CN117592784A - Big data analysis real estate market development risk early warning device and method thereof - Google Patents
Big data analysis real estate market development risk early warning device and method thereof Download PDFInfo
- Publication number
- CN117592784A CN117592784A CN202311663498.5A CN202311663498A CN117592784A CN 117592784 A CN117592784 A CN 117592784A CN 202311663498 A CN202311663498 A CN 202311663498A CN 117592784 A CN117592784 A CN 117592784A
- Authority
- CN
- China
- Prior art keywords
- risk
- data
- geographic
- real estate
- index
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000011161 development Methods 0.000 title claims abstract description 54
- 238000007405 data analysis Methods 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000012545 processing Methods 0.000 claims abstract description 29
- 238000005070 sampling Methods 0.000 claims abstract description 19
- 238000012502 risk assessment Methods 0.000 claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims abstract description 6
- 239000013598 vector Substances 0.000 claims description 37
- 239000011159 matrix material Substances 0.000 claims description 22
- 238000004140 cleaning Methods 0.000 claims description 18
- 230000009467 reduction Effects 0.000 claims description 13
- 230000007246 mechanism Effects 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 11
- 230000006870 function Effects 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 3
- 238000012847 principal component analysis method Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000012876 topography Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000011425 standardization method Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/16—Real estate
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Data Mining & Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- Bioinformatics & Computational Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of risk analysis, and discloses a large data analysis real estate market development risk early warning device and a method thereof, wherein the device comprises the following steps: the data acquisition module is used for acquiring the risk index data of the real estate at the data sampling points, and classifying the risk index data to obtain economic index data and geographic index data; the geographic risk analysis module is used for extracting geographic risk characteristics corresponding to the geographic index data and calculating geographic risk coefficients corresponding to the real estate; the economic risk analysis module is used for extracting the characteristics of the economic index data to obtain economic characteristics and predicting economic risk coefficients corresponding to real estate; the early warning coefficient calculation module is used for analyzing index correlation among risk indexes and determining risk early warning coefficients of real estate; and the risk early-warning processing module is used for making a risk early-warning scheme of the real estate according to the risk early-warning coefficient and the risk index. The real estate market development risk early warning analysis method aims at improving accuracy of real estate market development risk early warning analysis.
Description
Technical Field
The invention relates to the technical field of risk analysis, in particular to a large data analysis real estate market development risk early warning device and a method thereof.
Background
Real estate market development risk early warning is a system aimed at identifying, evaluating and reminding related potential risks so as to take measures in time to cope with the risks, and the real estate market development risk early warning is used for quantifying and analyzing various data and information related to real estate market by collecting, arranging and analyzing various data and information related to real estate market, and predicting possible risks and problems by using methods of statistics, economics, finance and the like so as to facilitate corresponding risk preventive measures to be taken in advance.
The existing real estate market development risk early warning mainly adopts a comprehensive simulation method, adopts a method similar to a traffic control signal system to reflect economic conditions and change trends, and comprises the following specific processes: firstly, a group of risk index systems are selected according to the principles of sensitivity, advance, stability and the like, then real estate economic fluctuation is divided into a plurality of judging sections, critical values corresponding to the judging sections are calculated, and the risk degree of each risk index is judged according to the critical values.
Disclosure of Invention
The invention provides a large data analysis real estate market development risk early warning device and a method thereof, and mainly aims to improve accuracy of real estate market development risk early warning analysis.
In order to achieve the above object, the present invention provides a big data analysis real estate market development risk early warning device, the device includes:
the data acquisition module is used for inquiring risk indexes corresponding to real estate to be early-warning analyzed, setting data sampling points of the real estate according to the risk indexes, acquiring risk index data of the real estate at the data sampling points, and classifying the risk index data to obtain economic index data and geographic index data;
the geographic risk analysis module is used for analyzing geographic risk factors corresponding to the geographic index data, extracting geographic risk characteristics corresponding to the geographic index data according to the geographic risk factors, and calculating geographic risk coefficients corresponding to the real estate according to the geographic risk factors and the geographic risk characteristics;
the economic risk analysis module is used for identifying economic institutions in the economic index data, extracting characteristics of the economic index data to obtain economic characteristics, analyzing economic trends of the economic institutions according to the economic characteristics, and predicting economic risk coefficients corresponding to the real estate according to the economic trends;
The early warning coefficient calculation module is used for analyzing the index correlation among the risk indexes and determining the risk early warning coefficient of the real estate according to the index correlation, the geographic risk coefficient and the economic risk coefficient;
and the risk early-warning processing module is used for making a risk early-warning scheme of the real estate according to the risk early-warning coefficient and the risk index.
