Spatial data gridding statistical device and method

Abstract

The invention relates to the technical field of data gridding statistics, in particular to a spatial data gridding statistics device and method, the device comprises a data acquisition module, a model establishment module, a data analysis module, a multi-source verification module and a data output and management module, and the method comprises the following steps: step 1, data acquisition and model establishment; step 2, data analysis and multi-source verification; and step 3, outputting and managing data. According to the method, the DBSCAN algorithm is adopted to carry out data analysis operation on the actual reference data, the dense area and the sparse area in the data can be automatically identified, the size or shape of the grid does not need to be preset, the cluster of any shape can be processed, and then the efficiency and the accuracy of the spatial data gridding analysis result are improved.

Description

Technical Field

[0001] This invention relates to the field of data gridding and statistical technology, specifically to a spatial data gridding and statistical device and method. Background Technology

[0002] Spatial data gridding statistics is a method that divides continuous spatial data into several regular or irregular grid cells according to specific rules, and then performs statistical analysis on the spatial data within each grid cell. In spatial data research, raw data is often widely distributed and diverse in form. Through gridding, complex spatial information can be transformed into a discrete set of grid cells, each assigned specific attribute values ​​(such as the number of buildings, total area, population density, and other statistical indicators within the grid). This not only simplifies the complexity of spatial data and facilitates its storage, management, and computation, but also more intuitively displays the distribution characteristics and trends of spatial data.

[0003] Existing technologies, such as spatial data gridding statistical methods based on mathematical interpolation, use regular grids to cover the area to be statistically analyzed. The value of each grid is calculated by dividing the statistical value of the area where the grid center is located by the number of grids in that area. Then, the average value of each grid is used to replace the original value of that grid. Under the premise that the total value of the statistical area remains unchanged, the smoothed grid value is used as the weight to adjust the grid value to form a cycle. Through multiple smoothing and adjustment, a continuous surface of statistical values ​​can be obtained.

[0004] While the above methods can achieve gridded statistics of spatial data to a certain extent, because they are based on regular grids for interpolation, the interpolation results may not accurately reflect the real situation when faced with complex distribution data. This may lead to bias in the statistical results and reduce the accuracy of the gridded analysis results of spatial data.

[0005] In summary, the spatial data gridding statistical method based on mathematical interpolation may lead to biased statistical results when dealing with complex data distributions, which reduces the accuracy of spatial data gridding analysis results. This problem has become an urgent issue to be solved in this field. Therefore, it is necessary to propose a spatial data gridding statistical device and method. Summary of the Invention

[0006] To address the aforementioned issues, this invention provides a spatial data gridding statistical device and method. By employing the DBSCAN algorithm to perform data analysis on actual reference data, it can automatically identify dense and sparse regions in the data without pre-setting the grid size or shape, enabling it to handle clusters of arbitrary shapes, thereby improving the efficiency and accuracy of spatial data gridding analysis results.

[0007] In order to achieve the above object, the technical scheme of the present application is as follows: A spatial data gridding statistical device comprises the following modules: A data acquisition module is configured to integrate satellite remote sensing image technology, mobile survey vehicle real-time point cloud technology, IoT sensor network and historical archive digitization platform, build an aerospace-terrestrial integrated acquisition system, and acquire remote sensing image information, real-time geographic information, field measurement information and historical archive information of a region to be counted.

[0008] A model establishment module is configured to establish a building distribution probability model based on historical archive information and real-time geographic information acquired by the data acquisition module, establish a real-time information database based on remote sensing image information, real-time geographic information and field measurement information, establish a building change increment model through the real-time information database, dynamically weight and integrate the building distribution probability model and the building change increment model, and obtain a comprehensive analysis model.

[0009] A data analysis module is configured to perform data analysis on actual reference data by using a DBSCAN algorithm through the comprehensive analysis model, obtain an analysis result, and calculate a matching rate between the obtained analysis result and the actual reference data, wherein the actual reference data comprises remote sensing image information, real-time geographic information and field measurement information in the real-time information database.

[0010] A multi-source verification module is configured to adjust output of the analysis result by using a multi-level decision mechanism according to the matching rate between the analysis result and the actual reference data.

[0011] A data output and management module is configured to classify and encode the output analysis result, create a chart based on the analysis result, and archive and backup all data in the acquisition process and the analysis process.

[0012] Further, the data acquisition module comprises the following units: A remote sensing image acquisition unit is configured to interface with a satellite API, and automatically pull the latest remote sensing image information.

