Space asset investment attraction customer source information optimization method, system and device and medium
Through data cleaning and clustering analysis, combined with gravity model calculation and visual display, the problem of lack of personalized strategies in office space asset management and investment promotion is solved, efficient management and precise investment promotion are achieved, and market competitiveness and financial health are improved.
Patent Information
- Application Number
- CN202411322124.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-23
- Publication Date
- 2025-02-11
AI Technical Summary
In the existing office space asset management and investment promotion practice, there is a lack of differentiation and personalized identification of customer needs, resulting in a lack of targeted investment promotion strategies, mismatch and waste of resources, increasing work burden, and damaging corporate image and brand value. And it is not cost-effective and difficult to meet the rapidly changing market demand.
Through data cleaning, K-means clustering algorithm analysis, gravity model calculation and visual display, house groups with similar characteristics are identified, and the attraction of each house to customers is calculated, and the attraction of detailed houses to customers is generated. table, formulate differentiated investment strategies, and evaluate and optimize strategies in real time.
It has achieved efficient management and precise investment promotion of office space assets, improved the scientificity and pertinence of investment promotion work, reduced resource waste and work burden, and improved the market competitiveness and financial health of the enterprise.
Smart Images
Figure CN119417498A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of information optimization technology, and more specifically to a method, system, device and medium for optimizing spatial asset investment promotion customer source information. Background Art
[0002] With the acceleration of urbanization and the increasing frequency of commercial activities, office buildings, as important carriers of urban economy, have a direct impact on the operating costs and market competitiveness of enterprises through the management of space resources and investment promotion efficiency.
[0003] In the current office space asset management and investment promotion practices, there is a common phenomenon of "casting a wide net to catch more fish". Although this strategy seems to have a wide coverage, it actually exposes multiple shortcomings. First, it ignores the differences and personalization of customer needs, resulting in a lack of pertinence in investment promotion strategies, making it difficult to accurately match customer needs, and thus causing resource mismatch and waste. Secondly, this undifferentiated promotion method often leads to a large number of ineffective communications and low-quality inquiries, which increases the workload of investment promotion staff, reduces work efficiency, and also damages the professional image and brand value of the company. Furthermore, due to the failure to effectively identify and prioritize high-potential customers, companies may miss important market opportunities, affecting long-term development and competitiveness. Finally, from a cost-effectiveness perspective, the "casting a wide net" approach means high marketing costs, but the actual conversion rate is difficult to guarantee. In the long run, it will be detrimental to the financial health and sustainable development of the company.
[0004] It can be seen that in the current real estate investment market, traditional investment methods often rely on manual experience and subjective judgment, and lack in-depth analysis and effective use of massive data. This leads to low investment efficiency, irrational resource allocation, and difficulty in meeting the rapidly changing needs of the market. At the same time, due to incomplete data cleaning and in-depth analysis, the formulation of investment strategies lacks scientific basis and it is difficult to accurately meet customer needs. Summary of the invention
[0005] In view of the above problems, the purpose of the present invention is to provide a method, system, device and medium for optimizing information on space asset investment promotion, which realizes efficient management and precise investment promotion of office space assets through data cleaning, cluster analysis, property attractiveness calculation and visual display.
[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions: In a first aspect, the present invention discloses a method for optimizing information on space asset investment promotion and customer source, comprising: Collect property information and customer information as property data, and perform data cleaning and data standardization on the property data; Use the K-means clustering algorithm to perform cluster analysis on the cleaned property data, perform cluster division based on preset dimensions, and identify property groups with similar characteristics; Based on the results of cluster analysis, the gravity model formula is used to calculate the attractiveness of each clustered property to customers within the cluster, and a detailed attractiveness table of the properties to customers within the cluster is generated; Based on the attractiveness table of properties to customers in the cluster, the attractiveness distribution of properties in each cluster is displayed through visualization; Based on the attractiveness table of properties to customers in the cluster, the properties are ranked by attractiveness, and investment strategies are generated based on the ranking results; Collect the execution effect information of investment promotion strategies in real time and adjust the investment promotion strategies based on the execution effect information.