Optionally, the classifying the risk index data to obtain economic index data and geographic index data includes:
performing data cleaning treatment on the risk index data to obtain cleaning index data;
analyzing data attributes corresponding to the cleaning index data, and calculating attribute weights corresponding to the data attributes;
according to the attribute weight, carrying out data screening processing on the cleaning index data to obtain target index data;
analyzing index semantics corresponding to the risk indexes, and identifying economic indexes and geographic indexes in the risk indexes according to the index semantics;
and classifying the target index data according to the economic index and the geographic index to obtain the economic index data and the geographic index data.
Optionally, the extracting, according to the geographic risk factor, a geographic risk feature corresponding to the geographic indicator data includes:
extracting geographic risk data of the geographic index data according to the geographic risk factors, and calculating a data linear value corresponding to the geographic risk data;
calculating a data variance corresponding to the geographic risk data according to the data linear value;
determining key risk data in the geographic risk data according to the data variance and the data linear value;
extracting features of the key risk data to obtain initial data features;
combining the initial data features to obtain target data features;
and obtaining the geographic risk characteristics corresponding to the geographic index data according to the target data characteristics.
Optionally, the merging processing is performed on the initial data features to obtain target data features, including:
performing dimension reduction processing on the initial data characteristics to obtain dimension reduction data characteristics;
carrying out standardization processing on the dimensionality reduction data characteristics to obtain standardized characteristics;
vectorizing the standardized features to obtain feature vectors;
Calculating the feature matching degree between the feature vectors;
combining the feature vectors according to the feature matching degree to obtain target feature vectors;
and performing feature conversion processing on the target feature vector to obtain target data features.
Optionally, the calculating the geographic risk coefficient corresponding to the real estate according to the geographic risk factor and the geographic risk feature includes:
the historical disaster data corresponding to the geographic risk factors are scheduled, wherein the historical disaster data comprises disaster type data and disaster loss data;
counting the disaster frequency of each type in the disaster type data, and calculating the disaster loss of each type in the disaster type data according to the disaster loss data;
combining the disaster loss and the disaster frequency, and distributing factor weights corresponding to the geographic risk factors;
calculating a risk characteristic value corresponding to the geographic risk characteristic, and calculating a factor risk coefficient corresponding to the geographic risk factor according to the risk characteristic value;
and carrying out weighted summation on the factor risk coefficients according to the factor weights to obtain the geographic risk coefficients corresponding to the real estate.
Optionally, the predicting the economic risk coefficient corresponding to the real estate according to the economic trend includes:
the economic risk factor corresponding to the real estate can be predicted by the following formula:
A＝βB a +(1-β)D a
wherein A represents economic risk coefficient corresponding to real estate, beta represents smooth index corresponding to economic trend, and B a Representing the true value corresponding to the time a in the economic trend, D a And the predicted value corresponding to the time a in the economic trend is shown.
Optionally, the analyzing the index correlation between the risk indexes includes:
analyzing index variables corresponding to the risk indexes;
constructing a variable matrix corresponding to the risk index according to the index variable, and calculating a matrix covariance corresponding to the variable matrix;
extracting core variables from the index variables according to the matrix covariance, and calculating association coefficients among the core variables;
and analyzing index correlation among the risk indexes according to the correlation coefficient.
Optionally, the calculating the correlation coefficient between the core variables includes:
calculating the correlation coefficient between the core variables by the following formula:
wherein M represents the correlation coefficient between the core variables, delta represents the total number of variables of the core variables, b and b+1 each represent the sequence number of the core variables, G b Representing the vector value corresponding to the b-th variable in the core variables, ln G b Representing the corresponding logarithmic value of the vector value of the b-th variable in the core variables, G b+1 Represents the vector value corresponding to the (b+1) th variable in the core variables, ln G b+1 And the corresponding logarithmic value of the vector value corresponding to the (b+1) th variable in the core variables is represented.
Optionally, the determining the risk early warning coefficient of the real estate according to the index correlation, the geographic risk coefficient and the economic risk coefficient includes:
determining a risk early warning coefficient of the real estate by the following formula:
wherein H represents risk early-warning coefficient of real estate, ω represents prediction probability of early-warning factor of real estate,the correlation value corresponding to the index correlation is represented, i and i+1 respectively represent serial numbers corresponding to the geographic risk coefficient and the economic risk coefficient, t represents the total number corresponding to the geographic risk coefficient and the economic risk coefficient, and N i Representing the geographical risk factorsIth coefficient, P i Representing the ith coefficient of the economic risk coefficients.
A big data analysis real estate market development risk early warning method, characterized in that the method comprises:
inquiring a risk index corresponding to the real estate to be early-warning analyzed, setting a data sampling point of the real estate according to the risk index, collecting risk index data of the real estate at the data sampling point, and classifying the risk index data to obtain economic index data and geographic index data;
Analyzing geographic risk factors corresponding to the geographic index data, extracting geographic risk features corresponding to the geographic index data according to the geographic risk factors, and calculating geographic risk coefficients corresponding to the real estate according to the geographic risk factors and the geographic risk features;
identifying an economic mechanism in the economic index data, extracting characteristics of the economic index data to obtain economic characteristics, analyzing economic trend of the economic mechanism according to the economic characteristics, and predicting economic risk coefficients corresponding to the real estate according to the economic trend;
analyzing index correlation among the risk indexes, and determining a risk early warning coefficient of the real estate according to the index correlation, the geographic risk coefficient and the economic risk coefficient;
and according to the risk early-warning coefficient and the risk index, formulating a risk early-warning scheme of the real estate.
