Evaluation method, device and equipment based on map digital platform and storage medium

Abstract

This invention provides an evaluation method, apparatus, device, and storage medium based on a map digital platform. The method includes: acquiring land parcel data; inputting the land parcel data into a pre-constructed map digital platform to obtain an evaluation result for the land parcel; and visually representing the land parcel on a map based on the evaluation result. The map digital platform is used to objectively evaluate the land parcel based on its raster fundamental score, raster quantiles, detailed indicator information, sector fundamental score ranking, and market evaluation requirements. The map digital platform-based evaluation method provided by this invention ensures the objectivity of land value evaluation results, allows for intuitive viewing of evaluation results, and improves user experience.

Description

Technical Field

[0001] This invention relates to the field of real estate technology, and in particular to an evaluation method, apparatus, equipment and storage medium based on a map digital platform. Background Technology

[0002] Currently, there are two main approaches to evaluating land value: one is to make a subjective judgment based on factors such as the surrounding population, supporting facilities, and industries to form a qualitative comprehensive evaluation; the other is to use a more subjective expert scoring method to weight each dimension and ultimately form a comprehensive evaluation.

[0003] The current method of judgment is highly subjective and lacks objective basis. The judgment results are strongly correlated with expert experience. Moreover, the current market health evaluation system is lacking. There are too many market indicators, most of which can only reflect the short-term situation of the market or the situation of a single project, and lack attention to risks. Furthermore, data resources are insufficient, resulting in the lack of a unified format and inaccuracy of the data used for evaluation. Summary of the Invention

[0004] This invention provides an evaluation method, apparatus, device, and storage medium based on a map digital platform to solve the technical problems of subjective land value judgment and the lack of a market evaluation system in the prior art, which makes accurate evaluation and analysis impossible. By constructing a map digital platform, the invention aims to achieve objective evaluation of land value and improve the accuracy of land value assessment.

[0005] In a first aspect, the present invention provides an evaluation method based on a map digital platform, the method comprising:

[0006] Obtain land parcel data;

[0007] The land parcel data is input into a pre-built map digital platform to obtain the evaluation results of the land parcel;

[0008] Based on the evaluation results, the land parcel will be visualized on a map;

[0009] The map digital platform is used to objectively evaluate the land parcels based on their grid fundamentals score, grid quantiles, detailed indicators, sector fundamentals score ranking, and market evaluation requirements.

[0010] Furthermore, according to the evaluation method based on a map digital platform provided by the present invention, the map digital platform includes a fundamental processing model and a market evaluation system, wherein the step of inputting the land parcel data into the pre-constructed map digital platform to obtain the evaluation result of the land parcel includes:

[0011] The land parcel data is input into the fundamental surface processing model to obtain the fundamental surface data of each grid cell of the land parcel.

[0012] The fundamental data is transmitted to the market evaluation system to obtain the evaluation results of the land parcel.

[0013] Furthermore, according to the evaluation method based on a map digital platform provided by the present invention, before inputting the land parcel data into the fundamental surface processing model to obtain the fundamental surface data of each grid cell of the land parcel, the method includes:

[0014] Obtain the first raw data of the sample plots, wherein the first raw data includes the average housing price data of the city and the urban raster surface index data;

[0015] The fundamental surface processing model to be trained is based on PCA and entropy algorithm, the average urban housing price data of the sample plots and the urban raster surface index data, to obtain the trained fundamental surface processing model.

[0016] The average urban housing price data serves as the Y-axis data for the fundamental analysis model to be trained, and the urban raster surface index data of the sample plots serves as the X-axis data for the fundamental analysis model to be trained.

[0017] Furthermore, according to the evaluation method based on a map digital platform provided by the present invention, before transmitting the fundamental data to the market evaluation system to obtain the evaluation result of the land parcel, the method includes:

[0018] Obtain multiple second raw data points for the sample plots;

[0019] Based on the multiple second raw data and the preset evaluation tags, multiple corresponding relationships are determined;

[0020] Based on the identified multiple correspondences, a market evaluation system is formed;

[0021] The second original data is different from the first original data, and includes at least new housing supply and demand data, new housing inventory data, land auction data, sector boundary data, and generalized inventory data.