[0007] Furthermore, the data cleaning and data standardization processing of the house data includes: Clean the property data to remove invalid or erroneous data items; the invalid or erroneous data items include: missing key information, duplicate records or property information that does not meet the investment conditions; Convert the property data into XML or JSON format.
[0008] Furthermore, the clustering analysis of the cleaned house data using the K-means clustering algorithm includes: Take the initial random k listings as the initial cluster centers, calculate the distance between all the listings that are not currently clustered and the clusters, and divide the clusters. Use the longitude and latitude in the listings as the coordinates of the corresponding listings, and recalculate the cluster centers each time a listing is added to a cluster. The coordinates A(a, b) of the cluster center are calculated by the following formula: A(a,b)=( , ); Each cluster is considered as a polygon with n vertices. The coordinates of the i-th vertex Ai are (a i , b i ), 1≤i≤n; The cluster distance Calculated by the following formula: ; in, The property information currently being iterated. It is the cluster center information currently verified; is the house information after the jth iteration, The cluster center information verified after the jth iteration.
[0009] Furthermore, the preset dimensions include: Geographic location information, area information, rental information and supporting facilities information.
[0010] Furthermore, the gravity model formula is as follows: ; in, For customer i's own business scale, is the expected commercial value of property j, is the geographical distance between customer i and listing j, is the attractiveness of property j to customer i.
[0011] Furthermore, the process of ranking the attractiveness of the properties and generating an investment attraction strategy based on the ranking results includes: Arrange the properties in descending order of attractiveness, determine the customer recommendation order for each property based on the sorting results, and generate an investment strategy for each property.
[0012] Furthermore, the execution effect information of the investment attraction strategy includes: Information on investment promotion success rate, customer satisfaction and investment promotion cost.
[0013] In a second aspect, the present invention further discloses a space asset investment promotion customer source information optimization system, comprising: The data preparation and cleaning module is used to collect property information and customer information as property data, and to perform data cleaning and data standardization on the property data; The clustering analysis module is used to perform cluster analysis on the cleaned property data using the K-means clustering algorithm, perform clustering division based on preset dimensions, and identify property groups with similar characteristics; The housing attraction calculation module is used to calculate the attraction of each clustered housing to customers in the cluster based on the results of cluster analysis using the gravity model formula, and generate a detailed attractiveness table of the housing to customers in the cluster; A display module is used to display the attractiveness distribution of each cluster of houses through visualization means based on the attractiveness table of houses to customers in the cluster; The strategy formulation and execution module ranks the attractiveness of properties based on the attractiveness table of properties to customers in the cluster, generates investment attraction strategies based on the ranking results, and executes the investment attraction strategies; The investment promotion effect evaluation and optimization module is used to collect the execution effect information of the investment promotion strategy in real time and adjust the investment promotion strategy based on the execution effect information.
[0014] In a third aspect, the present invention further discloses a space asset investment promotion customer source information optimization device, comprising: Memory, used to store space asset investment and customer source information optimization program; A processor is used to implement the steps of the space asset investment promotion and customer source information optimization method as described in any of the above items when executing the space asset investment promotion and customer source information optimization program.
[0015] In a fourth aspect, the present invention further discloses a readable storage medium, on which is stored a space asset investment promotion and customer source information optimization program, which, when executed by a processor, implements the steps of the space asset investment promotion and customer source information optimization method as described in any one of the above items.
[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention discloses a method, system, device and medium for optimizing the source information of spatial asset investment promotion, which ensures the accuracy and completeness of data through data cleaning and standardization processing; utilizes cluster analysis and housing attractiveness calculation model to achieve accurate classification of housing sources and effective docking with customer needs; improves the scientific nature and pertinence of investment promotion work through visual display and differentiated strategy formulation; and achieves continuous improvement and optimization of investment promotion work through investment promotion effect evaluation and optimization. The present invention adopts an intelligent and data-driven approach to comprehensively sort out and deeply analyze source information, and realizes accurate display of source information through multi-dimensional data labels and intelligent classification technology. This not only enables investment promotion personnel to quickly capture the core needs and preferences of potential customers, but also provides personalized recommendations and services based on the characteristics of different customers, thereby greatly improving the effectiveness and pertinence of information display.