According to the real estate early warning system and method, the specific indexes corresponding to the real estate are inquired so as to be convenient for knowing the specific indexes for measuring the risk degree of the real estate, the related data of the risk indexes can be conveniently obtained through collecting the risk index data of the real estate at the data sampling points, so that the subsequent risk early warning analysis is facilitated. Therefore, the real estate market development risk early warning device and the real estate market development risk early warning method based on the big data analysis can improve accuracy of real estate market development risk early warning analysis.
Drawings
FIG. 1 is a functional block diagram of a real estate market development risk early warning device with big data analysis according to an embodiment of the present invention;
fig. 2 is a flow chart of a method for analyzing real estate market development risk early warning according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In addition, the sequence of steps in the method embodiments described below is only an example and is not strictly limited.
In practice, the server-side equipment deployed by the big data analysis real estate market development risk early warning device may be composed of one or more pieces of equipment. The big data analysis real estate market development risk early warning device can be realized as: service instance, virtual machine, hardware device. For example, the big data analysis real estate market development risk early warning device may be implemented as a service instance deployed on one or more devices in a cloud node. In short, the live broadcast service system can be understood as a software deployed on a cloud node, and is used for providing services of big data analysis real estate market development risk early warning modes for each user side. Alternatively, the big data analysis real estate market development risk early warning device may also be implemented as a virtual machine deployed on one or more devices in the cloud node. The virtual machine is provided with application software for managing each user side. Or, the big data analysis real estate market development risk early warning device can be realized as a service end formed by a plurality of hardware devices of the same or different types, and one or a plurality of hardware devices are arranged for providing big data analysis real estate market development risk early warning mode service for each user end.
In the implementation form, the real estate market development risk early warning device and the user side are mutually adapted. Namely, the big data analysis real estate market development risk early warning device is used as an application installed on the cloud service platform, and the user side is used as a client side for establishing communication connection with the application; or the real estate market development risk early warning device for realizing big data analysis is realized as a website, and the user side is realized as a webpage; and then, or the real estate market development risk early warning device for analyzing the big data is realized as a cloud service platform, and the user side is realized as an applet in the instant messaging application.
Referring to fig. 1, a functional block diagram of a real estate market development risk early warning device is shown according to an embodiment of the present invention.
The big data analysis real estate market development risk early warning device 100 of the present invention may be disposed in a cloud server, and in terms of implementation, may be used as one or more service devices, may also be used as an application installed on the cloud (e.g. servers of a live service operator, a server cluster, etc.), or may also be developed as a website. According to the implemented functions, the large data analysis real estate market development risk early warning device 100 comprises a data acquisition module 101, a geographic risk analysis module 102, an economic risk analysis module 103, an early warning coefficient calculation module 104 and a risk early warning processing module 105.
In the embodiment of the invention, all the modules can be independently realized and called with other modules in the tracking of the real estate market development risk early warning mode based on big data analysis. The calling can be understood that a certain module can be connected with a plurality of modules of another type and provide corresponding services for the plurality of modules connected with the certain module, and in the large data analysis real estate market development risk early warning device provided by the embodiment of the invention, the application range of the large data analysis real estate market development risk early warning mode framework can be adjusted by adding the modules and directly calling without modifying program codes, so that the cluster type horizontal expansion is realized, and the purpose of rapidly and flexibly expanding the large data analysis real estate market development risk early warning device is achieved. In practical applications, the modules may be disposed in the same device or different devices, or may be service instances disposed in virtual devices, for example, in a cloud server.
The following description is directed to various components of the real estate market development risk early warning device and specific workflow for big data analysis, respectively, in conjunction with specific embodiments.
The data acquisition module 101 is configured to query risk indexes corresponding to real estate to be early-warning analyzed, set data sampling points of the real estate according to the risk indexes, acquire risk index data of the real estate at the data sampling points, and perform classification processing on the risk index data to obtain economic index data and geographic index data.
According to the real estate real-time risk early warning system and method, the risk indexes corresponding to real estate to be early-warning analyzed are inquired so as to be convenient for knowing the specific indexes for measuring the risk degree of the real estate, the risk index data of the real estate are collected at the data sampling points, the related data of the risk indexes can be conveniently obtained, so that subsequent risk early warning analysis is facilitated, wherein the risk indexes are indexes which can influence the development of the real estate, such as real estate supply and demand relation indexes, the data sampling points are points of the collected data corresponding to the risk indexes, the risk index data are data which have relation with the risk indexes, the economic index data are data about economic aspects in the risk index data, the geographic index data are data about geographic positions in the risk index data, and optionally, the risk indexes corresponding to real estate to be early-warning analyzed can be generally issued through organizations such as real estate associations, real estate developer associations and the like, such as market trading data and investment rate and the like. The risk index may be obtained by accessing their official website or attending a meeting of related industries, and the data sampling points of the real estate may be set by random sampling method.
