CN110837604A - Data analysis method and device based on housing monitoring platform - Google Patents
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Abstract
The embodiment of the invention provides a data analysis method based on a housing monitoring platform, which comprises the following steps: selecting an area to be analyzed and data dimensions related to users in the area from the housing monitoring platform to obtain a data matrix to be analyzed; extracting important dimensions from the data dimensions according to the characteristic vectors in the data matrix; and screening out users with the correlation with the important dimension larger than a set threshold value from the housing monitoring platform to form target users. Meanwhile, a data analysis device based on the housing monitoring platform is also provided. According to the technical scheme, the statistical data are more comprehensive, the clients can be intelligently recommended, the marketing efficiency and the transaction rate are improved, and the data cost of a company is saved.
Description
Technical Field
The invention relates to the field of data information, in particular to a data analysis method based on a housing monitoring platform, a data analysis device based on the housing monitoring platform and a corresponding storage medium.
Background
At present, data mining companies mainly mine data of real estate companies by data acquisition, data fusion and data analysis to form data clients. This approach has the following disadvantages: because data mining company often relates to more fields, often wide and not deep between each field, therefore the data of production have to a great extent unreliable, can't be close to the business reality of house property company moreover, and then can't produce accurate guide effect to house property company's business. Compared with the house company, the received client data is static and cannot reflect the dynamic change of the client data. That is, the customer data is the output data at the time of data analysis, and the change of the customer information will cause the change of the customer data, and the change part will not be reflected in the customer data of the real estate company.
Disclosure of Invention
The invention aims to provide a data analysis method and device based on a housing monitoring platform, and at least solves the problem that the existing acquired customer data is insufficient in accuracy and dynamic performance.
In order to achieve the above object, in a first aspect of the present invention, there is provided a data analysis method based on a housing monitoring platform, the method including:
selecting an area to be analyzed and data dimensions related to users in the area from the housing monitoring platform to obtain a data matrix to be analyzed;
extracting important dimensions from the data dimensions according to the characteristic vectors in the data matrix;
and screening out users with the correlation with the important dimension larger than a set threshold value from the housing monitoring platform to form target users.
Optionally, the data matrix to be analyzed includes: and a matrix formed by taking the selected area as a row and the selected data dimension as a column.
Optionally, the important dimensions are extracted from the data dimensions, and a principal component analysis method is adopted.
Optionally, the screening out, from the housing monitoring platform, users whose correlation with the important dimension is greater than a set threshold includes:
acquiring a data value of an important dimension of a rendezvous user in a certain area;
calculating the correlation between the data value of the important dimension of the user in the area and the data value of the important dimension of the user in the area;
and setting the user with the relevance larger than the set threshold value as the target user.
In a second aspect of the present invention, there is also provided a data analysis apparatus based on a housing monitoring platform, the apparatus comprising:
the data extraction module is used for selecting an area to be analyzed and data dimensions related to users in the area from the housing monitoring platform to obtain a data matrix to be analyzed;
the data analysis module is used for extracting important dimensions from the data dimensions according to the characteristic vectors in the data matrix; and
and the user screening module is used for screening out users of which the correlation with the important dimension is greater than a set threshold value from the housing monitoring platform to form target users.
Optionally, the data matrix to be analyzed includes: and a matrix formed by taking the selected area as a row and the selected data dimension as a column.
Optionally, the data analysis module extracts the important dimension from the data dimension by using a principal component analysis method.
Optionally, the user filtering module includes:
the trading user submodule is used for acquiring a data value of an important dimension of a trading user in a certain area;
a correlation calculation submodule for calculating a correlation between the data value of the important dimension of the user in the region and the data value of the important dimension of the intersecting user in the region; and
and the target user submodule is used for setting the user with the relevance larger than the set threshold as the target user.
In a third aspect of the present invention, there is also provided a server, including the aforementioned data analysis device based on a housing monitoring platform.
In a fourth aspect of the present invention, there is also provided a storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the aforementioned data analysis method based on a housing monitoring platform.
