Automatic layout method and system applied to intelligent apartment planning

Abstract

The invention relates to the technical field of apartment planning, and particularly discloses an automatic layout method and system applied to intelligent apartment planning, and the method comprises the steps: obtaining a potential user corresponding to a target region and a corresponding personalized tag; recombining the potential users according to the personalized tags to obtain a plurality of potential user groups and corresponding group tags and volumes; obtaining user tags of rented users in a preset adjacent area of the target area and existing layout information of a corresponding rental apartment; comparing the group tag with the user tag to screen the existing layout information so as to determine the expected layout information of the potential user group; screening expected layout information according to the volumes of different potential user groups and the number of target apartment blocks so as to determine layout suggestion information; according to the method, full-process automation from user demand analysis to specific layout suggestion is realized, the problem of lack of pertinence and personalization in intelligent apartment planning is effectively solved, and the demands of different user groups can be accurately met.

Description

Technical Field

[0001] The present application relates to the technical field of apartment planning, and more specifically, to an automatic layout method and system thereof applied to smart apartment planning. Background Art

[0002] In contemporary society, the needs of tenants are increasingly showing distinct personalized characteristics. This trend is not only reflected in higher requirements for the comfort and convenience of the living environment, but also in unique preferences for space layout, decoration style and even functional configuration. However, traditional apartment layouts often adopt a "one-size-fits-all" model, that is, the supplier selects and determines from the preset apartment layout styles, which seriously ignores the personalized needs of tenants.

[0003] With the improvement of living standards and changes in aesthetic concepts, tenants increasingly hope that their living space can reflect their personal taste and lifestyle. However, the existing apartment layout model lacks flexibility and cannot meet the diverse needs of tenants in terms of space layout and decoration style. This not only limits the choice of tenants, but also reduces the attractiveness and market competitiveness of apartments.

[0004] Therefore, how to break the limitations of traditional apartment layouts and provide more personalized and customized apartment products has become an urgent problem to be solved in the current real estate market. By introducing more flexible and changeable layout designs and decoration styles, it can not only better meet the personalized needs of tenants, but also improve the overall quality and living experience of the apartment, thereby attracting more tenants to choose and move in.

[0005] There is currently no effective technical solution to the above problems. Summary of the invention

[0006] The purpose of this application is to provide an automatic layout method and system for smart apartment planning, so as to realize the automation of the whole process from user demand analysis to specific layout suggestions, so as to solve the problem of lack of pertinence and personalization in smart apartment planning.

[0007] In a first aspect, the present application provides an automatic layout method for smart apartment planning, which is used to plan and layout smart apartments. The method comprises the following steps: S1. Obtain potential users and corresponding personality tags corresponding to the target area; S2. Reorganize the potential users according to their individual tags to obtain multiple potential user groups and corresponding group tags and volumes; S3, obtaining user tags of rented users in a preset neighboring area of ​​the target area and existing layout information of the corresponding rental apartments; S4, comparing the group labels and the user labels to filter the existing layout information to determine the desired layout information of the potential user groups; S5. Filter the desired layout information according to the size of different potential user groups and the target number of apartment buildings to determine the layout suggestion information.

[0008] The automatic layout method applied to smart apartment planning of the present application is used to plan and layout smart apartments. It obtains and analyzes the individual tags of potential users, groups potential users and matches them with existing successful layout cases, so as to formulate a layout plan that meets their characteristics for each potential user group, realizes the automation of the entire process from user demand analysis to specific layout suggestions, effectively solves the problem of lack of targeting and personalization in smart apartment planning, can accurately meet the needs of different user groups, improve occupancy rate and user satisfaction, and thus enhance the market competitiveness of the project.

[0009] In the automatic layout method applied to smart apartment planning, the personality tag includes one or more of age information, gender information, occupation information, family structure information, and income information.

[0010] The above-mentioned definition method of personalized tags can more comprehensively reflect the personal characteristics and life needs of potential users; by including multiple types of information in personalized tags, this application can more accurately capture the characteristics and needs of potential users, thereby providing a richer and more accurate data basis for subsequent user grouping and layout matching processes.

[0011] The automatic layout method applied to smart apartment planning, wherein S1 includes: Acquire potential users and corresponding personality tags based on the target area’s geographic location information, traffic information, and job information.

[0012] The automatic layout method applied to smart apartment planning in this application obtains potential users and their personality tags by utilizing multi-dimensional regional information. Compared with the traditional single information acquisition method, it can grasp the user characteristics of the target area more comprehensively and accurately, and provide a more reliable data basis for subsequent smart apartment planning. It not only improves the accuracy of potential user identification, but also provides more targeted information support for personalized apartment layout.