As an embodiment of the present invention, the classifying the risk indicator data to obtain economic indicator data and geographic indicator data includes: performing data cleaning processing on the risk index data to obtain cleaning index data, analyzing data attributes corresponding to the cleaning index data, calculating attribute weights corresponding to the data attributes, performing data screening processing on the cleaning index data according to the attribute weights to obtain target index data, analyzing index semantics corresponding to the risk index, identifying economic indexes and geographic indexes in the risk index according to the index semantics, and performing classification processing on the target index data according to the economic indexes and the geographic indexes to obtain the economic index data and the geographic index data.
The cleaning index data is data obtained after incomplete data in the risk index data are removed, the data attribute is a data property corresponding to the cleaning index data, the attribute weight represents an importance degree corresponding to the data attribute, the index semantic is an index meaning corresponding to the risk index, and the economic index and the geographic index are indexes related to economy and geography in the risk index respectively.
Optionally, the data cleaning process for the risk index data may be implemented by a cleaning tool, where the cleaning tool is compiled by a scripting language, analyzing a data attribute corresponding to the cleaning index data may be implemented by a principal component analysis method, calculating an attribute weight corresponding to the data attribute may be implemented by a weight calculator, and analyzing an index semantic corresponding to the risk index may be implemented by a semantic analysis method.
The geographic risk analysis module 102 is configured to analyze the geographic risk factor corresponding to the geographic index data, extract the geographic risk feature corresponding to the geographic index data according to the geographic risk factor, and calculate the geographic risk coefficient corresponding to the real estate according to the geographic risk factor and the geographic risk feature.
According to the real estate real-time analysis method, the geographical region characteristics of the real estate can be known by extracting the geographical features corresponding to the geographical index data, the knowledge of the geography around the real estate is increased, and the accuracy of calculating the geographical risk coefficient corresponding to the real estate is conveniently improved, wherein the geographical risk factors are geographical related risk factors in the geographical index data, such as the geographic topography, the climate environment, the hydrologic topography and the like, the geographical risk features are geographical features with risks for the development of the real estate in the geographical index data, and optionally, the analysis of the geographical risk factors corresponding to the geographical index data can be realized through the principal component analysis method.
As an embodiment of the present invention, the extracting, according to the geographic risk factor, a geographic risk feature corresponding to the geographic indicator data includes: extracting geographic risk data of the geographic index data according to the geographic risk factors, calculating a data linear value corresponding to the geographic risk data, calculating a data variance corresponding to the geographic risk data according to the data linear value, determining key risk data in the geographic risk data according to the data variance and the data linear value, extracting features of the key risk data to obtain initial data features, merging the initial data features to obtain target data features, and obtaining the geographic risk features corresponding to the geographic index data according to the target data features.
The data linear value is a numerical expression form corresponding to the geographic risk data, the data variance represents the stability degree corresponding to the geographic risk data, the key risk data is representative data in the geographic risk data, and the initial data features are all sub-features corresponding to the key risk data.
Optionally, extracting the geographic risk data of the geographic index data may be implemented by a left function, calculating a data linear value corresponding to the geographic risk data may be implemented by a primary linear regression model, calculating a data variance corresponding to the geographic risk data may be implemented by a variance calculator, comparing the data variance with the data linear value, if the data linear value is greater than the data variance, indicating that the data is critical risk data, and performing feature extraction on the critical risk data may be implemented by a lda dimension reduction method.
Optionally, as an optional embodiment of the present invention, the merging the initial data features to obtain a target data feature includes: performing dimension reduction processing on the initial data features to obtain dimension reduction features, performing standardization processing on the dimension reduction features to obtain standardization features, performing vectorization processing on the standardization features to obtain feature vectors, calculating feature matching degrees among the feature vectors, performing merging processing on the feature vectors according to the feature matching degrees to obtain target feature vectors, and performing feature conversion processing on the target feature vectors to obtain target data features.
The feature vector is a vector expression form corresponding to the standardized feature, the feature matching degree represents the matching degree between the feature vectors, and the target feature vector is a vector obtained by combining the feature vectors according to the numerical value of the feature matching degree.
Optionally, the dimension reduction processing on the initial data features may be implemented by a pca dimension reduction method, the standardization processing on the dimension reduction features may be implemented by a Z-score standardization method, the vectorization processing word2vec algorithm on the standardization features may be implemented, the feature matching degree between the feature vectors may be calculated by an euclidean distance algorithm, the closer the distance is, the higher the matching degree is, the merging processing on the feature vectors may be implemented by a vector algorithm, such as vector addition, the feature conversion processing on the target feature vectors may be implemented by a feature generator, and the feature generator is compiled by a script language.