[0013] A geographic information acquisition unit is configured to acquire road network and terrain data of a region to be counted in real time through a GeoServer, and integrate the data into real-time geographic information.

[0014] A measurement information acquisition unit is configured to measure buildings in a region to be counted in real time through mobile survey vehicle real-time point cloud technology and IoT sensor network, and obtain field measurement information.

[0015] A historical archive acquisition unit is configured to query historical archive information of a region to be counted through an Internet large model.

[0016] Further, the model establishment module comprises the following units: The probability model establishing unit is configured to perform key factor extraction on the historical archive information and real-time geographic information, and establish a probability model of building distribution based on the extracted key factors, and the specific algorithm formula is as follows: (1).

[0017] Wherein, y=1 is the probability of existing buildings, x is the feature vector, is the weight parameter obtained by training historical data.

[0018] The incremental model establishing unit is configured to perform real-time monitoring and prediction on the change of buildings according to remote sensing image information, perform identification operation on new buildings according to real-time geographic information, and analyze the use state of buildings based on field measurement information, establish a real-time information database, and then establish a building change incremental model based on the real-time information database.

[0019] The model integration unit is configured to dynamically weight and integrate the probability model of building distribution and the building change incremental model by using Kalman filtering technology to obtain a comprehensive analysis model.

[0020] Further, the specific process of the DBSCAN algorithm is as follows: Given an object p, define its -neighborhood, and the specific formula is as follows: (2).

[0021] Wherein, D is the data set, is the distance from point p to point q.

[0022] When the -neighborhood of point p contains MinPts points, p is a core point, which is specifically represented as: (3).

[0023] At this time, when point q is in the -neighborhood of point p, and p is a core point, q is directly density reachable from p; when there is a point sequence p1, p2, …, p n , and p1=p, p n =q, and p i+1 is directly density reachable from p i , then q is density reachable from p; when there is a point o, and point p and point q are both density reachable from point o, then p and q are density connected.

[0024] Further, the calculation formula of the matching rate is as follows: (4).

[0025] Wherein, g is the matching rate, f is the analysis result, and c is the reference data.

[0026] Further, the operation process of the multi-source verification module is as follows: When the matching rate is greater than 90%, the multi-source verification module directly outputs the analysis result.

[0027] When the matching rate is between 70%-90%, the multi-source verification module adjusts the analysis result and shows the adjusted analysis result to the staff, and after the staff confirms, the analysis result is output.

[0028] When the matching rate is less than 70%, the multi-source verification module alarms the staff, and the staff manually intervenes to correct the analysis result, and after the correction is completed, the corrected analysis result is output.

[0029] Further, the historical archive collection unit is also used to identify paper documents through OCR technology, and extract data in the paper documents through NLP technology to obtain historical archives in the paper documents.

[0030] Further, the multi-source verification module includes a self-learning unit, when the matching rate is between 70%-90%, the multi-source verification module adjusts the analysis result through the self-learning unit, records the adjustment parameters and feeds back to the model establishment module, so that the model establishment module updates the parameters of the comprehensive analysis model according to the adjustment parameters.

[0031] Further, the data output and management module includes a blockchain storage unit for generating a hash value of the analysis result, all collected data and processing logs and uploading them to the blockchain for storage.

[0032] Further, a spatial data gridding statistical method includes the following steps: Step 1, data collection and model establishment: collect remote sensing image information, real-time geographic information, field measurement information and historical archive information of the area to be counted, establish a probability model of building distribution based on its historical archive information and real-time geographic information, establish a real-time information database based on remote sensing image information, real-time geographic information and field measurement information, and then establish a building change increment model based on the real-time information database. Finally, integrate the probability distribution model and the building change increment model to obtain a comprehensive analysis model.

[0033] Step 2, data analysis and multi-source verification: analyze the actual reference data through the DBSCAN algorithm and the comprehensive analysis model, calculate the matching rate between the analysis result and the actual reference data after obtaining the analysis result, and establish a multi-level decision mechanism based on the matching rate to adjust the output of the analysis result.

[0034] Step three, output and management of data: after the output of the analysis result is completed, all data in this analysis are archived and backed up, and the output analysis result is classified and coded, and a chart is created according to the output analysis result.

