CN116911884A - Natural resource public land price management system and method based on big data - Google Patents
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Abstract
The application discloses a natural resource public land price management system and method based on big data, wherein the system comprises the following steps: the data crawling module crawls the original data of public land price system construction result information on the network; the big data processing module performs data cleaning and screening processing on the original data to obtain processed data; the land price management module extracts the public land price related information, and correspondingly fills the public land price related information into a public land price management table of the unified template; the input information acquisition module acquires to-be-estimated object information input by a user; the query module queries the public land price information, the calculation factors and the correction factors; the calculation module calculates the land block price of the object to be estimated. And acquiring public land price system construction result information data of each region by adopting a big data technology, extracting public land price related information to form public land price management data of a unified template after big data processing, and enabling a user to quickly inquire automatic valuations of objects to be estimated in the corresponding region for auxiliary reference.
Description
Technical Field
The application relates to the technical field of public land price management, in particular to a public land price management system and method for natural resources based on big data.
Background
Land price is understood to be the income obtained by the land owner from the land consumer's yielding of the land use rights, which is a manifestation of the land owner's rights. The public land price is a legal land price system in China, and comprises a reference land price, a calibrated land price and the like, which are used as cores of land price, and the public land price system maintains the stable development of market economy and promotes the fair disclosure of market value. The standard land price and the calibrated land price of the urban real estate management method of the people's republic of China are regulated to be determined and published regularly. As an important component of public land price, the two achievements have wide application scenes in daily land valuation, the reference land price reflects the average land price of the area, the price level is obviously lower than the market price on a macro scale, and each city and county department needs to comprehensively update the reference land price every three years; the market price of a specific land is reflected by the calibrated land price, the calibrated land price is focused on a microscopic scale, the calibrated land price is equal to or slightly lower than the market price level, the market guidance is stronger, and the calibrated land price updating period is one year. The land price system is various, although the respective application range is generally clear, the application cross still exists, and the public of multiple land price easily causes cognitive interference to the public, weakens the guiding effect of land price achievements on market participation main bodies, and urgently needs to integrate and optimize the current land price system. Therefore, it is necessary to construct an informationized public land price management system to facilitate the user's inquiry of the land price of each region.
Disclosure of Invention
Aiming at the defects in the prior art, the natural resource public land price management system and method based on big data provided by the application comprehensively acquire public land price system construction result information data of each region by adopting a big data technology, extract public land price related information to form public land price management data of a unified template through big data processing, realize automatic valuation by a user only by inputting information of an object to be estimated, realize quick reflection of potential market value of the object to be estimated and provide important reference for decision of the user.
In a first aspect, a natural resource public land price management system based on big data provided by an embodiment of the present application includes: the system comprises a data crawling module, a big data processing module, a land price management module, an input information acquisition module, a query module and a calculation module;
the data crawling module is used for crawling original data of public land price system construction result information published by relevant departments of each place on the network through a web crawler;
the big data processing module is used for carrying out data cleaning and screening processing on the original data to obtain processed data;
the land price management module is used for extracting the public land price related information from the processed data and correspondingly filling the public land price related information into a public land price management table of the unified template;
the input information acquisition module is used for acquiring to-be-estimated object information input by a user;
the query module is used for querying corresponding public land price information, calculation factors and correction coefficients from the public land price management table according to the information of the object to be estimated;
the calculation module is used for calculating the land parcel price of the object to be estimated according to the public land price information, the calculation factors and the correction factors.
In a second aspect, a method for managing a natural resource public land price based on big data provided by an embodiment of the present application includes:
crawling original data of public land price system construction result information published by relevant departments of each place on a network through a web crawler;
performing data cleaning and screening treatment on the original data to obtain treated data;
extracting the related information of the public land price from the processed data, and correspondingly filling the related information of the public land price into a corresponding public land price management table;
acquiring information of an object to be estimated input by a user;
inquiring corresponding public land price information, calculation factors and correction coefficients from the public land price management table according to the information of the object to be estimated;
and calculating the land parcel price of the object to be estimated according to the public land parcel information, the calculation factors and the correction factors.