According to the method, the risk level of the geographical area corresponding to the real estate can be known by calculating the geographical risk coefficient corresponding to the real estate according to the geographical risk factor and the geographical risk characteristic, and a relatively safe or potential place is found to carry out real estate investment, wherein the geographical risk coefficient represents the risk level of the real estate area.
As one embodiment of the present invention, the calculating the geographic risk factor corresponding to the real estate according to the geographic risk factor and the geographic risk feature includes: and dispatching historical disaster data corresponding to the geographic risk factors, wherein the historical disaster data comprises disaster type data and disaster loss data, counting disaster frequency of each type in the disaster type data, calculating disaster loss of each type in the disaster type data according to the disaster loss data, distributing factor weights corresponding to the geographic risk factors according to the disaster loss and the disaster frequency, calculating risk characteristic values corresponding to the geographic risk characteristics, calculating factor risk coefficients corresponding to the geographic risk factors according to the risk characteristic values, and carrying out weighted summation on the factor risk coefficients according to the factor weights to obtain the geographic risk coefficients corresponding to the real estate.
The historical disaster data are disaster record data generated before the geographic risk factors, the disaster loss data are related data of economic losses caused in the historical disaster data, the disaster frequency represents the occurrence times of each type in the disaster type data, the factor weight represents the importance degree corresponding to the geographic risk factors, the risk characteristic value is a characteristic value corresponding to the geographic risk factors, the standard of measuring the characteristic importance degree is measured, and the factor risk coefficient represents the risk level corresponding to the geographic risk factors.
Optionally, the step of dispatching the historical disaster data corresponding to the geographic risk factors can be achieved through a random dispatching algorithm, economic losses can be caused by analyzing the disaster loss data, total economic losses of the economic losses are calculated, so that disaster losses of each type in the disaster type data can be obtained, the importance degree corresponding to the geographic risk factors can be determined through the disaster losses and the disaster frequency values, factor weights corresponding to the geographic risk factors are further distributed, the calculation of the risk feature values corresponding to the geographic risk factors can be achieved through a power iteration method, the risk probability of the geographic risk factors is evaluated according to the risk feature values, and the risk probability is used as the factor risk coefficient corresponding to the geographic risk factors.
The economic risk analysis module 103 is configured to identify an economic institution in the economic index data, perform feature extraction on the economic index data to obtain economic features, analyze an economic trend of the economic institution according to the economic features, and predict an economic risk coefficient corresponding to the real estate according to the economic trend.
According to the invention, the economic performance corresponding to the economic index data can be known by extracting the characteristics of the economic index data, so that the economic trend of the economic institution is conveniently analyzed, and a guarantee is provided for improving the accuracy of the risk analysis of the real estate, wherein the economic institution is a commercial institution corresponding to the economic index data, such as a mall, the economic characteristics are the economic prominence in the economic index data, optionally, the data entropy corresponding to the economic index data can be calculated, the characteristic extraction can be performed on the economic index data according to the data entropy, a scatter diagram of the economic institution can be constructed according to the economic characteristics, and the economic trend of the economic institution can be analyzed according to the slope of the scatter diagram.
According to the method, the economic risk coefficient corresponding to the real estate is predicted according to the economic trend, so that investors can avoid investing in high-geographic risk areas, potential economic losses and risks are reduced, wherein the economic trend represents the economic trend corresponding to the economic institution, and the economic risk coefficient represents the economic risk degree corresponding to the real estate.
As one embodiment of the present invention, the predicting the economic risk coefficient corresponding to the real estate according to the economic trend includes:
the economic risk factor corresponding to the real estate can be predicted by the following formula:
A＝βB a +(1-β)D a
wherein A represents economic risk coefficient corresponding to real estate, beta represents smooth index corresponding to economic trend, and B a Representing the true value corresponding to the time a in the economic trend, D a And the predicted value corresponding to the time a in the economic trend is shown.
The early warning coefficient calculation module 104 is configured to analyze the index correlation between the risk indexes, allocate an index weight of the risk indexes according to the index correlation, and determine a risk early warning coefficient of the real estate according to the index weight, the geographic risk coefficient and the economic risk coefficient.
The invention can reveal potential association and mutual influence among the risk indexes by analyzing the index correlation among the risk indexes, which is helpful for identifying possible systematic risks and provides guarantee for the subsequent risk early warning scheme formulation, wherein the index correlation represents the association relation among the risk indexes.
As an embodiment of the present invention, the analyzing the index correlation between the risk indexes includes: analyzing index variables corresponding to the risk indexes, constructing a variable matrix corresponding to the risk indexes according to the index variables, calculating matrix covariance corresponding to the variable matrix, extracting core variables from the index variables according to the matrix covariance, calculating correlation coefficients among the core variables, and analyzing index correlation among the risk indexes according to the correlation coefficients.