[0013] The automatic layout method applied to smart apartment planning, wherein step S2 comprises: S21, dividing and reorganizing the potential users according to the segmented intervals of a plurality of preset personality tags to obtain a plurality of potential user groups; S22. Count the number of potential users in each potential user group as the volume of the potential user group, and use the segment interval where the personality label corresponding to each potential user group is located as the group label.

[0014] The automatic layout method applied to smart apartment planning, wherein step S4 comprises: S41, screening multiple existing layout information matching each potential user group according to the matching relationship between the group tag and the user tag, each existing layout information including multiple different types of subdivided layout information; S42: According to the largest number of subdivided layout information of different types of subdivided layout information in the plurality of existing layout information corresponding to each potential user group, select different types of subdivided layout information corresponding to each potential user group to form the desired layout information.

[0015] The automatic layout method applied to smart apartment planning, wherein step S5 comprises: S51, filtering and obtaining potential user groups equal in number to the preset number of target apartment buildings according to the volume of the potential user groups; S52: Construct layout suggestion information about different apartment types based on the expected layout information of the screened potential user groups.

[0016] The automatic layout method applied to smart apartment planning, wherein step S5 comprises: S51', obtaining the proportion of different potential user groups according to the volume of the potential user groups; S52', screening potential user groups based on a preset percentage threshold and the percentage of the potential user groups; S53', constructing layout suggestion information about different apartment types based on the expected layout information of the screened potential user groups, and determining the number of different apartment types based on the ratio of the volumes of each screened potential user group and the target number of apartment buildings.

[0017] The automatic layout method applied to smart apartment planning, wherein the steps between step S4 and step S5 further include: SB. Obtain the volume of vacant apartments corresponding to the expected layout information of the potential user group in a preset neighboring area of ​​the target area, and adjust the volume of each potential user group according to the volume of the vacant apartments.

[0018] The automatic layout method applied to smart apartment planning, wherein the steps between step S4 and step S5 further include: SA. Obtain user preset weights, and modify and adjust the size of each potential user group based on the user preset weights.

[0019] In a second aspect, the present application also provides an automatic layout system for smart apartment planning, the system comprising: A data collection module, used to obtain potential users and corresponding personality tags corresponding to the target area, and to obtain user tags of rented users in a preset neighboring area of ​​the target area and existing layout information of the corresponding rental apartments; A data integration module, used for reorganizing the potential users according to the individual tags to obtain multiple potential user groups and corresponding group tags and volumes; The data analysis module is used to compare group labels and user labels to filter existing layout information to determine the expected layout information of potential user groups, and to filter the expected layout information according to the volume and target number of apartment buildings of different potential user groups to determine layout suggestion information.

[0020] The automatic layout system applied to smart apartment planning in this application is used to plan and layout smart apartments, realizing full process automation from user demand analysis to specific layout suggestions, effectively solving the problem of lack of targeting and personalization in smart apartment planning, and being able to accurately meet the needs of different user groups, improve occupancy rate and user satisfaction, thereby enhancing the market competitiveness of the project.

[0021] From the above, it can be seen that the present application provides an automatic layout method and system for smart apartment planning, wherein the automatic layout method for smart apartment planning is used to plan and layout smart apartments. It obtains and analyzes the personality tags of potential users, groups potential users and matches them with existing successful layout cases, thereby formulating a layout plan that meets their characteristics for each potential user group, realizing full process automation from user demand analysis to specific layout suggestions, effectively solving the problem of lack of targeting and personalization in smart apartment planning, and being able to accurately meet the needs of different user groups, improve occupancy rate and user satisfaction, thereby enhancing the market competitiveness of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of an automatic layout method for smart apartment planning provided in some embodiments of the present application.

[0023] Figure 2 A flowchart of an automatic layout method for smart apartment planning provided in some other embodiments of the present application.

[0024] Figure 3 A schematic diagram of the structure of an automatic layout system for smart apartment planning provided in an embodiment of the present application.

[0025] Figure numerals: 201, data collection module; 202, data integration module; 203, data analysis module. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0027] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0028] Traditional apartment layout methods usually adopt standardized design patterns and lack consideration for the personalized needs of tenants. This method cannot meet the diverse requirements of different user groups for living space, resulting in a decline in the attractiveness and market competitiveness of apartments. For example, in a typical smart apartment project, the developer plans to build a large apartment complex in the core area of ​​a city. The project faces a diverse group of potential tenants, including young white-collar workers, small families, students, etc. The traditional layout method cannot accurately capture the demand characteristics of these different groups, resulting in a significant gap between apartment design and actual market demand. Specifically, it is impossible to automatically recommend a suitable room layout based on factors such as user occupation, age, and family structure; it is difficult to predict the optimal proportion of different types of apartments based on historical data; there is a lack of analysis and utilization mechanism for the preferences of surrounding tenants, etc. These problems lead to a significant difference between apartment design and actual needs, affecting the market positioning and operation effect of the project.

