Land survey stratification and physicalization methods, apparatus, equipment and storage media

Abstract

This invention discloses a method, apparatus, equipment, and storage medium for hierarchical materialization in land surveys, relating to the field of database statistical processing technology. The method includes: extracting a raw dataset from a land use status survey database; performing hierarchical materialization processing on the raw dataset to obtain multi-level intermediate materialization results; generating report data based on the multi-level intermediate materialization results; comparing the report data with preset verification values ​​to obtain deviation data; and when the deviation data exceeds a preset deviation threshold, performing a layer-by-layer backtracking comparison of the multi-level intermediate materialization results to determine the error level. This achieves the technical effect of determining the error level through layer-by-layer backtracking comparison when multi-level report statistical results are inconsistent.

Description

Technical Field

[0001] This invention relates to the field of database statistical processing technology, and in particular to a method, apparatus, equipment and storage medium for the hierarchical materialization of land surveys. Background Technology

[0002] With the continuous advancement of informatization in land resource management, the scale and complexity of the land use status database continue to grow. Its report statistics involve multi-dimensional indicator calculations and multi-level administrative unit aggregations, resulting in a lengthy statistical chain and numerous data processing steps. During data quality inspection and statistical result verification, higher technical requirements are placed on the traceability of intermediate data at each processing stage and the ability to locate errors.

[0003] Existing database statistical techniques typically generate final reports through direct calculation or logical views, with intermediate processing existing only as temporary computational states in memory and not physically persisted. Since the statistical process involves multiple data processing stages, such as field standardization, grouping and aggregation, area adjustment correction, and multi-level administrative unit summarization, when deviations occur in the final statistical values, the lack of traceable physical intermediate data at each level makes it impossible to determine the specific processing stage where the error originated. In particular, adjustment correction alters the base area values; without physical data records before and after adjustment, it is even more difficult to trace the source of error back layer by layer, resulting in low efficiency in investigating statistical errors.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, equipment, and storage medium for stratified land surveying, aiming to solve the technical problem of determining the specific level at which an error occurs when statistical results from multi-level reports are inconsistent.

[0006] To achieve the above objectives, the present invention provides a method for stratified and progressively materialized land surveys, the method comprising the following steps: The original dataset was extracted from the land use status survey database; The original dataset is subjected to hierarchical materialization processing to obtain multi-level intermediate materialization results; Report data is generated based on the multi-level intermediate materialization results; The report data is compared with the preset verification value to obtain the deviation data; When the deviation data exceeds a preset deviation threshold, the multi-level intermediate materialization results are backtracked and compared layer by layer to determine the level at which the error occurred.

[0007] In one embodiment, the step of performing hierarchical materialization processing on the original dataset to obtain multi-level intermediate materialization results includes: The original dataset is cleaned to obtain a standardized dataset; The standardized dataset is subjected to basic processing to obtain basic processed data; The basic processed data is then subjected to topic-oriented dimensional summarization to obtain topic-oriented dimensional summarization data. The dimensional summary data is hierarchically aggregated to obtain hierarchical summary data; The hierarchical summary data is materialized into corresponding intermediate materialized tables to obtain multi-level intermediate materialized results.

[0008] In one embodiment, the step of cleaning the original dataset to obtain a standardized dataset includes: Read all dimension fields and area fields from the original dataset to obtain the set of fields to be processed; Perform case-consistent processing on all dimension fields in the set of fields to be processed to obtain case-consistent data. The case-consistent data is processed by removing spaces to obtain coded standardized data; Null values ​​are removed from the area field in the set of fields to be processed to obtain data after null value removal. The data after removing null values ​​is subjected to numerical precision conversion to obtain area-standardized data; The standardized dataset is obtained by integrating the coded standardized data and the area standardized data.

[0009] In one embodiment, the step of performing basic processing on the standardized dataset to obtain basic processed data includes: Read all statistical dimension fields from the standardized dataset to obtain the grouping field set; The standardized dataset is grouped according to the grouping field set to obtain multiple minimum granularity grouped datasets; The area fields in the multiple smallest granularity grouped datasets are summed to obtain multiple smallest granularity area sum values; The sum of the multiple smallest granularity areas is associated with the corresponding field values ​​in the grouping fields of the basic calculation table to obtain the basic aggregate data; The basic aggregated data is converted to obtain the converted data; The converted data is then subjected to controlled area adjustment correction to obtain the basic processed data.

[0010] In one embodiment, the step of performing topic-oriented dimensional aggregation on the basic processed data to obtain topic-oriented dimensional aggregation data includes: Read the statistical dimension fields of the corresponding standard report in the basic processing data to obtain the dimension field set; The basic processed data is then grouped a second time based on the set of dimensional fields to obtain multiple secondary grouped datasets. Summing the area fields in the multiple quadratic grouping datasets yields multiple quadratic grouping area sums. The sum of the areas and values ​​of the multiple secondary groupings are associated with the corresponding field values ​​in the set of dimensional fields to obtain the thematic dimensional summary data.

[0011] In one embodiment, the step of performing hierarchical aggregation on the dimensional summary data to obtain hierarchical summary data includes: Read the village-level data to be summarized from the summarized data of the dimensions to obtain the village-level dataset; The village-level dataset is grouped and aggregated based on the village-level administrative region identifier to obtain the village-level computing layer results; Based on the township-level administrative region identifier, the village-level calculation layer results are grouped and aggregated to obtain the township-level summary layer results; Based on the county-level administrative region identifier, the township-level summary layer results are grouped and aggregated to obtain the county-level summary layer results; The results of the village-level calculation layer, the township-level summary layer, and the county-level summary layer are integrated to obtain hierarchical summary data.

[0012] In one embodiment, the step of performing a layer-by-layer backtracking comparison of the multi-level intermediate materialization results to determine the error occurrence level when the deviation data is greater than a preset deviation threshold includes: Read the statistical values ​​and preset verification values ​​from the report data to obtain the data to be compared; The difference between the data to be compared is calculated to obtain the deviation value; When the deviation value is greater than the preset deviation threshold, the intermediate materialization tables of each level are read forward from the last level intermediate materialization table corresponding to the multi-level intermediate materialization result to obtain the dataset to be backtracked. The statistical values ​​of the intermediate materialized tables of two adjacent layers in the dataset to be traced back are compared to obtain the hierarchical difference results. The error occurrence level with statistical bias is determined based on the hierarchical difference results.

[0013] Furthermore, to achieve the above objectives, the present invention also proposes a land survey stratification and physicalization device, the device comprising: The raw data acquisition module is used to extract raw datasets from the land use status survey database. The hierarchical materialization module is used to perform hierarchical materialization processing on the original dataset to obtain multi-level intermediate materialization results. The report generation module is used to generate report data based on the multi-level intermediate materialization results; The comparison module is used to compare the report data with preset verification values ​​to obtain deviation data; The backtracking comparison module is used to perform a layer-by-layer backtracking comparison of the multi-level intermediate materialization results when the deviation data is greater than a preset deviation threshold, in order to determine the level at which the error occurred.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes a land survey stratification and step-by-step materialization device, the device comprising: a memory, a processor, and a land survey stratification and step-by-step materialization program stored in the memory and executable on the processor, the land survey stratification and step-by-step materialization program being configured to implement the steps of the land survey stratification and step-by-step materialization method as described above.

