CN112862575A - Intelligent residential land auction price evaluation method based on big data analysis and cloud platform - Google Patents
Intelligent residential land auction price evaluation method based on big data analysis and cloud platform Download PDFInfo
- Publication number
- CN112862575A CN112862575A CN202110073278.1A CN202110073278A CN112862575A CN 112862575 A CN112862575 A CN 112862575A CN 202110073278 A CN202110073278 A CN 202110073278A CN 112862575 A CN112862575 A CN 112862575A
- Authority
- CN
- China
- Prior art keywords
- residential land
- land
- soil
- infrastructure
- auction price
- 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
- 238000011156 evaluation Methods 0.000 title claims abstract description 59
- 238000007405 data analysis Methods 0.000 title claims abstract description 18
- 239000002689 soil Substances 0.000 claims abstract description 141
- 238000001514 detection method Methods 0.000 claims description 106
- 238000003912 environmental pollution Methods 0.000 claims description 20
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 claims description 18
- 230000007613 environmental effect Effects 0.000 claims description 17
- 238000000034 method Methods 0.000 claims description 12
- VYZAMTAEIAYCRO-UHFFFAOYSA-N Chromium Chemical compound [Cr] VYZAMTAEIAYCRO-UHFFFAOYSA-N 0.000 claims description 10
- 229910052804 chromium Inorganic materials 0.000 claims description 10
- 239000011651 chromium Substances 0.000 claims description 10
- MGWGWNFMUOTEHG-UHFFFAOYSA-N 4-(3,5-dimethylphenyl)-1,3-thiazol-2-amine Chemical compound CC1=CC(C)=CC(C=2N=C(N)SC=2)=C1 MGWGWNFMUOTEHG-UHFFFAOYSA-N 0.000 claims description 9
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 9
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 9
- QSHDDOUJBYECFT-UHFFFAOYSA-N mercury Chemical compound [Hg] QSHDDOUJBYECFT-UHFFFAOYSA-N 0.000 claims description 9
- 229910052753 mercury Inorganic materials 0.000 claims description 9
- JCXJVPUVTGWSNB-UHFFFAOYSA-N nitrogen dioxide Inorganic materials O=[N]=O JCXJVPUVTGWSNB-UHFFFAOYSA-N 0.000 claims description 9
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims description 8
- 229910052698 phosphorus Inorganic materials 0.000 claims description 8
- 239000011574 phosphorus Substances 0.000 claims description 8
- 239000007789 gas Substances 0.000 claims description 7
- 229910001385 heavy metal Inorganic materials 0.000 claims description 6
- 230000001788 irregular Effects 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims 1
- 230000007547 defect Effects 0.000 abstract description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- BHEPBYXIRTUNPN-UHFFFAOYSA-N hydridophosphorus(.) (triplet) Chemical compound [PH] BHEPBYXIRTUNPN-UHFFFAOYSA-N 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
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
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/08—Auctions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- 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
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Primary Health Care (AREA)
- Remote Sensing (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a residential land auction price intelligent evaluation method based on big data analysis and a cloud platform, which analyze the basic facility condition of the region around the residential land, detect the soil environment parameter and the atmospheric environment parameter of the residential land, simultaneously acquire the shape of the contour of the residential land, further respectively count the auction price influence coefficient, the residential land soil environment pollution coefficient, the residential land atmospheric environment pollution coefficient and the shape category influence coefficient corresponding to various basic facilities, and count the comprehensive auction price influence coefficient of the residential land comprehensively, thereby calculating the residential land auction evaluation price according to the counted comprehensive auction price influence coefficient of the residential land, overcoming the defect that the evaluation index corresponding to the current residential land evaluation is too single, improving the comprehensiveness and comprehensiveness of the evaluation index and the accuracy of the evaluation result, the auction price evaluated can comprehensively reflect the value of the residential land.
Description
Technical Field
The invention belongs to the technical field of land auction evaluation, and particularly relates to a residential land auction price intelligent evaluation method based on big data analysis and a cloud platform.
Background
In recent years, with the increasing urban population, the increasing urban scale and the accelerating urbanization process, the land resource becomes more and more important, and the scarcity is increasingly obvious. In order to promote the value of land assets to be displayed, the land evaluation industry is started, land evaluation is that land auction price is evaluated frequently, and the value of land is accurately judged, so that the method is beneficial to transactions such as transfer, buying and selling and the like of people. The evaluation accuracy of the land auction price directly influences the period of land trading, so that how to correctly and reasonably evaluate the land auction price has important significance for countries, real estate organizations and people and individuals.
For the evaluation of the residential land auction price, the evaluation mode at present is mostly to evaluate the residential land auction price according to the blooming degree of the surrounding environment of the geographic position of the residential land, the evaluation index is too single, the influence of the soil environment and the atmospheric environment of the residential land on the residential land auction price is not considered, because the residential land is used for building the residential building, if the soil environment and the atmospheric environment are polluted, the influence of the soil environment and the atmospheric environment on the pricing of the residential building is bound to be caused, and further the influence of the residential land auction price is caused, so that the evaluation result of the evaluation mode of the residential land auction price at present can not comprehensively reflect the value of the residential land, and the evaluation accuracy is not high.
Disclosure of Invention
In order to overcome at least the above disadvantages in the prior art, the present invention provides a method for intelligently evaluating a price of a residential land auction based on big data analysis and a cloud platform, so as to solve or improve the above problems.