The application has the beneficial effects that:
according to the natural resource public land price management system and method based on big data, public land price system construction result information data of all areas are comprehensively obtained by adopting a big data technology, public land price management data of a unified template is formed by extracting public land price related information through big data processing, a user can realize automatic valuation by only inputting information of an object to be estimated, the potential market value of the object to be estimated is rapidly reflected, and important references are provided for decision making of the user.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
Fig. 1 is a block diagram showing a natural resource public land price management system based on big data according to a first embodiment of the present application;
fig. 2 is a flowchart of a method for managing a natural resource public land price based on big data according to another embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs.
As shown in fig. 1, there is shown a block diagram of a natural resource public land price management system based on big data according to a first embodiment of the present application, the system comprising: the system comprises a data crawling module, a big data processing module, a land price management module, an input information acquisition module, a query module and a calculation module, wherein the data crawling module is used for crawling original data of public land price system construction result information published by relevant departments of each place on a network through a web crawler; the big data processing module is used for carrying out data cleaning and screening processing on the original data to obtain processed data; the land price management module is used for extracting the public land price related information from the processed data and correspondingly filling the public land price related information into a public land price management table of the unified template; the input information acquisition module is used for acquiring the information of the object to be estimated input by the user; the query module is used for querying corresponding public land price information, calculation factors and correction coefficients from the public land price management table according to the information of the object to be estimated; the calculation module is used for calculating the land parcel price of the object to be estimated according to the public land price information, the calculation factors and the correction factors.
Because the related departments of each area publish the construction result information of the land price system and the time of the public information is inconsistent, the application is provided with a big data crawling module, and the original data of the construction result information of the public land price system published on the network is crawled by a web crawler, wherein the original data comprises: reference ground price and nominal ground price. The basic land price categories of each land mainly comprise: (1) the land price for construction comprises a town reference land price, a collective land price for construction and a land price for transfer construction; (2) the agricultural land price includes national agricultural land standard land price and collective agricultural land standard land price; (3) other types include underground space reference ground prices, new industrial reference ground prices, and the like. And the big data processing module performs data cleaning and screening processing on the original data to obtain processed data. The land price management module extracts the needed relevant public land price information from the processed data, and fills the extracted information into the public land price management table. And constructing urban and rural unified operation reference land price by adopting a unified template for all public land price information. The user inputs information of an object to be estimated in a search box, wherein the object to be estimated is a certain land block in a certain area, and the information of the object to be estimated comprises a land block name, a grade, an area and the like. The inquiring module inquires corresponding public land price information, calculation factors and correction coefficients from the public land price management table according to the information of the to-be-estimated object input by the user, and the calculating module calculates land price of the to-be-estimated object according to the relation among the public land price information, the calculation factors and the correction coefficients. The calculation factors include a regional factor, a capacity factor, a land development degree factor, and a land remaining life factor.
According to the natural resource public land price management system based on big data, public land price system construction result information data of all areas are comprehensively obtained by adopting a big data technology, public land price management data of a unified template is formed by extracting public land price related information through big data processing, a user can realize automatic valuation by only inputting information of an object to be estimated, the potential market value of the object to be estimated is rapidly reflected, and important references are provided for decision making of the user.
In another embodiment of the present application, unlike the first embodiment described above, the system further includes a map module that displays a map of each administrative area and a calculated block price on the area map of the object to be estimated, so that a user can intuitively see information of the query block and the block price through the map module.
In another embodiment of the present application, unlike the first embodiment, the big data processing module of the system includes a data cleaning unit, where the data cleaning unit is configured to denoise, parse and word-segment the original data by using a text processing technology to obtain text data, and analyze attributes of the text data by using a data mining technology to obtain cleaning data. The big data processing module further comprises a data screening unit, wherein the data screening unit is used for screening information published by the municipal level and the county level related departments from the cleaning data respectively, and unifying and correcting the land value connotation published by the county level departments according to the land value connotation published by the municipal level departments to obtain data with identical land value connotation elements. The land price management module comprises an information extraction unit, wherein the information extraction unit is used for extracting information of construction land price, agricultural land standard land price and calibration land price from data with identical land price connotation factors, the construction land standard land price comprises town standard land price, collective construction land standard land price and transfer construction land standard land price, and the agricultural land standard land price comprises national agricultural land standard land price and collective agricultural land standard land price.