[0015] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a land survey stratification step-by-step materialization program, wherein when the land survey stratification step-by-step materialization program is executed by a processor, it implements the steps of the land survey stratification step-by-step materialization method as described above.

[0016] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the land survey stratification method described above.

[0017] One or more technical solutions proposed in this application have at least the following technical effects: By materializing the original dataset in layers and levels and materializing the results of each layer into corresponding intermediate materialized tables, the processing data at each stage of the statistical chain is persistently stored in the form of physical tables. This provides a traceable physical data foundation for layer-by-layer verification of statistical errors. Based on this, the generated report data is compared with preset verification values ​​to obtain deviation data. When the deviation data exceeds the preset deviation threshold, the multi-level intermediate materialized results are backtracked and compared layer by layer. By reading each layer of intermediate materialized tables from the last level backward and comparing the statistical values ​​of adjacent layers, the technical effect of directly determining the level of error when inconsistencies occur in the statistical results of multi-level reports is achieved. This avoids the difficulty of error investigation caused by the lengthy and coupled statistical chain and the lack of intermediate physical data records, and significantly improves the efficiency of statistical error location and data quality control capabilities. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the first embodiment of the land survey stratification method in this application. Figure 2 This is a schematic diagram of the land survey stratification process provided in Example 1 of the land survey stratification method of this application. Figure 3 This is a flowchart illustrating the second embodiment of the land survey stratification method in this application. Figure 4 This is a schematic diagram of the module structure of the land survey stratification device according to an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the land survey stratification method in this application embodiment.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0024] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a land survey stratification and physicalization device. The following description uses a land survey stratification and physicalization device as an example to illustrate this embodiment and the subsequent embodiments.

[0025] Based on this, the embodiments of this application provide a method for stratified and progressively materialized land surveys, referring to... Figure 1 , Figure 1This is a flowchart illustrating the first embodiment of the land survey stratification method of this application.

[0026] In this embodiment, the land survey stratification method includes steps S10 to S50: Step S10: Extract the raw dataset from the land use status survey database; like Figure 2 As shown, Figure 2 This diagram illustrates the hierarchical materialization process of land surveys. The process includes the original map patch layer, field value standardization layer, basic calculation table, basic statistical table, light summary layer, village-level calculation layer, township and county-level summary layer, and standard results layer. The basic calculation table includes a left-hand label indicating the tiered materialization process for land use status surveys; a field value standardization layer to eliminate differences in capitalization, spaces, null values, and numerical precision; a basic statistical table to convert area fields from square meters to hectares and adjust them using control areas to achieve horizontal consistency and ensure the total area matches the control area; a light summary layer to perform secondary grouping statistics on the basic statistical table based on the statistical dimensions of each standard report, such as the "Cultivated Land Slope Classification Table," which performs secondary grouping based on cultivated land slope level and cultivated land type; a village-level calculation layer to group based on village-level administrative region codes; township and county-level summary layers to group based on township and county-level administrative region codes, respectively; and a standard results layer to merge the village-level calculation layer, township-level summary layer, and county-level summary layer to obtain a standard report containing statistical results from the county, township, and village levels.

[0027] It should be noted that the land use status survey database refers to a database used to store and manage data related to the current status of land use, which includes land parcel data and corresponding attribute information. It should also be noted that the raw dataset refers to the unprocessed initial data set extracted from the database, that is, the data set containing land parcel data and corresponding attribute information.

[0028] Understandably, extracting the raw dataset from the land use status survey database means reading the land parcel data and corresponding attribute information stored in the database to form the raw dataset that can be processed later.

[0029] This step provides the data foundation for subsequent hierarchical materialization processing. By directly extracting raw data from the database, the integrity and accuracy of the data source are ensured, laying the initial data foundation for the traceability of the entire statistical chain.

[0030] Step S20: Perform hierarchical materialization processing on the original dataset to obtain multi-level intermediate materialization results; It should be noted that hierarchical materialization processing refers to dividing the data processing process into multiple processing stages according to a preset data hierarchy. Each stage performs only limited statistical logic processing, i.e., a data processing method that proceeds step by step according to the data hierarchy. It should also be noted that multi-level intermediate materialization results refer to the data sets formed by storing the processing results of each level in the form of physical tables after hierarchical materialization processing; that is, the intermediate data persistently stored after each stage of processing is completed.

[0031] Understandably, performing hierarchical materialization processing on the original dataset to obtain multi-level intermediate materialization results means that the original dataset is processed sequentially through multiple stages according to a preset data hierarchy, such as field value standardization, basic calculation, basic statistics, light summarization, and hierarchical summarization at the village, township, and county levels. The data after each stage of processing is stored in the form of physical tables, ultimately forming a multi-level intermediate materialization result containing materialized data at each level.

[0032] This step breaks down the lengthy statistical chain into multiple layers and materializes the results of each layer, so that each step in the statistical process forms verifiable physical intermediate data. This avoids the drawback of untraceable intermediate data in the traditional logical view approach and provides a physical data foundation for locating statistical errors layer by layer.

[0033] Step S30: Generate report data based on the multi-level intermediate materialization results; It should be noted that the report data refers to the final statistical data generated after summarizing the results of multiple intermediate materialization levels upwards, that is, the standard report data that includes the statistical results of the county, township, and village levels.

[0034] As is understandable, generating report data based on multi-level intermediate materialization results means reading the summary data of the last level in the multi-level intermediate materialization results, organizing it into a standard report format that conforms to statistical norms, and forming the final report data.

[0035] This step transforms materialized intermediate data into standard reports that can be directly used for quality inspection and output. By generating reports based on the materialized intermediate results, the certainty and auditability of the report data source are ensured.

[0036] Step S40: Compare the report data with the preset verification value to obtain the deviation data; It should be noted that the preset check value refers to a standard value set in advance to verify the accuracy of report data, that is, a reference statistical value used as a comparison benchmark. It should also be noted that the deviation data refers to the difference obtained after comparing the report data with the preset check value, that is, data reflecting the degree of deviation between the statistical value and the standard value.

[0037] Understandably, comparing report data with preset verification values ​​to obtain deviation data means comparing the statistical values ​​in the generated report data with the preset verification values, calculating the difference between the two, and obtaining deviation data.

[0038] This step verifies the accuracy of the report data by introducing preset verification values, which can promptly detect any inconsistencies in the statistical results and provide a basis for subsequent layer-by-layer backtracking comparison.

[0039] Step S50: When the deviation data is greater than the preset deviation threshold, perform a layer-by-layer backtracking comparison of the multi-level intermediate materialization results to determine the level at which the error occurred.

[0040] It should be noted that the preset deviation threshold refers to a pre-set critical value used to determine the existence of statistical deviation, i.e., the numerical limit that triggers the backtracking comparison operation. It should also be noted that layer-by-layer backtracking comparison refers to the operation of reading intermediate materialized tables at each level backward from the final level materialized table and comparing the differences between adjacent levels of data; that is, a data comparison method that checks data layer by layer from the end to the beginning. Finally, it should be noted that the error occurrence level refers to the processing stage at which the statistical deviation first appears, as determined during the layer-by-layer backtracking comparison process; that is, the specific level at which data inconsistency exists.