In a first aspect, the invention provides a residential land auction price intelligent evaluation method based on big data analysis, which comprises the following steps:
s1, determining a region around residential land: acquiring the geographical position of the residential land through a GPS positioning instrument, and determining the surrounding area of the residential land by taking the geographical position of the residential land as a circle center and a preset length distance as a radius;
s2, statistics of infrastructure of the area around the residential land: counting the existing infrastructure in the determined area around the residential land, dividing the counted infrastructure into schools, hospitals, supermarkets and banks according to the types of the infrastructure, counting the number of the types of the counted infrastructures, and then numbering the schools corresponding to the statistical school category infrastructures according to a preset sequence, wherein the numbers are marked as 1,2. Numbering hospitals corresponding to hospital classification infrastructures according to a preset sequence, sequentially marking as 1,2.. b.. v, numbering each supermarket corresponding to the supermarket type infrastructure according to a preset sequence, sequentially marking as 1,2.. c.. x, numbering each bank corresponding to the bank type infrastructure according to a preset sequence, and sequentially marking the banks as 1,2.. d.. y;
s3, constructing a set of distances between each category of infrastructure and the residential land: for each school corresponding to the school category infrastructure, the distance between the geographic position of each school and the geographic position of the residence land is counted to form a school distance to residence land distance set Lε(lε1,lε2,...,lεa,...lεu)，lεa is the distance between the geographic position of the a-th school and the geographic position of the residential land, the distance between the geographic position of each hospital and the geographic position of the residential land is counted for each hospital corresponding to the hospital category infrastructure, and a hospital-to-residential land distance set L is formedτ(lτ1,lτ2,...,lτb,...lτv)，lτb is the distance between the geographic position of the b-th hospital and the geographic position of the residential land, the distance between the geographic position of each supermarket and the geographic position of the residential land is counted for each supermarket corresponding to the supermarket type infrastructure, and a supermarket-to-residential land distance set L is formedν(lν1,lν2,...,lνc,...lνx)，lνc represents the distance between the geographic position of the c-th supermarket and the geographic position of the residential land, and the geographic position of each bank is counted for each bank corresponding to the bank type infrastructureThe distance from the residence land to the geographical position is set to form a bank distance from residence land set Lψ(lψ1,lψ2,...,lψd,…lψy)，lψd is the distance from the geographical position of the d-th bank to the geographical position of the residential land;
s4, carrying out statistics on auction price influence coefficients corresponding to each type of infrastructure: extracting influence values corresponding to various types of basic facilities from a database, and respectively counting auction price influence coefficients corresponding to the types of basic facilities of schools, hospitals, supermarkets and banks in the surrounding area of the residential land according to a school-to-residential land distance set, a hospital-to-residential land distance set, a supermarket-to-residential land distance set and a bank-to-residential land distance set;
s5, sub-region division and detection point distribution: dividing the area of the residential land into a plurality of sub-areas according to the dividing mode of a plane grid, and arranging a single soil environment detection point and a single atmospheric environment detection point in each divided sub-area, thereby obtaining a plurality of arranged soil environment detection points and atmospheric environment detection points, numbering the plurality of arranged soil environment detection points, respectively marking the plurality of arranged soil environment detection points as 1,2.. i.. n, and numbering the plurality of arranged atmospheric environment detection points as 1,2.. j.. m;
s6, constructing a soil environment parameter set and an atmospheric environment parameter set: respectively installing soil environment parameter detection terminals at the distributed soil environment detection points for detecting the soil environment parameters of the soil environment detection points, respectively installing atmospheric environment parameter detection terminals at the distributed atmospheric environment detection points for detecting the atmospheric environment parameters of the atmospheric environment detection points, and further forming a soil environment parameter set Q by the detected soil environment parameters of the soil environment detection pointsw(qw1,qw2,...,qwi,...qwn)，qwi is a numerical value corresponding to the w-th soil environment parameter of the ith soil environment detection point, w is a soil environment parameter, and w is d1, d2, d3, d4, d5, d1, d2, d3, d4 and d5 are respectively expressed as pH value, phosphorus content, mercury content, lead content and chromium content,and the atmospheric environment parameters of the detected atmospheric environment detection points form an atmospheric environment parameter set Pr(pr1,pr2,...,prj,...prm)，prj is a numerical value corresponding to the r-th atmospheric environment parameter of the j-th atmospheric environment detection point, r is an atmospheric environment parameter, and r is the concentration of sulfur dioxide, carbon monoxide, nitrogen dioxide and PM2.5 respectively represented by e1, e2, e3, e4, e1, e2, e3 and e 4;
s7, statistics of soil environmental pollution coefficients and atmospheric environmental pollution coefficients: respectively comparing the soil environment parameter set and the atmospheric environment parameter set with the safe soil environment parameters and the safe atmospheric environment parameters stored in the database to obtain a soil environment parameter comparison set delta Qw(Δqw1,Δqw2,...,Δqwi,...Δqwn) and atmospheric environmental parameter comparison set Δ Pr(Δpr1,Δpr2,...,Δprj,...Δprm), further counting the soil environment pollution coefficient and the atmospheric environment pollution coefficient of the residential land according to the soil environment parameter comparison set and the atmospheric environment parameter comparison set;
s8, counting shape category influence coefficients: acquiring a shape corresponding to the residential land outline, extracting the characteristics of the shape, comparing the extracted characteristics of the shape with the characteristics corresponding to the shapes of various types in a database respectively, and determining the shape type corresponding to the residential land, wherein the shape type comprises a regular shape and an irregular shape, meanwhile, comparing the shape type corresponding to the residential land with the shape type influence coefficient corresponding to the shapes of various types in the database, and screening out the shape type influence coefficient corresponding to the residential land;
s9, comprehensive auction price influence coefficient statistics: counting the comprehensive auction price influence coefficient of the residential land according to the auction price influence coefficient corresponding to the school, hospital, supermarket and bank type infrastructure in the region around the residential land, the soil environment pollution coefficient, the atmospheric environment pollution coefficient and the shape type influence coefficient of the residential land;
s10, calculating the evaluation price of the residential land auction: and extracting the auction average price corresponding to the unit land area of the region of the residential land from the database, acquiring the area of the residential land, and calculating the auction evaluation price of the residential land according to the auction average price corresponding to the unit land area of the region of the residential land, the area of the residential land and the comprehensive auction price influence coefficient of the residential land.