The data cleaning process includes preprocessing the data, feature selection, data cleaning and cleaning results. The data preprocessing is to perform simple constraint processing on the original data; feature selection refers to extracting data features and eliminating redundant information; the data cleaning refers to cleaning dirty data according to actual application conditions; the cleaning result inspection refers to checking the quality of data according to the cleaning criteria. The Hadoop distributed data cleaning method can effectively improve the efficiency of cleaning big data. Early work of distributed data cleansing with Hadoop: the data is divided into a storage layer and a cleaning calculation layer. And (3) storing data by adopting an HDFS, realizing the transfer of the data by adopting a Hive data warehouse on the HDFS, and then processing the data of the database according to a specified format to finish the preparation work of the data cleaning earlier stage. The distributed data cleaning process adopting Hadoop comprises the following steps: (1) data source loading: and loading and unloading the acquired data by adopting Sqoop and Hive. (2) data preprocessing: and carrying out comprehensive analysis according to the data requirement so as to ensure the usability of the data during data analysis. (3) feature selection: and selecting main characteristics affecting the data quality, eliminating redundant characteristics and realizing the dimension reduction processing of big data. (4) identifying abnormal data: and the improved K-means algorithm is adopted, the larger the distance difference is, the stronger the abnormality is, and the MapReduce method is adopted to realize parallelization calculation. (5) checking the cleaning result: and (5) performing data quality inspection on the cleaned data to ensure the data quality.
The specific method for identifying the abnormal data comprises the following steps:
from training samples { x } 1 ，…，x m }，x i ∈R n K center points are randomly taken.
Step 1: from { x 1 ，…，x m K samples are randomly taken and recorded as an initial cluster center mu 1 ，μ 2 ，…，μ k ∈R n 。
Step 2: obtaining the distance between other data and the center sample, wherein the data samples are classified according to the distance, as shown in formula (1):
step 3: for each class j, the center point is determined by averaging as in equation (2):
wherein each sample contains d attributes, and m data in the j th class, wherein sample x i Sample x belonging to the j-th class i Summing the D features of the model, and averaging to obtain the centroid of the D features.
Step 4: if the objective function converges, terminating the program; otherwise go to Step 2.
The specific steps of optimizing the K-means algorithm by adopting the Capopy algorithm are as follows:
(1) The raw data is stored in the dataset D.
(2) Center points were randomly determined, put in canopy centerlist, and the data was deleted from D.
(3) The distance of other data from canopy centerlist is calculated. Dist [ i ]]Samples < T1 are taken as a class, and already categorized samples are deleted from D. According to the classification method, the rest sample data are respectively classified into T 2 ,T 3 …, up to data in DAll the classification is completed.
(4) K Canopy are obtained after categorization.
(5) K cluster center points are calculated.
(6) The distance of each data from the center point is calculated. Data is classified into the class where Dist [ i ] is smallest.
(7) The average value of each class is taken as a new center point.
(8) And (3) calculating the distance between the new center point and the Canopy center point, and classifying according to the step (3).
(9) If the convergence condition is reached, stopping circulation; otherwise, continuing to the steps (6) - (8).
And when data cleaning is carried out, the K-means algorithm is improved by adopting the Cappy algorithm to clean abnormal data, and the data MapReduce method with larger distance difference and stronger abnormality is used for realizing parallelization calculation. The improved K-means algorithm has higher accuracy and faster processing speed than the traditional K-means algorithm after data are cleaned. When the data screening processing is carried out, a Bayesian classification algorithm is adopted for screening, and the cleaning data is filtered again, so that the accuracy of data filtering is improved.
The Bayesian classification algorithm calculates posterior probability by using a Bayesian formula through a prior probability model of a certain object; namely, which class of subject the object source belongs to, and selecting the class with the maximum posterior probability as the subject to which the object source belongs; the probability of each data information in a small class is obtained by a Bayesian theory through training a source data set, and a Bayesian model is constructed; the naive Bayes are the Bayes classification model with the smallest error rate, and have few required estimation parameters, and the realization algorithm is simple; the minimum risk Bayesian classification algorithm solves the problem of error rate based on Bayes and naive Bayes, and is the optimization in the sense of minimum error rate.