[0041] Understandably, when the deviation data exceeds the preset deviation threshold, the multi-level intermediate materialized results are backtracked and compared layer by layer to determine the error occurrence level. This means that after confirming that there is a statistical deviation in the report data, starting from the last-level intermediate materialized table corresponding to the multi-level intermediate materialized results, each level of intermediate materialized table is read forward, and the statistical values ​​of adjacent intermediate materialized tables are compared. Based on the hierarchical difference results obtained from the comparison, the processing level at which the statistical deviation first appears is determined, thus obtaining the error occurrence level.

[0042] This step utilizes the materialized multi-level intermediate materialized results for layer-by-layer backtracking and comparison, which can quickly locate the specific level at which the error occurred when the statistical results are inconsistent. This significantly improves the efficiency and accuracy of statistical error investigation and avoids the difficulty in error location caused by the lengthy and coupled statistical links.

[0043] In one feasible implementation, step S50 includes steps A11 to A15: Step A11: Read the statistical values ​​and preset verification values ​​from the report data to obtain the data to be compared; It should be noted that statistical values ​​refer to specific numerical values ​​obtained through hierarchical aggregation and calculation in the report data, i.e., the quantitative statistical results in the report data. It should also be noted that the data to be compared refers to the data set composed of statistical values ​​extracted from the report data and preset verification values, i.e., the data combination used to perform the comparison operation.

[0044] Understandably, reading statistical values ​​and preset verification values ​​from report data to obtain comparison data means extracting statistical values ​​from generated report data and obtaining preset verification values, combining the two to form comparison data for subsequent difference calculation.

[0045] This step extracts the actual statistical values ​​from the report data and compares them with the preset standard verification values, providing a complete comparative data foundation for subsequent deviation calculations and ensuring the data integrity of deviation detection.

[0046] Step A12: Calculate the difference in the data to be compared to obtain the deviation value; It should be noted that difference calculation refers to the mathematical operation of subtracting two values ​​in the comparison data to determine the magnitude of the difference; that is, calculating the difference between the statistical value and the verification value. It should also be noted that the deviation value refers to the specific numerical difference obtained after difference calculation, that is, the quantitative result reflecting the degree of deviation between the statistical value and the verification value.

[0047] Understandably, calculating the difference between the data to be compared and obtaining the deviation value involves subtracting the statistical value from the preset verification value in the data to be compared and calculating the difference between the two to obtain the deviation value.

[0048] This step quantifies the deviation between the statistical value and the verification value into a specific deviation value through difference calculation, providing a precise numerical basis for subsequent judgment on whether to trigger backtracking comparison.

[0049] Step A13: When the deviation value is greater than the preset deviation threshold, start from the last intermediate materialization table corresponding to the multi-level intermediate materialization result and read the intermediate materialization tables of each level backward to obtain the dataset to be backtracked. It should be noted that the final intermediate materialization table refers to the materialization table at the very end of the multi-level intermediate materialization results, i.e., the physical storage table generated in the final aggregation stage. It should also be noted that the dataset to be backtracked refers to the data set composed of the intermediate materialization tables read level by level from the multi-level intermediate materialization results, i.e., the data combination used for layer-by-layer backtracking comparison.

[0050] Understandably, when the deviation value is greater than the preset deviation threshold, the intermediate materialization tables of each level are read backward from the last level intermediate materialization table corresponding to the multi-level intermediate materialization result to obtain the dataset to be backtracked. This means that after confirming the existence of statistical deviation, the last level intermediate materialization table and its predecessor intermediate materialization tables are read in reverse order from back to front, and the data of each level of materialization table read are integrated into the dataset to be backtracked.

[0051] This step constructs a complete backtracking data chain by reading the materialized tables level by level from the end to the beginning, so that the processing results at each level can be retrieved and verified, providing complete data support for locating errors layer by layer.

[0052] Step A14: Compare the statistical values ​​of the intermediate materialized tables of two adjacent layers in the dataset to be backtracked to obtain the hierarchical difference results; It should be noted that two adjacent intermediate materialized tables refer to two materialized tables in the dataset to be backtracked that are in a continuous sequential relationship, that is, two intermediate materialized tables that are directly adjacent in hierarchy. It should also be noted that the hierarchy difference result refers to the comparison result obtained after comparing the statistical values ​​of two adjacent intermediate materialized tables, that is, the output reflecting the differences between adjacent levels of data.

[0053] Understandably, comparing the statistical values ​​of two adjacent intermediate materialized tables in the dataset to be backtracked to obtain the hierarchical difference result means performing statistical value comparison operations on the two adjacent intermediate materialized tables in the dataset to be backtracked in turn to determine whether there are differences between the two layers of data and obtain the hierarchical difference result.

[0054] This step, by comparing the differences between two adjacent materialized tables, can narrow the error range to a specific hierarchical interval, thereby accurately locating the hierarchical boundary where statistical deviations occur.

[0055] Step A15: Determine the error level where statistical bias exists based on the hierarchical difference results.

[0056] It should be noted that statistical deviation refers to the difference between the statistical value and the standard value that exceeds the allowable range, i.e., an abnormal state of data inconsistency.

[0057] Understandably, determining the error level where statistical bias occurs based on the hierarchical difference results means analyzing the comparison between adjacent hierarchical levels in the hierarchical difference results, identifying the level where the data difference first appears as the source of the statistical bias, and thus determining the error level.

[0058] This step, by analyzing the hierarchical difference results, can directly output the specific hierarchical position where the statistical deviation first appears, achieving accurate location of statistical errors and significantly shortening the investigation time of the statistical chain.

[0059] This embodiment provides a hierarchical materialization method for land surveys. By performing hierarchical materialization processing on the original dataset and materializing the results of each layer into corresponding intermediate materialized tables, the processing data at each stage of the statistical chain is persistently stored in the form of physical tables. This provides a traceable physical data foundation for the layer-by-layer verification of statistical errors. When the report data deviates from the preset verification value and the deviation exceeds the preset deviation threshold, the intermediate materialized tables at each level are read backward from the last-level intermediate materialized table, and the statistical values ​​of adjacent intermediate materialized tables are compared to directly determine the level of error. This achieves the technical effect of quickly locating the source of errors when inconsistencies occur in the statistical results of multi-level reports, avoiding the difficulties in investigation caused by the lengthy and coupled statistical chain and the lack of intermediate physical data records, and significantly improving the efficiency of statistical error location and data quality control capabilities.

[0060] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S20 includes steps S201 to S205: Step S201: Clean the original dataset to obtain a standardized dataset; It's important to note that data cleaning refers to the process of inspecting and transforming data in a raw dataset to eliminate inconsistencies in data format and outliers; in other words, it's the process of improving data quality through normalization. Furthermore, a standardized dataset refers to a collection of data that, after data cleaning, conforms to a unified format specification, meaning that both field values ​​and numerical precision are standardized.