[0022] Furthermore, according to the evaluation method based on a map digital platform provided by the present invention, the step of transmitting the fundamental data to the market evaluation system to obtain the evaluation result of the land parcel includes:

[0023] Based on the multiple pre-set correspondences in the market evaluation system and the fundamental data, the evaluation tags corresponding to the fundamental data are determined;

[0024] The evaluation result of the land parcel is determined based on the evaluation labels.

[0025] Furthermore, according to the evaluation method based on a map digital platform provided by the present invention, the step of visualizing the land parcel on a map based on the evaluation results includes:

[0026] Based on the evaluation results, the land parcel will be visualized on the map using corresponding colors.

[0027] Furthermore, according to the evaluation method based on a map digital platform provided by the present invention, before inputting the land parcel data into a pre-constructed map digital platform, the method includes:

[0028] The land parcel data is preprocessed, cleaned, and inspected to obtain the processed land parcel data.

[0029] Secondly, the present invention provides a processing apparatus based on a map digital platform, the apparatus comprising:

[0030] The acquisition module is used to acquire land parcel data;

[0031] The input module is used to input the land parcel data into a pre-built map digital platform to obtain the evaluation results of the land parcel.

[0032] A visualization module is used to visualize the land parcel on a map based on the evaluation results;

[0033] The map digital platform is used to objectively evaluate the land parcels based on their grid fundamentals score, grid quantiles, detailed indicators, sector fundamentals score ranking, and market evaluation requirements.

[0034] Thirdly, the present invention also provides an electronic device, comprising:

[0035] Processor, memory, and bus, among which,

[0036] The processor and the memory communicate with each other via the bus;

[0037] The memory stores program instructions that can be executed by the processor, which can invoke the program instructions to perform the steps of the evaluation method based on the map digital platform as described in any of the preceding items.

[0038] Fourthly, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the steps of the evaluation method based on a map digital platform as described above.

[0039] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the evaluation method based on a map digital platform as described in any of the preceding claims.

[0040] This invention provides an evaluation method, apparatus, device, and storage medium based on a map digital platform. The method includes: acquiring land parcel data; inputting the land parcel data into a pre-constructed map digital platform to obtain an evaluation result for the land parcel; and visually representing the land parcel on a map based on the evaluation result. The map digital platform is used to objectively evaluate the land parcel based on its raster fundamental score, raster quantiles, detailed indicator information, sector fundamental score ranking, and market evaluation requirements. The map digital platform-based evaluation method provided by this invention ensures the objectivity and accuracy of land value evaluation results, allows for intuitive viewing of evaluation results, and improves user experience. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating an evaluation method based on a map digital platform provided by the present invention.

[0043] Figure 2 This is a schematic diagram of the evaluation process of a fundamental surface processing model provided by the present invention;

[0044] Figure 3 This is a schematic diagram of the overall process of an evaluation method based on a map digital platform provided by the present invention;

[0045] Figure 4 This is a schematic diagram of the structure of a processing device based on a map digital platform provided by the present invention;

[0046] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0048] Figure 1 This is a flowchart illustrating the evaluation method based on a map digital platform provided by the present invention, as shown below. Figure 1 As shown, the evaluation method based on a map digital platform provided by the present invention includes:

[0049] Step 101: Obtain land parcel data.

[0050] In this embodiment, land parcel data needs to be acquired. This land parcel data refers to relevant data about the land parcel to be evaluated, such as housing price data, fundamental data, special supporting facilities data, and city boundary data. The specific details can be configured according to the user's actual needs. It should be noted that in this embodiment, data is acquired through API interface calls, database access, and publicly available online data acquisition.

[0051] Step 102: Input the land parcel data into a pre-built map digital platform to obtain the evaluation results of the land parcel.

[0052] In this embodiment, the acquired land parcel data needs to be input into a pre-built map digital platform to obtain the evaluation result of the land parcel. In this embodiment, the evaluation result obtained through the constructed map digital platform is an objective judgment, such as the quality level of the land parcel itself (good, medium, poor) and its market performance (red, yellow, green).

[0053] It should be noted that this map digital platform is used to objectively evaluate land parcels based on their raster fundamentals score, raster quantiles, detailed indicator information, sector fundamentals score ranking, and market evaluation needs. The platform integrates the best or second-best data from various city companies, performs standardized governance and spatial processing on it, and automatically updates the data at agreed-upon time frequencies. For common data pain points in the market, it establishes a manual calibration-verification-feedback mechanism, effectively improving data accuracy and stability.