In an alternative embodiment of the first aspect of the present invention, the method of determining the area around the residential land in S1 is to calculate an area of a circle with a radius of a preset length distance and a center of the geographical position of the residential land according to the preset length distance, where the area inside the circle is the area around the residential land.
In an alternative embodiment of the first aspect of the present invention, the auction price influence coefficient corresponding to the school category infrastructure in the area around the residential land is calculated by the formula
In the formula etaεThe auction price influence coefficient corresponding to the school class infrastructure in the region around the residential land is expressed, alpha is the influence value corresponding to the school class infrastructure, and the calculation formula of the auction price influence coefficient corresponding to the school class infrastructure in the region around the residential land is
In the formula etaτThe auction price influence coefficient corresponding to the hospital category infrastructure in the area around the residential land is expressed, the beta is expressed as the influence value corresponding to the hospital category infrastructure, and the calculation formula of the auction price influence coefficient corresponding to the supermarket category infrastructure in the area around the residential land is
In the formula etaνThe auction price influence coefficient is expressed by supermarket type infrastructure in the area around the residence land, and the x is expressed by the influence value corresponding to the supermarket type infrastructureThe calculation formula of the auction price influence coefficient corresponding to the bank category infrastructure in the region is
In the formula etaψThe auction price influence coefficient is expressed for the bank type infrastructure in the area around the residential land, and λ is expressed as the influence value for the bank type infrastructure.
In an alternative embodiment of the first aspect of the present invention, the soil environment parameter detection terminal includes a soil acidity meter, a soil tester and a soil heavy metal detector, wherein the soil acidity meter is configured to detect an acidity and alkalinity of each soil environment detection point, the soil tester is configured to detect a phosphorus content of each soil environment detection point, the soil heavy metal detector is configured to detect a mercury content, a lead content and a chromium content of each soil environment detection point, the atmospheric environment parameter detection terminal includes a gas sensor and a PM2.5 detector, wherein the gas sensor is configured to detect a sulfur dioxide concentration, a carbon monoxide concentration and a nitrogen dioxide concentration of each atmospheric environment detection point, and the PM2.5 detector is configured to detect a PM2.5 concentration of each atmospheric environment detection point.
In an alternative embodiment of the first aspect of the invention, the safe soil environment parameters include safe values corresponding to ph, phosphorous, mercury, lead and chromium levels, and the safe atmospheric environment parameters include safe values corresponding to sulfur dioxide, carbon monoxide, nitrogen dioxide and PM2.5 concentrations.
In an alternative embodiment of the first aspect of the present invention, the soil environmental pollution coefficient of the residential land is calculated by the formula
Wherein sigma is expressed as the soil environmental pollution coefficient of the residential land, delta qwn is the difference between the w soil environment parameter of the ith soil environment detection point and the corresponding safety value of the soil environment parameter,
expressed as the safety value corresponding to the w-th soil environment parameter, the calculation formula of the atmospheric environmental pollution coefficient of the residential land is
Where xi is the atmospheric environmental pollution coefficient of the residential land, Δ prj is the difference between the r-th atmospheric environmental parameter of the j-th atmospheric environmental detection point and the corresponding safety value of the atmospheric environmental parameter,
expressed as the safety value corresponding to the r-th atmospheric environmental parameter.
In an alternative embodiment of the first aspect of the present invention, the calculation formula of the auction price influence factor for the residential site is
The auction price influence coefficient is expressed as a total auction price influence coefficient of the residential land, and γ is expressed as a shape category influence coefficient corresponding to the residential land.
In an alternative embodiment of the first aspect of the present invention, the estimated price for the residential land auction is calculated by the formula
Wherein G represents the auction evaluation price of the residential land, G represents the auction average price corresponding to the unit land area of the region where the residential land is located, and s represents the area of the residential land.
In a second aspect, the present invention provides a cloud platform, which includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected with at least one residential land auction price intelligent evaluation device, the machine-readable storage medium is configured to store a program, an instruction, or code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the residential land auction price intelligent evaluation method based on big data analysis according to the present invention.
Based on any one of the above aspects, the invention has the following beneficial effects:
(1) according to the invention, the situation of the infrastructure of the area around the residential land is analyzed, the soil environment parameter and the atmospheric environment parameter of the residential land are detected, and the shape of the contour of the residential land is obtained, so that the auction price influence coefficient, the residential land soil environment pollution coefficient, the residential land atmospheric environment pollution coefficient and the shape category influence coefficient corresponding to various infrastructures are respectively counted, thus the comprehensive auction price influence coefficient of the residential land is comprehensively counted, the defect that the evaluation index corresponding to the current residential land evaluation is too single is overcome, the comprehensiveness and comprehensiveness of the evaluation index are improved, and an accurate and reliable reference basis is provided for the subsequent residential land auction evaluation price calculation.
(2) The method and the device calculate the residential land auction evaluation price according to the statistical comprehensive auction price influence coefficient of the residential land, the evaluated auction price can comprehensively reflect the value of the residential land, the accuracy of the evaluation result is improved, the period of trading of the residential land is further reduced, and the working efficiency of residential land evaluators is improved.
