Real estate data analysis method, device, equipment, medium and product

Abstract

The invention discloses a real estate data analysis method and device, equipment, a medium and a product. The method comprises the following steps: acquiring real estate registration data and real estate market data of a target area; determining a market change interval based on the market values at different time points; dividing the real estate registration data and the market environment data according to market change intervals to obtain interval registration data and interval environment data in each change interval, and calculating an interval index corresponding to each change interval; for each change interval, dividing the interval environment data according to information types to obtain a plurality of single information sets, and associating the interval indexes with all the single information sets to obtain associated data groups; obtaining an association curve set based on the association data set; drawing an image according to the association curve set, and inputting the drawn image into the terminal equipment for display; the real estate data processing and analysis efficiency and the real estate data change trend intuition can be improved, resource configuration can be optimized, and market risks can be identified.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a real estate data analysis method, device, equipment, medium and product. Background Art

[0002] With the rapid development of computer technology, especially the widespread application of database technology, network technology and cloud computing technology, real estate registration projects are undergoing a profound digital transformation.

[0003] However, due to the large amount of real estate registration data and the relatively scattered data, it is impossible to intuitively observe the changing trend of real estate registration data. At present, real estate resources are allocated solely based on manual experience, which has a large number of subjective influencing factors and cannot give full play to the data value of real estate registration data. As a result, decision makers are unable to quickly obtain valuable information from the data, and the efficiency of real estate data processing and analysis is low. Summary of the invention

[0004] The embodiment of the present invention provides a real estate data analysis method, which can improve the efficiency of real estate data processing and analysis, as well as the intuitiveness of the change trend of real estate registration data.

[0005] In a first aspect, an embodiment of the present invention provides a real estate data analysis method, comprising:

[0006] Acquire real estate registration data and real estate market data of the target area; wherein the real estate market data includes market environment data and market values ​​at different time points;

[0007] Determine the market change range based on the market values ​​at different time points;

[0008] Dividing the real estate registration data and the market environment data according to the market change intervals, obtaining interval registration data and interval environment data within each change interval, and calculating the interval index corresponding to each change interval based on the interval registration data;

[0009] For each of the change intervals, the interval environment data is divided according to a preset information type to obtain a plurality of single information sets, and the interval indicator is associated with all of the single information sets to obtain an associated data group;

[0010] Based on all the association data groups in all the change intervals, obtaining an association curve set;

[0011] An image is drawn according to the association curve set, and the drawn image is input into a terminal device for display.

[0012] Furthermore, the determining of the market change range based on the market values ​​at different time points includes:

[0013] Taking the time point as the independent variable and the market value as the dependent variable, a market change curve within a preset time period is drawn;

[0014] Using a simulation tool to perform linear function fitting on the market change curve to obtain a fitted market change function;

[0015] The market change function is first-order differentiated, and the value of the independent variable when the first-order derivative is zero is recorded as the interval endpoint, and the market change interval is obtained according to the interval endpoint.

[0016] Furthermore, the interval registration data includes the registration quantity, the registration area and the registration price, then, the calculating of the interval index corresponding to each of the change intervals based on the interval registration data includes:

[0017] The interval index corresponding to each interval registration data is calculated according to the registration quantity, the registration area and the registration price. The specific formula is as follows:

[0018]

[0019] Among them, Z i is the interval indicator, i is the interval number corresponding to the interval registration data, α1 is a fixed coefficient with a value greater than 1, DL is the number of registrations, DS is the registration area, DE is the registration price, β1 is the influencing factor of the number of registrations, β2 is the influencing factor of the registration area, and β3 is the influencing factor of the registration price.

[0020] Furthermore, the information type includes at least one of market policies, economic growth rate, tax policies and social factors.

[0021] Furthermore, the obtaining of a set of association curves based on all the association data groups of all the change intervals includes:

[0022] Fitting all associated data sets containing single information sets of the same information type into a single associated curve;

[0023] All the unidirectional association curves are aggregated to obtain an association curve set; wherein the number of the unidirectional association curves is the same as the number of the information types.