[0061] Understandably, data cleaning of the original dataset to obtain a standardized dataset refers to performing processing operations such as standardizing field formats and removing outliers on the original dataset extracted from the land use status survey database, and integrating the processed data into a standardized dataset with a consistent format.

[0062] This step eliminates format differences and outliers in the original data, providing a unified and reliable data foundation for subsequent hierarchical materialization processing, and avoiding the downward propagation of statistical errors caused by inconsistent data formats.

[0063] In one feasible implementation, step S201 includes steps A21 to A26: Step A21: Read all dimension fields and area fields in the original dataset to obtain the set of fields to be processed; It should be noted that all dimension fields refer to the complete set of fields in the original dataset used to describe the various attribute characteristics of land parcels, that is, a combination of fields including location unit identifier, land parcel code field, ownership unit identifier, detailed annotation, planting attribute, and flyover identifier.

[0064] It should be noted that the area field refers to the database field used to record the area values ​​of land parcels, that is, the field that stores the actual area data of each parcel.

[0065] It should be noted that the set of fields to be processed refers to the set of target fields selected from the original dataset that need to be cleaned, that is, a combination of fields including all dimension fields and area fields.

[0066] Understandably, reading the land category code field and area field from the original dataset to obtain the set of fields to be processed means identifying and extracting all dimensional fields and area fields from the original dataset, and combining these fields into a set of fields to be processed that need to be cleaned later.

[0067] This step, by clearly defining the target fields that need to be cleaned, enables the data cleaning operation to be targeted at key business fields, thereby improving the efficiency and accuracy of data processing.

[0068] Step A22: Perform case-neutralization on all dimension fields in the set of fields to be processed to obtain case-neutralized data; It should be noted that case unification refers to the process of converting all characters in a field to the same uppercase and lowercase format, that is, unifying uppercase and lowercase letters into a standard format. Case-unified data refers to data that has undergone case unification, meaning data whose character format has been standardized to the same uppercase and lowercase standard.

[0069] Understandably, performing case-consistent processing on the land category code field in the set of fields to be processed to obtain case-consistent data means performing a character case conversion operation on the land category code field in the set of fields to be processed, unifying the mixed uppercase and lowercase characters into the same uppercase and lowercase format, and obtaining case-consistent data.

[0070] This step eliminates the problem of the same land category being identified as different categories due to differences in capitalization by standardizing the character case format of the land category code, thus ensuring the accuracy of land category classification statistics.

[0071] Step A23: Remove spaces from the case-consistent data to obtain standardized encoded data; It should be noted that whitespace removal refers to the process of deleting redundant whitespace characters from all dimension field values; that is, the cleaning process of removing invalid whitespace characters before, after, and within the string. It should also be noted that standardized data refers to all dimension field data after undergoing both case-consistency and whitespace removal processing; that is, all dimension data with fully standardized formatting.

[0072] Understandably, removing spaces from case-sensitive data to obtain standardized encoded data means identifying and deleting extra space characters in case-sensitive data to obtain standardized encoded data without invalid spaces.

[0073] This step removes extra spaces from land use codes, avoiding code matching failures or classification errors caused by space characters, and further improving the standardization and usability of land use data.

[0074] Step A24: Perform null value removal on the area field in the set of fields to be processed to obtain the null value-removed data; It should be noted that null value removal refers to the process of identifying and filtering null or missing values ​​in a field, i.e., the cleaning process of deleting or marking invalid blank data. It should also be noted that the data after null value removal refers to the area field data after null value removal processing, i.e., the valid area data after excluding null value records.

[0075] Understandably, performing null value removal on the area field in the set of fields to be processed to obtain null value-removed data means performing null value detection on the area field in the set of fields to be processed, filtering out records with null or missing values, and obtaining null value-removed data containing only valid area values.

[0076] This step removes null records from the area field, preventing null values ​​from being included in subsequent summation calculations and ensuring the data integrity of the area statistics.

[0077] Step A25: Perform numerical precision conversion on the data after removing null values ​​to obtain area-standardized data; It should be noted that numerical precision conversion refers to the process of uniformly converting the decimal places or measurement precision of a field value, that is, unifying values ​​with different precisions to the same precision standard. It should also be noted that area standardized data refers to area field data after numerical precision conversion, i.e., area data with unified measurement precision.

[0078] Understandably, performing numerical precision conversion on the data after removing null values ​​to obtain area-standardized data means identifying and uniformly converting the precision of each area value in the data after removing null values, converting area values ​​with different precisions into the same precision standard, and thus obtaining area-standardized data.

[0079] This step avoids calculation errors caused by differences in precision by standardizing the measurement accuracy of area values, thus ensuring the consistency of area statistics results at all levels.

[0080] Step A26: Integrate the data based on the coded standardized data and the area standardized data to obtain a standardized dataset.

[0081] It should be noted that data integration refers to the operation of merging multiple independently processed datasets into a unified dataset according to their corresponding relationships, that is, the process of combining data from different sources or different processing stages into a complete dataset.

[0082] Understandably, integrating coded standardized data and area standardized data to obtain a standardized dataset means matching and merging coded standardized data and area standardized data according to the record correspondence to form a standardized dataset that simultaneously contains standardized dimension fields and area data with uniform precision.

[0083] This step integrates the separately cleaned dimensional and area data to form a standardized dataset with uniform field formats and reliable data quality, providing complete and standardized data input for subsequent grouped statistical calculations.

[0084] Step S202: Perform basic processing on the standardized dataset to obtain basic processed data; It should be noted that basic processing refers to the statistical processing steps performed on the standardized dataset, including grouping and aggregation, unit conversion, and control area adjustment correction; that is, the data processing stage that establishes the basic statistical framework. Furthermore, the basic processed data refers to the dataset containing the grouping and aggregation results and the adjusted area after basic processing; in other words, data with completed basic statistical calculations and standardized area units.

[0085] Understandably, performing basic processing on a standardized dataset to obtain basic processed data means grouping, aggregating, and summing the standardized dataset according to preset grouping fields, and then performing unit conversion and control area adjustment correction on the summation results to obtain basic processed data.

[0086] This step establishes a basic statistical framework through grouping aggregation and adjustment correction, enabling the original map data to be initially summarized according to administrative units and land categories, and ensuring the consistency between the summarized area and the actual controlled area through control area adjustment.

[0087] In one feasible implementation, step S202 includes steps A31 to A36: Step A31: Read all statistical dimension fields in the standardized dataset to obtain the grouping field set; It should be noted that "all statistical dimension fields" refers to all dimension attribute fields involved in the grouping statistics of the basic calculation table, that is, the set of fields including location unit identifier, land type code field, ownership unit identifier, detailed annotation, planting attribute, and flyover identifier. It should also be noted that the grouping field set refers to the set of target fields extracted from the standardized dataset for the grouping operation of the basic calculation table, that is, the field combination formed after reading all statistical dimension fields.

[0088] Understandably, reading all statistical dimension fields from a standardized dataset to obtain a grouping field set means identifying and extracting all dimension fields that participate in the grouping statistics of the basic calculation table from the standardized dataset and combining these fields into a grouping field set.