The index variable is a transformable quantity in the risk index, such as income, general expansion and the like, the variable matrix is a square matrix corresponding to the risk index, the matrix covariance represents the discrete degree of the variable matrix, the core variable is a key variable in the index variable, and the association coefficient represents the association degree between the core variables.
Optionally, analyzing the index variable corresponding to the risk index may be implemented by a descriptive statistical method, constructing a variable matrix corresponding to the risk index may be implemented by a matrix function, for example, a zero matrix function, calculating a matrix covariance corresponding to the variable matrix may be implemented by a covariance calculator, where the covariance calculator is compiled by Java language, and may extract a core variable from the index variable according to a value of the matrix covariance, and analyze an index correlation between the risk indexes according to an interval range where the value of the correlation coefficient is located.
Optionally, as an optional embodiment of the present invention, the calculating the association coefficient between the core variables includes:
calculating the correlation coefficient between the core variables by the following formula:
Wherein M represents the correlation coefficient between the core variables, delta represents the total number of variables of the core variables, b and b+1 each represent the sequence number of the core variables, G b Representing the vector value corresponding to the b-th variable in the core variables, ln G b Representing the corresponding logarithmic value of the vector value of the b-th variable in the core variables, G b+1 Represents the vector value corresponding to the (b+1) th variable in the core variables, ln G b+1 And the corresponding logarithmic value of the vector value corresponding to the (b+1) th variable in the core variables is represented.
According to the method and the system, the risk early-warning coefficient of the real estate is determined according to the index correlation, the geographic risk coefficient and the economic risk coefficient, so that the comprehensive risk coefficient of the real estate can be obtained, and the accuracy of subsequent risk early-warning analysis of the real estate is improved, wherein the risk early-warning coefficient represents the possibility of potential risks corresponding to the real estate.
As one embodiment of the present invention, the determining the risk early warning coefficient of the real estate according to the index correlation, the geographic risk coefficient and the economic risk coefficient includes:
determining a risk early warning coefficient of the real estate by the following formula:
wherein H represents risk early-warning coefficient of real estate, ω represents prediction probability of early-warning factor of real estate, The correlation value corresponding to the index correlation is represented, i and i+1 respectively represent serial numbers corresponding to the geographic risk coefficient and the economic risk coefficient, t represents the total number corresponding to the geographic risk coefficient and the economic risk coefficient, and N i Representing the ith coefficient, P, of the geographic risk coefficients i Representing the ith coefficient of the economic risk coefficients.
The risk early-warning processing module 105 is configured to formulate a risk early-warning scheme of the real estate according to the risk early-warning coefficient and the risk index.
According to the risk early-warning system and method, a risk early-warning scheme of the real estate is formulated according to the risk early-warning coefficient and the risk index, so that the risk existing in later development of the real estate can be visually displayed, corresponding investment and other measures can be controlled through the risk early-warning scheme, and occurrence and influence of the risk are reduced, wherein the risk early-warning scheme is a risk early-warning visual report of the real estate.
According to the real estate early warning system and method, the specific indexes corresponding to the real estate are inquired so as to be convenient for knowing the specific indexes for measuring the risk degree of the real estate, the related data of the risk indexes can be conveniently obtained through collecting the risk index data of the real estate at the data sampling points, so that the subsequent risk early warning analysis is facilitated. Therefore, the real estate market development risk early warning method based on the big data analysis provided by the embodiment of the invention can improve the accuracy of real estate market development risk early warning analysis.
Referring to fig. 2, a flow chart of a method for analyzing real estate market development risk early warning according to an embodiment of the present invention is shown. In this embodiment, the big data analysis real estate market development risk early warning method includes:
inquiring a risk index corresponding to the real estate to be early-warning analyzed, setting a data sampling point of the real estate according to the risk index, collecting risk index data of the real estate at the data sampling point, and classifying the risk index data to obtain economic index data and geographic index data;
analyzing geographic risk factors corresponding to the geographic index data, extracting geographic risk features corresponding to the geographic index data according to the geographic risk factors, and calculating geographic risk coefficients corresponding to the real estate according to the geographic risk factors and the geographic risk features;
identifying an economic mechanism in the economic index data, extracting characteristics of the economic index data to obtain economic characteristics, analyzing economic trend of the economic mechanism according to the economic characteristics, and predicting economic risk coefficients corresponding to the real estate according to the economic trend;
Analyzing index correlation among the risk indexes, and determining a risk early warning coefficient of the real estate according to the index correlation, the geographic risk coefficient and the economic risk coefficient;
and according to the risk early-warning coefficient and the risk index, formulating a risk early-warning scheme of the real estate.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A big data analysis real estate market development risk early warning device, characterized in that the device comprises:
the data acquisition module is used for inquiring risk indexes corresponding to real estate to be early-warning analyzed, setting data sampling points of the real estate according to the risk indexes, acquiring risk index data of the real estate at the data sampling points, and classifying the risk index data to obtain economic index data and geographic index data;
the geographic risk analysis module is used for analyzing geographic risk factors corresponding to the geographic index data, extracting geographic risk characteristics corresponding to the geographic index data according to the geographic risk factors, and calculating geographic risk coefficients corresponding to the real estate according to the geographic risk factors and the geographic risk characteristics;
the economic risk analysis module is used for identifying economic institutions in the economic index data, extracting characteristics of the economic index data to obtain economic characteristics, analyzing economic trends of the economic institutions according to the economic characteristics, and predicting economic risk coefficients corresponding to the real estate according to the economic trends;
the early warning coefficient calculation module is used for analyzing the index correlation among the risk indexes and determining the risk early warning coefficient of the real estate according to the index correlation, the geographic risk coefficient and the economic risk coefficient;
And the risk early-warning processing module is used for making a risk early-warning scheme of the real estate according to the risk early-warning coefficient and the risk index.