[0054] Step 103: Visualize the land parcel on a map based on the evaluation results.

[0055] In this embodiment, the land parcel can also be visualized on a map based on the evaluation results. For example, if the evaluation result for parcel 1 is "good," its corresponding market performance is displayed in red; if the evaluation result for parcel 2 is "medium," its corresponding market performance is displayed in yellow, and so on. It should be noted that in other embodiments, market performance can also be displayed in the form of grid density or numbers, which is not specifically limited here.

[0056] This invention provides an evaluation method based on a map digital platform. By acquiring land parcel data and inputting it into a pre-constructed map digital platform, an evaluation result for the land parcel is obtained. Based on the evaluation result, the land parcel is visualized on a map. The map digital platform is used to objectively evaluate the land parcel based on its raster fundamental score, raster quantiles, detailed indicator information, sector fundamental score ranking, and market evaluation requirements. This map digital platform-based evaluation method ensures the objectivity and accuracy of land value evaluation results, allows for intuitive viewing of the evaluation results, and improves the user experience.

[0057] Based on any of the above embodiments, in one embodiment of the present invention, the map digital platform includes a fundamental processing model and a market evaluation system, wherein the step of inputting the land parcel data into the pre-constructed map digital platform to obtain the evaluation result of the land parcel includes:

[0058] The land parcel data is input into the fundamental surface processing model to obtain the fundamental surface data of each grid cell of the land parcel.

[0059] The fundamental data is transmitted to the market evaluation system to obtain the evaluation results of the land parcel.

[0060] In this embodiment, the acquired land parcel data needs to be input into a fundamental analysis model to obtain the fundamental data of each grid cell for the land parcel. Then, the obtained fundamental data is input into a market evaluation system to obtain the evaluation result of the land parcel. The fundamental data includes the fundamental score of each grid cell, the grid quantile, and the overall fundamental score. For different types of land, their value is affected by different factors to varying degrees. To quantify these "influences" and avoid human interference, this invention uses housing price data from both new and used homes to perform regression segmentation on the grid (250*250m) type, forming land fundamental scores with different weights for influencing factors.

[0061] The fundamental score algorithm is based on the current land value, using housing price data for regression segmentation to generate grid-level fundamental scores with different influencing factor weights, rather than a simple weighted average. The fundamental score calculation incorporates population, industry, and supporting infrastructure data, as well as new and used housing data, as the basis for assigning weights to each factor. Furthermore, the score is not an absolute value; it considers the overall distribution, specifically the quantile of that grid cell across all grid cells.

[0062] The present invention provides an evaluation method based on a map digital platform. By inputting land parcel data into a fundamental data processing model, the fundamental data of each grid cell of the land parcel is obtained. Then, the fundamental data is transmitted to a market evaluation system to obtain the evaluation result of the land parcel. Through the construction of the fundamental data processing model and the market evaluation system, the efficiency and accuracy of land value evaluation processing can be improved, and the user experience can be enhanced.

[0063] Based on any of the above embodiments, in one embodiment of the present invention, before inputting the land parcel data into the fundamental surface processing model to obtain the fundamental surface data of each grid cell of the land parcel, the following steps are included:

[0064] Obtain the first raw data of the sample plots, wherein the first raw data includes the average housing price data of the city and the urban raster surface index data;

[0065] The fundamental surface processing model to be trained is based on PCA and entropy algorithm, the average urban housing price data of the sample plots and the urban raster surface index data, to obtain the trained fundamental surface processing model.

[0066] The average urban housing price data serves as the Y-axis data for the fundamental analysis model to be trained, and the urban raster surface index data of the sample plots serves as the X-axis data for the fundamental analysis model to be trained.

[0067] In this embodiment, it is necessary to obtain the first raw data of the sample plots. This first raw data includes average city housing price data and city raster surface indicator data. It is also necessary to rapidly collect fundamental city data, including but not limited to population, infrastructure, industry, and housing price data. The city data is then organized, and relevant city data indicators are statistically analyzed, including but not limited to distance and quantity indicators. Comprehensive evaluation algorithms such as PCA and entropy methods are used to comprehensively evaluate the city raster and calculate the fundamental score. Further, new and used housing price data are used to perform a secondary correction on the fundamental score. Specifically, the raster fundamental score and quantiles are used for large-scale cross-city land comparisons, while detailed indicator information is used to compare the differences in different dimensions for land plots with the same fundamental score.