[0017] The present invention proposes an improved usage of the gravity model, which calculates the attractiveness of the current housing resources to customers based on the business scale of the customer source itself and the commercial value of the rental housing resources as evaluation parameters. The model can locate suitable customers in the target customer group through the needs of both parties.
[0018] The present invention performs cluster analysis on housing resources by using the K-means clustering method, calculates appropriate housing resource clusters, and conducts investment promotion work on the housing resources within the cluster partitions, which can effectively improve investment promotion efficiency.
[0019] The present invention generates a ranking of investment promotion success rates in combination with the calculation results of the gravity model, realizes customer tagging based on the attractiveness of the house source, and pushes the results to investment promotion personnel for targeted work advancement, thereby improving the work efficiency of investment promotion personnel and the success rate of investment promotion work.
[0020] It can be seen that compared with the prior art, the present invention has outstanding substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0022] Figure 1 It is a method flow chart of a specific implementation mode of the present invention.
[0023] Figure 2 It is a system structure diagram of a specific implementation mode of the present invention.
[0024] In the figure, 1. Data preparation and cleaning module; 2. Cluster analysis module; 3. Property attractiveness calculation module; 4. Display module; 5. Strategy formulation and execution module; 6. Investment promotion effect evaluation and optimization module. DETAILED DESCRIPTION
[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.
[0026] See also Figure 1 As shown, this embodiment provides a method for optimizing space asset investment source information, including the following steps: S1: Collect property information and customer information as property data, and perform data cleaning and data standardization on the property data.
[0027] In a specific implementation, first, the house data is cleaned to remove invalid or erroneous data items; the invalid or erroneous data items include: missing key information, duplicate records, or house information that does not meet the investment conditions. Then, the house data is uniformly converted into XML or JSON format to ensure that the format and unit of all data are unified, which is convenient for subsequent processing and analysis.
[0028] S2: Use the K-means clustering algorithm to perform cluster analysis on the cleaned property data, perform cluster division according to preset dimensions, and identify property groups with similar characteristics.
[0029] In a specific implementation method, the K-means clustering algorithm is used to conduct an in-depth analysis of the cleaned housing data, and clustering is performed based on multiple dimensions such as geographical location, area, rental level, supporting facilities, etc., to identify housing groups with similar characteristics.
[0030] Specifically, the initial random k housing information is used as the initial cluster center, and the distance between all the current housing information that is not included in the cluster and the cluster is calculated and divided into clusters. The longitude and latitude of the current housing object are used as the specific coordinates, and the cluster center is recalculated each time a housing object is included in the cluster.
[0031] In this step, the coordinates A(a, b) of the cluster center are calculated by the following formula: A(a,b)=( , ); Each cluster is considered as a polygon with n vertices. The coordinates of the i-th vertex Ai are (a i , b i ), 1≤i≤n.
[0032] In this step, the cluster distance Calculated by the following formula: ; in, The property information currently being iterated. It is the cluster center information currently verified; is the house information after the jth iteration, The cluster center information verified after the jth iteration.
[0033] It should be noted that when determining the number of clusters, the elbow method or silhouette coefficient method should be used to determine the optimal number of clusters to ensure that the clustering results are neither too fragmented nor too general.
[0034] S3: Based on the results of cluster analysis, the gravity model formula is used to calculate the attractiveness of each clustered property to customers within the cluster, and a table of the attractiveness of the property to customers within the cluster is generated.
[0035] In the specific implementation, first, a gravity model is constructed: the house is regarded as the "center of mass", the potential customers are regarded as the "objects" to be attracted, and multiple variables (such as geographical location, rental price, physical properties, market demand, etc.) are considered as the influencing factors of "gravity". This method focuses on the current commercial value of the house and the tenant's own business scale, and takes whether the house can meet the business needs of the merchant as the main source of attraction, and constructs an optimized display of investment information based on the attraction model.