[0089] This step extracts all statistical dimension fields to form a set of grouping fields, ensuring that the basic calculation table can be grouped according to the most complete combination of dimensions, providing a complete dimensional basis for generating the most granular grouped statistical results.

[0090] Step A32: Group the standardized dataset according to the grouping field set to obtain multiple smallest granularity grouped datasets; It should be noted that the smallest granularity grouped dataset refers to multiple data subsets obtained after dividing a standardized dataset according to the set of grouping fields, that is, the most granular grouped data set after classification according to the combination of values ​​of all statistical dimension fields.

[0091] Understandably, grouping a standardized dataset according to a set of grouping fields to obtain multiple smallest-granularity grouped datasets means dividing the standardized dataset according to the combination of values ​​of each field in the set of grouping fields, and classifying records with the same values ​​of all statistical dimension fields into the same subset, thus obtaining multiple smallest-granularity grouped datasets.

[0092] This step achieves the finest-grained division of the standardized dataset by grouping the data according to the combination of all statistical dimension fields. This ensures that the data in each group have completely consistent attribute characteristics across all dimensions, providing data units with clear boundaries for subsequent summation operations.

[0093] Step A33: Sum the area fields in multiple minimum granularity grouped datasets to obtain multiple minimum granularity area sum values; It should be noted that the sum of the smallest granularity area values ​​refers to the total value obtained by summing the area field values ​​in each smallest granularity group dataset, that is, the sum of the area data of all patches within the same smallest subdivision group.

[0094] Understandably, summing the area fields in multiple smallest granularity group datasets to obtain multiple smallest granularity area sums means performing an accumulation operation on the area field values ​​within each smallest granularity group dataset, calculating the total area of ​​each subdivided group, and obtaining multiple smallest granularity area sums.

[0095] This step, by calculating the sum of the areas of each of the smallest subgroups, forms basic area statistics classified according to the combination of all statistical dimensions, providing quantitative results for establishing a basic statistical framework with the smallest granularity.

[0096] Step A34: Associate the sum of multiple smallest granular area values ​​with the corresponding field values ​​in the grouping fields of the basic calculation table to obtain the basic aggregate data; It should be noted that basic aggregated data refers to a data set containing complete dimension identifiers and area values, formed by associating and binding the smallest granularity area and value with the corresponding grouping field value. In other words, it is the smallest granularity statistical result data with a clear dimension affiliation.

[0097] Understandably, associating multiple minimum granularity area sums with the corresponding field values ​​in the grouping fields of the basic calculation table to obtain basic aggregate data means matching and binding each minimum granularity area sum with the field value in the grouping field of the basic calculation table corresponding to that group, forming basic aggregate data with complete dimension identifiers.

[0098] This step establishes a correspondence between the smallest granular area and value and all dimension fields, giving the basic statistical data a clear dimension affiliation identifier. This provides a traceable, smallest granular data foundation for subsequent unit conversion, control area adjustment correction, and secondary grouping and summarization of various standard reports according to different dimensions.

[0099] Step A35: Perform unit conversion on the basic aggregated data to obtain the converted data; It should be noted that unit conversion refers to the process of changing the unit of measurement of data from one standard to another; that is, the process of changing the unit of the physical quantity represented by the numerical value. It should also be noted that the converted data refers to the data that has undergone unit conversion processing, i.e., data whose unit of measurement has been changed.

[0100] Understandably, performing unit conversion on the basic aggregated data to obtain the converted data refers to performing a unit conversion operation on the area values ​​in the basic aggregated data, converting the area data from the original unit of measurement to the unit of measurement required by the target report, and obtaining the converted data.

[0101] This step ensures consistency in the measurement standards of area values ​​in statistical reports at all levels by standardizing the units of measurement for area data, thus avoiding deviations in the summary results caused by inconsistent units.

[0102] Step A36: Perform controlled area adjustment correction on the transformed data to obtain the basic processed data.

[0103] It should be noted that control area adjustment correction refers to the process of comparing the converted area data with the preset control area and correcting the area value according to the proportional allocation principle when there is a deviation. In other words, it is a data correction operation to ensure that the total area at each level is consistent with the overall control area.

[0104] Understandably, adjusting the control area of ​​the converted data to obtain the basic processed data means comparing the total area in the converted data with the preset control area, and when there is an area deviation, proportionally distributing and correcting the area of ​​each group according to preset rules to obtain basic processed data consistent with the control area.

[0105] This step eliminates the cumulative area error caused by rounding or boundary cutting by controlling area adjustment correction, ensuring that the total area of ​​the basic statistical data matches the actual controlled area, and providing an accurate area benchmark for subsequent hierarchical aggregation.

[0106] Step S203: Perform topic-oriented dimensional summarization on the basic processing data to obtain topic-oriented dimensional summarization data; It should be noted that topic-oriented dimensional aggregation refers to the data processing process of secondary grouping and summing of basic processed data according to the statistical dimensions preset in standard reports. In other words, it involves re-aggregating and statistically analyzing data based on specific dimension fields. Furthermore, topic-oriented dimensional aggregation data refers to the aggregated data set categorized according to statistical dimensions after dimensional aggregation processing; that is, area statistics grouped according to specific dimensions.

[0107] Understandably, performing dimensional summarization on basic processing data to obtain dimensional summary data means grouping the basic processing data a second time according to preset statistical dimension fields, and summing and correlating the area data within each group to obtain dimensional summary data categorized by dimension.

[0108] This step adds a dimension aggregation layer on top of the basic processing, enabling data to be independently statistically analyzed according to specific dimensions such as slope level and farmland type, thus meeting the data requirements of special statistical reports for multi-dimensional cross-analysis.

[0109] In one feasible implementation, step S203 includes steps A41-A44: Step A41: Read the statistical dimension fields of the corresponding standard report in the basic processing data to obtain the set of dimension fields; It should be noted that a standard report refers to a report template with a fixed format and statistical scope, formulated according to the land use status survey and statistical specifications; that is, a standardized report format used to output the final statistical results. It should also be noted that statistical dimension fields refer to the dimension attribute fields in a standard report used for grouping and statistically analyzing data; that is, field identifiers that determine which dimension the report data is categorized and summarized by.

[0110] Understandably, reading the statistical dimension fields of the corresponding standard report in the basic processing data to obtain the dimension field set means identifying and extracting the required statistical dimension fields of the standard report from the basic processing data according to the statistical requirements of the target standard report, and combining these fields into a dimension field set.

[0111] This step dynamically determines the set of dimension fields based on the statistical requirements of standard reports, enabling dimension summarization to be flexibly adapted to the specific statistical calibers of different standard reports. This avoids the problem of rigid statistical dimensions caused by using fixed dimension fields and improves the flexibility and reusability of generating multiple reports.

[0112] Step A42: Perform secondary grouping of the basic processing data based on the set of dimension fields to obtain multiple secondary grouped datasets; It should be noted that secondary grouping refers to a grouping operation that further divides data according to new dimension fields after the basic grouping has been completed. In other words, it's a process of reclassifying the same dataset based on different dimensions. Furthermore, a secondary grouped dataset refers to multiple subsets of data obtained after secondary grouping, i.e., data grouped according to the set of dimension fields.