[0068] It should be noted that this embodiment includes approximately 50 basic indicators. Depending on the completeness of urban infrastructure, some indicators may be missing; for example, cities without subways will not have subway-related indicators. These basic indicators are divided into three categories: Quantity of Range: the number of POIs within a certain radius; Nearest Distance: the distance to the nearest POI (in meters); and Size: the size of the shopping center.

[0069] It should be noted that in this embodiment, secondary indicators are also designed. Based on the classification of the basic indicators, PCA is used to reduce the dimensionality to secondary indicators. There are ten categories of secondary indicators in total: population, transportation, education, landscape, commerce, business, leisure, medical care, industry, and disadvantages. Among the secondary indicators, the disadvantage indicators are only used for data display and are not included in the subsequent model calculation.

[0070] It should be noted that the first raw data obtained is housing price data: city new home opening data, city second-hand home transaction data, city second-hand home listing data; fundamental data: population, industry and supporting data, to calculate city grid base table indicators; special supporting data: city supporting facilities, landscape and core water system; city boundary data: sector data / district / county data / street and town data.

[0071] It should be noted that the urban grid surface indicators include: collecting all population grids, industry, and supporting data to calculate the grid base table indicators; if additional planning data is required to calculate the corresponding indicators, it needs to be calculated separately offline; the data needs to include all indicators in the indicator system, including: primary indicators and secondary indicators.

[0072] Among these, housing price data also needs to undergo data preprocessing (either second-hand housing data or new housing data):

[0073] 0. Delete missing latitude and longitude data, or re-encode geocoding;

[0074] 1. Correct erroneous latitude and longitude data, such as mismatched districts and counties;

[0075] 2. Delete data for latitude and longitude errors that cannot be calibrated;

[0076] 3. Market data is used to filter housing types, sales time, area, average price, and construction time.

[0077] Data cleaning is also required: 0. Summarize the data on second-hand housing listings and new housing launches by sector, calculate the price inversion rate for each sector, and identify the sectors with inverted prices; 1. Summarize the data on second-hand housing listings and new housing launches by grid, and take one of the two as the reference average price and reference source; 2. Multiply the grid of the sector with inverted prices and the reference source is the average price of new housing by the price inversion rate to obtain the corrected reference average price.

[0078] It should be noted that the housing price data still needs to be verified.

[0079] 0. Outlier detection: The grid reference average price is higher / lower than the surrounding 8 neighboring values ​​within a certain range;

[0080] 1. Outlier deletion: If the reference source is new home data, delete it; or if the reference source is second-hand home data but the transaction volume is low.

[0081] 2. Kernel density smoothing: Each grid cell is weighted by its eight neighbors and then subjected to kernel density smoothing.

[0082] The data output is: 0. Output a raster-level map of housing price distribution for the entire city, with reference housing prices and reference sources marked.

[0083] It should be noted that the model training process is as follows: Model input: The city's average housing price map serves as the model's y, and the city's grid base table indicators serve as the model's x; Model flow: Using all secondary indicators as features, grids with housing price data are used as training data, and grids without housing price data are used as prediction data. 80% of the training data is used as the training set, and the remaining 20% ​​is used as the test set. A simple regression decision tree model is used to divide housing prices into 4 or 8 categories. A random forest model is built for each category of dataset. The indicator weights obtained from the model are used as weights, and the weighted sum is normalized to a score of 0-100 to obtain the city's fundamental score. The city's grids are divided into 8 or 4 categories according to housing prices. Different scoring weights are referenced when calculating the fundamental score using the weighted sum of the base scores for different housing price categories (i.e., different indicator weights are referenced for different housing price ranges).