[0035] The technical principle of the above scheme is as follows: The data acquisition module first acquires remote sensing image information, real-time geographic information, field measurement information and historical archive information of the region to be counted, then a probability model of building distribution is established based on the historical archive information and the real-time geographic information, a real-time information database is established based on the remote sensing image information, the real-time geographic information and the field measurement information, a building change increment model is established based on the real-time information database, and the probability model of building distribution and the building change increment model are dynamically weighted and integrated to obtain a comprehensive analysis model.

[0036] After the comprehensive analysis model is obtained, the data analysis module uses the DBSCAN algorithm to analyze the actual reference data to obtain an analysis result, and synchronously calculates the matching rate with the actual reference data, after the matching rate is obtained, the multi-source verification module adjusts and outputs the output of the analysis result according to the specific situation of the matching rate, and finally the data output and management module archives and backs up all data, and classifies and codes the output analysis result, and creates a chart according to the analysis result.

[0037] The above scheme has the following beneficial effects: 1、The present application, by using the DBSCAN algorithm to analyze the actual reference data, can automatically identify the dense and sparse areas in the data, without the need to pre-set the grid size or shape, so that it can process clusters of any shape, thereby improving the efficiency and accuracy of the spatial data grid analysis result.

[0038] 2、The present application, by dynamically weighting and integrating the building distribution probability model and the building change increment model to obtain a comprehensive analysis model, fully considers the historical distribution and real-time change of the building, and improves the accuracy and adaptability of the comprehensive analysis model.

[0039] 3、The present application, by classifying and coding the output analysis result and creating a chart based on the analysis result, provides an intuitive analysis result for the staff. At the same time, the operation of archiving and backing up all data in the acquisition process and the analysis process also ensures the safety and traceability of the data.

[0040] Additional aspects and advantages of the application will be made apparent by the following description. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 This is a schematic diagram of the spatial data gridding statistical device of the present invention.

[0042] Figure 2 This is a schematic diagram of the data acquisition module in the spatial data gridding statistical device of the present invention.

[0043] Figure 3 This is a schematic diagram of the model building module in the spatial data gridding statistical device of the present invention.

[0044] Figure 4 This is a schematic diagram illustrating the steps of the spatial data gridding statistical method of the present invention. Detailed Implementation

[0045] The following detailed description illustrates the specific implementation methods: Example 1: As attached Figure 1 As shown: A spatial data gridding statistical device includes a data acquisition module for data acquisition operations, a model building module for establishing a comprehensive analysis model, a data analysis module for analyzing actual reference data using the DBSCAN algorithm and the comprehensive analysis model, a multi-source verification module for adjusting the output of the analysis results using a multi-level decision-making mechanism, and a data output and management module for managing all data.

[0046] The functions of each module are explained in detail below: As attached Figure 2 As shown, the data acquisition module integrates satellite remote sensing imagery technology, real-time point cloud technology from mobile surveying vehicles, IoT sensor networks, and a historical archive digitization platform to construct an integrated air-space-ground acquisition system. This system collects remote sensing image information, real-time geographic information, field measurement information, and historical archive information for the area to be surveyed. It includes a remote sensing image acquisition unit, a geographic information acquisition unit, a measurement information acquisition unit, and a historical archive acquisition unit.

[0047] The remote sensing image acquisition unit is used to interface with the satellite API and automatically retrieve the latest remote sensing image information.

[0048] The geographic information acquisition unit is used to acquire road network and terrain data of the area to be statistically analyzed in real time through GeoServer and integrate them into real-time geographic information.

[0049] The measurement information acquisition unit is used to conduct on-site measurements of buildings in the area to be surveyed using real-time point cloud technology from a mobile measurement vehicle and an IoT sensor network, thereby obtaining on-site measurement information.

[0050] The historical archive collection unit is configured to query historical archive information of the region to be counted through an Internet large model, and is also configured to recognize paper documents through an OCR technique, and extract data in the paper documents through an NLP technique to obtain historical archives in the paper documents.

[0051] Specifically, the data collection module first interfaces with a satellite API through the remote sensing image collection unit to pull the latest remote sensing image information, and interfaces with a GeoServer through the geographic information collection unit to obtain road network data and terrain data of the region to be counted, and integrates the road network data and the terrain data of the region to be counted to form real-time geographic information. After obtaining the real-time geographic information, the data collection module uses a mobile survey vehicle real-time point cloud technology and an IoT sensor network to measure the buildings in the region to be counted through the measurement information collection unit, integrates the measurement results into field measurement information, and further queries historical archive information of the region to be counted through the historical archive collection unit using an Internet large model. When information reading of historical archives in paper documents is required, the data collection module first recognizes the paper documents through an OCR technique, and then extracts data in the paper documents through an NLP technique.