(3) In the process of detecting the soil environment parameters and the atmospheric environment parameters of the residential land, the soil environment detection points and the atmospheric environment detection points are arranged on the residential land to obtain the soil environment parameters of all the soil environment detection points and the atmospheric environment parameters of all the atmospheric environment detection points, so that detection errors caused by the fact that only single soil environment detection points and only single atmospheric environment detection points of the residential land are detected are avoided, and the soil environment pollution coefficients and the atmospheric environment pollution coefficients obtained according to the soil environment parameters of all the soil environment detection points and the atmospheric environment parameters of all the atmospheric environment detection points are closer to real values.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a flow chart of the method steps of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, in a first aspect, the present invention provides a residential land auction price intelligent evaluation method based on big data analysis, comprising the following steps:
s1, determining a region around residential land: acquiring the geographical position of the residential land through a GPS locator, and determining the surrounding area of the residential land by taking the geographical position of the residential land as the center of a circle and a preset length distance as a radius, wherein the method for determining the surrounding area of the residential land is to calculate the area of a circle by taking the geographical position of the residential land as the center of a circle and the preset length distance as the radius according to the preset length distance, and the area in the circle area is the surrounding area of the residential land;
the embodiment provides convenience for later statistics of infrastructure of the surrounding area of the residential land by determining the surrounding area of the residential land;
s2, statistics of infrastructure of the area around the residential land: counting the existing infrastructure in the determined area around the residential land, dividing the counted infrastructure into schools, hospitals, supermarkets and banks according to the types of the infrastructure, counting the number of the types of the counted infrastructures, and then numbering the schools corresponding to the statistical school category infrastructures according to a preset sequence, wherein the numbers are marked as 1,2. Numbering hospitals corresponding to hospital classification infrastructures according to a preset sequence, sequentially marking as 1,2.. b.. v, numbering each supermarket corresponding to the supermarket type infrastructure according to a preset sequence, sequentially marking as 1,2.. c.. x, numbering each bank corresponding to the bank type infrastructure according to a preset sequence, and sequentially marking the banks as 1,2.. d.. y;
s3, constructing a set of distances between each category of infrastructure and the residential land: for each school corresponding to the school category infrastructure, the distance between the geographic position of each school and the geographic position of the residence land is counted to form a school distance to residence land distance set Lε(lε1,lε2,...,lεa,...lεu)，lεa is the distance between the geographic position of the a-th school and the geographic position of the residential land, the distance between the geographic position of each hospital and the geographic position of the residential land is counted for each hospital corresponding to the hospital category infrastructure, and a hospital-to-residential land distance set L is formedτ(lτ1,lτ2,...,lτb,...lτv)，lτb is the distance between the geographic position of the b-th hospital and the geographic position of the residential land, the distance between the geographic position of each supermarket and the geographic position of the residential land is counted for each supermarket corresponding to the supermarket type infrastructure, and a supermarket-to-residential land distance set L is formedν(lν1,lν2,...,lνc,...lνx)，lνc represents the distance between the geographic position of the c-th supermarket and the geographic position of the residential land, the distance between the geographic position of each bank and the geographic position of the residential land is counted for each bank corresponding to the bank type infrastructure, and a bank-to-residential land distance set L is formedψ(lψ1,lψ2,...,lψd,...lψy)，lψd is the distance from the geographical position of the d-th bank to the geographical position of the residential land;
s4, carrying out statistics on auction price influence coefficients corresponding to each type of infrastructure: extracting the influence value corresponding to each category of infrastructure from the database,and according to the school distance to residence land distance set, the hospital distance to residence land distance set, the supermarket distance to residence land distance set and the bank distance to residence land distance set, respectively counting auction price influence coefficients corresponding to the school, hospital, supermarket and bank type infrastructure in the region around the residence land, wherein the calculation formula of the auction price influence coefficients corresponding to the school type infrastructure in the region around the residence land is
In the formula etaεThe auction price influence coefficient corresponding to the school class infrastructure in the region around the residential land is expressed, alpha is the influence value corresponding to the school class infrastructure, and the calculation formula of the auction price influence coefficient corresponding to the school class infrastructure in the region around the residential land is
In the formula etaτThe auction price influence coefficient corresponding to the hospital category infrastructure in the area around the residential land is expressed, the beta is expressed as the influence value corresponding to the hospital category infrastructure, and the calculation formula of the auction price influence coefficient corresponding to the supermarket category infrastructure in the area around the residential land is
In the formula etaνThe auction price influence coefficient corresponding to the supermarket type infrastructure in the area around the residential land is expressed, x is expressed as the influence value corresponding to the supermarket type infrastructure, and the calculation formula of the auction price influence coefficient corresponding to the bank type infrastructure in the area around the residential land is
In the formula etaψThe auction price influence coefficient corresponding to the bank type infrastructure in the region around the residential land is represented, and lambda is represented as the influence value corresponding to the bank type infrastructure;
in the embodiment, the auction price influence coefficients corresponding to the various types of infrastructure are counted, the statistical result comprehensively and intuitively reflects the bloom degree of the area around the residential land, and the closer the distance between all the infrastructures in the various types of infrastructures and the geographic position of the residential land is, the more the bloom of the area around the residential land is indicated;
s5, sub-region division and detection point distribution: dividing the area of the residential land into a plurality of sub-areas according to the dividing mode of a plane grid, and arranging a single soil environment detection point and a single atmospheric environment detection point in each divided sub-area, thereby obtaining a plurality of arranged soil environment detection points and atmospheric environment detection points, numbering the plurality of arranged soil environment detection points, respectively marking the plurality of arranged soil environment detection points as 1,2.. i.. n, and numbering the plurality of arranged atmospheric environment detection points as 1,2.. j.. m;