[0029] First, please refer to Figure 1 and Figure 2 Some embodiments of the present application provide an automatic layout method for smart apartment planning, which is used to plan and layout smart apartments. The method includes the following steps: S1. Obtain potential users and corresponding personality tags corresponding to the target area; S2. Reorganize potential users according to their personality tags to obtain multiple potential user groups and corresponding group tags and volumes; S3, obtaining user tags of rented users in a preset neighboring area of ​​the target area and existing layout information of the corresponding rental apartments; S4, comparing the group labels and the user labels to filter the existing layout information to determine the desired layout information of the potential user groups; S5. Filter the desired layout information according to the size of different potential user groups and the target number of apartment buildings to determine the layout suggestion information.

[0030] Specifically, the target area is an area where smart apartments are being planned to be built or an area where smart apartments have been built and the internal layout of the apartments is yet to be carried out. The potential users are the target population who are inferred to have a demand for apartment rental based on data analysis and may rent apartments in the target area and its preset neighboring areas.

[0031] More specifically, a personal label refers to attribute information describing the characteristics of a potential user, which may include age, occupation, income level, family structure, etc. A user group refers to a set of potential users with similar characteristics, which can be obtained by analyzing personal labels through a clustering algorithm. A group label refers to an identifier used to describe the characteristics of a user group, which can be represented by the most representative combination of personal labels in the group. Volume refers to the size of a user group, which can be measured by the number of potential users in the group. Existing layout information refers to the spatial arrangement and functional configuration data of the apartment where the rented user lives, which can be obtained through research and data collection. Expected layout information refers to the apartment layout plan that is most suitable for the group, which is inferred based on the characteristics of the potential user group, and can be obtained by comparing and analyzing the existing layout information. Layout suggestion information is the expected layout information pushed to the layout designer after confirmation, which refers to specific apartment design suggestions given for different user groups, which may include decoration style, room division, supporting facilities, etc.

[0032] More specifically, considering that the traditional layout method cannot meet the diverse needs of different user groups, this application proposes an automatic layout method based on user feature analysis. Specifically, by obtaining the personality tags of potential users in the target area, user needs can be understood more accurately. This method can overcome the problem of insufficient understanding of user needs in traditional methods.

[0033] More specifically, in order to effectively utilize the existing successful experience, the present application proposes an idea of ​​combining existing layout information. The automatic layout method applied to smart apartment planning in the embodiment of the present application can establish a database of actual market demand and layout references by obtaining the user tags of rented users in the preset neighboring area of ​​the target area and the existing layout information of the corresponding rental apartments, and then perform layout reasoning on this basis. This processing method can avoid relying entirely on theoretical speculation and make decisions based on actual data; wherein, the user tags of rented users are of the same type as the aforementioned personal tags, so that the automatic layout method applied to smart apartment planning in the embodiment of the present application can determine whether the corresponding potential users and rented users belong to the same group of people based on the difference between personal tags and user tags.

[0034] It should be noted that the relevant data of potential users and existing users can be collected through questionnaires, interviews, focus group discussions, application authorization, etc. that do not violate the privacy agreement.

[0035] More specifically, step S2 is equivalent to the process of classifying potential users. By reorganizing potential users according to personality tags, multiple potential user groups and corresponding group tags and volumes can be obtained. The purpose is to group users with similar needs together to provide a basis for subsequent targeted layout.

[0036] More specifically, after obtaining the potential user groups and existing layout information, the automatic layout method applied to smart apartment planning in the embodiment of the present application proposes a processing method of comparing group labels and user labels to filter the existing layout information, thereby determining the expected layout information of these potential user groups, so as to quickly find the layout plan that best suits the potential users in a large amount of data.

[0037] More specifically, taking into account the size differences of different user groups, this application proposes a method for screening the desired layout information according to the size of the potential user group and the target number of apartment buildings to determine the final layout recommendation information; this processing method can ensure that the layout recommendation is not only personalized, but also meets the actual market demand ratio.