According to the technical scheme, data analysis is carried out based on the housing monitoring platform, so that statistical data are more comprehensive, clients can be intelligently recommended, marketing efficiency and success rate are improved, and data cost of companies is saved.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a schematic diagram of a data analysis method based on a housing monitoring platform according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a data analysis device based on a housing monitoring platform according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a method in a data analysis method based on a housing monitoring platform according to an alternative embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
In the embodiments of the present invention, unless otherwise specified, the use of directional terms such as "upper, lower, top, and bottom" is generally used with respect to the orientation shown in the drawings or the positional relationship of the components with respect to each other in the vertical, or gravitational direction.
Fig. 1 is a schematic diagram of a data analysis method based on a housing monitoring platform according to an embodiment of the present invention. As shown in fig. 1, a data analysis method based on a housing monitoring platform includes:
selecting an area to be analyzed and data dimensions related to users in the area from the housing monitoring platform to obtain a data matrix to be analyzed;
extracting important dimensions from the data dimensions according to the characteristic vectors in the data matrix;
and screening out users with the correlation with the important dimension larger than a set threshold value from the housing monitoring platform to form target users.
Therefore, data with the largest amount of information can be selected from massive house property data, and potential customers with the largest transaction probability are screened out according to the characteristics with the largest amount of information, so that the demand analysis of the customers is more accurate, and the companies are helped to make more favorable decisions.
Specifically, the housing monitoring platform has massive data, and when data analysis is performed, it is required to know which data have important information. The embodiment of the invention therefore forms the region to be analyzed and the dimensions to be analyzed into a data matrix and extracts important dimensions from it by means of matrix analysis. The important dimensionality marks the most information of original data carried in the data dimensionality, massive clients in the housing monitoring platform are analyzed by the important dimensionality, potential clients with the largest relation with transaction users are selected, and a client manager can implement more accurate marketing, so that marketing effect is improved, and transaction efficiency is improved.
In an embodiment, the data matrix to be analyzed includes: and a matrix formed by taking the selected area as a row and the selected data dimension as a column. For example, in city research, seven dimensions are selected in a region for analysis, and the research dimensions comprise seven dimensions such as the equal price of house bargaining in each region, the community environment in each region, the second-hand house occupation ratio in each region, the per-capita income in each region, the house traffic condition in each region, the house bargaining period in each region, the house bargaining condition of specific population in each region and the like, and data entry is performed. The seven dimensions are respectively set as A1, B1, C1, D1, E1, F1 and G1; similarly, the seven dimensions of the nth region are An, Bn, Cn, Dn, En, Fn, Gn, respectively, so that the raw data are arranged in rows to form a matrix X in two dimensions, each row represents a region, and each column represents a statistical dimension, where two regions are taken as An example:
and performing dimension reduction analysis on the matrix, and projecting the sample data in the matrix into a new space. For a matrix, diagonalizing the matrix is a process of generating a feature root and a feature vector, and is a process of projecting the matrix on an orthonormal basis, and the feature value corresponds to a projection length in the direction of the feature vector, so that more information of original data is carried in the direction.
Through the process, the analysis of customer requirements is more accurate, and a company is helped to make more favorable decisions.
In an embodiment provided by the present invention, the extracting important dimensions in the data matrix includes: and extracting the important dimensionality from the data matrix by adopting a principal component analysis method. And (3) performing dimensionality reduction analysis on the seven-dimensional data by adopting a Principal Component Analysis (PCA). The judgment is made by converting the length of the matrix (eigenvector). A long eigenvector means that the variation is significant in this dimension (e.g., usability as described above), whereas a short eigenvalue means that the data has substantially no variance and therefore no analytical value in this dimension, since all data has the same or similar data values in this dimension. The method for extracting the important dimension specifically comprises the following steps:
normalizing X to enable each line of X to subtract the corresponding mean value thereof to obtain:
solving the covariance matrix of X':
solving the characteristic value of C, and obtaining by using linear algebra knowledge or an eig function in MATLAB:
μ1＝M
μ2＝N
the corresponding feature vectors are respectively:
reducing the original data to one dimension, and selecting the eigenvector corresponding to the largest eigenvalue, so that P is:
P＝[L O]
the data after dimensionality reduction are:
Y＝PX′＝[A″B″C″D″E″F″G″]
in an embodiment provided by the present invention, the screening users from the housing monitoring platform whose correlation with the important dimension is greater than a set threshold includes: acquiring a data value of an important dimension of a rendezvous user in a certain area; calculating the correlation between the data value of the important dimension of the user in the area and the data value of the important dimension of the user in the area; and setting the user with the relevance larger than the set threshold value as the target user.