[0036] Then, the improved gravity model formula is used to calculate the attractiveness of each listing to customers within the cluster, and a detailed attractiveness table of listings to customers within the cluster is generated.
[0037] The gravity model formula is as follows: ; in, For customer i's own business scale, is the expected commercial value of property j, is the geographical distance between customer i and listing j, is the attractiveness of property j to customer i.
[0038] S4: Based on the attractiveness table of properties to customers in the cluster, the attractiveness distribution of properties in each cluster is displayed through visualization means.
[0039] In a specific implementation, the attractiveness distribution of each cluster of properties is displayed by means of visualization, which intuitively shows which properties are more attractive to which customer groups. Based on the attractiveness analysis results, differentiated investment strategies can be formulated for different customer groups, including pricing strategies, promotion channels, service content, etc.
[0040] S5: Based on the attractiveness table of properties to customers in the cluster, sort the attractiveness of the properties and generate investment strategies based on the sorting results.
[0041] In a specific implementation, the attractiveness of the properties is sorted in descending order, the customer recommendation order of each property is determined based on the sorting results, and an investment strategy for each property is generated.
[0042] For example, based on the attractiveness of the properties, the properties with high attractiveness are recommended to the corresponding potential customers first, avoiding blind recommendation or inefficient matching. In actual application, the business personnel can also communicate with the customers in depth to understand their needs and preferences, so as to form a personalized business strategy.
[0043] S6: Collect the execution effect information of the investment promotion strategy in real time, and adjust the investment promotion strategy based on the execution effect information.
[0044] In the specific implementation method, the investment promotion effect is regularly evaluated, including indicators such as investment promotion success rate, customer satisfaction, investment promotion cost, etc. According to the evaluation results, the investment promotion process, strategy, model, etc. are optimized and adjusted to continuously improve the investment promotion efficiency and effect.
[0045] The present invention provides a method for optimizing spatial asset investment source information. First, a data cleaning process is started to eliminate all invalid or erroneous data items, such as missing key information, duplicate records, or housing records that obviously do not meet investment conditions.
[0046] Subsequently, clustering algorithms are used to conduct in-depth analysis of the current cleaned property data, and clustering is performed based on multiple dimensions such as geographical location, area, rental level, and supporting facilities to identify groups of properties with similar characteristics, laying the foundation for the subsequent formulation of differentiated strategies for different customer groups. Based on the results of cluster analysis, the attractiveness of each clustered property to potential customers is further calculated.
[0047] Finally, a detailed table of the attractiveness of properties to customers in the cluster will be generated and displayed through visualization, which will intuitively present the attractiveness distribution of each cluster of properties, making it easier for investment promotion staff to quickly understand market trends and changes in customer demand. Through visualization charts, you can clearly see which properties are more attractive to which customer groups, thereby providing strong support for the formulation of more targeted investment promotion strategies and resource allocation plans. Ultimately, this series of rigorous and coherent steps together constitute the core process of optimizing space asset investment promotion planning, thereby achieving efficient management and precise investment promotion of office space assets.
[0048] See also Figure 2 As shown, the present invention also discloses a spatial asset investment attraction source information optimization system, including: a data preparation and cleaning module 1, a cluster analysis module 2, a property attraction calculation module 3, a display module 4, a strategy formulation and execution module 5 and an investment attraction effect evaluation and optimization module 6. The data preparation and cleaning module 1 is used to collect house information and customer information as house data, and perform data cleaning and data standardization on the house data.
[0049] Cluster analysis module 2 is used to perform cluster analysis on the cleaned housing data using the K-means clustering algorithm, perform cluster division according to preset dimensions, and identify housing groups with similar characteristics.
[0050] The housing attraction calculation module 3 is used to calculate the attraction of each cluster housing to the customers in the cluster based on the results of cluster analysis using the gravity model formula, and generate a detailed attractiveness table of the housing to the customers in the cluster.