[0084] It should be noted that the model's water system correction also needs to be completed. Grids with a water system area exceeding a certain proportion (80%) are designated as water system grids, and their fundamental score is equal to 0. Quantile calculation includes: obtaining city sector data and investment evaluation fields, and filtering investable sectors; sorting the grids within the investable sectors according to their scores, and calculating the percentage of grids whose scores exceed the target score, which is the grid quantile; if there are no investable grids in a city sector, the quantile ranking will still be based on grids covering the entire administrative region.

[0085] The overall improvement in fundamental scores includes: Processing requirements: Considering the generally low scores in basic calculations, a unified standard is used to improve the overall final fundamental scores for each city; Evaluation method:

[0086]

[0087] Score improvement characteristics: Since the total score is out of 100, the score improvement is lower for high-scoring grids and higher for low-scoring grids (for example, the original score of 100 points remains 100 points after improvement, the original score of 81 points becomes 90 points after improvement, and the original score of 36 points becomes 60 points after improvement).

[0088] The segment evaluation includes: acquiring segment data and performing topology checks and repairs; summarizing grid fundamental scores according to segment range; calculating the average score of secondary indicators for all grids in a segment, which is the segment-related score; and calculating the average score of fundamental scores for all grids in a segment, which is the segment fundamental score. A new city grid "passing boundary" is added to facilitate sales staff in accurately identifying investable / uninvestable segment boundaries. Map grid passing boundary data is drawn based on city fundamental scores. The default passing boundary standard for basic cities is 60 points. However, due to differences in the distribution of supporting facilities (clustered or dispersed layout), the 60-point passing boundary may be too large or too small, inconsistent with common understanding. Therefore, a certain range of fluctuation is made above or below the 60-point standard: for example, Shijiazhuang A: 64, XX: 64.

[0089] The evaluation method based on a map digital platform provided by this invention ensures the objectivity of the evaluation and improves the accuracy of land value assessment by constructing a fundamental surface processing model.

[0090] Based on any of the above embodiments, in one embodiment of the present invention, before transmitting the fundamental data to the market evaluation system to obtain the evaluation result of the land parcel, the following steps are included:

[0091] Obtain multiple second raw data points for the sample plots;

[0092] Based on the multiple second raw data and the preset evaluation tags, multiple corresponding relationships are determined;

[0093] Based on the identified multiple correspondences, a market evaluation system is formed;

[0094] The second original data is different from the first original data, and includes at least new housing supply and demand data, new housing inventory data, land auction data, sector boundary data, and generalized inventory data.

[0095] In this embodiment, it is necessary to obtain multiple second raw data of sample plots, and then determine multiple corresponding relationships based on the multiple second raw data and preset evaluation labels. Then, a market evaluation system is formed based on the determined multiple corresponding relationships. Among them, the raw data to be obtained includes: new housing supply and demand data: used to calculate new housing related indicators, local high-quality data source; new housing inventory data: used to calculate inventory related indicators, local high-quality data source; land bidding and auction data: used to assist in the estimation of generalized inventory indicators; sector boundary data: used to depict statistical boundaries; generalized inventory data: (1) provided by local high-quality data source; (2) for cities where the data source cannot provide accurate generalized inventory, it is estimated from this:

[0096] a. Determine the list of properties based on explicit inventory and land parcels with no associated properties;

[0097] b. The general inventory of properties on land already associated with the land = explicit inventory + land gross floor area (saleable residential area) - supply area (or general inventory = land gross floor area - transaction area);

[0098] c. Broad inventory of buildings not associated with land = explicit inventory + gross floor area of ​​buildings - supply area (or broad inventory = gross floor area of ​​land - transaction area);

[0099] d. The generalized inventory of land parcels not associated with any housing developments converted into housing developments = gross floor area (or saleable residential building area, depending on the data);

[0100] e. Inventory Adjustment: If the difference between broad inventory and narrow inventory is less than 3,000 cubic meters, it is set as if all pre-sale permits have been obtained, and the broad inventory equals the narrow inventory.

[0101] It should be noted that the indicator intervals also need to be divided according to business needs. These intervals are completely independent and encompass all possible scenarios for the data. Supply and demand relationship evaluation labels are assigned to the data in different intervals, and these labels are visualized on a map using corresponding colors. Specific rules are shown in Table 1 below:

[0102] Table 1

[0103]

[0104]

[0105] It should be noted that the evaluation results need to be revised:

[0106] Sectors initially rated as "Risk" will have their rating adjusted to "Supply Shortage" (in light green) if the supply-demand ratio is less than 1 or there is a supply shortage.