And performing database dropping according to the analysis result, and storing the relevant relation data of the reflecting area and the transaction user. Through the analysis, the important dimension of the relationship in the mass database is obtained, so that the correlation between the user and the transaction user in the dimension needs to be analyzed, so that the broker spends time on the client with the most transaction intention, the transaction efficiency is promoted, and the performance of the broker is promoted.
The specific analysis process is as follows: the correlation coefficient is a measure of the degree of linear correlation between the study variables and is generally denoted by the letter r. There are several ways of defining the correlation coefficient depending on the subject. The correlation table and the correlation graph may reflect the correlation between two variables and the direction of the correlation, but may not exactly indicate the degree of correlation between two variables. The correlation coefficient is a statistical index used for reflecting the degree of closeness of correlation between variables. In this embodiment, the calculation of the correlation value will be described using SPSS as software.
The important dimensionalities of the transaction users in a certain area are obtained, for example, the 'residential building traffic conditions of each area' selected by most of the transaction users, and the value of the important dimensionalities of the transaction users and the actual value of the important dimensionalities of each user are calculated by clicking 'analysis' -correlation '-bivariate'. "Pearson" may be selected in the "correlation coefficient" box, and the mean and standard deviation may be selected if descriptive analysis is required. Therefore, each user can obtain a correlation value delta with the important dimension of the transaction user, and the larger the correlation value delta is, the more similar the user and the transaction user are, the larger the potential transaction probability is. The user is recommended to the broker, and the marketing accuracy of the broker can be improved. In actual use, the correlation values δ can be sorted, and several top-ranked customers are recommended to the broker every day for accurate marketing. A certain threshold value may be set for the correlation value δ, and a client exceeding the threshold value δ may be recommended to the broker.
Fig. 2 is a schematic structural diagram of a data analysis device based on a housing monitoring platform according to an embodiment of the present invention. As shown in fig. 2, in an embodiment provided by the present invention, there is further provided a data analysis apparatus based on a housing monitoring platform, the apparatus including:
the data extraction module is used for selecting an area to be analyzed and data dimensions related to users in the area from the housing monitoring platform to obtain a data matrix to be analyzed;
the data analysis module is used for extracting important dimensions from the data dimensions according to the characteristic vectors in the data matrix; and
and the user screening module is used for screening out users of which the correlation with the important dimension is greater than a set threshold value from the housing monitoring platform to form target users.
In some embodiments, the data matrix to be analyzed includes: and a matrix formed by taking the selected area as a row and the selected data dimension as a column.
In some embodiments, the data analysis module extracts the important dimensions from the data dimensions using principal component analysis.
In some embodiments, the user filtering module comprises: the trading user submodule is used for acquiring a data value of an important dimension of a trading user in a certain area; a correlation calculation submodule for calculating a correlation between the data value of the important dimension of the user in the region and the data value of the important dimension of the intersecting user in the region; and the target user submodule is used for setting the user with the relevance larger than the set threshold as the target user.
For more details of the system according to the embodiment of the present invention, reference may be made to the above description of the data analysis method based on the housing monitoring platform, and the same or corresponding technical effects as those of the data analysis method based on the housing monitoring platform can be obtained, so that the details are not repeated herein.
In an embodiment, the present invention further provides a data analysis apparatus based on a housing monitoring platform, where the apparatus includes: a memory and a processor;
the memory to store program instructions;
the processor is used for calling the program instructions stored in the memory to realize the data analysis method based on the housing monitoring platform to form a target user. The processor may include, but is not limited to, a general purpose processor, a special purpose processor, a conventional processor, a plurality of microprocessors, a controller, a microcontroller, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) circuit, any other type of Integrated Circuit (IC), a state machine, and the like. In a common scenario, the device is preferably a server.
Fig. 3 is a schematic method flow diagram of a data analysis method based on a housing monitoring platform according to an alternative embodiment of the present invention, as shown in fig. 3:
the method comprises the following steps that (1) each city is investigated, and investigation dimensionalities comprise multiple dimensionalities such as each regional house bargaining average price, each regional community environment and the like, and data input is carried out;
and performing dimensionality reduction analysis on the multiple dimensionality data by adopting a Principal Component Analysis (PCA). The judgment is made by converting the matrix, i.e. the length of the eigenvector. A long eigenvector means that the variation in this dimension is significant, whereas a short eigenvalue means: in this dimension, the data is substantially non-discriminatory and therefore of no analytical value.