in the embodiment, the soil environment detection points and the atmospheric environment detection points are distributed in the area where the residential land is located, so that a foundation is laid for soil environment parameter detection of the soil environment detection points and atmospheric environment parameter detection of the atmospheric environment detection points which are carried out later;
s6, constructing a soil environment parameter set and an atmospheric environment parameter set: the method comprises the steps of respectively installing soil environment parameter detection terminals at all laid soil environment detection points for detecting soil environment parameters of all the soil environment detection points, wherein each soil environment parameter detection terminal comprises a soil acidity meter, a soil tester and a soil heavy metal detector, the soil acidity meter is used for detecting the pH value of each soil environment detection point, the soil tester is used for detecting the phosphorus content of each soil environment detection point, the soil heavy metal detector is used for detecting the mercury content, the lead content and the chromium content of each soil environment detection point, the atmospheric environment parameter detection terminals are respectively installed at all the laid atmospheric environment detection points for detecting the atmospheric environment parameters of each atmospheric environment detection point, each atmospheric environment parameter detection terminal comprises a gas sensor and a PM2.5 detector, the gas sensor is used for detecting the sulfur dioxide concentration, the lead content and the chromium content of each soil environment detection point, and the gas sensor is used for detecting, Carbon monoxide concentration and nitrogen dioxide concentration, wherein the PM2.5 detector is used for detecting the PM2.5 concentration at each atmospheric environment detection point, and further detectingThe soil environment parameters of each soil environment detection point form a soil environment parameter set Qw(qw1,qw2,...,qwi,...qwn)，qwi is a numerical value corresponding to the w soil environment parameter of the ith soil environment detection point, w is a soil environment parameter, w is d1, d2, d3, d4, d5, d1, d2, d3, d4 and d5 are respectively expressed as pH value, phosphorus content, mercury content, lead content and chromium content, and the detected atmospheric environment parameters of the atmospheric environment detection points form an atmospheric environment parameter set Pr(pr1,pr2,...,prj,...prm)，prj is a numerical value corresponding to the r-th atmospheric environment parameter of the j-th atmospheric environment detection point, r is an atmospheric environment parameter, and r is the concentration of sulfur dioxide, carbon monoxide, nitrogen dioxide and PM2.5 respectively represented by e1, e2, e3, e4, e1, e2, e3 and e 4;
in the embodiment, soil environment parameter detection is carried out on each laid soil environment detection point, and atmospheric environment parameter detection is carried out on each laid atmospheric environment detection point, so that detection errors caused by detection only on a single soil environment detection point and the atmospheric environment detection points of the residential land are avoided, and the soil environment pollution coefficient and the atmospheric environment pollution coefficient obtained according to the soil environment parameters of each soil environment detection point and the atmospheric environment parameters of each atmospheric environment detection point are closer to the real values;
s7, statistics of soil environmental pollution coefficients and atmospheric environmental pollution coefficients: respectively comparing the soil environment parameter set and the atmospheric environment parameter set with safe soil environment parameters and safe atmospheric environment parameters stored in a database, wherein the safe soil environment parameters comprise safety values corresponding to the pH value, the phosphorus content, the mercury content, the lead content and the chromium content, the safe atmospheric environment parameters comprise safety values corresponding to the sulfur dioxide concentration, the carbon monoxide concentration, the nitrogen dioxide concentration and the PM2.5 concentration, and obtaining a soil environment parameter comparison set delta Qw(Δqw1,Δqw2,...,Δqwi,...Δqwn) and atmospheric environmental parameter comparison set Δ Pr(Δpr1,Δpr2,...,Δprj,...Δprm) according to the soilThe environmental parameter comparison set and the atmospheric environmental parameter comparison set count the soil environmental pollution coefficient of the residential land
Wherein sigma is expressed as the soil environmental pollution coefficient of the residential land, delta qwn is the difference between the w soil environment parameter of the ith soil environment detection point and the corresponding safety value of the soil environment parameter,
expressed as the safety value and the atmospheric environmental pollution coefficient corresponding to the w-th soil environmental parameter
Where xi is the atmospheric environmental pollution coefficient of the residential land, Δ prj is the difference between the r-th atmospheric environmental parameter of the j-th atmospheric environmental detection point and the corresponding safety value of the atmospheric environmental parameter,
expressing the safety value corresponding to the r atmospheric environment parameter;
s8, counting shape category influence coefficients: acquiring a shape corresponding to the residential land outline, extracting the characteristics of the shape, comparing the extracted characteristics of the shape with the characteristics corresponding to the shapes of various types in a database respectively, and determining the shape type corresponding to the residential land, wherein the shape type comprises a regular shape and an irregular shape, meanwhile, comparing the shape type corresponding to the residential land with the shape type influence coefficient corresponding to the shapes of various types in the database, and screening out the shape type influence coefficient corresponding to the residential land;
s9, comprehensive auction price influence coefficient statistics: counting the comprehensive beat of the residential land according to the auction price influence coefficient corresponding to the basic facilities of the classes of schools, hospitals, supermarkets and banks in the area around the residential land, the soil environmental pollution coefficient of the residential land, the atmospheric environmental pollution coefficient and the shape class influence coefficientSelling price influence coefficient
A general auction price influence coefficient for the residential land, and γ is a shape category influence coefficient corresponding to the residential land;
the comprehensive auction price influence coefficient of the residential land counted in the embodiment synthesizes the influence conditions of various infrastructures around the residential land, the influence conditions of the soil environment, the influence conditions of the atmospheric environment and the influence conditions of parameters of the land, realizes the quantitative display of the influence results corresponding to various auction price influence factors, overcomes the defect that the evaluation index corresponding to the current residential land evaluation is too single, improves the comprehensiveness and the comprehensiveness of the evaluation index, and provides a precise and reliable reference basis for the subsequent residential land auction evaluation price calculation;
s10, calculating the evaluation price of the residential land auction: extracting the auction average price corresponding to the unit land area of the region of the residential land from the database, acquiring the area of the residential land, and calculating the auction evaluation price of the residential land according to the auction average price corresponding to the unit land area of the region of the residential land, the area of the residential land and the comprehensive auction price influence coefficient of the residential land
Wherein G represents the auction evaluation price of the residential land, G represents the auction average price corresponding to the unit land area of the region where the residential land is located, and s represents the area of the residential land.