[0113] Understandably, regrouping the basic processing data according to the set of dimension fields to obtain multiple secondary grouped datasets means that the basic processing data is reclassified and divided according to the set of dimension fields to obtain multiple secondary grouped datasets.

[0114] This step involves regrouping the basic data to reorganize it according to specific dimensions, enabling the same basic data to support statistical needs across different dimensions and improving the flexibility and reusability of data processing.

[0115] Step A43: Sum the area fields in multiple quadratic grouping datasets to obtain multiple quadratic grouping area sums; It should be noted that the sum of the areas in the second-order grouping refers to the total value obtained by summing the area field values ​​in each second-order grouping dataset, that is, the sum of all area data within the same dimension group.

[0116] Understandably, summing the area fields in multiple quadratic grouping datasets to obtain multiple quadratic grouping area sums means performing an accumulation operation on the area field values ​​within each quadratic grouping dataset, calculating the total area of ​​each dimension group, and obtaining multiple quadratic grouping area sums.

[0117] This step provides a direct numerical source for generating specific statistical reports by calculating the total area of ​​each dimension group.

[0118] Step A44: Associate the area sums of multiple secondary groupings with the corresponding field values ​​in the dimensional field set to obtain the topic-specific dimensional summary data.

[0119] Understandably, associating the sum of areas of multiple secondary groups with the corresponding field values ​​in the set of dimension fields to obtain dimension summary data means matching and binding each sum of areas of a secondary group with the dimension field corresponding to that group, forming dimension summary data with clear dimension identifiers.

[0120] This step establishes a correspondence between the area and value of the secondary grouping and the dimension fields, so that the dimension summary data has a clear dimension attribution identifier, which facilitates subsequent report generation and data verification according to different dimensions.

[0121] Step S204: Perform hierarchical aggregation on the dimensional summary data to obtain hierarchical summary data; It should be noted that hierarchical aggregation refers to the process of grouping and summarizing data level by level according to administrative hierarchy, that is, aggregating data according to the three levels of administrative units: village, township, and county. It should also be noted that hierarchical aggregated data refers to the data set that includes the summarized results of multiple levels of administrative units after hierarchical aggregation, that is, the integrated result of the area statistics data at the village, township, and county levels.

[0122] Understandably, performing hierarchical aggregation on dimensional summary data to obtain hierarchical summary data means grouping and aggregating the dimensional summary data according to the administrative levels of village, township, and county in sequence, and integrating the aggregation results of each level into hierarchical summary data containing statistical data of the three levels of administrative units.

[0123] This step aggregates data level by level according to administrative hierarchy, achieving progressive generation from village-level atomic data to township-level and county-level aggregated data, thus meeting the standard report's requirements for statistical data at the county, township, and village levels.

[0124] In one feasible implementation, step S204 includes steps A51-A55: Step A51: Read the village-level data to be summarized from the dimension summary data to obtain the village-level dataset; It should be noted that village-level data to be aggregated refers to the subset of data in the dimensional aggregated data that requires village-level hierarchical aggregation processing; that is, the basic data within the scope of village-level statistics. It should also be noted that the village-level dataset refers to the set of data separated from the dimensional aggregated data for village-level grouping and aggregation; that is, the subset of data divided according to village-level administrative units.

[0125] Understandably, reading village-level data to be aggregated from dimensional summary data to obtain village-level datasets means identifying and extracting data records that need to be aggregated at the village level from dimensional summary data to form village-level datasets.

[0126] This step separates village-level data from the dimensional aggregated data, clarifies the starting level and data range of the hierarchical aggregation, and provides accurate village-level basic data for subsequent hierarchical upward aggregation.

[0127] Step A52: Group and aggregate the village-level dataset based on the village-level administrative region identifier to obtain the village-level computational layer results; It should be noted that the village-level administrative region identifier refers to the coded information used to uniquely identify village-level administrative units, that is, the identifier that distinguishes different village-level administrative units. It should also be noted that the village-level calculation layer result refers to the village-level hierarchical statistical data obtained after grouping and aggregating according to the village-level administrative region identifier, that is, the sum of the areas of each village-level administrative unit.

[0128] Understandably, grouping and aggregating village-level datasets based on village-level administrative region identifiers to obtain village-level computational layer results means grouping village-level datasets according to village-level administrative region identifiers, aggregating and calculating the area data within each group, and obtaining the area summary result of each village-level administrative unit, i.e., the village-level computational layer result.

[0129] This step generates independent statistical results at the village level by grouping and aggregating data according to village-level administrative units. These results serve as the basic atomic data for township and county-level aggregation, ensuring the traceability of the smallest administrative unit in the statistical results.

[0130] Step A53: Group and aggregate the village-level calculation layer results based on the township-level administrative region identifier to obtain the township-level summary layer results; It should be noted that the township-level administrative region identifier refers to the coded information used to uniquely identify township-level administrative units, that is, the identifier that distinguishes different township-level administrative units. It should also be noted that the township-level aggregated results refer to the township-level statistical data obtained after grouping and aggregating according to the township-level administrative region identifiers, that is, the aggregated area results of each township-level administrative unit.

[0131] Understandably, grouping and aggregating the village-level calculation layer results based on the township-level administrative region identifier to obtain the township-level summary layer results means grouping the village-level calculation layer results according to the township-level administrative region identifier, and aggregating and calculating the village-level area data within each group to obtain the area summary result of each township-level administrative unit, i.e., the township-level summary layer result.

[0132] This step aggregates the village-level calculation results according to the township-level administrative units, realizing the progressive summarization of statistical results from the village level to the township level, so that the township-level statistical data can be directly traced back to the data sources of each subordinate village.

[0133] Step A54: Group and aggregate the township-level summary layer results based on the county-level administrative region identifier to obtain the county-level summary layer results; It should be noted that the county-level administrative region identifier refers to the coded information used to uniquely identify county-level administrative units, that is, the identifier that distinguishes different county-level administrative units. It should also be noted that the county-level aggregated results refer to the county-level statistical data obtained after grouping and aggregating according to the county-level administrative region identifiers, that is, the aggregated area results of each county-level administrative unit.

[0134] Understandably, grouping and aggregating the township-level summary layer results based on the county-level administrative region identifier to obtain the county-level summary layer results means grouping the township-level summary layer results according to the county-level administrative region identifier, and aggregating and calculating the township-level area data within each group to obtain the area summary results of each county-level administrative unit, i.e., the county-level summary layer results.

[0135] This step aggregates the township-level summary results according to the county-level administrative units, completing the final summary from the township to the county level and forming the county-level top-level statistical data required for the standard report.

[0136] Step A55: Integrate the results of the village-level calculation layer, the township-level summary layer, and the county-level summary layer to obtain the hierarchical summary data.

[0137] Understandably, integrating the results of village-level calculations, township-level aggregations, and county-level aggregations to obtain hierarchical aggregated data means merging and organizing these results according to administrative levels to form hierarchical aggregated data that simultaneously includes statistical data from the county, township, and village levels.