In this embodiment, the cleaning data is screened by using a minimum risk bayesian algorithm, and the specific steps of screening include:
known as P (omega) s ),P(X|ω t ) In the case of s=1, 2 …, c and X to be identified (data packet to be filtered), the post-calculation is calculated according to bayesian formulaThe probability of a test is determined by,
wherein P (omega) s ) The prior probability is obtained by analyzing the requirement of the user on the network data in the past; p (omega) t I X) is a posterior probability, which is the probability of correcting again after obtaining the information X, P (x|ω) s ) Judging whether the received X to be identified is the probability of the junk network data according to the past experience of the user on the network data;
recording the data loss as alpha, and defining a decision rule as:
1) When the network data is junk data, judging that the junk data cannot cause any loss, wherein alpha=0;
2) When the junk network data is judged to be legal data, the loss alpha=0;
3) When the network data required by the user is judged to be junk data, the loss is immeasurable, and alpha is more than 0 and less than infinity;
calculating r according to the posterior probability obtained after calculation and a set decision rule according to the following formula s Conditional risk of s=1, 2, … … a:
it is considered that the data will be lost after erroneous judgment. Alpha-0 is reduced to the minimum, so that the R condition risk values R (rs|X) obtained before are compared, and the decision of minimizing the condition risk is found out and is recorded as R h ,r h The minimum risk Bayesian classification decision is the method. The big data processing method has higher filtering accuracy and system robustness, and the sample data after cleaning is filtered to obtain more accurate data.
According to the system provided by the embodiment of the application, the big data processing module is arranged to clean and filter the crawled data, so that high-quality public land price data can be obtained. Due to differences in the land price system from place to place, for example: from the construction of land price system of each land in a certain province, national agricultural land price is limited to the use of the agricultural reclamation system, the value connotation is set as yielding use right, and the highest use is unified for 50 years; the collective agricultural land price is set as the contract management right, the highest year is set according to the land management method, and the collective agricultural land price is inconsistent with the national standard land price greatly; the land value of the group construction land is set (limited) to the internal circulation of the village group, and the land value is greatly different from the land value of the domestic house, and the land value of other business construction lands is also different in the aspect of volume rate setting. Connotation form unification is the first step in building a unifying land price system. In the embodiment of the application, the crawled data is processed through the big data processing module, the information published by the related departments of the city level and the county level is respectively screened from the cleaning data, and the land value connotations published by the county level departments are unified and corrected according to the land value connotations published by the city level departments, so that the data with the same element of the land value connotations is obtained. The public land price management table adopts a unified template, and content elements in the table are the same. Wherein, connotation elements include: rights type, date of valuation, etc. By unifying and correcting the land value connotations of all land level cities in the province or all county areas in the same land level city, a transverse comparable and visual public land value system is established, and the applicability and the practicability of the public land value system are improved.
In another embodiment of the present application, the system further includes a valuation comparing module, configured to compare the ground price estimated according to the reference ground price with the ground price estimated according to the calibrated ground price to obtain a comparison result, compare the comparison result with a set threshold, and if the comparison result is smaller than the set threshold, respectively display the two ground prices to be referred to by the user; if the value is larger than the set threshold value, displaying two land prices respectively, and giving out the reason for large land price difference. Through the price comparison module, a user can intuitively and rapidly obtain the difference of the prices of the objects to be estimated according to the reference price and the calibrated price and the reason for the large difference. The system also comprises an analysis module which is used for analyzing the actual cases according to the published land price and giving selection suggestions to the user. Through the provided analysis module, the user can make scientific selections with reference to the suggestions given.
In the first embodiment, a natural resource public land price management system based on big data is provided, and correspondingly, the application also provides a natural resource public land price management method based on big data. Fig. 2 is a flowchart of a method for managing a natural resource public land price based on big data according to another embodiment of the present application. Since the method embodiments are substantially similar to the apparatus embodiments, the description is relatively simple, and reference is made to the description of the apparatus embodiments for relevant points. The method embodiments described below are merely illustrative.