[0107] Sectors whose market capacity does not meet the safety threshold are defined as "weak supply and demand" (marked in dark gray).

[0108] According to the evaluation method based on a map digital platform provided by the present invention, land can be objectively evaluated by constructing a market evaluation system, achieving a unified format for land evaluation, and displaying it through visualization to improve user experience.

[0109] Based on any of the above embodiments, in one embodiment of the present invention, the step of transmitting the fundamental data to the market evaluation system to obtain the evaluation result of the land parcel includes:

[0110] Based on the multiple pre-set correspondences in the market evaluation system and the fundamental data, the evaluation tags corresponding to the fundamental data are determined;

[0111] The evaluation result of the land parcel is determined based on the evaluation labels.

[0112] In this embodiment, to avoid blind optimism about the market, the supply and demand relationship is used as the starting point for judging the market situation when creating the urban sector market health evaluation system. That is, the focus is on the inventory situation in the short and long term, and risk warnings are issued for sectors with high destocking pressure. Corresponding evaluation labels are determined through the corresponding relationship, and the specific evaluation label information is shown in Table 1 above.

[0113] According to the evaluation method based on a map digital platform provided by this invention, land can be objectively evaluated through a market evaluation system, achieving a unified format for land evaluation and displaying it through visualization, thereby improving user experience.

[0114] Based on any of the above embodiments, in one embodiment of the present invention, the step of visualizing the land parcel on a map according to the evaluation result includes:

[0115] Based on the evaluation results, the land parcel will be visualized on the map using corresponding colors;

[0116] Before inputting the land parcel data into a pre-built map digital platform, the method includes: performing data preprocessing, cleaning, and verification on the land parcel data to obtain processed land parcel data.

[0117] In this embodiment, the corresponding land parcels need to be visualized on the map using corresponding colors based on the evaluation results. The specific display method is as described above and will not be described in detail here. Furthermore, before inputting the land parcel data into the pre-built map digital platform, the land parcel data needs to undergo data preprocessing, cleaning, and verification to obtain processed land parcel data. The specific processing procedure is described in the above embodiment and will not be described in detail here.

[0118] It should be noted that this map digital platform also developed a geographic data entry system, supporting batch data import and online editing. Collected data undergoes unified standardization and processing, and is integrated with external data before being stored in a database. The stored data can be flexibly extracted for use in different functional modules through data engineering, enabling data activation and high-frequency use; importing data from different sources allows for internal consolidation within the company. Implementing the import function requires careful prior review of the data format to ensure rapid analysis upon import without the need for secondary verification. Simultaneously, it automatically acquires facility spatial location information based on open-source internet map data, automatically spatializing the data and displaying it on a map. It integrates the best or second-best data from various city companies, performing unified standardization and spatial processing, and automatically updating it at agreed-upon time frequencies. For common data pain points in the market, a manual calibration-verification-feedback mechanism is established to effectively improve data accuracy and stability.

[0119] Furthermore, in another embodiment of the invention, such as Figure 3As shown, in this embodiment, preferably, the process includes: Data Requirements – organizing all project requirements, breaking down the required input and output data, determining the number of data output tables needed, designing the structure of each data table, including the names and types of each field, and assessing the data volume of each data table to adapt to various storage technologies; Data Acquisition – API interface calls, database access, and acquisition from publicly available network data sources; Data Cleaning – data cleaning is a collective term for a series of data preprocessing processes, including handling null values, duplicate values, and outliers. Directly acquired data may be very dirty and needs to be cleaned to improve data quality; Data Column Naming: using more recognizable data column names. Mark units and other information when necessary; convert field types: convert character data to numeric or other calculable data, and handle time data uniformly; handle null values: when the data volume is large, some null values ​​can be deleted; when the data volume is small, consider various techniques to calculate and fill null values; handle duplicate values: determine whether a value is duplicated based on oversight in the entire column or the primary key column data, and generally delete duplicate values; handle outliers: identify outliers such as excessively large, excessively small, negative values, and unreasonable distributions, and delete or replace reasonable values; feature engineering - feature engineering is essentially an engineering activity, the purpose of which is to extract features from the raw data to the maximum extent possible for use by algorithms and models. Data transformation – applying logarithmic, exponential, and negative number calculations to important data columns to reflect their key characteristics; Dimensionless processing: converting data of different dimensions to the same dimension, common methods include standardization and interval scaling; Data feature selection – selecting meaningful features manually or using dimensionality reduction algorithms (such as PCA) to input into subsequent algorithms and models for training; Feature monitoring – monitoring feature effectiveness and weights to prevent feature quality degradation from affecting model performance; Data modeling – the process of abstracting patterns and concepts from existing data through systematic analysis to establish a data model for an information system; Statistical modeling – using simple statistical methods... Model building involves various steps, including: model construction (e.g., mean fitting, median segmentation, label classification); machine learning model building (combining one or more machine learning models to construct an overall model); model data correction (performing statistical or manual corrections to the model outputs to prevent outliers); data validation (verifying data to ensure its integrity); correctness verification (checking data integrity, table structure, and missing fields); accuracy verification (checking the accuracy of results, data distribution, and whether it follows a normal distribution); and data import (importing results into a database or API interface).