[0038] The automatic layout method applied to smart apartment planning in the embodiment of the present application is used to plan and layout smart apartments. It obtains and analyzes the individual tags of potential users, groups potential users and matches them with existing successful layout cases, so as to formulate a layout plan that meets their characteristics for each potential user group, and realizes the automation of the entire process from user demand analysis to specific layout suggestions, effectively solving the problem of lack of targeting and personalization in smart apartment planning, and can accurately meet the needs of different user groups, improve occupancy rate and user satisfaction, thereby enhancing the market competitiveness of the project.

[0039] In some preferred implementations, the personality tag includes one or more of age information, gender information, occupation information, family structure information, and income information.

[0040] Specifically, the above-mentioned definition method of personalized tags can more comprehensively reflect the personal characteristics and life needs of potential users; by including multiple types of information in personalized tags, the present application can more accurately capture the characteristics and needs of potential users, thereby providing a richer and more accurate data basis for subsequent user grouping and layout matching processes.

[0041] More specifically, age information can reflect the differences in users' needs at different stages of life. For example, young people may prefer open layouts and modern facilities, while middle-aged or elderly people may pay more attention to comfort and safety. Gender information may affect preferences for spatial layout and facility configuration, such as the size of a cloakroom or the design of a bathroom. Occupational information may be related to working hours and living habits, which in turn affects the demand for home office space or leisure areas. Family structure information directly affects the number of rooms and layout requirements. For example, singles, couples, or families with children have different requirements for the number of bedrooms and public spaces. Income information may be related to the requirements for apartment grade and supporting facilities, affecting the choice of decoration materials and the configuration of smart home devices.

[0042] More specifically, this multi-dimensional personalized label definition enables the subsequent user grouping and layout matching process to be more accurate and personalized. By comprehensively considering these factors, the automatic layout method applied to smart apartment planning in the embodiment of the present application can provide potential user groups with different characteristics with an apartment layout plan that is more in line with their needs.

[0043] More specifically, in practical applications, these personality tags can be obtained and used in a variety of ways. For example, relevant information about potential users can be collected through questionnaires, big data analysis, or docking with other databases. The collected information can be coded and quantified to facilitate subsequent data processing and analysis.

[0044] In some preferred embodiments, S1 comprises: Acquire potential users and corresponding personality tags based on the target area’s geographic location information, traffic information, and job information.

[0045] Specifically, by utilizing the geographic location information, traffic information, and job information of the target area, potential user groups can be effectively identified and located. Geographic location information helps determine the specific scope and surrounding environment of the target area; traffic information can reflect the accessibility and convenience of the area; and job information can reveal the employment opportunities and occupational distribution in the area. Comprehensive analysis of this information can help more accurately predict and locate potential users who may be interested in apartments in the area and their distribution.

[0046] More specifically, after determining the potential users and their distribution, relevant information of the potential users can be collected through questionnaires, big data analysis, or docking with other databases based on the potential users and their distribution to obtain corresponding personality labels; in other embodiments, the personality labels of potential users can also be inferred based on "geographic location information, traffic information, and job information", such as inferring the age information, gender information, occupation information, and income information of potential users based on job information.

[0047] More specifically, the automatic layout method applied to smart apartment planning in the embodiment of the present application obtains potential users and their personality tags by utilizing multi-dimensional regional information. Compared with the traditional single information acquisition method, it can grasp the user characteristics of the target area more comprehensively and accurately, and provide a more reliable data basis for subsequent smart apartment planning. It not only improves the accuracy of potential user identification, but also provides more targeted information support for personalized apartment layout.

[0048] More specifically, geographic location information may include the specific coordinates of the target area, the urban area (such as the city center, suburbs, etc.), the surrounding environment (such as commercial areas, residential areas, schools, etc.), etc. For example, a target area near the city center business district may attract more young white-collar workers or business people; transportation information may include the convenience of public transportation (such as the number and distance of subway stations and bus stations), the accessibility of major roads, and the distance to the airport or railway station. For example, a target area close to a subway station may be more popular with commuters and will also affect the distribution range of potential users; job position information may include major surrounding enterprises, industrial parks, business centers, etc. For example, a target area close to a science and technology park may attract more IT practitioners.

[0049] More specifically, the automatic layout method for smart apartment planning in the embodiment of the present application can also improve the recognition accuracy of potential users and their personality tags through big data analysis and machine learning algorithms. For example, a prediction model can be established to input various information of the target area and output the characteristics and personality tags of potential users. This model can be continuously optimized through training with historical data to improve the accuracy of prediction.

[0050] In some preferred embodiments, step S2 comprises: S21, dividing and reorganizing potential users according to multiple segmented intervals of preset personality tags to obtain multiple potential user groups; S22. Count the number of potential users in each potential user group as the volume of the potential user group, and use the segment interval where the personality label corresponding to each potential user group is located as the group label.