[0051] Display module 4 is used to display the attractiveness distribution of each cluster of housing resources through visualization means based on the attractiveness table of housing resources to customers in the cluster.
[0052] The strategy formulation and execution module 5 ranks the attractiveness of the properties based on the attractiveness table of the properties to the customers in the cluster, generates an investment attraction strategy according to the ranking results, and executes the investment attraction strategy.
[0053] The investment attraction effect evaluation and optimization module 6 is used to collect the execution effect information of the investment attraction strategy in real time and adjust the investment attraction strategy based on the execution effect information.
[0054] The specific implementation of the space asset investment promotion customer source information optimization system of this embodiment is basically the same as the specific implementation of the above-mentioned space asset investment promotion customer source information optimization method, and will not be repeated here.
[0055] The present invention also discloses a device for optimizing space asset investment promotion and customer source information, comprising a processor and a memory; wherein, when the processor executes the space asset investment promotion and customer source information optimization program stored in the memory, the steps of the space asset investment promotion and customer source information optimization method as described in any one of the above items are implemented.
[0056] Furthermore, the spatial asset investment promotion customer source information optimization device in this embodiment may also include: The input interface is used to obtain the space asset investment promotion and customer source information optimization program imported from the outside, and save the obtained space asset investment promotion and customer source information optimization program to the memory, and can also be used to obtain various instructions and parameters transmitted by external terminal devices, and transmit them to the processor, so that the processor can use the above various instructions and parameters to carry out corresponding processing. In this embodiment, the input interface can specifically include but is not limited to a USB interface, a serial interface, a voice input interface, a fingerprint input interface, a hard disk reading interface, etc.
[0057] The output interface is used to output various data generated by the processor to the terminal device connected thereto, so that other terminal devices connected to the output interface can obtain various data generated by the processor. In this embodiment, the output interface may specifically include but is not limited to a USB interface, a serial interface, etc.
[0058] The communication unit is used to establish a remote communication connection between the space asset investment promotion customer source information optimization device and the external server, so that the space asset investment promotion customer source information optimization device can mount the image file to the external server. In this embodiment, the communication unit may specifically include but is not limited to a remote communication unit based on wireless communication technology or wired communication technology.
[0059] The keyboard is used to obtain various parameter data or instructions input by the user by tapping the keycaps in real time.
[0060] The display is used to display the relevant information of the space asset investment and customer source information optimization process in real time.
[0061] The mouse can be used to assist users in inputting data and simplify user operations.
[0062] The present invention also discloses a readable storage medium, wherein the readable storage medium includes a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable hard disk, a CD-ROM or any other form of storage medium known in the technical field. The readable storage medium stores a space asset investment promotion customer source information optimization program, and when the space asset investment promotion customer source information optimization program is executed by a processor, the steps of the space asset investment promotion customer source information optimization method described in any one of the above items are implemented.
[0063] In summary, the present invention realizes efficient management and precise investment promotion of office space assets through data cleaning, cluster analysis, property attractiveness calculation and visual display.
[0064] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the method disclosed in the embodiment, since it corresponds to the system disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.
[0065] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.
[0066] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as 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 through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.
[0067] 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.
[0068] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each module may exist physically separately, or two or more modules may be integrated into one unit.
[0069] Similarly, each processing unit in each embodiment of the present invention may be integrated into one functional module, or each processing unit may exist physically, or two or more processing units may be integrated into one functional module.
[0070] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
[0071] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.
[0072] The above is a detailed introduction to the space asset investment promotion customer source information optimization method, system, device and readable storage medium provided by the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified, and these improvements and modifications also fall within the scope of protection of the present invention.