Referring to fig. 2, a flowchart of a method for managing a natural resource public land price based on big data according to another embodiment of the present application is shown, the method includes the following steps:
crawling original data of public land price system construction result information published by relevant departments of each place on a network through a web crawler;
performing data cleaning and screening treatment on the original data to obtain treated data;
extracting the related information of the public land price from the processed data, and correspondingly filling the related information of the public land price into a corresponding public land price management table;
acquiring information of an object to be estimated input by a user;
inquiring corresponding public land price information, calculation factors and correction coefficients from the public land price management table according to the information of the object to be estimated;
and calculating the land parcel price of the object to be estimated according to the public land parcel information, the calculation factors and the correction factors.
According to the natural resource public land price management method based on big data, the public land price system construction result information data of each region is crawled by adopting the big data technology, big data processing is carried out, public land price related information is extracted to form public land price management data of a unified template, and a user can quickly inquire automatic valuations of objects to be estimated in the corresponding region for auxiliary reference.
In order to facilitate the user to intuitively see the information of the query parcel and the parcel price through the map module, the method further comprises: a map of each administrative area is displayed, and a block price is displayed on the area map of the object to be estimated.
The specific method for carrying out data cleaning and screening treatment on the original data comprises the following steps:
denoising, analyzing and word segmentation are carried out on the original data to obtain text data, and the attribute of the text data is analyzed by adopting a data mining technology to obtain cleaning data;
and respectively screening information published by related departments of the city level and the county level from the cleaning data, and unifying and correcting the land value connotations published by the county level departments according to the land value connotations published by the city level departments to obtain the data with the same element of the land value connotations.
Specifically, the data cleansing process includes preprocessing data, feature selection, data cleansing and cleansing results. The data preprocessing is to perform simple constraint processing on the original data; feature selection refers to extracting data features and eliminating redundant information; the data cleaning refers to cleaning dirty data according to actual application conditions; the cleaning result inspection refers to checking the quality of data according to the cleaning criteria. The Hadoop distributed data cleaning method can effectively improve the efficiency of cleaning big data. And when data cleaning is carried out, the K-means algorithm is improved by adopting the Cappy algorithm to clean abnormal data, and the data MapReduce method with larger distance difference and stronger abnormality is used for realizing parallelization calculation. The improved K-means algorithm has higher accuracy and faster processing speed than the traditional K-means algorithm after data are cleaned. When the data screening processing is carried out, a Bayesian classification algorithm is adopted for screening, and the cleaning data is filtered again, so that the accuracy of data filtering is improved. For specific data processing reference is made to the description of the system above. By unifying and correcting the land value connotations of all land level cities in the province or all county areas in the same land level city, a transverse comparable and visual public land value system is established, and the applicability and the practicability of the public land value system are improved.
The specific method for extracting the public land price related information from the processed data comprises the following steps: and extracting information of construction standard land price, agricultural standard land price and calibration land price from the data with the same land price connotation factors, wherein the construction standard land price comprises town standard land price, collective construction standard land price and transfer construction standard land price, and the agricultural standard land price comprises national agricultural standard land price and collective agricultural standard land price.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application, and are intended to be included within the scope of the appended claims and description.
Claims (10)
1. A big data based natural resource utility management system, comprising: the system comprises a data crawling module, a big data processing module, a land price management module, an input information acquisition module, a query module and a calculation module;
the data crawling module is used for crawling original data of public land price system construction result information published by relevant departments of each place on the network through a web crawler;
the big data processing module is used for carrying out data cleaning and screening processing on the original data to obtain processed data;
the land price management module is used for extracting the public land price related information from the processed data and correspondingly filling the public land price related information into a public land price management table of the unified template;
the input information acquisition module is used for acquiring to-be-estimated object information input by a user;
the query module is used for querying corresponding public land price information, calculation factors and correction coefficients from the public land price management table according to the information of the object to be estimated;
the calculation module is used for calculating the land parcel price of the object to be estimated according to the public land price information, the calculation factors and the correction factors.