[0052] As shown in FIG. 1, Figure 3 The model establishment module is configured to establish a probability model of building distribution based on the historical archive information and the real-time geographic information collected by the data collection module, and establish a real-time information database based on the remote sensing image information, the real-time geographic information, and the field measurement information, and dynamically integrate the probability model of building distribution and an incremental model of building change to obtain a comprehensive analysis model, which includes a probability model establishment unit, an incremental model establishment unit, and a model integration unit.

[0053] The probability model establishment unit is configured to perform key factor extraction on the historical archive information and the real-time geographic information, and establish a probability model of building distribution based on the extracted key factors. The specific algorithm formula is as follows: (1).

[0054] wherein y = 1 is the probability of the existence of a building, x is a feature vector, is a weight parameter trained through historical data.

[0055] The incremental model establishment unit is configured to monitor and predict changes in buildings in real time according to the remote sensing image information, identify new buildings according to the real-time geographic information, analyze the use state of the buildings based on the field measurement information, establish a real-time information database, and establish an incremental model of building change based on the real-time information database.

[0056] The model integration unit is configured to dynamically integrate the probability model of building distribution and the building change increment model by using Kalman filtering technology to obtain a comprehensive analysis model.

[0057] Specifically, the model establishing module first extracts key factors from historical archive information and real-time geographic information by the probability model establishing unit, and establishes a probability model of building distribution based on the extracted key factors. After the probability model is established, the model establishing module establishes a building change increment model based on remote sensing image information to monitor and predict the change of buildings in real time, identifies new buildings based on real-time geographic information, analyzes the use state of buildings based on field measurement information, and establishes a real-time information database. Finally, the model establishing module establishes the building change increment model based on the real-time information database.

[0058] After the probability model and the building change increment model are established, the model establishing module dynamically integrates the probability model of building distribution and the building change increment model by using Kalman filtering technology through the model integration unit to obtain a comprehensive analysis model.

[0059] The data analysis module is configured to perform data analysis on actual reference data by using the DBSCAN algorithm based on the comprehensive analysis model to obtain an analysis result and calculate a matching rate between the obtained analysis result and the actual reference data. The actual reference data includes remote sensing image information, real-time geographic information, and field measurement information in the real-time information database.

[0060] The specific process of the DBSCAN algorithm is as follows: Given an object p, define its -neighborhood as follows: (2).

[0061] Where D is a data set, is the distance from point p to point q.

[0062] When the -neighborhood of point p contains MinPts points, p is a core point, which is specifically represented as: (3).

[0063] At this time, when point q is in the -neighborhood of point p and p is a core point, q is directly density reachable from p; when there is a point sequence p1, p2, …, p n , and p1=p, p n =q, and p i+1 is density reachable from p iIf a direct density is reachable for q, then q is density reachable from p; if there is a point o such that both p and q are density reachable from o, then p and q are density connected.

[0064] Specifically, after the establishment of the comprehensive analysis model is completed, the data analysis module performs data analysis operations on the actual reference data through the comprehensive analysis model and the DBSCAN algorithm, obtains an analysis result, and synchronously calculates a matching rate between the analysis result and the actual reference data, to obtain a specific value of the matching rate.

[0065] The multi-source verification module is configured to adjust an output of the analysis result by using a multi-level decision mechanism according to the matching rate between the analysis result and the actual reference data.

[0066] The calculation formula of the matching rate is as follows: (4).

[0067] The calculation formula of the matching rate is as follows:

[0068] The operation process of the multi-source verification module is as follows: When the matching rate is greater than 90%, the multi-source verification module directly outputs the analysis result.

[0069] When the matching rate is between 70% and 90%, the multi-source verification module adjusts the analysis result, and displays the adjusted analysis result to the staff for confirmation, and then outputs the analysis result after the staff confirms.

[0070] When the matching rate is less than 70%, the multi-source verification module alarms the staff, and the staff manually intervenes to correct the analysis result, and then outputs the corrected analysis result.

[0071] The multi-source verification module includes a self-learning unit, and when the matching rate is between 70% and 90%, the multi-source verification module adjusts the analysis result through the self-learning unit, records adjustment parameters, and feeds back the adjustment parameters to the model establishment module, so that the model establishment module updates parameters of the comprehensive analysis model according to the adjustment parameters.