[0024] Furthermore, the step of fitting all associated data groups of a single information set containing the same information type into a single associated curve includes:

[0025] Use the bag-of-words model algorithm to convert all associated data sets containing single information sets of the same information type into vector form;

[0026] Using a cosine similarity algorithm to calculate data correlation values ​​in the associated data set in the vector form;

[0027] Plotting all the data-related values ​​in a plane coordinate system according to the interval order of the change interval to obtain a discrete image;

[0028] The discrete image is fitted using a fitting tool to obtain a single correlation curve.

[0029] In a second aspect, an embodiment of the present invention provides a real estate data analysis device, including:

[0030] A data acquisition module, used to acquire real estate registration data and real estate market data of a target area; wherein the real estate market data includes market environment data and market values ​​at different time points;

[0031] An interval division module, used for determining a market change interval based on the market values ​​at different time points;

[0032] An index calculation module, used for dividing the real estate registration data and the market environment data according to the market change intervals, obtaining interval registration data and interval environment data within each change interval, and calculating the interval index corresponding to each change interval based on the interval registration data;

[0033] A data association module, for dividing the interval environment data according to preset information types for each change interval to obtain multiple single information sets, and associating the interval indicator with all the single information sets to obtain an associated data group;

[0034] A curve fitting module, used for obtaining a set of correlation curves based on all the correlation data groups in all the change intervals;

[0035] The data display module is used to draw an image according to the association curve set, and input the drawn image into the terminal device for display.

[0036] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0037] Memory for storing computer programs;

[0038] A processor, configured to execute the computer program;

[0039] Wherein, when the processor executes the computer program, it implements the real estate data analysis method described in any one of the first aspects above.

[0040] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the real estate data analysis method described in any one of the first aspects above is implemented.

[0041] In a fifth aspect, an embodiment of the present invention provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the real estate data analysis method described in any one of the first aspects above.

[0042] Compared with the prior art, a real estate data analysis method provided by an embodiment of the present invention has the following beneficial effects: by acquiring real estate registration data and real estate market data of a target area; wherein the real estate market data includes market environment data and market values ​​at different time points; based on the market values ​​at different time points, determining a market change interval; dividing the real estate registration data and the market environment data according to the market change interval to obtain interval registration data and interval environment data within each change interval, and calculating an interval index corresponding to each change interval based on the interval registration data; for each change interval, dividing the interval environment data according to a preset information type to obtain a plurality of single information sets, associating the interval index with all the single information sets to obtain an associated data group; based on all the associated data groups of all the change intervals, obtaining an associated curve set; performing image drawing according to the associated curve set, and inputting the drawn image into a terminal device for display; the present invention can improve the efficiency of real estate data processing and analysis, as well as the intuitiveness of the change trend of real estate registration data. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical features of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 It is a flowchart of an embodiment of a real estate data analysis method of the present invention;

[0045] Figure 2 is a structural schematic diagram of an embodiment of a real estate data analysis device of the present invention;

[0046] Figure 3 It is a structural schematic diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] In a first aspect, an embodiment of the present invention provides a real estate data analysis method, see Figure 1 , which is a flow chart of an embodiment of a real estate data analysis method provided by the present invention.

[0049] like Figure 1 As shown, the method comprises the following steps:

[0050] S1: Acquire real estate registration data and real estate market data of a target area; wherein the real estate market data includes market environment data and market values ​​at different time points;

[0051] S2: determining a market change range based on the market values ​​at different time points;

[0052] S3: Dividing the real estate registration data and the market environment data according to the market change intervals, obtaining interval registration data and interval environment data within each change interval, and calculating an interval index corresponding to each change interval based on the interval registration data;

[0053] S4: for each of the change intervals, dividing the interval environment data according to preset information types to obtain multiple single information sets, and associating the interval indicator with all the single information sets to obtain an associated data group;

[0054] S5: obtaining a set of association curves based on all the association data groups in all the change intervals;

[0055] S6: Drawing an image according to the set of associated curves, and inputting the drawn image into a terminal device for display.