[0120] The evaluation method based on a map-based digital platform provided by this invention enables rapid scanning of the fundamental land value of each grid cell. Through model calculations, it corrects empirical biases, unifies standards, and improves the efficiency of research and policy collaboration. Simultaneously, it applies standardized processing of underlying market data to the market model, forming a complete market health evaluation system. Conclusions are directly generated within the system, and the system supports querying of underlying data to provide a basis for market research. After the segment data is located, it is standardized, stored in the database, and extracted to other modules of the system, serving as an important self-developed granularity for Vanke's market and land value analysis (i.e., any underlying data can be aggregated for analysis under Vanke's segment-based framework). Furthermore, it stores externally accessed land planning indicators and internally predicted operational indicators in a unified data table, achieving data integration between internal and external sources.

[0121] Figure 4 The evaluation device based on a map digital platform provided by the present invention, such as Figure 4 As shown, the evaluation device based on the map digital platform includes:

[0122] Module 401 is used to acquire land parcel data;

[0123] Input module 402 is used to input the land parcel data into a pre-built map digital platform to obtain the evaluation result of the land parcel;

[0124] The visualization module 403 is used to visualize the land parcel on a map based on the evaluation results;

[0125] The map digital platform is used to objectively evaluate the land parcels based on their grid fundamentals score, grid quantiles, detailed indicators, sector fundamentals score ranking, and market evaluation requirements.

[0126] This invention provides an evaluation device based on a map digital platform. By acquiring land parcel data and inputting it into a pre-constructed map digital platform, an evaluation result for the land parcel is obtained. Based on the evaluation result, the land parcel is visualized on a map. The map digital platform is used to objectively evaluate the land parcel based on its raster fundamental score, raster quantiles, detailed indicator information, sector fundamental score ranking, and market evaluation requirements. The map digital platform-based evaluation device provided by this invention ensures the objectivity and accuracy of land value evaluation results, allows for intuitive viewing of the evaluation results, and improves the user experience.

[0127] Since the device described in this embodiment of the invention is based on the same principle as the method described in the above embodiments, more detailed explanations will not be repeated here.

[0128] Figure 5This is a schematic diagram of the physical structure of the electronic device provided in the embodiments of the present invention, such as... Figure 5 As shown, the present invention provides an electronic device, including: a processor 501, a memory 502, and a bus 503;

[0129] The processor 501 and the memory 502 communicate with each other via the bus 503.

[0130] The processor 501 is used to call program instructions in the memory 502 to execute the methods provided in the above method embodiments, such as: acquiring land parcel data, inputting the land parcel data into a pre-built map digital platform to obtain the evaluation results of the land parcel; and visually presenting the land parcel on the map according to the evaluation results; wherein the map digital platform is used to objectively evaluate the land parcel according to the land parcel's grid fundamental score, grid quantile, detailed indicator details, sector fundamental score ranking, and market evaluation needs.

[0131] This invention provides a non-transitory computer-readable storage medium storing computer instructions that cause the computer to execute the methods provided in the above-described method embodiments. These instructions include, for example, acquiring land parcel data, inputting the land parcel data into a pre-constructed map digital platform to obtain evaluation results for the land parcels, and visualizing the land parcels on a map based on the evaluation results. The map digital platform is used to objectively evaluate land parcels based on their raster fundamentals score, raster quantiles, detailed indicator information, sector fundamentals score ranking, and market evaluation requirements.