[0138] This step integrates the statistical results of the three levels of administrative units into a unified data set, enabling the hierarchical summary data to simultaneously support the output requirements of county, township, and village-level data in standard reports, thus achieving unified management of statistical results of multi-level administrative units.

[0139] Step S205: Materialize the hierarchical summary data into corresponding intermediate materialized tables to obtain multi-level intermediate materialized results.

[0140] It should be noted that an intermediate materialized table refers to a data table that can be read independently after the data processing results are written into the storage medium in the form of a physical table; that is, a persistent data table with a physical storage structure.

[0141] Understandably, materializing hierarchical summary data into corresponding intermediate materialized tables to obtain multi-level intermediate materialized results means writing the village-level calculation layer results, township-level summary layer results, and county-level summary layer results in the hierarchical summary data into independent physical tablespaces to form multiple independently accessible intermediate materialized tables, and then combining these intermediate materialized tables into multi-level intermediate materialized results.

[0142] This step materializes the hierarchical summary data into an intermediate materialized table for physical storage, so that the statistical results at the county, township, and village levels all exist in the form of independent physical tables. This provides a definite data source for subsequent report generation and provides directly readable physical data nodes for hierarchical backtracking and comparison of statistical deviations.

[0143] This embodiment provides a hierarchical materialization method for land surveys. By cleaning the original dataset, case differences and spaces in the land use code field are eliminated, null values ​​in the area field are removed, and numerical precision is standardized, resulting in a standardized dataset with a consistent format. This provides a reliable data foundation for subsequent processing. Furthermore, the standardized dataset undergoes basic processing, grouping, aggregating, and summing data according to location unit code and land use code, followed by unit conversion and control area adjustment correction. This yields basic processed data consistent with the actual control area, establishing an accurate area statistical benchmark. Additionally, the basic processed data is dimensionally summarized, undergoing secondary grouping and aggregation according to farmland slope level and farmland type. This yields dimensional summary data that meets specific statistical needs, supporting multi-dimensional cross-analysis. Further, the dimensional summary data undergoes hierarchical aggregation, sequentially grouping and aggregating according to village, township, and county-level administrative region identifiers. The results from these three levels are integrated into hierarchical summary data, forming a statistical framework of county, township, and village-level progressive aggregation. Finally, the hierarchical summary data is materialized into corresponding intermediate materialized tables, ensuring that the processing results at each level are persistently stored in independent physical tables.

[0144] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the land survey stratification method of this application. Any simple modifications based on this technical concept are within the scope of protection of this application.

[0145] This application also provides a land survey stratification device, please refer to... Figure 4 The land survey stratified physicalization device includes: Raw data acquisition module 10 is used to extract raw datasets from the land use status survey database; The hierarchical materialization module 20 is used to perform hierarchical materialization processing on the original dataset to obtain multi-level intermediate materialization results. The report generation module 30 is used to generate report data based on multi-level intermediate materialization results. The comparison module 40 is used to compare the report data with the preset verification value to obtain the deviation data; The backtracking comparison module 50 is used to perform a layer-by-layer backtracking comparison of multi-level intermediate materialization results when the deviation data is greater than the preset deviation threshold, in order to determine the level at which the error occurred.

[0146] The land survey stratified materialization device provided in this application, employing the land survey stratified materialization method described in the above embodiments, can solve the technical problem of determining the specific level at which an error occurs when statistical results in multi-level reports are inconsistent. Compared with the prior art, the beneficial effects of the land survey stratified materialization device provided in this application are the same as those of the land survey stratified materialization method described in the above embodiments, and other technical features in the land survey stratified materialization device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0147] In one embodiment, the hierarchical materialization module 20 is also used to perform data cleaning on the original dataset to obtain a standardized dataset; Perform basic processing on the standardized dataset to obtain basic processed data; The basic processed data is summarized into thematic dimensions to obtain thematic dimensional summary data; Perform hierarchical aggregation on the dimensional summary data to obtain hierarchical summary data; The hierarchical summary data is materialized into corresponding intermediate materialized tables to obtain multi-level intermediate materialized results.

[0148] In one embodiment, the hierarchical materialization module 20 is also used to read all dimension fields and area fields in the original dataset to obtain a set of fields to be processed; Perform case-consistent processing on all dimension fields in the set of fields to be processed to obtain case-consistent data. By removing spaces from data that is case-insensitive, we obtain standardized encoded data. Perform null value removal on the area field in the set of fields to be processed to obtain the data after null value removal; After removing null values, the data is subjected to numerical precision conversion to obtain area-standardized data. The data is integrated based on the coded standardized data and the area standardized data to obtain a standardized dataset.

[0149] In one embodiment, the hierarchical materialization module 20 is also used to read all statistical dimension fields in the standardized dataset to obtain a set of grouping fields; The standardized dataset is grouped according to the set of grouping fields to obtain multiple datasets with the smallest granularity. Summing the area field in multiple minimum granularity grouped datasets yields multiple minimum granularity area sum values; The basic aggregated data is obtained by associating multiple minimum granularity area sums with the corresponding field values ​​in the grouping fields of the basic calculation table. Perform unit conversion on the basic aggregated data to obtain the converted data; The transformed data is then subjected to controlled area adjustment correction to obtain the basic processed data.

[0150] In one embodiment, the hierarchical materialization module 20 is further configured to perform topic-oriented dimensional aggregation on the basic processing data to obtain topic-oriented dimensional aggregation data, including the following steps: Read the statistical dimension fields of the corresponding standard report in the basic processing data to obtain the set of dimension fields; The basic processed data is then grouped a second time based on the set of dimension fields to obtain multiple secondary grouped datasets. Summing the area field in multiple quadratic grouping datasets yields multiple quadratic grouping area sums. By associating the area sums of multiple secondary groupings with the corresponding field values ​​in the dimensional field set, we obtain the dimensional summary data oriented towards the topic.

[0151] In one embodiment, the hierarchical materialization module 20 is also used to read the village-level data to be summarized in the dimension summary data to obtain the village-level dataset; The village-level dataset is grouped and aggregated based on the village-level administrative region identifier to obtain the village-level computational layer results; Based on the township-level administrative region identifier, the village-level calculation layer results are grouped and aggregated to obtain the township-level summary layer results; Based on the county-level administrative region identifier, the township-level summary layer results are grouped and aggregated to obtain the county-level summary layer results; The results from the village-level calculation layer, the township-level summary layer, and the county-level summary layer are integrated to obtain hierarchical summary data.

[0152] In one embodiment, the backtracking comparison module 50 is further used to read statistical values ​​and preset verification values ​​from the report data to obtain the data to be compared; The difference between the comparison data is calculated to obtain the deviation value; When the deviation value is greater than the preset deviation threshold, the intermediate materialization tables of each level are read backward from the last level intermediate materialization table corresponding to the multi-level intermediate materialization result to obtain the dataset to be backtracked. The statistical values ​​of the intermediate materialized tables of two adjacent layers in the dataset to be backtracked are compared to obtain the hierarchical difference results. The error level at which statistical bias occurs is determined based on the hierarchical difference results.

[0153] This application provides a land survey stratification and materialization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the land survey stratification and materialization method in Embodiment 1 described above.