[0056] In a specific implementation, real estate registration data within a preset time period of a target area is collected in the real estate registration electronic database, wherein the target area is a geographical area covered by the real estate registration electronic database, and the real estate registration information includes the owner, real estate area, price, and purpose, etc. In this embodiment, the preset time period is set to five years, that is, the real estate registration data within five years of the target area is collected. The specific time range is set by those skilled in the art based on big data experience and is not specifically limited here. At the same time, real estate market data in the target area is collected, including market environment data and market values ​​at different time points. Market environment data refers to market policies, economic growth rates, tax policies, and social factors related to real estate. Based on the market values ​​at different time points, the market change interval B is determined. i , divide the real estate registration data and market environment data according to the market change interval, obtain the interval registration data and interval environment data in each change interval, and calculate the interval index corresponding to each change interval based on the interval registration data.

[0057] For each change interval B i , divide the interval environmental data according to the preset information type, and obtain multiple single information sets C ji , where the information type can be at least one of market policy, economic growth rate, tax policy and social factors, j represents different information types, j=1,2,3…n, n represents the number of types, i represents the interval number of different market change intervals, i=1,2,3…m, m represents the number of intervals.

[0058] For example, for the change interval B1, the corresponding interval index is Z1, and the corresponding single information set is C 11 , C 21 …C n1 For the change interval B2, the corresponding interval index is Z2, and the corresponding single information set is C 12 , C 22 …C n2 , the interval index Z i With all single information sets C ji Associating to obtain the associated data group, that is, the interval index Z1 is respectively associated with the single information set C 11 , C 21 …C n1 Bind and get the associated data group (Z1,C 11 )、(Z1,C 21 )…(Z1,C n1 ), the interval index Z2 is respectively associated with the single information set C 12 , C 22 …C n2Bind and get the associated data group (Z2,C 12 )、(Z2,C 22 )…(Z2,C n2 ).

[0059] Based on all the associated data groups in all the change intervals, a set of associated curves is obtained, and all the curves in the associated curve set are plotted in the same plane coordinate system to obtain plane-related images. The plane-related images are then transmitted to the terminal display device. Relevant staff can view the plane-related images through the terminal display device, and can make real-time adjustments to real estate resources based on the changing trends of the curves, providing more comprehensive and accurate data support for urban planning, policy formulation, and urban management.

[0060] In summary, the present invention obtains real estate registration data and real estate market data of the target area; wherein the real estate market data includes market environment data and market values ​​at different time points; based on the market values ​​at different time points, the market change interval is determined; the real estate registration data and the market environment data are divided according to the market change interval to obtain interval registration data and interval environment data in each change interval, and the interval index corresponding to each change interval is calculated based on the interval registration data; for each change interval, the interval environment data is divided according to a preset information type to obtain multiple single information sets, and the interval index is associated with all the single information sets to obtain an associated data group; based on all the associated data groups of all the change intervals, an associated curve set is obtained; an image is drawn according to the associated curve set, and the drawn image is input into a terminal device for display; the efficiency of real estate data processing and analysis can be improved, and the intuitiveness of the change trend of real estate registration data and the effectiveness of decision support are significantly enhanced, which is helpful to optimize resource allocation, identify market risks, and provide strong technical support for the real estate market.

[0061] In an optional implementation manner, determining the market change interval based on the market values ​​at different time points includes:

[0062] Taking the time point as the independent variable and the market value as the dependent variable, a market change curve within a preset time period is drawn;

[0063] Using a simulation tool to perform linear function fitting on the market change curve to obtain a fitted market change function;

[0064] The market change function is first-order differentiated, and the value of the independent variable when the first-order derivative is zero is recorded as the interval endpoint, and the market change interval is obtained according to the interval endpoint.