[0072] Specifically, after the specific value of the matching rate is obtained, the staff classifies the matching rate according to specific conditions of the matching rate, different levels correspond to different analysis result processing manners, and the staff is connected to form a multi-level decision mechanism.

[0073] When the value of the matching rate is above 90%, the analysis result is directly output by the multi-source verification module; when the value of the matching rate is between 70% and 90%, the multi-source verification module adjusts the analysis result through the self-learning unit, and after the parameters are adjusted, the analysis result is displayed to the staff, and after the staff confirms, the analysis result is output; when the matching rate is below 70%, the multi-source verification module will alarm the staff, and the staff intervenes to modify the analysis result, and after the modification is completed, the modified analysis result is output. In this process, when the matching rate is between 70% and 90%, the multi-source verification module will record the adjustment parameters and feed back to the model establishment module after adjusting the analysis result through the self-learning unit, so that the model establishment module updates the parameters of the comprehensive analysis model according to the adjustment parameters.

[0074] The data output and management module is used for classifying and coding the output analysis result, and creating a chart based on the analysis result, and archiving and backing up all data in the collection process and analysis process.

[0075] The data output and management module includes a blockchain storage unit for generating hash values for the analysis result, all collected data and processing logs and uploading them to the blockchain for storage.

[0076] Specifically, after the output of the analysis result is completed, the data output and management module classifies and codes the output analysis result, and creates a chart based on the analysis result to bring an intuitive visual display to the staff, and archives and backs up all data generated in the collection and analysis process for subsequent staff to query and use. Specifically, the data output and management module generates hash values for the analysis result, all collected data and processing logs through the blockchain storage unit and uploads them to the blockchain for storage.

[0077] Embodiment 2: As shown in the accompanying drawings, Figure 4 The spatial data gridding statistical method comprises the following steps: Step one, data collection and model establishment: first, collect remote sensing image information, real-time geographic information, field measurement information and historical archive information of the area to be counted, and establish a probability model of building distribution according to the historical archive information and real-time geographic information, and establish a real-time information database based on the remote sensing image information, real-time geographic information and field measurement information, and then establish a building change increment model based on the real-time information database, and finally integrate the probability distribution model and the building change increment model to obtain a comprehensive analysis model.

[0078] Step two, data analysis and multi-source verification: after obtaining the comprehensive analysis model, the actual reference data is analyzed by DBSCAN algorithm and the comprehensive analysis model to obtain the analysis result, and after obtaining the analysis result, the matching rate between the analysis result and the actual reference data is calculated, the matching rate is taken as the reference data, a multi-level decision mechanism is established, and the output of the analysis result is adjusted according to the multi-level decision mechanism.

[0079] Step three, output and management of data: after the output of the analysis result is completed, all the data in this analysis are archived and backed up, the output analysis result is classified and coded, and a chart is created according to the output analysis result to bring more intuitive visual display to the staff.

[0080] Obviously, the above embodiments are only examples for clear illustration, and are not limitations to the embodiments. For ordinary skilled in the art, other different forms of changes or variations can be made on the basis of the above description. Here, all the embodiments cannot be exhausted, and the obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A spatial data gridding statistics apparatus, characterized by, Comprise the following modules: Data acquisition module, for integrating satellite remote sensing image technology, mobile survey vehicle real-time point cloud technology, IoT sensor network and historical archives digitization platform, building an integrated space-ground system, collecting remote sensing image information, real-time geographic information, field measurement information and historical archive information of the area to be counted; Model establishment module, for establishing a probability model of building distribution based on historical archive information and real-time geographic information collected by the data acquisition module, and establishing a real-time information database based on remote sensing image information, real-time geographic information and field measurement information, dynamically weighting and integrating the probability model of building distribution and the building change increment model through the real-time information database to obtain a comprehensive analysis model; Data analysis module, for performing data analysis operations on actual reference data using DBSCAN algorithm through the comprehensive analysis model to obtain analysis results and calculate the matching rate between the obtained analysis results and actual reference data; wherein the actual reference data are remote sensing image information, real-time geographic information and field measurement information in the real-time information database; Multi-source verification module, for adjusting the output of the analysis results according to the matching rate between the analysis results and the actual reference data using a multi-level decision mechanism; Data output and management module, for classifying and encoding the output analysis results, creating charts based on the analysis results, and archiving and backing up all data in the collection and analysis processes.