2. The apparatus for pre-warning risk of real estate market development based on big data analysis according to claim 1, wherein the classifying the risk index data to obtain economic index data and geographic index data comprises:
performing data cleaning treatment on the risk index data to obtain cleaning index data;
analyzing data attributes corresponding to the cleaning index data, and calculating attribute weights corresponding to the data attributes;
according to the attribute weight, carrying out data screening processing on the cleaning index data to obtain target index data;
analyzing index semantics corresponding to the risk indexes, and identifying economic indexes and geographic indexes in the risk indexes according to the index semantics;
and classifying the target index data according to the economic index and the geographic index to obtain the economic index data and the geographic index data.
3. The apparatus for pre-warning the risk of real estate market development according to claim 1, wherein the extracting the geographic risk feature corresponding to the geographic index data according to the geographic risk factor comprises:
Extracting geographic risk data of the geographic index data according to the geographic risk factors, and calculating a data linear value corresponding to the geographic risk data;
calculating a data variance corresponding to the geographic risk data according to the data linear value;
determining key risk data in the geographic risk data according to the data variance and the data linear value;
extracting features of the key risk data to obtain initial data features;
combining the initial data features to obtain target data features;
and obtaining the geographic risk characteristics corresponding to the geographic index data according to the target data characteristics.
4. The apparatus for pre-warning risk of real estate market development based on big data analysis according to claim 3, wherein the merging the initial data features to obtain target data features comprises:
performing dimension reduction processing on the initial data characteristics to obtain dimension reduction data characteristics;
carrying out standardization processing on the dimensionality reduction data characteristics to obtain standardized characteristics;
vectorizing the standardized features to obtain feature vectors;
calculating the feature matching degree between the feature vectors;
Combining the feature vectors according to the feature matching degree to obtain target feature vectors;
and performing feature conversion processing on the target feature vector to obtain target data features.
5. The big data analysis real estate market development risk early warning device of claim 1 wherein the calculating the geographical risk coefficient corresponding to the real estate based on the geographical risk factor and the geographical risk feature includes:
the historical disaster data corresponding to the geographic risk factors are scheduled, wherein the historical disaster data comprises disaster type data and disaster loss data;
counting the disaster frequency of each type in the disaster type data, and calculating the disaster loss of each type in the disaster type data according to the disaster loss data;
combining the disaster loss and the disaster frequency, and distributing factor weights corresponding to the geographic risk factors;
calculating a risk characteristic value corresponding to the geographic risk characteristic, and calculating a factor risk coefficient corresponding to the geographic risk factor according to the risk characteristic value;
and carrying out weighted summation on the factor risk coefficients according to the factor weights to obtain the geographic risk coefficients corresponding to the real estate.
6. The apparatus for pre-warning risk of real estate market development based on big data analysis of claim 1 wherein the predicting the economic risk coefficient corresponding to real estate based on the economic trend includes:
the economic risk factor corresponding to the real estate can be predicted by the following formula:
A＝βB a +(1-β)D a
wherein A represents economic risk coefficient corresponding to real estate, beta represents smooth index corresponding to economic trend, and B a Representing the true value corresponding to the time a in the economic trend, D a And the predicted value corresponding to the time a in the economic trend is shown.
7. The big data analysis real estate market development risk early warning device of claim 1 wherein the analyzing the index correlation between the risk indices includes:
analyzing index variables corresponding to the risk indexes;
constructing a variable matrix corresponding to the risk index according to the index variable, and calculating a matrix covariance corresponding to the variable matrix;
extracting core variables from the index variables according to the matrix covariance, and calculating association coefficients among the core variables;
and analyzing index correlation among the risk indexes according to the correlation coefficient.