[0051] Specifically, the core of the above steps is to integrate the characteristics of potential users and integrate scattered individual tags into group tags for different groups.

[0052] More specifically, the segmented intervals may be intervals determined by preset division rules, and different types of characteristic information in personality tags may have different division rules, such as dividing age into intervals such as 18-25, 26-35, 36-50, and dividing income levels into three levels: low, medium, and high. Step S21 may classify potential users in multiple dimensions according to these segmented intervals to form user groups with similar characteristics. At the same time, step S22 may count the number of users in each potential user group as the size of the potential user group.

[0053] More specifically, a group label refers to an identifier used to describe the characteristics of a user group, and can be represented by the most representative combination of personality labels in the group. In a more preferred embodiment, the segmented intervals where different types of characteristic information in the personality labels of each potential user group are located constitute the group label of the corresponding potential user group.

[0054] More specifically, step S21 may group these potential users by using a clustering algorithm, such as using a K-means clustering algorithm to group these potential users.

[0055] More specifically, the automatic layout method applied to smart apartment planning in the embodiment of the present application obtains the size and group labels of potential user groups based on the above steps, so as to simplify the subsequent data analysis and comparison process through data integration, and to achieve the screening of desired layout information through a comprehensive analysis processing method, thereby improving data processing efficiency and data representativeness.

[0056] In some preferred embodiments, step S4 comprises: S41, screening multiple existing layout information matching each potential user group according to the matching relationship between the group tag and the user tag, each existing layout information including multiple different types of subdivided layout information; S42: According to the largest number of subdivided layout information of different types of subdivided layout information in the plurality of existing layout information corresponding to each potential user group, different types of subdivided layout information corresponding to each potential user group are selected to form desired layout information.

[0057] Specifically, the automatic layout method applied to smart apartment planning in the embodiment of the present application proposes to filter multiple existing layout information matching each potential user group according to the matching relationship between group tags and user tags. Each existing layout information includes multiple different types of subdivided layout information, involving specific layout subdivision items such as decoration style, room division, supporting facilities, such as whether a kitchen is configured, whether the bathroom is separated into dry and wet areas, whether air conditioning is provided, etc.

[0058] More specifically, the matching relationship in step S41 can be similarity or consistency. The process of screening based on similarity belongs to the screening process of fuzzy retrieval, and the process of screening based on consistency belongs to the screening process of precise retrieval. The former can ensure that enough existing layout information is screened out for subsequent analysis, and the latter can ensure that the screened existing layout information accurately meets the needs of potential user groups. Both can be set according to user needs.

[0059] More specifically, the automatic layout method applied to smart apartment planning in the embodiment of the present application compares group labels and user labels to screen out existing layout information that matches potential user groups, and then selects the largest (most common) combination of segmented layout information as the expected layout information by analyzing the frequency of occurrence of each type of segmented layout information in these matching layout information. This method can effectively utilize existing layout experience while taking into account the characteristics of potential user groups, thereby generating a layout plan that better meets user needs. The method of selecting the largest number of segmented layout information also ensures that the desired layout information finally generated can reflect the preferences of most matching users.

[0060] More specifically, the matching relationship between group labels and user labels: This feature can be implemented in a variety of ways. For example, the cosine similarity can be used to calculate the similarity between label vectors, or the Jaccard similarity coefficient can be used to calculate the similarity between label sets. Another method is to use a machine learning algorithm, such as a support vector machine (SVM) or a random forest, to train a classifier to determine whether the labels match; in addition, in an implementation where the group label corresponds to the segmented interval where the different types of feature information in the aforementioned personal labels are located, the automatic layout method applied to smart apartment planning in an embodiment of the present application can achieve matching analysis by determining whether the user labels are all within the group label.

[0061] More specifically, matching relationships include similarity or consistency: This feature provides flexible matching criteria. Similarity can be measured by calculating the distance or correlation between labels, such as using cosine similarity or Pearson correlation coefficient. Consistency can be determined by checking whether the labels are exactly the same or within a predefined set of synonyms.

[0062] In some preferred embodiments, step S5 comprises: S51, filtering and obtaining potential user groups equal in number to the preset number of target apartment buildings according to the volume of the potential user groups; S52: Construct layout suggestion information about different apartment types based on the expected layout information of the screened potential user groups.

[0063] Specifically, the target number of apartment buildings is the number of apartment buildings to be built in the target area. For ease of management, certain apartment layout constructions generally maintain a consistent layout for all apartments in the entire apartment building. Therefore, the automatic layout method applied to smart apartment planning in an embodiment of the present application screens the desired layout information based on the target number of apartment buildings to provide different layout suggestion information for different apartment buildings.