[0065] Specifically, the time point is used as an independent variable factor, and the market value of the real estate data is used as a dependent variable factor. An image is drawn in a plane coordinate system to obtain a market change curve of the real estate data within a preset time period. The preset time period in this embodiment is a quarter, and can also be set to a month or a year according to actual needs. A simulation tool is used to perform linear function fitting on the drawn market change curve. The simulation tool can use Excel, MATLAB or Python, etc., and the output result after fitting is marked as a market change function. The market change function is referenced as follows:

[0066] BS = f(t);

[0067] Where BS represents the market value of real estate, t represents time, and f(t) represents the linear function expression about time t;

[0068] Perform derivative operation on the market change function, calculate the first-order derivative of the market change function BS′=f′(t), set the first-order derivative of the market change function to 0, that is, let f ′ (t) = 0, solve for the t value, record the multiple t values ​​obtained as interval endpoints, arrange the interval endpoints in chronological order, and obtain multiple market change intervals, which are recorded as B i , where i represents the interval number of different market change intervals.

[0069] In an optional implementation manner, the interval registration data includes the registration quantity, the registration area and the registration price, then, calculating the interval index corresponding to each of the change intervals based on the interval registration data includes:

[0070] The interval index corresponding to each interval registration data is calculated according to the registration quantity, the registration area and the registration price. The specific formula is as follows:

[0071]

[0072] Among them, Z i is the interval indicator, i is the interval number corresponding to the interval registration data, α1 is a fixed coefficient with a value greater than 1, DL is the number of registrations, DS is the registration area, DE is the registration price, β1 is the influencing factor of the number of registrations, β2 is the influencing factor of the registration area, and β3 is the influencing factor of the registration price.

[0073] Specifically, the interval registration data of each change interval includes the registration quantity, registration area and registration price, wherein the registration price refers to the transfer price of the real estate. The registration quantity, registration area and registration price are respectively recorded as DL, DS and DE, and the interval index corresponding to each of the interval registration data is calculated. The specific formula is as follows:

[0074]

[0075] Among them, Z i is the interval index, i is the interval number corresponding to the interval registration data, α1 is a fixed coefficient with a value greater than 1, β1 is the influencing factor of the registration quantity, β2 is the influencing factor of the registration area, and β3 is the influencing factor of the registration price. The specific values ​​of β1, β2, β3 and α1 are obtained by technicians in this field after big data calculation. The interval index Z i i in the market and the market change interval B i The i in is consistent.

[0076] In an optional implementation, the information type includes at least one of market policy, economic growth rate, tax policy and social factors.

[0077] In an optional implementation, obtaining a set of association curves based on all the association data groups of all the change intervals includes:

[0078] Fitting all associated data sets containing single information sets of the same information type into a single associated curve;

[0079] All the unidirectional association curves are aggregated to obtain an association curve set; wherein the number of the unidirectional association curves is the same as the number of the information types.

[0080] Specifically, it will contain a single information set C of the same information type. ji All associated data groups are fitted into a single association curve, that is, the associated data groups with the same j value are fitted into a single association curve, and all single association curves are summarized to obtain a set of association curves. It can be understood that there are as many one-way association curves as there are information types, and the number of one-way association curves is the same as the number of information types, both of which are n.

[0081] In an optional implementation manner, fitting all associated data groups containing single information sets of the same information type into a single associated curve includes:

[0082] Use the bag-of-words model algorithm to convert all associated data sets containing single information sets of the same information type into vector form;

[0083] Using a cosine similarity algorithm to calculate data correlation values ​​in the associated data set in the vector form;

[0084] Plotting all the data-related values ​​in a plane coordinate system according to the interval order of the change interval to obtain a discrete image;

[0085] The discrete image is fitted using a fitting tool to obtain a single correlation curve.

[0086] Specifically, obtain an associated data group with the same j value, use the bag-of-words model algorithm to first convert the associated data group into a vector form, and use the cosine similarity algorithm to calculate the data correlation value in each associated data group, and plot the data correlation values ​​in the plane coordinate system in sequence according to the interval order of the market change interval to obtain a discrete image of the data correlation value, and then use mathematical tools to perform linear fitting on discrete points in the discrete image, and mark the fitting output result as a single association curve, wherein the mathematical tool can be Matlab tool.

[0087] In a second aspect, an embodiment of the present invention provides a real estate data analysis device, see Figure 2 , which is a structural diagram of an embodiment of a real estate data analysis device provided by the present invention.