According to the method and the device, the evaluation price of the residential land auction is calculated according to the statistical comprehensive auction price influence coefficient of the residential land, the evaluation price can comprehensively reflect the value of the residential land, the accuracy of an evaluation result is improved, the period of trading of the residential land is further shortened, and the working efficiency of a residential land evaluator is improved.
In a second aspect, the present invention provides a cloud platform, which includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected with at least one residential land auction price intelligent evaluation device, the machine-readable storage medium is configured to store a program, an instruction, or code, such as a residential land auction price intelligent evaluation instruction/module in an embodiment of the present invention, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium, so as to execute the residential land auction price intelligent evaluation method based on big data analysis according to the present invention.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (9)
1. A residential land auction price intelligent evaluation method based on big data analysis is characterized by comprising the following steps: the method comprises the following steps:
s1, determining a region around residential land: acquiring the geographical position of the residential land through a GPS positioning instrument, and determining the surrounding area of the residential land by taking the geographical position of the residential land as a circle center and a preset length distance as a radius;
s2, statistics of infrastructure of the area around the residential land: counting the existing infrastructure in the determined area around the residential land, dividing the counted infrastructure into schools, hospitals, supermarkets and banks according to the types of the infrastructure, counting the number of the types of the counted infrastructures, and then numbering the schools corresponding to the statistical school category infrastructures according to a preset sequence, wherein the numbers are marked as 1,2. Numbering hospitals corresponding to hospital classification infrastructures according to a preset sequence, sequentially marking as 1,2.. b.. v, numbering each supermarket corresponding to the supermarket type infrastructure according to a preset sequence, sequentially marking as 1,2.. c.. x, numbering each bank corresponding to the bank type infrastructure according to a preset sequence, and sequentially marking the banks as 1,2.. d.. y;
s3, constructing a set of distances between each category of infrastructure and the residential land: for each school corresponding to the school category infrastructure, the distance between the geographic position of each school and the geographic position of the residence land is counted to form a school distance to residence land distance set Lε(lε1,lε2,...,lεa,...lεu)，lεa is the distance between the geographic position of the a-th school and the geographic position of the residential land, the distance between the geographic position of each hospital and the geographic position of the residential land is counted for each hospital corresponding to the hospital category infrastructure, and a hospital-to-residential land distance set L is formedτ(lτ1,lτ2,...,lτb,...lτv)，lτb is the distance between the geographic position of the b-th hospital and the geographic position of the residential land, the distance between the geographic position of each supermarket and the geographic position of the residential land is counted for each supermarket corresponding to the supermarket type infrastructure, and a supermarket-to-residential land distance set L is formedν(lν1,lν2,...,lνc,...lνx)，lνc represents the distance between the geographic position of the c-th supermarket and the geographic position of the residential land, the distance between the geographic position of each bank and the geographic position of the residential land is counted for each bank corresponding to the bank type infrastructure, and a bank-to-residential land distance set L is formedψ(lψ1,lψ2,…,lψd,…lψy)，lψd is the distance from the geographical position of the d-th bank to the geographical position of the residential land;
s4, carrying out statistics on auction price influence coefficients corresponding to each type of infrastructure: extracting influence values corresponding to various types of basic facilities from a database, and respectively counting auction price influence coefficients corresponding to the types of basic facilities of schools, hospitals, supermarkets and banks in the surrounding area of the residential land according to a school-to-residential land distance set, a hospital-to-residential land distance set, a supermarket-to-residential land distance set and a bank-to-residential land distance set;
s5, sub-region division and detection point distribution: dividing the area of the residential land into a plurality of sub-areas according to the dividing mode of a plane grid, and arranging a single soil environment detection point and a single atmospheric environment detection point in each divided sub-area, thereby obtaining a plurality of arranged soil environment detection points and atmospheric environment detection points, numbering the plurality of arranged soil environment detection points, respectively marking the plurality of arranged soil environment detection points as 1,2.. i.. n, and numbering the plurality of arranged atmospheric environment detection points as 1,2.. j.. m;
s6, constructing a soil environment parameter set and an atmospheric environment parameter set: respectively installing soil environment parameter detection terminals at the distributed soil environment detection points for detecting the soil environment parameters of the soil environment detection points, respectively installing atmospheric environment parameter detection terminals at the distributed atmospheric environment detection points for detecting the atmospheric environment parameters of the atmospheric environment detection points, and further forming a soil environment parameter set Q by the detected soil environment parameters of the soil environment detection pointsw(qw1,qw2,…,qwi,...qwn)，qwi is a numerical value corresponding to the w soil environment parameter of the ith soil environment detection point, w is a soil environment parameter, w is d1, d2, d3, d4, d5, d1, d2, d3, d4 and d5 are respectively expressed as pH value, phosphorus content, mercury content, lead content and chromium content, and the detected atmospheric environment parameters of the atmospheric environment detection points form an atmospheric environment parameter set Pr(pr1,pr2,…,prj,…prm)，prj is a numerical value corresponding to the r-th atmospheric environment parameter of the j-th atmospheric environment detection point, r is an atmospheric environment parameter, and r is the concentration of sulfur dioxide, carbon monoxide, nitrogen dioxide and PM2.5 respectively represented by e1, e2, e3, e4, e1, e2, e3 and e 4;
s7, statistics of soil environmental pollution coefficients and atmospheric environmental pollution coefficients: respectively comparing the soil environment parameter set and the atmospheric environment parameter set with the safe soil environment parameters and the safe atmospheric environment parameters stored in the database to obtain a soil environment parameter comparison setResultant of Δ Qw(Δqw1,Δqw2,…,Δqwi,…Δqwn) and atmospheric environmental parameter comparison set Δ Pr(Δpr1,Δpr2,…,Δprj,…Δprm), further counting the soil environment pollution coefficient and the atmospheric environment pollution coefficient of the residential land according to the soil environment parameter comparison set and the atmospheric environment parameter comparison set;
s8, counting shape category influence coefficients: acquiring a shape corresponding to the residential land outline, extracting the characteristics of the shape, comparing the extracted characteristics of the shape with the characteristics corresponding to the shapes of various types in a database respectively, and determining the shape type corresponding to the residential land, wherein the shape type comprises a regular shape and an irregular shape, meanwhile, comparing the shape type corresponding to the residential land with the shape type influence coefficient corresponding to the shapes of various types in the database, and screening out the shape type influence coefficient corresponding to the residential land;
s9, comprehensive auction price influence coefficient statistics: counting the comprehensive auction price influence coefficient of the residential land according to the auction price influence coefficient corresponding to the school, hospital, supermarket and bank type infrastructure in the region around the residential land, the soil environment pollution coefficient, the atmospheric environment pollution coefficient and the shape type influence coefficient of the residential land;
s10, calculating the evaluation price of the residential land auction: and extracting the auction average price corresponding to the unit land area of the region of the residential land from the database, acquiring the area of the residential land, and calculating the auction evaluation price of the residential land according to the auction average price corresponding to the unit land area of the region of the residential land, the area of the residential land and the comprehensive auction price influence coefficient of the residential land.