Claims
1. A method for optimizing spatial asset investment source information, characterized in that: include: Collect property information and customer information as property data, and perform data cleaning and data standardization on the property data; Use the K-means clustering algorithm to perform cluster analysis on the cleaned property information, perform cluster division based on preset dimensions, and identify property groups with similar characteristics; Based on the results of cluster analysis, the gravity model formula is used to calculate the attractiveness of each clustered house to customers, and a table of the attractiveness of the house to customers is generated; Based on the attractiveness table of properties to customers, the attractiveness distribution of each cluster of properties is displayed through visualization; Based on the attractiveness table of properties to customers, sort the attractiveness of properties and generate investment strategies based on the sorting results; Collect the execution effect information of investment promotion strategies in real time and adjust the investment promotion strategies based on the execution effect information.
2. The method for optimizing space asset investment source information according to claim 1, characterized in that: The data cleaning and data standardization processing of the house data includes: Clean the property data to remove invalid or erroneous data items; the invalid or erroneous data items include: missing key information, duplicate records or property information that does not meet the investment conditions; Convert the property data into XML or JSON format.
3. The method for optimizing space asset investment source information according to claim 1, characterized in that: The K-means clustering algorithm is used to perform cluster analysis on the cleaned house data, including: Take the initial random k listings as the initial cluster centers, calculate the distance between all the listings that are not currently clustered and the clusters, and divide the clusters. Use the longitude and latitude in the listings as the coordinates of the corresponding listings, and recalculate the cluster centers each time a listing is added to a cluster. The coordinates A(a, b) of the cluster center are calculated by the following formula: A(a,b)=( , ); Each cluster is considered as a polygon with n vertices. The coordinates of the i-th vertex Ai are (a i , b i ), 1≤i≤n; The cluster distance Calculated by the following formula: ； in, The property information currently being iterated. It is the cluster center information currently verified; is the house information after the jth iteration, The cluster center information verified after the jth iteration.
4. The method for optimizing space asset investment source information according to claim 1, characterized in that: The preset dimensions include: Geographic location information, area information, rental information and supporting facilities information.
5. The method for optimizing space asset investment source information according to claim 1, characterized in that: The gravity model formula is as follows: ； in, For the customer's own business scale, is the expected commercial value of property j, is the geographical distance between customer i and listing j, is the attractiveness of property j to customer i.
6. The method for optimizing space asset investment source information according to claim 1, characterized in that: The process of ranking the attractiveness of the properties and generating an investment attraction strategy according to the ranking results includes: Arrange the properties in descending order of attractiveness, determine the customer recommendation order for each property based on the sorting results, and generate a business investment strategy for each property.
7. The method for optimizing space asset investment source information according to claim 1, characterized in that: The execution effect information of the investment attraction strategy includes: Information on investment promotion success rate, customer satisfaction and investment promotion cost.
8. A space asset investment source information optimization system, characterized in that: include: The data preparation and cleaning module is used to collect property information and customer information as property data, and to perform data cleaning and data standardization on the property data; The clustering analysis module is used to perform cluster analysis on the cleaned house information using the K-means clustering algorithm, perform clustering division according to preset dimensions, and identify groups of houses with similar characteristics; The housing attraction calculation module is used to calculate the attractiveness of each clustered housing to customers based on the results of cluster analysis using the gravity model formula, and generate a detailed attractiveness table of the housing to customers; A display module is used to display the attractiveness distribution of each cluster of houses through visualization means based on the attractiveness table of houses to customers; The strategy formulation and execution module ranks the attractiveness of properties based on the attractiveness table of properties to customers, generates investment attraction strategies based on the ranking results, and executes the investment attraction strategies; The investment promotion effect evaluation and optimization module is used to collect the execution effect information of the investment promotion strategy in real time and adjust the investment promotion strategy based on the execution effect information.
9. A spatial asset investment source information optimization device, characterized in that: include: Memory, used to store space asset investment and customer source information optimization program; A processor is used to implement the steps of the space asset investment promotion and customer source information optimization method as described in any one of claims 1 to 7 when executing the space asset investment promotion and customer source information optimization program.
10. A readable storage medium, characterized in that: The readable storage medium stores a space asset investment promotion and customer source information optimization program, and when the space asset investment promotion and customer source information optimization program is executed by the processor, the steps of the space asset investment promotion and customer source information optimization method as described in any one of claims 1 to 7 are implemented.