2. The spatial data gridding statistics device of claim 1, wherein, The data acquisition module comprises the following units: Remote sensing image acquisition unit, for interfacing with satellite API to automatically pull the latest remote sensing image information; Geographic information acquisition unit, for obtaining road network and terrain data of the area to be counted in real time through GeoServer and integrating them into real-time geographic information; Measurement information acquisition unit, for measuring buildings in the area to be counted in real time through mobile survey vehicle real-time point cloud technology and IoT sensor network to obtain field measurement information; Historical archive acquisition unit, for querying historical archive information of the area to be counted through an Internet large model.

3. The spatial data gridding statistics device of claim 2, wherein, The model establishment module comprises the following units: Probability model establishment unit, for performing key factor extraction operations on historical archive information and real-time geographic information, and establishing a probability model of building distribution based on the extracted key factors, with the specific algorithm formula as follows: （1）； Wherein, y=1 is the probability of the existence of the building, x is the feature vector, is the weight parameter obtained by training historical data; Increment model establishment unit, for monitoring and predicting changes in buildings in real time according to remote sensing image information, identifying new buildings according to real-time geographic information, analyzing the use status of buildings based on field measurement information, establishing a real-time information database, and establishing a building change increment model based on the real-time information database; Model integration unit, for dynamically weighting and integrating the probability model of building distribution and the building change increment model using Kalman filtering technology to obtain a comprehensive analysis model.

4. The spatial data gridding statistics apparatus of claim 3, wherein, The specific process of DBSCAN algorithm is as follows: Given an object p, define a neighborhood of p as follows: - neighborhood is defined as follows: （2）； where D is a dataset, is the distance from point p to point q; When the point p has - p is a core point if the neighborhood of p contains MinPts points, which is denoted as: （3）； At this time, when point q is in the neighborhood of point p , and p is the core point, q is directly density reachable from p; when there is a point sequence p1, p2, …, p n , and where p1 = p, p n = q, and p i+1 is directly density reachable from p i , then q is density reachable from p; when there is a point o, and points p and q are both density reachable from point o, then p and q are density connected.

5. The spatial data gridding statistics device of claim 4, wherein, The operation process of the multi-source verification module is as follows: When the matching rate is greater than 90%, the multi-source verification module directly outputs the analysis results; When the matching rate is between 70%-90%, the analysis result is adjusted by the multi-source verification module, and the adjusted analysis result is displayed to the staff, and after the staff confirms, the analysis result is output; When the matching rate is less than 70%, the multi-source verification module alarms the staff, and the staff manually intervenes to correct the analysis result, and after the correction is completed, the corrected analysis result is output.

6. The spatial data gridding statistics device of claim 5, wherein, The calculation formula of the matching rate is as follows: （4）； Wherein, g is the matching rate, f is the analysis result, and c is the reference data.

7. The spatial data gridding statistics device of claim 6, wherein, The historical archive acquisition unit is also used to identify paper documents through OCR technology, and then extract data in the paper documents through NLP technology to obtain historical archives in the paper documents.

8. The spatial data gridding statistics device of claim 7, wherein, The multi-source verification module includes a self-learning unit, when the matching rate is between 70%-90%, the multi-source verification module adjusts the analysis result through the self-learning unit, records the adjustment parameters and feeds back to the model establishment module, so that the model establishment module updates the parameters of the comprehensive analysis model according to the adjustment parameters.

9. The spatial data gridding statistics device of claim 8, wherein, The data output and management module includes a blockchain storage unit, which is used to generate hash values of the analysis result, all collected data and processing logs and upload them to the blockchain for storage.

10. A spatial data gridding statistical method based on the spatial data gridding statistical device according to any one of claims 1 to 9, characterized by, The method comprises the following steps: Step one, data acquisition and model establishment: collect remote sensing image information, real-time geographic information, field measurement information and historical archive information of the area to be counted, establish a probability model of building distribution based on the historical archive information and real-time geographic information, establish a real-time information library based on the remote sensing image information, real-time geographic information and field measurement information, and then establish a building change increment model based on the real-time information library, and finally integrate the probability distribution model and the building change increment model to obtain a comprehensive analysis model; Step two, data analysis and multi-source verification: analyze the actual reference data through the DBSCAN algorithm and the comprehensive analysis model, calculate the matching rate between the analysis result and the actual reference data, and establish a multi-level decision mechanism based on the matching rate to adjust the output of the analysis result; Step three, data output and management: after the output of the analysis result is completed, all data in this analysis are archived and backed up, and the output analysis result is classified and coded, and a chart is created according to the output analysis result.

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