2. The intelligent evaluation method for residential land auction price based on big data analysis according to claim 1, characterized in that: the method for determining the area around the residential land in S1 is to calculate the area of a circle with the geographical location of the residential land as the center and the preset length distance as the radius according to the preset length distance, where the area in the circle is the area around the residential land.
3. The intelligent evaluation method for residential land auction price based on big data analysis according to claim 1, characterized in that: the calculation formula of the auction price influence coefficient corresponding to the school category infrastructure in the area around the residential land is
In the formula etaεThe auction price influence coefficient corresponding to the school class infrastructure in the region around the residential land is expressed, alpha is the influence value corresponding to the school class infrastructure, and the calculation formula of the auction price influence coefficient corresponding to the school class infrastructure in the region around the residential land is
In the formula etaτThe auction price influence coefficient corresponding to the hospital category infrastructure in the area around the residential land is expressed, the beta is expressed as the influence value corresponding to the hospital category infrastructure, and the calculation formula of the auction price influence coefficient corresponding to the supermarket category infrastructure in the area around the residential land is
In the formula etaνThe auction price influence coefficient corresponding to the supermarket type infrastructure in the area around the residential land is expressed, x is expressed as the influence value corresponding to the supermarket type infrastructure, and the calculation formula of the auction price influence coefficient corresponding to the bank type infrastructure in the area around the residential land is
In the formula etaψThe auction price influence coefficient is expressed for the bank type infrastructure in the area around the residential land, and λ is expressed as the influence value for the bank type infrastructure.
4. The intelligent evaluation method for residential land auction price based on big data analysis according to claim 1, characterized in that: the soil environment parameter detection terminal comprises a soil acidity meter, a soil tester and a soil heavy metal detector, wherein the soil acidity meter is used for detecting the pH value of each soil environment detection point, the soil tester is used for detecting the phosphorus content of each soil environment detection point, the soil heavy metal detector is used for detecting the mercury content, the lead content and the chromium content of each soil environment detection point, the atmospheric environment parameter detection terminal comprises a gas sensor and a PM2.5 detector, wherein the gas sensor is used for detecting the sulfur dioxide concentration, the carbon monoxide concentration and the nitrogen dioxide concentration of each atmospheric environment detection point, and the PM2.5 detector is used for detecting the PM2.5 concentration of each atmospheric environment detection point.
5. The intelligent evaluation method for residential land auction price based on big data analysis according to claim 1, characterized in that: the safe soil environment parameters comprise safety values corresponding to the pH value, the phosphorus content, the mercury content, the lead content and the chromium content, and the safe atmospheric environment parameters comprise safety values corresponding to the sulfur dioxide concentration, the carbon monoxide concentration, the nitrogen dioxide concentration and the PM2.5 concentration.
6. The intelligent evaluation method for residential land auction price based on big data analysis according to claim 1, characterized in that: the calculation formula of the soil environmental pollution coefficient of the residential land is
Wherein sigma is expressed as the soil environmental pollution coefficient of the residential land, delta qwn is the difference between the w soil environment parameter of the ith soil environment detection point and the corresponding safety value of the soil environment parameter,
expressed as a safety value corresponding to the w-th soil environment parameter,the calculation formula of the atmospheric environmental pollution coefficient of the residential land is
Where xi is the atmospheric environmental pollution coefficient of the residential land, Δ prj is the difference between the r-th atmospheric environmental parameter of the j-th atmospheric environmental detection point and the corresponding safety value of the atmospheric environmental parameter,
expressed as the safety value corresponding to the r-th atmospheric environmental parameter.
7. The intelligent evaluation method for residential land auction price based on big data analysis according to claim 1, characterized in that: the calculation formula of the comprehensive auction price influence coefficient of the residential land is
The auction price influence coefficient is expressed as a total auction price influence coefficient of the residential land, and γ is expressed as a shape category influence coefficient corresponding to the residential land.
8. The intelligent evaluation method for residential land auction price based on big data analysis according to claim 1, characterized in that: the calculation formula of the residential land auction evaluation price is
Wherein G represents the auction evaluation price of the residential land, G represents the auction average price corresponding to the unit land area of the region where the residential land is located, and s represents the area of the residential land.