[0132] The present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the methods provided in the above embodiments, the method comprising: acquiring land parcel data, inputting the land parcel data into a pre-constructed map digital platform to obtain the evaluation results of the land parcel; and visually presenting the land parcel on a map based on the evaluation results; wherein the map digital platform is used to objectively evaluate the land parcel based on the land parcel's raster fundamental score, raster quantile, detailed indicator details, sector fundamental score ranking, and market evaluation requirements.

[0133] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An evaluation method based on a map digital platform, characterized in that, The map digital platform includes a fundamental data processing model and a market evaluation system, and the method includes: Obtain land parcel data; The land parcel data is input into a pre-built map digital platform to obtain the evaluation results of the land parcel; Based on the evaluation results, the land parcel will be visualized on a map; The map digital platform is used to objectively evaluate the land parcels based on their grid fundamentals score, grid quantiles, detailed indicators, sector fundamentals score ranking, and market evaluation requirements. The step of inputting the land parcel data into a pre-built map digital platform to obtain the evaluation result of the land parcel includes: The land parcel data is input into the fundamental surface processing model to obtain the fundamental surface data of each grid cell of the land parcel. The fundamental data is transmitted to the market evaluation system to obtain the evaluation result of the land parcel; The process, prior to inputting the land parcel data into the fundamental surface processing model to obtain the fundamental surface data of each grid cell of the land parcel, includes: Obtain the first raw data of the sample plots, wherein the first raw data includes the average housing price data of the city and the urban raster surface index data; The fundamental surface processing model to be trained is based on PCA and entropy algorithm, the average urban housing price data of the sample plots and the urban raster surface index data, to obtain the trained fundamental surface processing model. The average urban housing price data serves as the Y-axis data for the fundamental analysis model to be trained, and the urban raster surface index data of the sample plots serves as the X-axis data for the fundamental analysis model to be trained. Before transmitting the fundamental data to the market evaluation system to obtain the evaluation result of the land parcel, the following steps are included: Obtain multiple second raw data points for the sample plots; Based on the multiple second raw data and the preset evaluation tags, multiple corresponding relationships are determined; Based on the identified multiple correspondences, a market evaluation system is formed; The second original data is different from the first original data, and includes at least new housing supply and demand data, new housing inventory data, land auction data, sector boundary data, and generalized inventory data.

2. The evaluation method based on a map digital platform according to claim 1, characterized in that, The step of transmitting the fundamental data to the market evaluation system to obtain the evaluation result of the land parcel includes: Based on the multiple pre-set correspondences in the market evaluation system and the fundamental data, the evaluation tags corresponding to the fundamental data are determined; The evaluation result of the land parcel is determined based on the evaluation labels.

3. The evaluation method based on a map digital platform according to claim 1, characterized in that, The step of visualizing the land parcel on a map based on the evaluation results includes: Based on the evaluation results, the land parcel will be visualized on the map using corresponding colors.

4. The evaluation method based on a map digital platform according to claim 1, characterized in that, Before inputting the land parcel data into a pre-built map digital platform, the method includes: The land parcel data is preprocessed, cleaned, and inspected to obtain the processed land parcel data.

5. A processing apparatus based on a map digital platform, used to execute the evaluation method as described in any one of claims 1 to 4, characterized in that, The device includes: The acquisition module is used to acquire land parcel data; The input module is used to input the land parcel data into a pre-built map digital platform to obtain the evaluation results of the land parcel. A visualization module is used to visualize the land parcel on a map based on the evaluation results; The map digital platform is used to objectively evaluate the land parcels based on their grid fundamentals score, grid quantiles, detailed indicators, sector fundamentals score ranking, and market evaluation requirements.

6. An electronic device, characterized in that, include: A processor, a memory, and a bus, wherein the processor and the memory communicate with each other via the bus; The memory stores program instructions that can be executed by the processor, which can invoke the program instructions to perform the steps of the evaluation method based on the map digital platform as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to perform the steps of the evaluation method based on a map digital platform as described in any one of claims 1 to 4.

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