[0088] like Figure 2 As shown, the device comprises:

[0089] The data acquisition module 21 is used to acquire the real estate registration data and real estate market data of the target area; wherein the real estate market data includes market environment data and market values ​​at different time points;

[0090] An interval division module 22, for determining a market change interval based on the market values ​​at different time points;

[0091] An index calculation module 23, for dividing the real estate registration data and the market environment data according to the market change intervals, obtaining interval registration data and interval environment data within each change interval, and calculating the interval index corresponding to each change interval based on the interval registration data;

[0092] The data association module 24 is used to divide the interval environment data according to the preset information type for each change interval to obtain multiple single information sets, and associate the interval indicator with all the single information sets to obtain an associated data group;

[0093] A curve fitting module 25, configured to obtain a set of correlation curves based on all the correlation data groups in all the variation intervals;

[0094] The data display module 26 is used to draw an image according to the set of associated curves, and input the drawn image into the terminal device for display.

[0095] In an optional implementation, the interval division module 22 is further configured to:

[0096] Taking the time point as the independent variable and the market value as the dependent variable, a market change curve within a preset time period is drawn;

[0097] Using a simulation tool to perform linear function fitting on the market change curve to obtain a fitted market change function;

[0098] The market change function is first-order differentiated, and the value of the independent variable when the first-order derivative is zero is recorded as the interval endpoint, and the market change interval is obtained according to the interval endpoint.

[0099] In an optional implementation manner, the interval registration data includes the registration quantity, the registration area and the registration price, then the index calculation module 23 is further used for:

[0100] The interval index corresponding to each interval registration data is calculated according to the registration quantity, the registration area and the registration price. The specific formula is as follows:

[0101]

[0102] Among them, Z i is the interval indicator, i is the interval number corresponding to the interval registration data, α1 is a fixed coefficient with a value greater than 1, DL is the number of registrations, DS is the registration area, DE is the registration price, β1 is the influencing factor of the number of registrations, β2 is the influencing factor of the registration area, and β3 is the influencing factor of the registration price.

[0103] In an optional implementation, the information type includes at least one of market policy, economic growth rate, tax policy and social factors.

[0104] In an optional implementation, the curve fitting module 25 is further used for:

[0105] Fitting all associated data sets containing single information sets of the same information type into a single associated curve;

[0106] All the unidirectional association curves are aggregated to obtain an association curve set; wherein the number of the unidirectional association curves is the same as the number of the information types.

[0107] In an optional implementation, the curve fitting module 25 is further used for:

[0108] Use the bag-of-words model algorithm to convert all associated data sets containing single information sets of the same information type into vector form;

[0109] Using a cosine similarity algorithm to calculate data correlation values ​​in the associated data set in the vector form;

[0110] Plotting all the data-related values ​​in a plane coordinate system according to the interval order of the change interval to obtain a discrete image;

[0111] The discrete image is fitted using a fitting tool to obtain a single correlation curve.

[0112] In a third aspect, an embodiment of the present invention provides an electronic device, see Figure 3 , which is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0113] like Figure 3 As shown, the device includes:

[0114] A memory 31, used for storing computer programs;

[0115] A processor 32, configured to execute the computer program;

[0116] Wherein, when the processor 32 executes the computer program, the real estate data analysis method as described in any of the above embodiments is implemented.

[0117] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 32 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0118] The processor 32 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0119] The memory 31 can be used to store the computer program and / or module, and the processor 32 realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory 31, and calling the data stored in the memory 31. The memory 31 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 31 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0120] It should be noted that the above electronic device includes, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 3 The structural diagram is merely an example of the electronic device described above and does not constitute a limitation on the electronic device, and may include more components than shown in the figure, or a combination of certain components, or different components.

[0121] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the real estate data analysis method described in any of the above embodiments is implemented.

[0122] It should be understood that the present invention can implement all or part of the processes in the above-mentioned real estate data analysis method, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned real estate data analysis method. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0123] In a fifth aspect, an embodiment of the present application further provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the real estate data analysis method described in any of the above embodiments.