[0064] More specifically, step S51 obtains potential user groups equal to the number of preset target apartment buildings, so as to ensure that each apartment building has a corresponding target user group. Then step S52 constructs layout suggestion information for different apartment types based on the expected layout information of the screened potential user groups. This processing method can customize the apartment layout according to actual market demand and user preferences, and is a layout for all apartments in the entire apartment building for different apartment buildings, thereby improving the market adaptability and attractiveness of the apartments.

[0065] More specifically, a modular design approach can be used when constructing layout suggestion information. For example, an apartment can be divided into basic modules such as living room, bedroom, kitchen, and bathroom, and then the size, layout, and function of each module can be adjusted according to the needs of different user groups. This approach can both meet personalized needs and ensure the efficiency of design and construction.

[0066] In some preferred embodiments, step S5 comprises: S51', obtaining the proportion of different potential user groups according to the volume of the potential user groups; S52', screening potential user groups based on a preset percentage threshold and the percentage of potential user groups; S53', constructing layout suggestion information about different apartment types based on the expected layout information of the screened potential user groups, and determining the number of different apartment types based on the ratio of the volumes of each screened potential user group and the target number of apartment buildings.

[0067] Specifically, in actual design, some apartment suppliers will also design a variety of apartments with different layouts in the same apartment building in order to increase the occupancy rate of tenants; the automatic layout method applied to smart apartment planning in the embodiment of the present application can also screen out the main target group by analyzing the proportion and volume of potential user groups, and customize layout suggestions according to their needs, so as to allocate the number of apartment types with different layouts according to the expected demand proportion of potential users. This processing method can meet market demand more accurately and improve the pertinence and efficiency of apartment planning. It can avoid excessive dispersion of resources and concentrate on meeting the main needs by setting a proportion threshold to screen user groups. Secondly, the number of different types of apartments is allocated according to the volume ratio of the screened groups, ensuring the balance between supply and demand. This data-driven intelligent layout method not only improves the scientific nature and market adaptability of apartment planning, but also can better meet the personalized needs of different user groups, thereby enhancing the overall competitiveness and attractiveness of smart apartments.

[0068] It should be noted that, in the early stage of planning, the number of apartments in each apartment building is generally determined. Therefore, step S53' can determine the total number of apartments according to the target number of apartment buildings, and then allocate the number of different apartment types according to the ratio of the volume of each potential user group after screening and the total number of apartments.

[0069] More specifically, in step S51 ′, the proportion can be expressed as a percentage by dividing the volume of each group by the total volume to obtain the proportion; or a relative proportion method can be used to select a base group and compare the volumes of other groups with the base group to obtain a relative proportion.

[0070] More specifically, in step S52', the preset proportion threshold can be flexibly set according to the actual situation, for example, it can be set to 5%, 10% or 15%. When the proportion of a certain group is lower than this threshold, it can be excluded from the layout consideration. This can avoid dispersing resources to groups with smaller needs and improve resource utilization efficiency. In the embodiment of the present application, the preset proportion threshold is preferably set according to the target number of apartment buildings. The larger the number of target apartment buildings, the smaller the preset proportion threshold.

[0071] More specifically, the automatic layout method for smart apartment planning in the embodiment of the present application obtains the group ratio to provide basic data for screening; the screening process ensures that the subsequent layout suggestions and apartment quantity allocation are focused on the main demand groups; and the layout suggestions and apartment quantity allocation constructed based on the screening results directly affect the final smart apartment planning scheme. This progressive design not only ensures the accuracy of the planning, but also improves the efficiency of the entire process.

[0072] In some preferred embodiments, the method further includes the following steps between step S4 and step S5: SB. Obtain the volume of vacant apartments corresponding to the expected layout information of the potential user groups in a preset neighboring area of ​​the target area, and adjust the volume of each potential user group according to the volume of the vacant apartments.

[0073] Specifically, the automatic layout method applied to smart apartment planning in the embodiment of the present application corrects the size of the potential user group by considering the vacant apartments in the preset neighboring area of ​​the target area, which can more accurately reflect the actual needs of potential users. Among them, step SB can understand the supply of apartments that match the needs of potential users in the current market by obtaining the volume of vacant apartments corresponding to the expected layout of the potential user group. Then, the volume of each potential user group is corrected and adjusted according to the volume of these vacant apartments, so as to more fully and accurately consider the actual market demand; wherein, the correction and adjustment process is the process of reducing the volume of the corresponding potential user group based on the volume of these vacant apartments, so that the apartment corresponding to the layout suggestion information provided in step S5 is an apartment that is more needed by the current potential user, making up for the vacant market.