[0154] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing a land survey stratification and materialization device according to embodiments of this application. The land survey stratification and materialization device in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast acquisition devices, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle-mounted terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The land survey stratification and materialization device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0155] like Figure 5As shown, the land survey stratification and materialization device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the land survey stratification and materialization device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the land survey stratification equipment to exchange data wirelessly or via wired communication with other devices. Although the figure shows land survey stratification equipment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0156] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0157] The land survey stratified materialization device provided in this application, employing the land survey stratified materialization method described in the above embodiments, can solve the technical problem of determining the specific level at which an error occurs when statistical results in multi-level reports are inconsistent. Compared with the prior art, the beneficial effects of the land survey stratified materialization device provided in this application are the same as those of the land survey stratified materialization method described in the above embodiments, and other technical features of this land survey stratified materialization device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0158] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0159] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0160] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the land survey stratification method described in the above embodiments.

[0161] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0162] The aforementioned computer-readable storage medium may be included in the land survey stratification and physicalization equipment; or it may exist independently and not be assembled into the land survey stratification and physicalization equipment.

[0163] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the land survey stratification and materialization device, the land survey stratification and materialization device: extracts the original dataset from the land use status survey database; performs stratification and materialization processing on the original dataset to obtain multi-level intermediate materialization results; generates report data based on the multi-level intermediate materialization results; compares the report data with preset verification values ​​to obtain deviation data; and when the deviation data exceeds a preset deviation threshold, performs a layer-by-layer backtracking comparison of the multi-level intermediate materialization results to determine the error occurrence level.

[0164] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0165] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0166] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0167] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described land survey stratification method, thereby solving the technical problem of determining the specific level at which an error occurs when statistical results in multi-level reports are inconsistent. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the land survey stratification method provided in the above embodiments, and will not be repeated here.

[0168] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the land survey stratification method described above.

[0169] The computer program product provided in this application can solve the technical problem of determining the specific level at which an error occurs when statistical results in multi-level reports are inconsistent. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the land survey stratification method provided in the above embodiments, and will not be repeated here.

[0170] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for stratified and progressively materialized land surveys, characterized in that, The method includes: The original dataset was extracted from the land use status survey database; The original dataset is subjected to hierarchical materialization processing to obtain multi-level intermediate materialization results; Report data is generated based on the multi-level intermediate materialization results; The report data is compared with the preset verification value to obtain the deviation data; When the deviation data exceeds a preset deviation threshold, the multi-level intermediate materialization results are backtracked and compared layer by layer to determine the level at which the error occurred.

2. The method as described in claim 1, characterized in that, The step of performing hierarchical materialization processing on the original dataset to obtain multi-level intermediate materialization results includes: The original dataset is cleaned to obtain a standardized dataset; The standardized dataset is subjected to basic processing to obtain basic processed data; The basic processed data is then subjected to topic-oriented dimensional summarization to obtain topic-oriented dimensional summarization data. The dimensional summary data is hierarchically aggregated to obtain hierarchical summary data; The hierarchical summary data is materialized into corresponding intermediate materialized tables to obtain multi-level intermediate materialized results.

3. The method as described in claim 2, characterized in that, The step of cleaning the original dataset to obtain a standardized dataset includes: Read all dimension fields and area fields from the original dataset to obtain the set of fields to be processed; Perform case-consistent processing on all dimension fields in the set of fields to be processed to obtain case-consistent data. The case-consistent data is processed by removing spaces to obtain coded standardized data; Null values ​​are removed from the area field in the set of fields to be processed to obtain data after null value removal. The data after removing null values ​​is subjected to numerical precision conversion to obtain area-standardized data; The standardized dataset is obtained by integrating the coded standardized data and the area standardized data.

4. The method as described in claim 2, characterized in that, The step of performing basic processing on the standardized dataset to obtain basic processed data includes: Read all statistical dimension fields from the standardized dataset to obtain the grouping field set; The standardized dataset is grouped according to the grouping field set to obtain multiple minimum granularity grouped datasets; The area fields in the multiple smallest granularity grouped datasets are summed to obtain multiple smallest granularity area sum values; The sum of the multiple smallest granularity areas is associated with the corresponding field values ​​in the grouping fields of the basic calculation table to obtain the basic aggregate data; The basic aggregated data is converted to obtain the converted data; The converted data is then subjected to controlled area adjustment correction to obtain the basic processed data.

5. The method as described in claim 2, characterized in that, The steps of performing topic-oriented dimensional aggregation on the basic processed data to obtain topic-oriented dimensional aggregation data include: Read the statistical dimension fields of the corresponding standard report in the basic processing data to obtain the dimension field set; The basic processed data is then grouped a second time based on the set of dimensional fields to obtain multiple secondary grouped datasets. Summing the area fields in the multiple quadratic grouping datasets yields multiple quadratic grouping area sums. The sum of the areas and values ​​of the multiple secondary groupings are associated with the corresponding field values ​​in the set of dimensional fields to obtain the thematic dimensional summary data.

6. The method as described in claim 2, characterized in that, The step of performing hierarchical aggregation on the dimensional summary data to obtain hierarchical summary data includes: Read the village-level data to be summarized from the summarized data of the dimensions to obtain the village-level dataset; The village-level dataset is grouped and aggregated based on the village-level administrative region identifier to obtain the village-level computing layer results; Based on the township-level administrative region identifier, the village-level calculation layer results are grouped and aggregated to obtain the township-level summary layer results; Based on the county-level administrative region identifier, the township-level summary layer results are grouped and aggregated to obtain the county-level summary layer results; The results of the village-level calculation layer, the township-level summary layer, and the county-level summary layer are integrated to obtain hierarchical summary data.

7. The method as described in claim 1, characterized in that, When the deviation data exceeds a preset deviation threshold, the step of performing a layer-by-layer backtracking comparison of the multi-level intermediate materialization results to determine the error occurrence level includes: Read the statistical values ​​and preset verification values ​​from the report data to obtain the data to be compared; The difference between the data to be compared is calculated to obtain the deviation value; When the deviation value is greater than the preset deviation threshold, the intermediate materialization tables of each level are read forward from the last level intermediate materialization table corresponding to the multi-level intermediate materialization result to obtain the dataset to be backtracked. The statistical values ​​of the intermediate materialized tables of two adjacent layers in the dataset to be traced back are compared to obtain the hierarchical difference results. The error occurrence level with statistical bias is determined based on the hierarchical difference results.

8. A land survey stratification device, characterized in that, The device includes: The raw data acquisition module is used to extract raw datasets from the land use status survey database. The hierarchical materialization module is used to perform hierarchical materialization processing on the original dataset to obtain multi-level intermediate materialization results. The report generation module is used to generate report data based on the multi-level intermediate materialization results; The comparison module is used to compare the report data with preset verification values ​​to obtain deviation data; The backtracking comparison module is used to perform a layer-by-layer backtracking comparison of the multi-level intermediate materialization results when the deviation data is greater than a preset deviation threshold, in order to determine the level at which the error occurred.

9. A land survey stratification and physicalization device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the land survey stratification method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the land survey stratification method as described in any one of claims 1 to 7.

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