[0124] The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. It should be pointed out that for those skilled in the art, several equivalent obvious variations and / or equivalent substitutions can be made without departing from the technical principles of the present invention. These obvious variations and / or equivalent substitutions should also be regarded as the protection scope of the present invention.

Claims

1. A real estate data analysis method, characterized in that: include: Acquire real estate registration data and real estate market data of the target area; wherein the real estate market data includes market environment data and market values ​​at different time points; Determine the market change range based on the market values ​​at different time points; Dividing the real estate registration data and the market environment data according to the market change intervals, obtaining interval registration data and interval environment data within each change interval, and calculating the interval index corresponding to each change interval based on the interval registration data; For each of the change intervals, the interval environment data is divided according to a preset information type to obtain a plurality of single information sets, and the interval indicator is associated with all of the single information sets to obtain an associated data group; Based on all the association data groups in all the change intervals, obtaining an association curve set; An image is drawn according to the association curve set, and the drawn image is input into a terminal device for display.

2. The real estate data analysis method according to claim 1, characterized in that: The determining of the market change range based on the market values ​​at different time points includes: Taking the time point as the independent variable and the market value as the dependent variable, a market change curve within a preset time period is drawn; Using a simulation tool to perform linear function fitting on the market change curve to obtain a fitted market change function; The market change function is first-order differentiated, and the value of the independent variable when the first-order derivative is zero is recorded as the interval endpoint, and the market change interval is obtained according to the interval endpoint.

3. The real estate data analysis method according to claim 1, characterized in that: The interval registration data includes the registration quantity, the registration area and the registration price. Then, the calculating of the interval index corresponding to each change interval based on the interval registration data includes: The interval index corresponding to each interval registration data is calculated according to the registration quantity, the registration area and the registration price. The specific formula is as follows: Among them, Z i is the interval indicator, i is the interval number corresponding to the interval registration data, α1 is a fixed coefficient with a value greater than 1, DL is the number of registrations, DS is the registration area, DE is the registration price, β1 is the influencing factor of the number of registrations, β2 is the influencing factor of the registration area, and β3 is the influencing factor of the registration price.

4. The real estate data analysis method according to claim 1, characterized in that: The information type includes at least one of market policy, economic growth rate, tax policy and social factors.

5. The real estate data analysis method according to claim 1, characterized in that: The step of obtaining a set of association curves based on all the association data groups of all the change intervals includes: Fitting all associated data sets containing single information sets of the same information type into a single associated curve; All the unidirectional association curves are aggregated to obtain an association curve set; wherein the number of the unidirectional association curves is the same as the number of the information types.

6. The real estate data analysis method according to claim 5, characterized in that: The step of fitting all associated data groups containing a single information set of the same information type into a single associated curve comprises: Use the bag-of-words model algorithm to convert all associated data sets containing single information sets of the same information type into vector form; Using a cosine similarity algorithm to calculate data correlation values ​​in the associated data set in the vector form; Plotting all the data-related values ​​in a plane coordinate system according to the interval order of the change interval to obtain a discrete image; The discrete image is fitted using a fitting tool to obtain a single correlation curve.

7. A real estate data analysis device, characterized in that: include: A data acquisition module, used to acquire real estate registration data and real estate market data of a target area; wherein the real estate market data includes market environment data and market values ​​at different time points; An interval division module, used for determining a market change interval based on the market values ​​at different time points; An index calculation module, used for dividing the real estate registration data and the market environment data according to the market change intervals, obtaining interval registration data and interval environment data within each change interval, and calculating the interval index corresponding to each change interval based on the interval registration data; A data association module, for dividing the interval environment data according to preset information types for each change interval to obtain multiple single information sets, and associating the interval indicator with all the single information sets to obtain an associated data group; A curve fitting module, used for obtaining a set of correlation curves based on all the correlation data groups in all the change intervals; The data display module is used to draw an image according to the association curve set, and input the drawn image into the terminal device for display.

8. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to execute the computer program; Wherein, when the processor executes the computer program, the real estate data analysis method as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the real estate data analysis method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that It comprises computer instructions, which, when executed by a processor, implement the real estate data analysis method as described in any one of claims 1 to 6.

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