[0074] More specifically, the process of obtaining the volume of vacant apartments can be achieved in a variety of ways, for example, relevant information can be collected through real estate databases, real estate agency platforms, or field surveys. The volume of vacant apartments obtained should include the types and quantities of apartments corresponding to the desired layout of each potential user group.

[0075] More specifically, the process of modifying and adjusting the size of each potential user group can adopt different strategies. One possible method is to reduce the size of the corresponding potential user group proportionally. For example, if the volume of vacant apartments of a certain type accounts for 20% of the total volume of that type, the volume of the corresponding potential user group can be reduced by 20%. Another method is to adjust according to the absolute number of vacant apartments. If the number of vacant apartments of a certain type reaches a certain threshold, the volume of the potential user group of that type is reduced accordingly.

[0076] More specifically, this correction and adjustment mechanism is closely integrated with the potential user group information and the expected layout information obtained in the previous step. The automatic layout method applied to smart apartment planning in the embodiment of the present application takes into account the volume of vacant apartments. The method of the present application can more accurately reflect the market supply and demand situation and avoid the deviation that may be caused by layout planning based only on the characteristics of potential user groups.

[0077] More specifically, this correction and adjustment mechanism can be used in combination with other factors. For example, factors such as user preset preferences and market trend forecasts can be considered simultaneously to comprehensively adjust the size of each potential user group. This multi-dimensional adjustment method can more comprehensively reflect market demand and supply conditions.

[0078] If the apartment layout method only considers the characteristics and preferences of potential users, it may ignore the existing market supply situation, which will lead to oversupply or undersupply of certain types of apartments. Therefore, the automatic layout method applied to smart apartment planning in the embodiment of the present application can more comprehensively evaluate market demand and make more accurate layout decisions by introducing the key factor of vacant apartment volume. This can not only improve the efficiency of apartment use, but also provide tenants with living options that are more in line with actual needs.

[0079] In some preferred embodiments, the target number of apartment buildings is generated according to the expected layout of the target area.

[0080] Specifically, the automatic layout method applied to smart apartment planning in the embodiment of the present application can better adapt to the actual situation and development plan of the target area by generating the target number of apartment buildings according to the expected layout method of the target area. This method takes into account the characteristics and future development trends of the target area, can better meet the needs of different types of potential users, and improve the pertinence and adaptability of apartment planning. At the same time, this method can also help developers better grasp market demand, optimize resource allocation, and improve return on investment.

[0081] In some preferred embodiments, the method further includes the following steps between step S4 and step S5: SA: Obtain user preset weights and adjust the size of each potential user group based on the user preset weights.

[0082] Specifically, the above steps introduce a flexible adjustment mechanism of user preset bias, so that the apartment layout can more accurately match user needs. Specifically, the user preset bias is the bias weight set by the apartment supplier for different potential user groups; by obtaining these preset bias information, the automatic layout method applied to smart apartment planning in the embodiment of the present application can dynamically adjust the size of different potential user groups. Compared with the traditional static layout method, this solution can more flexibly respond to the personalized needs of users and the design needs of apartment suppliers, taking into account the adaptability of apartment planning, user satisfaction and the construction needs of apartment suppliers; for example, if the apartment supplier wants to rent the apartment to young people, it can set a larger bias weight for several potential user groups corresponding to young people to increase the size of the potential user groups, so that the final layout suggestion information is more inclined to meet the needs of young people.

[0083] More specifically, in an embodiment including step SB, step SA is preferably performed before step SA, that is, the volume corrected and adjusted in step SB is the volume corrected and adjusted according to the user's preset weight bias.

[0084] Second, please refer to Figure 3Some embodiments of the present application also provide an automatic layout system for smart apartment planning, the system comprising: Data collection module 201, used to obtain potential users and corresponding personality tags corresponding to the target area, and to obtain user tags of rented users in a preset neighboring area of ​​the target area and existing layout information of the corresponding rental apartments; A data integration module 202, for reorganizing potential users according to individual tags to obtain multiple potential user groups and corresponding group tags and volumes; The data analysis module 203 is used to compare the group tags and the user tags to filter the existing layout information to determine the desired layout information of the potential user groups, and to filter the desired layout information according to the size and target number of apartment buildings of different potential user groups to determine the layout suggestion information.

[0085] The automatic layout system applied to smart apartment planning in the embodiment of the present application is used to plan and layout smart apartments. It obtains and analyzes the individual tags of potential users, groups potential users and matches them with existing successful layout cases, so as to formulate a layout plan that meets their characteristics for each potential user group, and realizes the automation of the entire process from user demand analysis to specific layout suggestions, effectively solving the problem of lack of targeting and personalization in smart apartment planning, and can accurately meet the needs of different user groups, improve occupancy rate and user satisfaction, thereby enhancing the market competitiveness of the project.

