browsed and selected home design plans based on the number of times the label corresponding to each design element appears when the different users browse the home design plans and the number of times the label corresponding to each design element appears when the different users select the home design plans; The support analysis unit is used to determine the frequency of different users selecting labels under other design elements when selecting labels under corresponding design elements based on the feature vector set of labels corresponding to each design element in the home design solutions selected by different users, and use the frequency of labels under other design elements as the support of each label among the labels in other design elements; The automatic generation module is used to establish an intelligent model when the user clicks the one-click generation function, based on the browsing data sent by the data acquisition module, the different home design plans collected by the search database, the correlation between the tags in the home design plans browsed and selected by the user sent by the intelligent analysis module, and the support between the tags of each tag in other design elements, to automatically generate a home design plan that fits the user, and send the generated home design plan that fits the user to the interior home design platform; 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 home design plans browsed by users on the interior home design platform, segment the home design plans browsed by users, determine the feature vector of the label corresponding to each design element in the home design plans browsed by users; and send the browsing data analysis results to the model management unit; The model management unit is used to establish an intelligent model, perform retrieval analysis in the retrieval database using the Pearson correlation coefficient based on the browsing data analysis results sent by the browsing data analysis unit, and send the retrieval analysis results to the solution automatic generation unit; The automatic scheme generation unit is used to determine the correlation coefficients between different feature vectors in the retrieval database and feature vectors corresponding to labels in the home design scheme browsed by the user based on the retrieval analysis results sent by the model management unit, predict the feature vectors corresponding to labels of different design elements in the home design scheme that fits the user based on the correlation between the labels in the browsed and selected home design schemes and the support between the labels of each label in other design elements, obtain the label of the design element corresponding to each feature vector in the retrieval database, combine the obtained labels of the design elements, and obtain the automatically generated home design scheme that fits the user. The interior home design platform is used for users to log in after authorization and browse home design plans; the interior home design platform includes a one-click generation function; Used to push home design plans that suit users.

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