9. A cloud platform, characterized by: the cloud platform comprises a processor, a machine-readable storage medium and a network interface, wherein the machine-readable storage medium, the network interface and the processor are connected through a bus system, the network interface is used for being in communication connection with at least one intelligent residential land auction price evaluation device, the machine-readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, the instructions or the codes in the machine-readable storage medium to execute the intelligent residential land auction price evaluation method based on big data analysis according to any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110073278.1A CN112862575A (en) | 2021-01-20 | 2021-01-20 | Intelligent residential land auction price evaluation method based on big data analysis and cloud platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110073278.1A CN112862575A (en) | 2021-01-20 | 2021-01-20 | Intelligent residential land auction price evaluation method based on big data analysis and cloud platform |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112862575A true CN112862575A (en) | 2021-05-28 |
Family
ID=76007596
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110073278.1A Pending CN112862575A (en) | 2021-01-20 | 2021-01-20 | Intelligent residential land auction price evaluation method based on big data analysis and cloud platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112862575A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117313335A (en) * | 2023-09-14 | 2023-12-29 | 苏州圣蒙莱科技有限公司 | Digital twinning-based smart city componentization construction method and construction platform |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130144683A1 (en) * | 2011-12-06 | 2013-06-06 | HMVP Partners, LLC | Determining residential transaction price indices based on prior transaction prices |
| CN109447697A (en) * | 2018-10-17 | 2019-03-08 | 上海大雄资产评估有限公司 | Price Evaluation of Real Estate method and Price Evaluation of Real Estate system |
| CN109919653A (en) * | 2019-01-17 | 2019-06-21 | 平安城市建设科技(深圳)有限公司 | Source of houses price evaluation method, apparatus, equipment and storage medium |
| CN111080356A (en) * | 2019-12-11 | 2020-04-28 | 西南科技大学 | Method for calculating residence price influence factors by using machine learning regression model |
| CN111709854A (en) * | 2020-06-11 | 2020-09-25 | 曹巍 | Method and system for determining land value based on neural network and land equivalent circle |
| CN112241926A (en) * | 2020-11-17 | 2021-01-19 | 重庆金融资产交易所有限责任公司 | Land value evaluation method and device |
-
2021
- 2021-01-20 CN CN202110073278.1A patent/CN112862575A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130144683A1 (en) * | 2011-12-06 | 2013-06-06 | HMVP Partners, LLC | Determining residential transaction price indices based on prior transaction prices |
| CN109447697A (en) * | 2018-10-17 | 2019-03-08 | 上海大雄资产评估有限公司 | Price Evaluation of Real Estate method and Price Evaluation of Real Estate system |
| CN109919653A (en) * | 2019-01-17 | 2019-06-21 | 平安城市建设科技(深圳)有限公司 | Source of houses price evaluation method, apparatus, equipment and storage medium |
| CN111080356A (en) * | 2019-12-11 | 2020-04-28 | 西南科技大学 | Method for calculating residence price influence factors by using machine learning regression model |
| CN111709854A (en) * | 2020-06-11 | 2020-09-25 | 曹巍 | Method and system for determining land value based on neural network and land equivalent circle |
| CN112241926A (en) * | 2020-11-17 | 2021-01-19 | 重庆金融资产交易所有限责任公司 | Land value evaluation method and device |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117313335A (en) * | 2023-09-14 | 2023-12-29 | 苏州圣蒙莱科技有限公司 | Digital twinning-based smart city componentization construction method and construction platform |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Goodchild et al. | A simple positional accuracy measure for linear features | |
| CN110427533B (en) | Pollution propagation mode visual analysis method and system based on time sequence particle tracking | |
| CN110346517B (en) | Smart city industrial atmosphere pollution visual early warning method and system | |
| CN102750413B (en) | Data processing and mapping method of topographic surveying of electric transmission line tower positions | |
| CN112308292A (en) | Method for drawing fire risk grade distribution map | |
| CN111652195A (en) | Land utilization change analysis and prediction method | |
| CN112348290B (en) | River water quality prediction method, river water quality prediction device, storage medium and storage device | |
| US20100036793A1 (en) | Method and System for Geospatial Forecasting of Events Incorporating Data Error and Uncertainty | |
| CN102298705A (en) | Method for analyzing influence of landscape characteristic on remote sensing classification patch accuracy | |
| CN114708521A (en) | City functional area identification method and system based on street view image target relation perception network | |
| CN113051653A (en) | Urban planning road construction evaluation management system based on multi-dimensional data analysis | |
| RU2503042C1 (en) | Method for spatial quantitative evaluation of ambient air contamination level | |
| CN112862575A (en) | Intelligent residential land auction price evaluation method based on big data analysis and cloud platform | |
| CN110503348B (en) | Individual air pollution exposure simulation measurement method based on position matching | |
| Habtemichael et al. | Incident-induced delays on freeways: quantification method by grouping similar traffic patterns | |
| CN113486295B (en) | Fourier series-based ozone total amount change prediction method | |
| CN112241981A (en) | Method and device for verifying precision of secondary measurement data of crop planting area | |
| CN114004661A (en) | Store information processing method, device, equipment and storage medium | |
| CN115166874A (en) | Meteorological drought index SPI construction method based on machine learning | |
| CN115689337A (en) | Carbon emission spatial distribution inversion method, device, equipment and storage medium | |
| CN113963268B (en) | Construction state change monitoring method for large-scale engineering construction | |
| CN113128058B (en) | Inversion method and device of temperature profile, readable storage medium and electronic equipment | |
| CN111461163B (en) | Urban interior PM2.5 concentration simulation and population exposure evaluation method and device | |
| CN116125557A (en) | Electric power weather monitoring network layout optimization method and system | |
| CN115439287A (en) | Geological disaster risk evaluation method based on machine learning |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210528 |