According to the analysis result of the previous step, the data is dropped into a database; and taking out the relevant relation data of each area and the trading users, carrying out algorithm analysis by adopting a uniform variable method, and taking the relevant relation data of the trading users in the same area, wherein the larger the relevant relation of the trading users in the same area is, the higher the set relevant factor (delta) is, and recommending the users in the area with the large relevant factor to the broker every day.
Embodiments of the present invention also provide a storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the aforementioned housing monitoring platform-based data analysis method.
According to the technical scheme provided by the embodiment of the invention, the data analysis method can screen out potential transaction clients from mass data, so that the data cost of a company is saved, and the transaction rate is improved.
While the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solution of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications are within the scope of the embodiments of the present invention.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, the embodiments of the present invention will not be described separately for the various possible combinations.
Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be implemented by a program, which is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps in the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as disclosed in the embodiments of the present invention as long as it does not depart from the spirit of the embodiments of the present invention.
Claims (10)
1. A data analysis method based on a housing monitoring platform is characterized by comprising the following steps:
selecting an area to be analyzed and data dimensions related to users in the area from the housing monitoring platform to obtain a data matrix to be analyzed;
extracting important dimensions from the data dimensions according to the characteristic vectors in the data matrix;
and screening out users with the correlation with the important dimension larger than a set threshold value from the housing monitoring platform to form target users.
2. The data analysis method of claim 1, wherein the data matrix to be analyzed comprises: and a matrix formed by taking the selected area as a row and the selected data dimension as a column.
3. The data analysis method according to claim 1, wherein the important dimensions are extracted from the data dimensions, and a principal component analysis method is used.
4. The data analysis method of claim 1, wherein the screening users from the housing monitoring platform whose correlation with the important dimension is greater than a set threshold value to form target users comprises:
acquiring a data value of an important dimension of a rendezvous user in a certain area;
calculating the correlation between the data value of the important dimension of the user in the area and the data value of the important dimension of the user in the area;
and setting the user with the relevance larger than the set threshold value as the target user.
5. A data analysis apparatus based on a housing monitoring platform, the apparatus comprising:
the data extraction module is used for selecting an area to be analyzed and data dimensions related to users in the area from the housing monitoring platform to obtain a data matrix to be analyzed;
the data analysis module is used for extracting important dimensions from the data dimensions according to the characteristic vectors in the data matrix; and
and the user screening module is used for screening out users of which the correlation with the important dimension is greater than a set threshold value from the housing monitoring platform to form target users.
6. The data analysis device of claim 5, wherein the data matrix to be analyzed comprises: and a matrix formed by taking the selected area as a row and the selected data dimension as a column.
7. The data analysis device of claim 5, wherein the data analysis module extracts the important dimensions from the data dimensions using principal component analysis.
8. The data analysis device of claim 5, wherein the user filtering module comprises:
the trading user submodule is used for acquiring a data value of an important dimension of a trading user in a certain area;
a correlation calculation submodule for calculating a correlation between the data value of the important dimension of the user in the region and the data value of the important dimension of the intersecting user in the region; and
and the target user submodule is used for setting the user with the relevance larger than the set threshold as the target user.
9. A server, characterized in that the server comprises the housing monitoring platform based data analysis device of any one of claims 5 to 8.
10. A storage medium having stored thereon computer program instructions, which when executed by a processor, perform the steps of the housing monitoring platform based data analysis method of any of claims 1 to 4.
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| CN112330387A (en) * | 2020-09-29 | 2021-02-05 | 重庆锐云科技有限公司 | Virtual broker applied to house-watching software |
| CN112330387B (en) * | 2020-09-29 | 2023-07-18 | 重庆锐云科技有限公司 | Virtual broker applied to house watching software |
| CN113761346A (en) * | 2021-02-22 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Method, device, electronic device and storage medium for scenic spot recommendation |
| CN113761346B (en) * | 2021-02-22 | 2025-02-21 | 北京沃东天骏信息技术有限公司 | Method, device, electronic device and storage medium for recommending scenic spots |
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