[0086] In some preferred embodiments, the data collection module 201 is further used to obtain the volume of vacant apartments corresponding to the expected layout information of the potential user group in a preset neighboring area of ​​the target area, and the data analysis module 203 is further used to adjust the volume of each potential user group according to the volume correction of the vacant apartments.

[0087] In some preferred embodiments, the data collection module 201 is further used to obtain the user's preset weight, and the data analysis module 203 is further used to correct and adjust the size of each potential user group according to the user's preset weight.

[0088] In the embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. The system embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some communication interfaces, modules or units, which can be electrical, mechanical or other forms.

[0089] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0091] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0092] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An automatic layout method for smart apartment planning, used for planning and layout of smart apartments, characterized in that: The method comprises the following steps: S1. Obtain potential users and corresponding personality tags corresponding to the target area; S2. Reorganize the potential users according to their individual tags to obtain multiple potential user groups and corresponding group tags and volumes; S3, obtaining user tags of rented users in a preset neighboring area of ​​the target area and existing layout information of the corresponding rental apartments; S4, comparing the group labels and the user labels to filter the existing layout information to determine the desired layout information of the potential user groups; S5. Filter the desired layout information according to the size of different potential user groups and the target number of apartment buildings to determine the layout suggestion information.

2. The automatic layout method for smart apartment planning according to claim 1 is characterized in that: The personality tag includes: one or more of age information, gender information, occupation information, family structure information, and income information.

3. The automatic layout method for smart apartment planning according to claim 1 is characterized in that: S1 includes: Acquire potential users and corresponding personality tags based on the target area’s geographic location information, traffic information, and job information.

4. The automatic layout method for smart apartment planning according to claim 1 is characterized in that: Step S2 includes: S21, dividing and reorganizing the potential users according to the segmented intervals of a plurality of preset personality tags to obtain a plurality of potential user groups; S22. Count the number of potential users in each potential user group as the volume of the potential user group, and use the segment interval where the personality label corresponding to each potential user group is located as the group label.

5. The automatic layout method for smart apartment planning according to claim 1 is characterized in that: Step S4 includes: S41, screening multiple existing layout information matching each potential user group according to the matching relationship between the group tag and the user tag, each existing layout information including multiple different types of subdivided layout information; S42: According to the largest number of subdivided layout information of different types of subdivided layout information in the plurality of existing layout information corresponding to each potential user group, select different types of subdivided layout information corresponding to each potential user group to form the desired layout information.

6. The automatic layout method for smart apartment planning according to claim 1 is characterized in that: Step S5 includes: S51, filtering and obtaining potential user groups equal in number to the preset number of target apartment buildings according to the volume of the potential user groups; S52: Construct layout suggestion information about different apartment types based on the expected layout information of the screened potential user groups.

7. The automatic layout method for smart apartment planning according to claim 1 is characterized in that: Step S5 includes: S51', obtaining the proportion of different potential user groups according to the volume of the potential user groups; S52', screening potential user groups based on a preset percentage threshold and the percentage of the potential user groups; S53', constructing layout suggestion information about different apartment types based on the expected layout information of the screened potential user groups, and determining the number of different apartment types based on the ratio of the volumes of each screened potential user group and the target number of apartment buildings.

8. The automatic layout method for smart apartment planning according to claim 1 is characterized in that: The method further includes the following steps between step S4 and step S5: SB. Obtain the volume of vacant apartments corresponding to the expected layout information of the potential user group in a preset neighboring area of ​​the target area, and adjust the volume of each potential user group according to the volume of the vacant apartments.

9. The automatic layout method for smart apartment planning according to claim 1, characterized in that: The method further includes the following steps between step S4 and step S5: SA. Obtain user preset weights, and modify and adjust the size of each potential user group based on the user preset weights.

10. An automatic layout system for smart apartment planning, characterized in that: The system comprises: A data collection module, used to obtain potential users and corresponding personality tags corresponding to the target area, and to obtain user tags of rented users in a preset neighboring area of ​​the target area and existing layout information of the corresponding rental apartments; A data integration module, used for reorganizing the potential users according to the individual tags to obtain multiple potential user groups and corresponding group tags and volumes; The data analysis module is used to compare group labels and user labels to filter existing layout information to determine the expected layout information of potential user groups, and to filter the expected layout information according to the volume and target number of apartment buildings of different potential user groups to determine layout suggestion information.

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