2. The big data based natural resource exposure ground price management system of claim 1, further comprising a map module for displaying a map of each of the administrative areas of the land and displaying the calculated land block price on the regional map of the object to be estimated.
3. The big data-based natural resource public land price management system according to claim 1, wherein the big data processing module comprises a data cleaning unit, the data cleaning unit is used for denoising, analyzing and word segmentation of original data by using a text processing technology to obtain text data, and analyzing attributes of the text data by using a data mining technology to obtain cleaning data.
4. The big data-based natural resource public land price management system of claim 3, wherein the big data processing module further comprises a data screening unit, the data screening unit is used for screening information published by the municipal level and the county level related departments from the cleaning data respectively, and unifying and correcting the land price connotation published by the county level departments according to the land price connotation published by the municipal level departments to obtain data with identical land price connotation elements.
5. The big data based natural resource exposure ground price management system of claim 4, wherein the ground price management module comprises an information extraction unit for extracting information of a construction reference ground price, an agricultural reference ground price and a calibrated ground price from the same data of the ground price content elements, wherein the construction reference ground price comprises a town reference ground price, a collective construction reference ground price and a transfer construction reference ground price, and the agricultural reference ground price comprises a national agricultural reference ground price and a collective agricultural reference ground price.
6. The big data based natural resource exposure ground price management system of claim 1 or 5, wherein the calculation factors include a regional factor, a capacity factor, a land development level factor, and a land remaining life factor.
7. A natural resource public land price management method based on big data is characterized by comprising the following steps:
crawling original data of public land price system construction result information published by relevant departments of each place on a network through a web crawler;
performing data cleaning and screening treatment on the original data to obtain treated data;
extracting the related information of the public land price from the processed data, and correspondingly filling the related information of the public land price into a corresponding public land price management table;
acquiring information of an object to be estimated input by a user;
inquiring corresponding public land price information, calculation factors and correction coefficients from the public land price management table according to the information of the object to be estimated;
and calculating the land parcel price of the object to be estimated according to the public land parcel information, the calculation factors and the correction factors.
8. The big data based natural resource exposure ground price management method of claim 7, further comprising: a map of each administrative area is displayed, and a block price is displayed on the area map of the object to be estimated.
9. The method for managing natural resource public land price based on big data according to claim 7, wherein the specific method for performing data cleaning and screening processing on the original data comprises the following steps:
denoising, analyzing and word segmentation are carried out on the original data to obtain text data, and the attribute of the text data is analyzed by adopting a data mining technology to obtain cleaning data;
and respectively screening information published by related departments of the city level and the county level from the cleaning data, and unifying and correcting the land value connotations published by the county level departments according to the land value connotations published by the city level departments to obtain the data with the same element of the land value connotations.
10. The big data based natural resource utility management method of claim 9, wherein the specific method for extracting utility related information from the processed data comprises: and extracting information of construction standard land price, agricultural standard land price and calibration land price from the data with the same land price connotation factors, wherein the construction standard land price comprises town standard land price, collective construction standard land price and transfer construction standard land price, and the agricultural standard land price comprises national agricultural standard land price and collective agricultural standard land price.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109523446A (en) * | 2018-10-19 | 2019-03-26 | 北京北大软件工程股份有限公司 | A kind of big data processing analysis system towards price field |
| CN110659934A (en) * | 2019-09-06 | 2020-01-07 | 李俊鹏 | Big data benchmark land price and land price automatic evaluation updating system |
| CN115151940A (en) * | 2020-05-14 | 2022-10-04 | 韩国不动产院 | Land market estimation system and method with final price calculation determination unit |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109523446A (en) * | 2018-10-19 | 2019-03-26 | 北京北大软件工程股份有限公司 | A kind of big data processing analysis system towards price field |
| CN110659934A (en) * | 2019-09-06 | 2020-01-07 | 李俊鹏 | Big data benchmark land price and land price automatic evaluation updating system |
| CN115151940A (en) * | 2020-05-14 | 2022-10-04 | 韩国不动产院 | Land market estimation system and method with final price calculation determination unit |
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