8. The big data analysis real estate market development risk early warning device of claim 7 wherein the calculating the correlation coefficient between the core variables includes:
calculating the correlation coefficient between the core variables by the following formula:
wherein M represents the correlation coefficient between the core variables, delta represents the total number of variables of the core variables, b and b+1 each represent the sequence number of the core variables, G b Representing the direction corresponding to the b-th variable in the core variablesMagnitude, ln G b Representing the corresponding logarithmic value of the vector value of the b-th variable in the core variables, G b+1 Represents the vector value corresponding to the (b+1) th variable in the core variables, ln G b+1 And the corresponding logarithmic value of the vector value corresponding to the (b+1) th variable in the core variables is represented.
9. The big data analysis real estate market development risk early warning device of claim 1 wherein the determining the real estate risk early warning coefficient according to the index correlation, the geographic risk coefficient and the economic risk coefficient includes:
determining a risk early warning coefficient of the real estate by the following formula:
wherein H represents risk early-warning coefficient of real estate, ω represents prediction probability of early-warning factor of real estate, The correlation value corresponding to the index correlation is represented, i and i+1 respectively represent serial numbers corresponding to the geographic risk coefficient and the economic risk coefficient, t represents the total number corresponding to the geographic risk coefficient and the economic risk coefficient, and N i Representing the ith coefficient, P, of the geographic risk coefficients i Representing the ith coefficient of the economic risk coefficients.
10. A big data analysis real estate market development risk early warning method, characterized in that the method comprises:
inquiring a risk index corresponding to the real estate to be early-warning analyzed, setting a data sampling point of the real estate according to the risk index, collecting risk index data of the real estate at the data sampling point, and classifying the risk index data to obtain economic index data and geographic index data;
analyzing geographic risk factors corresponding to the geographic index data, extracting geographic risk features corresponding to the geographic index data according to the geographic risk factors, and calculating geographic risk coefficients corresponding to the real estate according to the geographic risk factors and the geographic risk features;
identifying an economic mechanism in the economic index data, extracting characteristics of the economic index data to obtain economic characteristics, analyzing economic trend of the economic mechanism according to the economic characteristics, and predicting economic risk coefficients corresponding to the real estate according to the economic trend;
Analyzing index correlation among the risk indexes, and determining a risk early warning coefficient of the real estate according to the index correlation, the geographic risk coefficient and the economic risk coefficient;
and according to the risk early-warning coefficient and the risk index, formulating a risk early-warning scheme of the real estate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311663498.5A CN117592784A (en) | 2023-12-05 | 2023-12-05 | Big data analysis real estate market development risk early warning device and method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311663498.5A CN117592784A (en) | 2023-12-05 | 2023-12-05 | Big data analysis real estate market development risk early warning device and method thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117592784A true CN117592784A (en) | 2024-02-23 |
Family
ID=89916620
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311663498.5A Pending CN117592784A (en) | 2023-12-05 | 2023-12-05 | Big data analysis real estate market development risk early warning device and method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117592784A (en) |
-
2023
- 2023-12-05 CN CN202311663498.5A patent/CN117592784A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sirisha et al. | Profit prediction using ARIMA, SARIMA and LSTM models in time series forecasting: A comparison | |
| US9921936B2 (en) | Method and system for IT resources performance analysis | |
| CN112258093A (en) | Risk level data processing method and device, storage medium and electronic equipment | |
| CN112734559A (en) | Enterprise credit risk evaluation method and device and electronic equipment | |
| CN112950359B (en) | User identification method and device | |
| CN106952190A (en) | False source of houses typing Activity recognition and early warning system | |
| CN113254542A (en) | Data visualization processing method and device and electronic equipment | |
| CN113034046A (en) | Data risk metering method and device, electronic equipment and storage medium | |
| CN114978877B (en) | Abnormality processing method, abnormality processing device, electronic equipment and computer readable medium | |
| CN115545790A (en) | Price data prediction method and device, electronic equipment and storage medium | |
| CN109711849B (en) | Ether house address portrait generation method and device, electronic equipment and storage medium | |
| CN117633249B (en) | Method and device for constructing basic variable for SDGs space type monitoring index | |
| CN114118793A (en) | Local exchange risk early warning method, device and equipment | |
| CN117593115A (en) | Feature value determining method, device, equipment and medium of credit risk assessment model | |
| CN112767126A (en) | Collateral grading method and device based on big data | |
| CN115879848A (en) | Transport vehicle safety monitoring method and device | |
| CN117592784A (en) | Big data analysis real estate market development risk early warning device and method thereof | |
| CN114581209A (en) | Method, device and equipment for training financial analysis model and storage medium | |
| CN113849464A (en) | Information processing method and apparatus | |
| CN112132689A (en) | Recommendation method and device based on time sequence factor event | |
| CN110570301A (en) | Risk identification method, device, equipment and medium | |
| CN117911194B (en) | Intelligent water management method and system based on big data | |
| CN114492877B (en) | Operation and maintenance analysis method and device of business system | |
| CN116484230A (en) | Method for identifying abnormal business data and training method of AI digital person | |
| CN118708977A (en) | Intelligent radiation source supervision method and system based on Internet of things |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |