Multi-level land space planning index monitoring system and method

Abstract

The application discloses a kind of multi-level land space planning index monitoring system and method, it is related to land space planning technical field, the method includes: after the table creation of database is completed and initial index basic information, spatial coordinate data are entered, the association between index and spatial unit is established, initialization database and associated data are obtained;Based on data synchronization parameter configuration initialization database and associated data, calculate and update index value, generate index business data and historical record data, and through traceability analysis, the index traceability analysis result obtained is shown in multiple forms, and the index multidimensional comprehensive achievement set is generated;According to the verification request and the verification rule, the index multidimensional comprehensive achievement set is verified, and the land space planning index monitoring result is integrated and generated.The application supports index whole-process monitoring by double database, solves the problem that land space planning effect is not good, realizes the effect that monitoring process standardization and achievement display visualization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of land space planning, and particularly relates to a multi-level land space planning index monitoring system and method. BACKGROUND

[0002] In the land space planning index management system, the index management of each standard unit level is a key link for implementing the land space development and protection target. The current related technology still relies on manual completion of index data statistics, and is throughout the whole process of data processing. However, manual operation is prone to index comparison errors and data update delays, which ultimately leads to poor land space planning effect.

[0003] The above content is only used to assist in understanding the technical solutions of the present application, and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0004] The main purpose of the present application is to provide a multi-level land space planning index monitoring system and method, which aims to solve the technical problem of poor land space planning effect.

[0005] To achieve the above purpose, the present application provides a multi-level land space planning index monitoring method, which comprises:

[0006] After completing the table creation of the database and the initial index basic information and the input of the spatial coordinate data, the association relationship between the index and the spatial unit is established, and the initialization database and the associated data are obtained;

[0007] Based on the data synchronization parameter configuration, the initialization database and the associated data are calculated and updated, the index business data and the historical record data are generated;

[0008] After tracing and analyzing the index business data and the historical record data, the obtained index tracing and analyzing result is displayed in multiple forms, and the index multi-dimensional comprehensive result set is generated;

[0009] According to the verification request and the verification rule, the index multi-dimensional comprehensive result set is verified, and the land space planning index monitoring result is integrated and generated.

[0010] In an embodiment, according to the business demand, the table creation is completed in the database, and a double database framework containing various empty table structures is output;

[0011] Based on the double database framework, the initial index basic information and the spatial coordinate data are input, and a double database filled with initial data is generated;

[0012] The association between the indicators and the spatial units is established through the corresponding matching of the indicator codes and the spatial unit codes, and is stored in the double database filled with the initial data, to obtain the initialization database and the association data.

[0013] In an embodiment, based on the initialization database and the association data, the corresponding data synchronization parameters are called, and a data synchronization service is started to obtain original indicator data from an external data source interface.

[0014] According to the preset data cleaning rules and operation logic, the preprocessing of the original indicator data and the calculation of derived data are completed, and standardized indicator data are output.

[0015] The standardized indicator data are compared with the existing data in the indicator value table in the initialization database, and the indicator business data and the historical record data are output according to the data comparison rules.

[0016] In an embodiment, according to a multi-dimensional interaction request, the indicator data corresponding to the level, year, type and indicator change related records in the indicator business data and the historical record data are retrieved, to obtain target retrieval data.

[0017] Based on the target retrieval data, a traceability analysis process is carried out, and an indicator traceability analysis result is output.

[0018] After integrating the target retrieval data and the indicator traceability analysis result, the result is displayed in three forms of map annotation, list presentation and card highlighting, and an indicator multi-dimensional comprehensive result set is output.

[0019] In an embodiment, the indicator planning values and implementation values corresponding to different spatial levels and years in the target retrieval data, and the indicator change information are extracted, to obtain an indicator traceability analysis core data set.

[0020] According to the indicator traceability analysis core data set, the change of the planning values in the full-dimensional time record is analyzed, and a time context is generated to obtain an indicator preliminary analysis result.

[0021] The indicator preliminary analysis result is integrated according to the structure of the level comparison data and the historical change data, the data source and the calculation logic are determined, and the indicator traceability analysis result is output.

[0022] In an embodiment, based on the verification request, the indicator planning values, implementation values, spatial unit association data and historical change data of different spatial levels in the indicator multi-dimensional comprehensive result set are extracted, the verification rules are combined, and an indicator connection verification data source is output.

[0023] The index connection verification data source is connected to the difference and proportion of the indexes across the hierarchy, the connection state of each index is judged by comparing the verification rules, and the abnormal reason and calculation basis are marked, and the verification result is output;

[0024] Through the verification result, the index traceability analysis details, spatial distribution display data, and abnormal rectification suggestions are integrated, and the national space planning index monitoring result is generated in a unified format.

[0025] In an embodiment, based on the business data storage requirements, the table types and table core fields that the index database and the graph database need to carry data are determined, and a double-database table structure scheme is output;

[0026] According to the double-database table structure scheme, corresponding table structures are created in the index database and the graph database to obtain a double-database initial framework;

[0027] The table structure adaptation information of the double-database initial framework is checked, and field abnormal problems are corrected, and a double-database empty table framework is output.

[0028] In an embodiment, based on the initialization database and the associated data, combined with the business scenario requirements, the parameter types that need to be configured and the business constraint conditions of each parameter are determined, and a data synchronization parameter configuration requirement list is output;

[0029] According to the data synchronization parameter configuration requirement list, a legal and effective data source interface address is obtained by connecting an external data source provider, and the synchronization frequency is determined according to the index type to obtain a preliminary data synchronization parameter scheme;

[0030] The preliminary data synchronization parameter scheme is checked, and the parameter details are adjusted for test abnormal conditions to generate the data synchronization parameters.

[0031] In an embodiment, according to the preset table structure classification of the double-database, the verification conclusion, the abnormal index list, the spatial position information and the historical traceability data in the national space planning index monitoring result are respectively stored in the corresponding data table to obtain a monitoring result data set;

[0032] According to the preset achievement delivery requirements, the core information in the monitoring result data set is extracted and arranged in a standard format to obtain index monitoring successful delivery data;

[0033] Based on the index monitoring successful delivery data, the table data and dynamic association information of the initialization database are updated, and an updated database is output.

[0034] In addition, to achieve the above purpose, the application also provides a multi-level national space planning index monitoring system, which comprises:

[0035] a database planning module, after completing table creation of the database and inputting initial index basic information and spatial coordinate data, establishing an association between the index and the spatial unit to obtain an initialized database and associated data;

[0036] a document management module, based on data synchronization parameter configuration of the initialized database and the associated data, calculating and updating index values, generating index business data and historical record data;

[0037] a historical tracing module, after tracing and analyzing the index business data and the historical record data, generating an index multi-dimensional comprehensive result set through multi-form display of the obtained index tracing analysis results;

[0038] a report generation module, verifying the index multi-dimensional comprehensive result set according to a verification request and verification rules, and integrating to generate a land space planning index monitoring result.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes a planning index monitoring device, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the multi-level land space planning index monitoring method as described above.

[0040] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the multi-level land space planning index monitoring method as described above.

[0041] The present application provides a multi-level land space planning index monitoring method, which comprises the following technical logic: firstly, completing double database table creation, initial index basic information and spatial coordinate data input, establishing an association between the index and the spatial unit to form an initialized database and associated data; secondly, based on data synchronization parameter configuration of the initialized database and the associated data, calculating and updating index values to generate index business data and historical record data; thirdly, carrying out tracing analysis on the above two types of data and generating an index multi-dimensional comprehensive result set through multi-form display; and finally, verifying the result set according to a verification request and verification rules, and integrating to generate a land space planning index monitoring result. The present application solves the technical problems of low monitoring efficiency, inaccurate results and insufficient visualization in traditional land space planning index monitoring, improves the data consistency, verification accuracy, analysis systematization and result display intuitiveness of land space planning index monitoring, and realizes the standardization, automation and traceability of the whole process of index monitoring.

[0042] In summary, the present application solves the problem of poor effect of land space planning by supporting the whole process monitoring of indexes through double databases, improves the automation degree, data consistency and checking accuracy of land space planning index monitoring, and realizes the effects of standardization of monitoring process and visualization of achievement display. BRIEF DESCRIPTION OF DRAWINGS

[0043] The accompanying drawings, which are incorporated herein and form part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the present application.

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings required to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative labor.

[0045] Figure 1 The flowchart of the first embodiment of the multi-level land space planning index monitoring method of the present application;

[0046] Figure 2 The overall architecture diagram of the present application;

[0047] Figure 3 The internal structure diagram of the data storage layer of the present application;

[0048] Figure 4 The adaptation relationship diagram of the domestic component of the present application;

[0049] Figure 5 The timing diagram of the data synchronization process of the present application;

[0050] Figure 6 The structural schematic diagram of the planning index monitoring device of the present application.

[0051] The purpose implementation, functional characteristics and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0052] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application, and are not used to limit the present application.

[0053] The related art still relies on manual completion of index data statistics, and runs through the whole process of data processing. Manual operation is prone to index comparison errors and data update not in time, which ultimately leads to poor effect of land space planning.

[0054] The application provides a solution: first, after completing the table creation of the database and the entry of the initial index basic information and spatial coordinate data, an association relationship between the index and the spatial unit is established, an initialization database and associated data are obtained, the initialization database and the associated data are configured based on data synchronization parameters, index values are calculated and updated, index business data and historical record data are generated, then, after tracing and analyzing the index business data and the historical record data, the obtained index tracing and analyzing result is displayed in multiple forms, an index multi-dimensional comprehensive result set is generated, finally, the index multi-dimensional comprehensive result set is verified according to a verification request and a verification rule, and a land space planning index monitoring result is generated by integration.

[0055] It should be noted that the execution subject of the embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone or the like, or an electronic device, a planning index monitoring device or the like capable of realizing the above functions. The embodiment and the following embodiments will be described below by taking the planning index monitoring device as an example.

[0056] In order to better understand the technical solutions of the application, the following will be described in detail in combination with the drawings of the specification and specific embodiments.

[0057] The embodiment of the application provides a monitoring method for multi-level land space planning indexes. Figure 1 , Figure 1 FIG. 1 is a flowchart of a first embodiment of the monitoring method for multi-level land space planning indexes.

[0058] In the embodiment, the monitoring method for multi-level land space planning indexes comprises steps S10-S40:

[0059] Step S10, after completing the table creation of the database and the entry of the initial index basic information and spatial coordinate data, an association relationship between the index and the spatial unit is established, an initialization database and associated data are obtained.

[0060] In the embodiment, the table of the database is a structured carrier for storing indexes and spatial related data, the initial index basic information is the original definition and attribute data of the index. The spatial coordinate data is geographical data describing the position of the spatial unit. The index is the core object of monitoring. The spatial unit is a geographical division unit of planning. The association relationship is the corresponding matching logic of the index and the spatial unit. The initialization database is a database with completed basic data configuration. The associated data is data recording the corresponding relationship between the index and the spatial unit.

[0061] As an optional implementation, the corresponding data tables such as the index basic information table, the spatial unit coordinate table and the index spatial correlation table are created according to the data storage requirements, and the initial index basic information and the spatial coordinate data are batch-entered in a preset data format to ensure the completeness and format uniformity of the entered data. Then the unique identification code corresponding to each index and the exclusive code of each spatial unit are extracted, and the coding matching is automatically performed according to the preset coding mapping rule to establish the correlation between the index and the spatial unit. The coding consistency and corresponding rationality are checked in real time during the matching process. After excluding invalid matching items, the correlation is stored in the corresponding data table together with the initial data, and the initialization database and the correlation data are obtained. This method has high correlation efficiency and strong matching accuracy, and realizes the rapid completion of the basic data correlation configuration.

[0062] As another optional implementation, the corresponding data tables are created according to the storage requirements of the index data and the spatial data, and the initial index basic information is checked attribute by attribute, and the spatial coordinate data is checked for position validity. After the verification, the data is entered into the data table in batches. Then the entered data is classified and arranged according to the field of the index and the administrative level of the spatial unit, and a plurality of classified data sets are formed. Then the index and the spatial unit in each classified data set are associated according to the mapping relationship between the field and the level, and the logical compatibility of the associated data in each group with other classified data sets is checked after each group is associated. After confirming that there is no logical conflict, the association information of all classified data sets is summarized and stored in the index spatial correlation table, all data in the data table is integrated, and finally the initialization database and the correlation data are obtained. This method adapts to the multi-dimensional data classification management requirement and has high fault tolerance.

[0063] In step S20, the initialization database and the correlation data are configured based on the data synchronization parameters, the index values are calculated and updated, the index business data and the historical record data are generated.

[0064] In this embodiment, the data synchronization parameters are configuration information for controlling the data synchronization process. The index value is the specific quantitative data of the index. The index business data is the effective data of the current available index. The historical record data is the original storage data before the index value is changed.

[0065] As an optional implementation, based on the initialized database and the associated data, preset data synchronization parameters are loaded, and an external data source is called to obtain original index data according to an interface address specified by the data synchronization parameters. According to a data cleaning rule in the data synchronization parameters, the original data is format-corrected, abnormal values are removed, and null values are supplemented. Then, in combination with the correspondence between the indexes and the spatial units in the associated data, the derived index values are calculated according to preset calculation logic. Subsequently, the calculated new index values are compared with existing index values in the initialized database in batches, the existing old values are moved into a historical record data storage area in batches, the index values in the database are updated in batches, and the latest index business data and complete historical record data are generated. This method has high data processing efficiency and fast synchronization speed, and can quickly complete large-scale index value updating.

[0066] As another optional implementation, the configured data synchronization parameters are called to determine the access rule and the data synchronization range of the external data source, and the external original index data is obtained in batches according to the synchronization frequency set by the parameters. Each batch of data is verified and processed according to the data cleaning rule one by one to ensure the standardization of a single data. Then, in combination with the accurate positioning of the spatial unit corresponding to each index by the associated data, the single index data is operated one by one to obtain the updated index value. Subsequently, for each index value, the corresponding old value in the database is extracted and stored in the historical record data area, and then the new value is updated to the corresponding position in the database one by one. After each data update is completed, the logic consistency of the data and the associated data is verified, and the next data processing is performed after the correctness is confirmed. Finally, accurate index business data and piece-by-piece historical record data are generated. This method has high data processing accuracy and is easy to troubleshoot abnormal data, and can accurately guarantee the accuracy of a single data.

[0067] Step S30, after the index business data and the historical record data are analyzed, the obtained index traceability analysis result is displayed in multiple forms to generate a set of index multi-dimensional comprehensive results.

[0068] In this embodiment, traceability analysis is a process of combing and analyzing the historical context, change logic and correlation of the two types of data. The index traceability analysis result is a structured data set formed after traceability analysis. Multi-form display is to display the analysis result through multiple visual presentation methods. The set of index multi-dimensional comprehensive results is a complete result carrier after integrating the analysis result and display materials.

[0069] As an optional implementation, the index business data and historical record data are integrated, the index values, change times, differences before and after changes, and core fields of associated spatial units are extracted, and the index values are classified and collected in chronological order and by index type. Through comparison of index value changes in different time periods, analysis of change triggers and transmission logic, structured index traceability analysis results including value trends, change details, and associated relationships are formed. Subsequently, map annotations, data lists, and core index cards are generated based on the results. Finally, the three types of display materials are integrated according to the preset framework, supplemented with data sources and analysis logic explanations, and the index multi-dimensional comprehensive results are generated. This method has high integration efficiency and fast result generation speed, and can quickly present overall analysis conclusions.

[0070] As another optional implementation, the index business data and historical record data are divided according to the index level and data type to form multiple sub-datasets. For each sub-dataset, traceability analysis is carried out one by one, the data integrity and logical consistency are first verified, then the value evolution track of this type of index, cross-level associated influence, and historical change reason are combed, and the index traceability analysis results are formed in a hierarchical and type-specific manner. Subsequently, each sub-result is matched with a dedicated display form, such as a line chart for hierarchical comparison results, a time series list for change detail results, and a partitioned map for spatial association results. The information density and presentation logic of each display form are optimized one by one to ensure that the sub-results are clear and easy to understand. Finally, all sub-display materials are summarized and integrated into a unified result carrier, supplemented with associated explanations between sub-results, and the index multi-dimensional comprehensive results are generated. This method has high degree of subdivision and strong display specificity, and can accurately support detailed review and special analysis.

[0071] Step S40, according to the verification request and the verification rule, verifying the index multi-dimensional comprehensive results, and integrating to generate the land space planning index monitoring results.

[0072] In this embodiment, the verification request is the index verification related demand instruction proposed by the user. The verification rule is the standard and logic for judging the compliance and consistency of the index. The land space planning index monitoring results are the final index monitoring results formed after verification and integration.

[0073] As an optional implementation, the index values, spatial correlation data, historical change records and analysis conclusions in the multi-dimensional comprehensive index result set are extracted. According to the verification range and core demand specified by the verification request, the target verification data is filtered, the preset verification rules are loaded, the target data is batch compared and verified, and it is automatically determined whether each index meets the rule requirements such as hierarchical constraint proportion, threshold standard, etc. The abnormal index is labeled with the violation type, trigger condition and judgment basis. Then, the compliance index list, abnormal index details and verification rule matching records are integrated, the index traceability analysis correlation information is supplemented, and the land space planning index monitoring result is generated according to the fixed structure of verification overview, compliance situation, abnormal details and judgment basis. This method has high verification efficiency and fast integration speed, and can quickly cover large-scale index verification requirements.

[0074] As another optional implementation, the multi-dimensional comprehensive index result set is split into multiple independent sub-verification units according to spatial levels and index types. For each sub-unit, the special verification points corresponding to the verification request are first decomposed, and then the verification rules adapted to the unit are called one by one to check the value accuracy, spatial correlation rationality and historical change compliance of the indexes in the unit. In the verification process, the logical consistency of the unit data and other related units is compared and recorded, and the suspicious indexes are rechecked and supplemented with rechecking explanations. Then, the verification results of all sub-units are summarized, and the compliance index details, abnormal index problem list, rechecking conclusions and rectification suggestions are classified and arranged. The data traceability path and rule adaptation explanation are supplemented, and the land space planning index monitoring result is generated according to the structure of general statement, hierarchical and type verification results, abnormal rectification guide and data traceability. This method has high verification accuracy and comprehensive detail control, and can effectively identify hidden index problems in special scenarios.

[0075] Exemplarily, referring to Figure 2 , Figure 2The figure is the overall architecture of the application. In the monitoring scenario of territorial space planning indicators, the system adopts a front-end and back-end separation architecture, which is divided into three levels: front-end display layer, back-end service layer, and data storage layer. The front-end display layer uses Vue3 as the development framework, Element-Plus as the UI component library, Axios to realize the HTTP / REST data interaction between the front-end and the back-end, Echarts to generate visual charts such as annual change graphs of indicators, and 3dSDK to present three-dimensional space scenes such as territorial space unit solid models. The back-end service layer is based on SpringBoot, which realizes request routing through SpringGateway gateway, completes database interaction through MyBatis, and deploys data synchronization services (interface external data sources to obtain planning indicator raw data), indicator standard services, core analysis services (conduct indicator level comparison and operation), historical tracking services (record indicator change trajectory), and scene services. The data storage layer adopts a "Dream Database and Domestic Graphics Database (such as Renmin Gold Warehouse Database or Mass Database)" dual architecture, which stores business tables such as indicator basic information table, indicator value table, and indicator history record table in the Dream Database, and stores spatial tables such as space unit coordinate table and indicator space association table in the graphics database, and realizes integrated storage of business and spatial data through table association.

[0076] As the territorial space planning indicators are visually presented through the front-end multi-component linkage, the back-end multi-service collaborative processing, and the dual-database integrated storage, the problems of low data analysis efficiency and non-intuitive result display in the monitoring of territorial space planning indicators are solved, and the timeliness, accuracy, and readability of indicator monitoring are improved.

[0077] Based on any of the above embodiments, in Embodiment Two of the application, the step S10 includes steps A11-A13:

[0078] Step A11, according to the business requirements, complete table creation in the database, and output a dual-database framework containing various empty table structures.

[0079] In this embodiment, business requirements are the functional and data storage requirements that drive table creation. Empty table structure is a table containing only field definitions and constraints. The dual-database framework is a storage architecture composed of Dream Database and domestic graphics database.

[0080] As an optional implementation, the storage fields, data types, constraint conditions and the like of the index business data and the spatial data in the business requirement are collected first, and are sorted into a table structure design document. Then, the business class empty table of the index basic information table and the index value table is created in the Dream Database in sequence, and the spatial class empty table of the spatial unit coordinate table and the index spatial association table is created in the domestic graphics database. In the creation, the accuracy of the fields and the constraints is verified in real time, and after completion, the double database framework containing the empty table structures is output. The table structure of the method has high matching degree with the business requirement, and lays an accurate foundation for the subsequent data storage.

[0081] Step A12, based on the double database framework, the initial index basic information and the spatial coordinate data are inputted, and the double database filled with initial data is generated.

[0082] In the embodiment, the double database filled with initial data is the Dream Database and the domestic graphics database storing the index basic information and the spatial coordinate data after the initial data inputting is completed.

[0083] As an optional implementation, the initial index basic information and the spatial coordinate data are split according to single data, and the single data inputting is performed on the business tables such as the index basic information table and the index value table of the Dream Database and the spatial tables such as the spatial unit coordinate table of the domestic graphics database. The logical consistency of the data with the table field type, the data constraint and the already inputted data is verified after each data inputting. If inconsistency is found, the data is immediately corrected or the inputting logic is adjusted. After confirming that there is no error, the inputting of the next data is continued. After all the data inputting is completed, the double database filled with initial data is generated. In the method, the data inputting accuracy is high, the single data exception can be quickly located and corrected, and the accuracy of the initial data inputting is realized.

[0084] Step A13, through the corresponding matching of the index code and the spatial unit code, the association relationship between the index and the spatial unit is established and stored in the double database filled with initial data, and the initialization database and the association data are obtained.

[0085] In the embodiment, the index code is a unique code identifying the index. The spatial unit code is a unique code identifying the spatial unit. The double database filled with initial data is the Dream Database and the domestic graphics database in which the initial index basic information and the spatial coordinate data have been inputted.

[0086] As an optional implementation, the index code of the index basic information table in the double database filled with initial data and the spatial unit code of the spatial unit coordinate table are extracted first, sorted into a code mapping list, and the range of spatial unit codes corresponding to each index code is determined. Then the index and the spatial unit are automatically matched according to the code mapping list to generate a correlation record. The correlation record is stored in the index spatial correlation table of the double database, and the uniqueness and rationality of the code matching are checked during storage. The code combinations that do not match are marked and processed separately, and then stored again after processing. Finally, the storage of all correlation relationships is completed, and the initialization database and the correlation data are obtained. This method has high correlation efficiency and can quickly complete large-scale code matching, ensuring the overall efficiency of the correlation configuration and the accuracy of the batch correlation.

[0087] Exemplarily, with reference to Figure 3 , Figure 3 is the internal structure diagram of the data storage layer of the present application. In the monitoring scenario of land space planning indicators, an index basic information table (storing index codes, names, units, spatial levels, etc., such as the city-level indicator "SJ-101", code "SJ-101", unit "hectare", spatial level "city level") is created in the Dameng database, an index value table (storing index values, time, etc.), an index history record table (storing values before index changes, time, reason, etc.), and a planning atlas table (storing atlas numbers, names, associated indicators, etc.). In the domestic graphics database, a spatial unit coordinate table (storing spatial unit codes, coordinate information, such as the administrative boundary coordinates of the partition spatial unit code "BA", the group range coordinates of the group spatial unit code "BA01", and the unit coordinates of the unit spatial unit code "BA01-01") and an index spatial correlation table (storing the correspondence between index codes and spatial unit codes, such as "SJ-101" associated with "BA", "FQ-101" associated with "BA01", and "DY-101" associated with "BA01-01") are created. Subsequently, initial index basic information is entered, such as the index "SJ-101", initial spatial coordinate data of each spatial level (coordinates of the partitions BA, groups BA01, and units BA01-01) are imported, and the correlation between the index and the spatial unit is established, such as "SJ-101" associated with "BA", "FQ-101" associated with "BA01", and "DY-101" associated with "BA01-01", forming an initialization database and correlation data.

[0088] Since the double database carries land space planning business data and spatial data respectively, specific index basic information and multi-level spatial coordinate data are entered and accurate correlation is established, the problems of low monitoring efficiency and inaccurate spatial matching are solved, and the data consistency and spatial correlation of index monitoring are improved.

[0089] Based on any of the above embodiments, in the third embodiment of the present application, the step S20 comprises steps B11-B13:

[0090] Step B11, based on the initialization database and the association data, calling the corresponding data synchronization parameters, and starting the data synchronization service to obtain the original index data from the external data source interface.

[0091] In this embodiment, the data synchronization service is a service module for obtaining data from the outside. The external data source interface is an access interface for providing data externally. The original index data is the original index information without processing.

[0092] As an optional implementation, the data synchronization parameters are first parsed to determine the acquisition rule of each piece of data and the single access mode of the external data source interface. The data synchronization service is started to acquire the original index data from the external data source interface one by one according to the parameter setting rule. After each piece of data is acquired, the single piece of data is accurately matched according to the corresponding relationship between the index and the space unit in the association data. After matching, the consistency of the data format and the parameter requirements is verified. If inconsistency is found, the acquisition logic is immediately adjusted or the data is corrected. After confirming that there is no error, the single piece of original index data is stored, and the acquisition of the next piece of data is continued, until all the original index data that meets the parameter requirements is acquired. This method has high data acquisition accuracy, and the matching and verification of single data are more detailed, which can effectively avoid data errors.

[0093] Step B12, according to the preset data cleaning rule and operation logic, completing the preprocessing of the original index data and the calculation of derived data, and outputting standardized index data.

[0094] In this embodiment, the data cleaning rule is a standard for specifying the format of the original index data and correcting abnormal values. The operation logic is a rule for calculating derived data that is preset in advance. The derived data calculation is a process of generating new data according to the original index data and the operation logic. The standardized index data is the index data that meets the unified specification after preprocessing and derived data calculation.

[0095] As an optional implementation, the preset data cleaning rule and operation logic are first loaded to batch process the original index data, covering the operations of format unification, abnormal value identification and correction, and null value supplement. After preprocessing, the batch data is calculated according to the operation logic to calculate the derived data. During the calculation process, the compatibility of the data and the data cleaning rule and the operation logic is verified synchronously. If incompatible batch data is found, it is marked and processed separately. After processing, the derived data calculation is performed again until all data processing is completed, and the standardized index data is output. This method has high data processing efficiency and can quickly process large-scale original index data.

[0096] Step B13, comparing the standardized index data with the existing data of the index value table in the initialization database, and outputting the index business data and the historical record data according to the data comparison rule.

[0097] In the embodiment, the index value table is a database table storing specific values of indexes. The existing data is the index data stored in the index value table. The data comparison rule is a preset rule for determining data update and distinguishing business data from historical data.

[0098] As an optional implementation, the existing data of the index value table in the initialization database is extracted, the standardized index data and the data comparison rule are loaded, and batch comparison is performed thereon. Whether the data is updated is determined according to the rule. The old data of the data to be updated is extracted as the historical record data, and the new standardized data is stored as the index business data in the corresponding table. When storing, the data consistency is verified, the abnormal data in batch comparison is marked and processed separately, and then it is executed again. Finally, the comparison is completed, and the index business data and the historical record data are output. This method has high comparison efficiency and can quickly process large-scale data.

[0099] Exemplarily, in the monitoring scene of territorial space planning indexes, the data synchronization parameters are configured: the interface address of the external data source, the synchronization frequency (such as real-time synchronization of implementation value data, synchronization of planning value data at 2 o'clock in the morning every day), and the data cleaning rule (such as replacing the null value with “no data” and uniformly keeping 2 decimal places for the numerical value format). The data synchronization service is started: the backend data synchronization service calls the external data source interface to obtain the index data according to the preset parameters, for example, obtains the implementation value data of the “urban construction land scale” index of the district in 2024 from the online Huijiao system. Data processing and storage: the data synchronization service automatically cleans the obtained raw data (such as correcting the numerical value with format error), calculates (such as calculating the remaining value = planning value - implementation value according to the implementation value and the planning value). After the processing is completed, the data is stored in the index value table of the Dream database. If the index data already exists, for example, the implementation value in 2023 is already stored, the old data is stored in the index historical record table, and the latest data of the index value table is updated.

[0100] Since the multi-level territorial space planning raw index data is automatically obtained by calling the data synchronization parameters, the data cleaning and derivation calculation are completed according to the rule, and the business data and the historical record data are distinguished through data comparison, the problems of data lag, disordered format and no trace of historical changes are solved, and the timeliness, standardization degree and traceability of the index data are improved.

[0101] Based on any of the above embodiments, in the fourth embodiment of the present application, the step S30 includes steps C11-C13.

[0102] Step C11, according to the multi-dimensional interaction request, retrieve the target search data corresponding to the level, year, type of the index data and the index change related record in the index business data and the historical record data.

[0103] In this embodiment, the multi-dimensional interaction request is a user request containing search conditions such as level, year, type. The index change related record refers to the time, reason and other related information of the index value change. The target search data is the filtered data set that meets all search conditions.

[0104] As an optional implementation, first parse the multi-dimensional interaction request, extract the core search conditions such as level, year, type and organize them into unified filtering rules. Then batch retrieve the index business data and historical record data according to the rules, and synchronize the matching of the index data and the index change related record corresponding to the level, year, type. During the retrieval process, verify the correspondence between the conditions and the data fields, filter the redundant data that does not meet the batch matching, and then filter all data that meet the conditions, and again verify the consistency of data integrity and search conditions, and finally get the target search data. This method has high retrieval efficiency and can quickly cover large-scale data filtering requirements, and is suitable for conventional multi-dimensional retrieval scenarios.

[0105] As another optional implementation, first split the search conditions such as level, year, type in the multi-dimensional interaction request, and set the retrieval priority according to the order of level, year, type. First, filter the index business data and historical record data according to the level condition to get the basic data set corresponding to the level, and then filter the basic data set based on the year condition to retain the data that meets the year requirement. Then, perform three times of filtering according to the type condition to extract the index data and the index change related record corresponding to the type. After each round of filtering, verify the precision matching degree of the filtering result and the current search condition, and if there is deviation, adjust the filtering logic. After three rounds of filtering, aggregate the data and supplement the association information between the index change record and the index data, and finally get the target search data. This method has high retrieval accuracy and can effectively adapt to complex multi-condition combination filtering requirements and avoid redundant data interference.

[0106] Step C12, based on the target search data, carry out a trace analysis process, and output the index trace analysis result.

[0107] In this embodiment, the trace analysis process is a process of analyzing and judging the value evolution track, change reason and related logic of the target search data. The index trace analysis result is a structured result containing trend characteristics, change details and related relationships formed after trace analysis.

[0108] As an optional implementation, the target retrieval data is split into multiple independent sub-datasets according to the index level and the change type, and the traceability analysis is carried out for each sub-dataset. First, the numerical value change trajectory of a single index is sorted out, and the time node, specific numerical difference and trigger reason of each change are determined. Then, the mutual influence logic of the index and the corresponding spatial unit and the associated index is analyzed, and the data integrity and logical rationality in the analysis process are checked one by one, and the special change explanation of a single data is supplemented. Finally, the analysis results of all sub-datasets are integrated according to the logic of sub-features and overall correlation, and the index traceability analysis results including hierarchical change details, single index evolution context and accurate correlation logic are produced. This method has high analysis accuracy and deep detail mining, and can effectively identify implicit data correlation.

[0109] Step C13, after integrating the target retrieval data and the index traceability analysis results, the results are displayed through map annotation, list presentation and card highlighting, and the index multi-dimensional comprehensive results set is output.

[0110] In this embodiment, the map annotation is a display form of visualizing the spatial correlation index data on a geographical map. The list presentation is a display form of listing the complete information of the index in a standard format. The card highlighting is a display form of highlighting the core index value and key conclusions.

[0111] As an optional implementation, the target retrieval data and the index traceability analysis results are first split into multiple sub-data modules according to the index level and the data type, and each module focuses on a type of special information. Then, the display form suitable for each sub-module is matched: the module with prominent spatial properties uses map annotation to accurately present its geographical coordinates and spatial correlation. The module with rich information dimensions uses list presentation to show all details in an organized manner. The core conclusion module uses card highlighting to strengthen the visual prominence of key information. The display content of each module is optimized in detail to ensure that the sub-information is clear and easy to understand. Then, the logical correlation between the sub-modules is sorted out, and the connection between the modules is supplemented. Finally, all sub-display materials are integrated according to the structure of classified display, logical correlation and core summary, and the index multi-dimensional comprehensive results set is obtained. This method has strong display pertinence and accurate detail presentation, and can accurately support special analysis and detail review.

[0112] By accurately retrieving the national space planning index data through multi-dimensional conditions, the system carries out traceability analysis and sorts out the evolution logic, and visualizes the display through maps, lists and cards, which solves the problems of complicated retrieval, fragmented analysis and non-intuitive display in traditional index monitoring, and improves the data retrieval accuracy, analysis systematicness and result readability.

[0113] Based on any of the above embodiments, in the fifth embodiment of the present application, the step C12 includes steps D11-D13:

[0114] Step D11, extract the target search data in different spatial levels, corresponding to the index planning value and implementation value of the year, and the index change information, and get the index traceability analysis core data set.

[0115] In this embodiment, the spatial level refers to the geographical division level corresponding to the index. The corresponding year is the specified time dimension. The index planning value is the preset index target value. The index implementation value is the actual value achieved by the index. The index change information is the time, reason, difference and other related records of the change of the index value. The index traceability analysis core data set is the key data set extracted for traceability analysis.

[0116] As an optional implementation, the spatial level dimension of the target search data is first split, and each level is processed in order from high to low. For each spatial level, the index data is filtered by year. The planning value and implementation value of each data are extracted, and the corresponding index change information is sorted item by item. The consistency and logical association of the change information with the planning value and implementation value are verified, and redundant change records without direct association are removed. The integrity of each data extraction is verified after each data extraction, and the next data is processed after confirmation. After all levels and year data are processed, the index traceability analysis core data set is integrated to form the index traceability analysis core data set. This method has high extraction accuracy and can ensure the strong association and integrity of the core data.

[0117] Step D12, according to the index traceability analysis core data set, according to the change of the planning value of the full-dimensional time record, comb the time context to generate the index preliminary analysis result.

[0118] In this embodiment, the full-dimensional time record refers to a complete time record form accurate to year, month, day, hour, and minute. The planning value of the index refers to the expected target value set at different time nodes. The change includes the change time and the related information of the change reason of the index planning value. The time context refers to the logical line of the index planning value change formed by combing in chronological order. The index preliminary analysis result includes the preliminary analysis result of the planning value time evolution track and the change details.

[0119] As an optional implementation, all the planning values, full-dimensional time records and corresponding change information of the indexes in the index traceability analysis core data set are integrated. The time stamps of the full-dimensional time records are batch sorted, the change time and reason corresponding to each planning value are batch associated, a unified time context framework is built, and only basic verification of data integrity is performed in the process. After ensuring that there is no missing key information, the index preliminary analysis result is directly integrated. This method has extremely high processing efficiency and can complete the sorting and integration of large-scale data in a short time, which is suitable for scenarios that require quick output of preliminary analysis conclusions.

[0120] Step D13, according to the structure integration of the hierarchical comparison data and the historical change data, the preliminary analysis results of the indicators are determined, the data sources and the calculation logic are determined, and the traceability analysis results of the indicators are output.

[0121] In this embodiment, the hierarchical comparison data is the comparison results of cross-hierarchical indicators such as differences and proportions obtained through hierarchical comparison analysis. The historical change data is a data set recording information such as change time, reason, and difference of indicator values.

[0122] As an optional implementation, the hierarchical comparison data is disassembled into difference details and proportion characteristics, and the historical change data is disassembled into change reasons, time nodes, and value differences. According to the smallest unit of a single indicator, a single level, and a single time node, the data in the preliminary analysis results of the indicators are split and matched one by one, the corresponding hierarchical comparison subdivision data and the historical change subdivision data are accurately associated and integrated, and the specific acquisition channel and complete operation process of each integrated data are traced and clarified one by one. The authenticity of the data source, the rigor of the calculation logic, and the logical consistency between the data are verified one by one. If problems are found, they are corrected and adjusted in time. After the integration and verification of all the smallest unit data are completed, the traceability analysis results of the indicators are formed by gradually summarizing according to the logic of the indicator type, the spatial level, and the time sequence. This method has high integration accuracy, the data sources and the calculation logic can be accurately traced, and there is no hidden bias.

[0123] Exemplarily, in the monitoring scene of the land space planning indicators, the target retrieval data is split into single data units according to the spatial level and the corresponding year, the indicator planning value, the implementation value, and the indicator change information of each unit are extracted one by one, the integrity of each piece of information, the consistency of the change time and the full-dimensional time record, and the logical rationality of the change reason and the planning value change are verified one by one, the records with abnormalities are separately marked and supplemented, and the core data set of the indicator traceability analysis is formed after verification. For each indicator in the core data set, the historical change of the planning value is sorted out according to the full-dimensional time record one by one, the associated logic of the change time, the change reason, and the planning value change is restored node by node, the detailed time context of each indicator is constructed, and the refined preliminary analysis results of the indicators are generated one by one. According to the exclusive needs of each spatial level and each year, the preliminary analysis results are integrated one by one, the specific data sources and the individual calculation logic of each data unit are determined, the contents after integration are logically verified and accuracy checked one by one, and the traceability analysis results of the indicators are output after all units are verified to be normal.

[0124] By precisely extracting multi-level multi-year index core data, quantitatively calculating cross-level differences and sorting out time context, structurally integrating analysis results and clearly specifying data sources and logic, the problems of no quantitative basis for comparison of land space planning index levels, no clear context for time evolution, and difficulty in data tracing are solved, and the precision and traceability of index traceability analysis are improved.

[0125] Based on any of the above embodiments, in the sixth embodiment of the present application, the step S40 comprises steps E11-E13:

[0126] Step E11, based on the verification request, extracting index planning values, implementation values, spatial unit association data and historical change data of different spatial levels in the multi-dimensional comprehensive index results, combining the verification rules, outputting index connection verification data sources.

[0127] In this embodiment, the spatial unit association data refers to the corresponding matching information of the index and the spatial unit. The index connection verification data source is a special data set for conducting connection verification.

[0128] As an optional implementation, the verification request is parsed to determine the core range of spatial levels and index types that need to be extracted, and the index planning values, implementation values, spatial unit association data and historical change data corresponding to different spatial levels in the multi-dimensional comprehensive index results are batch extracted. The batch data extracted is compared and screened with the verification rules to check the compatibility of the data format with the requirements of the rules, the items in the batch data that do not comply with the rules are marked and summarized, all compliant data is integrated after unified adjustment, and finally the index connection verification data source is output. This method has high extraction efficiency and can quickly cover large-scale data extraction requirements.

[0129] Step E12, calculating the difference and proportion of cross-level indexes in the index connection verification data source, judging the connection state of each index by comparing the verification rules, marking the abnormal reasons and calculation basis, and outputting the verification results.

[0130] In this embodiment, the connection state refers to the compliance or abnormal result of the index cross-level connection. The abnormal reason is the specific factor of the index connection not being compliant. The calculation basis is the operation logic of the difference and proportion. The verification result is the complete verification result containing the connection state, abnormal reason and calculation basis.

[0131] As an optional implementation, the values of all cross-level indicators in the batch extraction indicator connection verification data source are extracted first, and the differences and proportions of each indicator are uniformly calculated to form a batch calculation result set. Then the result set is compared with the verification rules in batches, and the connection state of all indicators is quickly determined according to the rule threshold. The historical change data and spatial association information in the data source of the indicators marked as abnormal are associated in batches, the common abnormal reasons are summarized, and the calculation basis of all indicators is uniformly determined. Finally, the state, reason and basis after batch processing are integrated, and the verification result is output. This method has high verification efficiency and can quickly complete batch verification of large-scale cross-level indicators, which is suitable for overall screening scenarios.

[0132] Step E13, based on the verification result, integrating the indicator traceability analysis details, spatial distribution display data and abnormal rectification suggestions, generating the land space planning indicator monitoring result in a unified format.

[0133] In this embodiment, the spatial distribution display data refers to the distribution and association visualization data of the indicators in geographical space. The abnormal rectification suggestion is an optimization measure proposed for the indicator connection abnormality.

[0134] As an optional implementation, the connection state, abnormal reason and calculation basis in the verification result are extracted first, and the hierarchical comparison data in the indicator traceability analysis details, the time evolution context, and the geographical association information in the spatial distribution display data are collected synchronously. The data is classified and integrated in batches according to the module division rules set in a unified format. Based on the common abnormal problems in the verification result, general abnormal rectification suggestions are generated in batches. In the integration process, the format consistency and logical association of each module data are verified, the format deviation data in batch processing is uniformly adjusted, the missing common information is supplemented, and finally all contents are integrated in a unified format to output the land space planning indicator monitoring result. This method has high integration efficiency and can quickly complete the standardized integration of large-scale data and suggestions, which is suitable for overall monitoring scenarios.

[0135] By accurately extracting the core data and associated information of cross-level indicators, quantitatively calculating the connection differences and comparing the rules to determine the state, and integrating multi-dimensional data to generate targeted rectification suggestions and standardized monitoring results, the problems of no clear data source, no quantitative basis for abnormal determination, and no accurate guidance for rectification measures in land space planning cross-level indicator connection verification are solved, and the efficiency and accuracy of indicator connection verification are improved.

[0136] Based on any of the above embodiments, in the seventh embodiment of the present application, the step S10 further includes steps F11-F13:

[0137] Step F11, based on the business data storage requirements, determining the table types and table core fields that the indicator database and the graph database need to carry respectively, and outputting a double-database table structure scheme.

[0138] In the present embodiment, the business data storage requirement is the storage demand for index-related data and space-related data. The index database is a database that carries index business data, such as the Dameng database. The graph database is a domestic database that carries space-related data. The table type is a database table category divided according to data function. The table core field is a core data item in the table for storing key information. The double-database table structure scheme is a planning result of clearly indicating the table type and corresponding core field of the index database and the graph database.

[0139] As an optional implementation, first, the business data storage requirements are comprehensively collected, and are divided into index-related data such as index basic information, index values, and index change records, and space-related data such as space unit coordinates and index space association according to data function attributes. The table types that the index database needs to carry are batch-determined, and the core fields of each table are refined. The table types that the graph database needs to carry are batch-determined at the same time, and the core fields are refined. In the integration process, the association and field compatibility of the two types of database table types are verified to ensure smooth data flow, and finally the double-database table structure scheme is output. This method has high planning efficiency and can quickly adapt to general storage scenarios and cover core storage requirements.

[0140] Step F12, according to the double-database table structure scheme, creating corresponding table structures in the index database and the graph database to obtain a double-database initial framework.

[0141] In the present embodiment, the table structure is the field setting, type definition, and association rule of the database table. The double-database initial framework is the double-database basic form with basic data storage capability after the table structure is created.

[0142] As an optional implementation, first, according to the double-database table structure scheme, the table types that the index database needs to create and the core fields of each table are batch-processed, the field types and basic constraints are uniformly determined, the table structure creation operation is batch-executed, and the construction of all planned tables such as the index basic information table and the index value table is completed. The table types and core fields of the graph database are batch-processed at the same time, and the table structures such as the space unit coordinate table and the index space association table are batch-created. In the creation process, the field association between the tables in the same database and the adaptability of the table structures of the two databases are batch-verified to ensure smooth data flow logic, and finally the double-database initial framework is formed. This method has high creation efficiency and can quickly complete the construction of full-amount table structures and adapt to general storage scenarios.

[0143] Step F13, verifying the table structure adaptation information of the double-database initial framework, correcting field abnormal problems, and outputting a double-database empty table framework.

[0144] In this embodiment, the table structure adaptation information is the adaptation related information between the two types of database tables, such as field association, type matching, and logical connection. The field exception problem is the problem affecting storage and use, such as field type error, constraint loss, and contradictory association logic. The double-database empty table framework is the final basic framework of the double database after verification and correction, which is compliant with the table structure and smooth adaptation without data storage.

[0145] As an optional implementation, first, the table structure list of the double-database initial framework is sorted, and the adaptation information is batch-verified in the order of index database table, graph database table, and cross-database table association. The batch-verification is performed on the field type consistency, associated field correspondence, and constraint rule rationality. The batch-identification is performed on the field exception problem and classified and summarized. The batch-formulation is performed on the correction scheme in the order of common problems and individual problems. The batch-verification is performed on the adaptation after correction to ensure that there is no missing exception. Finally, the double-database empty table framework is output. This method has high verification and correction efficiency, can quickly process large-scale table structure adaptation problems, and covers core exception types.

[0146] Exemplarily, referring to Figure 4 , Figure 4 The adaptation relationship diagram of the localization components of the present application is shown in the figure. In the monitoring scenario of land space planning indicators, the hardware layer is composed of domestic servers and domestic storage devices to form the basic support; the data storage layer includes Dream Database V8.1 and domestic graph database V98v6 to form a data storage link; the backend service layer realizes data interaction with Dream Database V8.1 through Spring Boot combined with Dream JDBC driver; the front-end display layer takes Vue3 as the core, and is matched with Element Plus and jupliot3D SDK, which is connected with the backend service layer on one hand and associated with domestic graph database V98v6 on the other hand, to finally complete the whole-process architecture of data storage, calling, and multi-form visual display.

[0147] Since the Dream Database and the domestic graph database respectively carry the business data and the spatial association data of automatic follow-up, the table structure and the core field are determined and accurately created and verified, and the problem of low follow-up data calling efficiency is solved, and the data storage standardization and spatial association accuracy are improved.

[0148] Based on any of the above embodiments, in the eighth embodiment of the present application, the step S40 is followed by steps G11-G13:

[0149] Step G11, based on the initialization database and the associated data, the parameter types to be configured and the business constraint conditions of each parameter are determined according to the business scenario demand, and the data synchronization parameter configuration demand list is output.

[0150] In this embodiment, the business scenario requirement is the actual business demand for data synchronization function, timeliness, scope, etc. The parameter type is the parameter category involved in data synchronization. The business constraint condition is the business rule that the parameter needs to meet. The data synchronization parameter configuration requirement list is a list of parameter types and corresponding business constraint conditions that need to be configured.

[0151] As an optional implementation, the table structure, field association, and matching logic of the initialization database are first integrated, the common functions and timeliness requirements in the business scenario requirement are batched, and the parameter types that need to be configured are batched. The business constraint conditions of each parameter are determined, the compatibility of the parameter type and the constraint is verified during the integration process, the general requirements of the core business scenarios are covered, and finally the data synchronization parameter configuration requirement list is output. This method has high configuration efficiency and can quickly cover the parameter requirements of general business scenarios.

[0152] Step G12, according to the data synchronization parameter configuration requirement list, interface the external data source provider to obtain a legal and valid data source interface address, and determine the synchronization frequency according to the index type to obtain a preliminary data synchronization parameter scheme.

[0153] In this embodiment, the external data source provider is an external institution or system that provides data to be synchronized. The data source interface address is the network address provided by the external data source for data access; the index type is the classification of the index; the synchronization frequency is the time interval rule of data synchronization; and the preliminary data synchronization parameter scheme is a preliminary configuration scheme containing core parameters such as interface address and synchronization frequency.

[0154] As an optional implementation, the interface requirements and frequency requirements in the data synchronization parameter configuration requirement list are first sorted, the external data source providers are batched, and the data source interface addresses that meet the legality are batched. The synchronization frequency is batched according to the common characteristics of the index type, the interface address and the frequency are integrated to form a preliminary scheme, the interface accessibility and the frequency adaptability are verified during the process, and the synchronization requirements of general index types are covered. This method has high configuration efficiency and is suitable for batch connection scenarios, which can quickly complete the basic parameter scheme and provide efficient configuration support for regular data synchronization tasks.

[0155] Step G13, verifying the preliminary data synchronization parameter scheme, adjusting parameter details for test abnormal conditions, and generating the data synchronization parameter.

[0156] In this embodiment, the test abnormal condition is an interface access failure, a synchronization frequency mismatch, etc. that occurs during the test process.

[0157] As an optional implementation, the test is carried out in batches according to the preliminary data synchronization parameter scheme, the test abnormal situation is identified in batches, the abnormal root cause is analyzed in batches and the general adjustment scheme is formulated, and the parameter details are adjusted in batches. The adjusted parameters are verified to ensure that most of the conventional abnormalities are covered, and finally the data synchronization parameters are generated. This method has high verification and adjustment efficiency, is suitable for batch synchronization scenarios, and quickly solves common abnormalities.

[0158] As another optional implementation, in the process of verifying the preliminary data synchronization parameter scheme and adjusting the parameter details, an authorization verification mechanism is added. The operation personnel of the parameter configuration are authorized by levels, and only the first-level authorized account is granted the core parameter adjustment permission. The second-level authorized account can only view the parameter adjustment records. All parameter adjustment operations need to be approved by the first-level authorized account before taking effect, and the authorized operation log and parameter change track are synchronized. Finally, the data synchronization parameters with authorization traceability identification are generated. This method has high parameter configuration security and can avoid data synchronization abnormalities caused by unauthorized modification.

[0159] Exemplarily, with reference to Figure 5 , Figure 5 is a time sequence diagram of the data synchronization process of the present application. In the monitoring scenario of territorial space planning indicators, based on the initialized database (automatic revisit task basic configuration table in Dameng database) and associated data (matching association information of customer ID and revisit task), combined with the automatic revisit business scenario demand (real-time revisit task pushing, batch synchronization of historical revisit data), the parameter types (synchronization trigger rule, data filtering condition, interface timeout setting) and business constraint conditions (synchronization trigger rule needs to be triggered within 10 minutes after customer transaction completion, data filtering condition needs to contain valid customer ID and exclude invalid revisit tasks) that need to be configured are determined, and the data synchronization parameter configuration demand list is output. According to the list, the legal and valid data source interface address (http: / / xxx.xxx.xxx / autoVisitData) is obtained by connecting with the external data source provider, the synchronization frequency (real-time type every 5 minutes, periodic type every day at 2 a.m. once) is determined according to the indicator type (real-time type revisit task, periodic type historical revisit data), and the preliminary data synchronization parameter scheme is obtained. When verifying the scheme, it is found that the real-time type synchronization frequency causes interface access timeout, and it is adjusted to every 8 minutes. The periodic type interface returns missing data fields, and the data filtering rule is adjusted to be compatible with the format, and the data synchronization parameters are generated.

[0160] By explicitly defining the parameter types and business constraints of automatic revisit data synchronization, accurately connecting with the external data source interface, matching the indicator type and adapting the frequency, verifying and adjusting the test abnormalities, the problems of automatic revisit scene data synchronization trigger not timely, poor interface compatibility and unreasonable frequency are solved, and the timeliness and accuracy of data synchronization are improved.

[0161] Based on any of the above embodiments, in the ninth embodiment of the present application, the step S40 is followed by steps H11-H13:

[0162] Step H11, according to the preset table structure of the double database, the verification conclusion, the list of abnormal indicators, the spatial position information and the historical tracing data in the national space planning index monitoring result are respectively stored into the corresponding data table, and the monitoring result data set is obtained.

[0163] In this embodiment, the verification conclusion refers to the determination result of the compliance of index cross-level connection. The list of abnormal indicators refers to the detailed list of abnormal index codes and reasons. The spatial position information refers to the coordinate and spatial unit matching of the index association. The historical tracing data refers to the historical information of index value change and evolution track. The monitoring result data set refers to the complete data set formed after the various types of monitoring data are stored into the double database according to the table structure.

[0164] As an optional implementation, the preset table structure list of the double database is sorted out to determine the verification conclusion table and the abnormal indicator table corresponding to the index database of the verification conclusion and the list of abnormal indicators. The spatial position information corresponds to the spatial position table of the graphic database. The historical tracing data corresponds to the historical tracing table of the index database. The various types of data in the national space planning index monitoring result are extracted in batches, and are stored into the corresponding table according to the table structure to obtain the monitoring result data set. This method has high storage efficiency and is suitable for large-scale data batch storage scenarios.

[0165] As another optional implementation, in the process of storing various types of monitoring data according to the preset table structure of the double database, a data warehouse authorization mechanism is embedded, and exclusive authorization permissions are set for different types of monitoring data. The verification conclusion and the list of abnormal indicators need to be verified by the data audit authorization account before being stored in the warehouse. The spatial position information and the historical tracing data need to be bound with the spatial data authorization key to complete the permission verification. After being stored in the warehouse, an authorized storage identification is added to each data, and the authorized operator and time are recorded. This method has strong monitoring data storage compliance and can prevent illegal data from being mixed in.

[0166] Step H12, according to the preset result delivery requirement, the core information in the monitoring result data set is extracted and arranged according to the standard format, and the index monitoring successful delivery data is obtained.

[0167] In this embodiment, the preset result delivery requirement refers to the requirements of the pre-determined result delivery in terms of format, content range and use scenario. The core information refers to the key data in the monitoring result data set which is valuable for the result delivery. The index monitoring successful delivery data refers to the index monitoring result data which can be directly used for delivery after the core information is extracted and arranged according to the standard format.

[0168] As an optional implementation, the preset achievement delivery requirements are combed to determine the extraction range and standard format structure module of core information. The core information in the monitoring result data set is batch extracted and classified and arranged according to the standard format module, the efficiency of batch arrangement is ensured, and the index monitoring success delivery data is obtained. This method has high arrangement efficiency and is suitable for the rapid arrangement demand of large-scale achievement delivery.

[0169] As another optional implementation, in the process of extracting core information from the monitoring result data set and arranging according to the standard format, a delivery data authorization screening mechanism is embedded, and the core information that can be displayed is screened according to the authorized level of the achievement delivery object. High-sensitive information is only open to high-level authorized delivery objects, and ordinary authorized delivery objects only display desensitized core information. After the arrangement is completed, the authorized level identifier and access permission description are added to the delivery data. This method has strong privacy protection for delivery data and can accurately control the information disclosure range.

[0170] Step H13, updating the table data and dynamic association information of the initialization database based on the index monitoring success delivery data, and outputting the updated database.

[0171] In this embodiment, the dynamic association information refers to the association relationship between the database tables that dynamically changes with data updates. The updated database refers to the database whose table data and dynamic association information are updated, and the data timeliness and accuracy meet the standards.

[0172] As an optional implementation, the index monitoring success delivery data is first split into single data entries, the initialization database table data and dynamic association information corresponding to each entry are analyzed one by one, and the data update operation is performed one by one. The accuracy of the data and the rationality of the association are checked separately after completing the update of each entry. After all entries are updated, the integrity and association of the database are checked as a whole, and the updated database is finally output. This method has high update accuracy, and there is no detail deviation in data and association information update.

[0173] Exemplarily, in the monitoring scene of land space planning indicators, according to the double-database preset table structure, 10 verification conclusions, 5 abnormal indicator lists, 5 groups of spatial position GIS coordinates, and 3 years of historical traceability data in the land space planning indicator monitoring result are respectively stored in the verification conclusion table, the abnormal indicator table, and the historical traceability table of the Dream database, and the spatial position table of the domestic graphics database, to obtain the monitoring result data set. The core information (abnormal indicator code, compliance state, spatial coordinates, and change node) is extracted and arranged according to the standard format, to obtain the index monitoring success delivery data. Based on the data, the initialization database is updated, 15 table data are added to the Dream database, the dynamic association information is improved, 5 spatial association data are updated in the domestic graphics database, and the updated database is output.

[0174] Through double-database classification storage, standardized extraction of delivery data, and updating of the initialization database, the problems of scattered storage of land space planning index monitoring data, non-uniform delivery format, and lagging database data are solved, and the data storage standardization, delivery efficiency, and database timeliness are improved.

[0175] Based on any of the above embodiments, in the tenth embodiment of the present application, after step H13, the method further comprises steps I11-I13:

[0176] In step I11, the latest values of the indexes of each spatial level, the connection state, the abnormal reason, the spatial location information, and the historical trace record in the updated database are extracted, and are classified and arranged into a display data set according to a display scenario.

[0177] In the present embodiment, the display scenario refers to a specific use scenario of data presentation. The display data set refers to a structured data collection that can be directly used for presentation after being classified and arranged according to the display scenario.

[0178] As an optional implementation, all display scenarios are split, and the data details and arrangement rules required for extraction are determined for each scenario. The target data of the corresponding spatial level is extracted for each scenario, and is finely arranged according to the requirements of each scenario. The adaptability is verified after completing each scenario, and the results of all scenarios are integrated to form a display data set. This method has strong adaptability and accurately matches the data to the requirements of each scenario.

[0179] In step I12, based on the display data set, the spatial distribution of the abnormal indexes is presented through map annotation, the complete data of the indexes and the calculation basis are presented in the form of a list, the core conclusions and key values are highlighted in the form of a card, and multi-form visual display materials are generated.

[0180] In the present embodiment, the spatial distribution of the abnormal indexes refers to the geographical distribution of the abnormal indexes. The multi-form visual display materials refer to visual results integrated from maps, lists, cards, and other presentation forms.

[0181] As an optional implementation, different data modules in the display data set are split, and a special map annotation style is customized for the spatial distribution of the abnormal indexes. An adaptive list layout is designed for the complete data of the indexes. Differentiated card highlighting logic is customized according to the importance of the core conclusions, and is integrated after being connected to each form of presentation details to form multi-form visual display materials. This method has strong adaptability and accurately matches the materials to the characteristics of the data.

[0182] In step I13, according to the multi-form visual display materials, a comprehensive display interface is integrated to form, and the display results of the index monitoring data are output.

[0183] In this embodiment, the multi-form visual display material refers to a collection of visual content carrying index monitoring related data in various forms such as map annotation, list presentation, card highlighting, etc. The comprehensive display interface refers to a unified and coherent data presentation interface formed by integrating various visual materials. The index monitoring data display result refers to the final visual result that can be directly used for index monitoring data display after integration and optimization.

[0184] As an optional implementation, the multi-form visual display materials are integrated in batches according to a preset standardized layout, the style, size and interaction logic of the materials are uniformly adjusted, the adaptability and coherence of each material in the interface are verified, and the comprehensive display interface is quickly formed and the result is output. This method is efficient and can quickly output display results with a unified style.

[0185] Exemplarily, in the monitoring scenario of land space planning indicators, the data storage alternative solution is: using "single domestic spatial database" instead of "double database architecture", such as using SuperMap GIS database (supporting both relational and spatial data). This method can reduce the number of database deployments and simplify the data association logic, but has limitations: the processing efficiency of relational data of SuperMap database (such as transaction concurrency and complex queries) is lower than that of Dream database, when the amount of index data exceeds 1 million, the query response time increases by more than 30%, which cannot meet the demand of large-scale data processing, therefore the double database architecture of the present application is more optimal. The data synchronization alternative solution is: using "ETL tool (such as Kettle domestic version)" instead of "custom synchronization service". Kettle tool supports visual configuration of synchronization process, reducing development cost, but has the problem of insufficient real-time performance: Kettle's real-time synchronization needs to rely on complex trigger configuration, when the external data source generates more than 100 data per second, data accumulation is easy to occur, while the custom synchronization service of the present application is based on Quartz + WebHook, the real-time performance can reach within 1 minute, which is more suitable for real-time monitoring of planning indicators. The visual display alternative solution is: using "ArcGIS for JavaScript" instead of "3dSDK". ArcGIS has richer visual effects, but has the problem of domestic adaptation: ArcGIS relies on foreign components, which does not meet the requirement of China's information creation, and high copyright fees need to be paid, while 3dSDK is a domestic component, supporting CGCS2000 coordinate system, with lower cost and higher security, therefore the solution of the present application is more practical.

[0186] By classifying and extracting multi-dimensional monitoring data, multi-form visual presentation and integrating and unifying the display interface, the problems of single display form, unhighlighted key information and poor scene adaptability of land space planning index monitoring data display are solved, and the data readability and decision support efficiency are improved.

[0187] To achieve the above-embodiment, the application further provides a multi-level land space planning index monitoring system, comprising: a database planning module, a document management module, a historical tracking module, and a report generation module, wherein:

[0188] The database planning module establishes an association between the index and the spatial unit after completing the table creation of the database and the entry of the initial index basic information and spatial coordinate data, to obtain an initialized database and associated data.

[0189] The document management module configures the initialized database and the associated data based on data synchronization parameters, calculates and updates index values, and generates index business data and historical record data.

[0190] The historical tracking module tracks and analyzes the index business data and the historical record data, displays the obtained index tracking analysis results in multiple forms, and generates an index multi-dimensional comprehensive result set.

[0191] The report generation module verifies the index multi-dimensional comprehensive result set according to verification requests and verification rules, and integrates and generates land space planning index monitoring results.

[0192] The application provides a planning index monitoring device, which comprises at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the multi-level land space planning index monitoring method in the above-embodiment one.

[0193] Reference will be made to Figure 6 which shows a structural schematic diagram of a planning index monitoring device suitable for implementing the embodiments of the application. The planning index monitoring device in the embodiments of the application can include but is not limited to mobile terminals such as mobile phones, notebook computers, Internet of Things monitoring devices, personal digital assistants (PDA), tablet computers (PAD), portable multimedia players (PMP), smart analysis and decision support devices, and fixed terminals such as digital twin systems and desktop computers. Figure 6 The planning index monitoring device shown is only an example and should not impose any limitation on the functions and application scope of the embodiments of the application.

[0194] As Figure 6As shown, the planning index monitoring device can include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 1002 or loaded from a storage device 1003 into a random access memory (RAM) 1004. Various programs and data required for operation of the planning index monitoring device are also stored in the random access memory 1004. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other by a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the planning index monitoring device to communicate wirelessly or by wire with other devices to exchange data. Although the planning index monitoring device is shown as having various systems, it should be understood that all of the shown systems are not required to be implemented or possessed. More or fewer systems can be alternatively implemented or possessed.

[0195] The planning index monitoring device provided by the present application adopts the multi-level land space planning index monitoring method in the above embodiments, and can solve the technical problem of poor land space planning effect. Compared with the prior art, the planning index monitoring device provided by the present application has the same beneficial effects as the multi-level land space planning index monitoring method provided by the above embodiments, and other technical features in the planning index monitoring device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0196] The above merely provides a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0197] The present application provides a computer readable storage medium having computer readable program instructions (i.e., computer programs) stored thereon, the computer readable program instructions being used to execute the multi-level land space planning index monitoring method in the above embodiments.

[0198] The computer readable storage medium provided in the application may be, for example, a U disk, but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination of the above. More specific examples of the computer readable storage medium may include, but are not limited to, an electrical connection with one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present 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 can be transmitted by any suitable medium, including but not limited to an electrical wire, an optical cable, a radio frequency (RF), and the like, or any suitable combination of the above.

[0199] The computer readable storage medium described above carries one or more programs, which, when executed by the planning index monitoring device, cause the planning index monitoring device to: after completing the table creation of the database and the initial index basic information, spatial coordinate data input, establish the association relationship between the index and the spatial unit, obtain the initialization database and the association data; based on the data synchronization parameter configuration, the initialization database and the association data, calculate and update the index value, generate index business data and historical record data; after tracing and analyzing the index business data and the historical record data, the obtained index tracing analysis result is displayed in multiple forms, and an index multi-dimensional comprehensive result set is generated; according to the verification request and the verification rule, the index multi-dimensional comprehensive result set is verified, and a land space planning index monitoring result is integrated and generated.

[0200] The readable storage medium provided in the application is a computer readable storage medium, which stores computer readable program instructions (i.e. computer programs) for executing the above-mentioned multi-level land space planning index monitoring method, and can solve the technical problem of poor land space planning effect. Compared with the prior art, the computer readable storage medium provided in the application has the same beneficial effects as the multi-level land space planning index monitoring method provided in the above-mentioned embodiments, and will not be repeated here.

[0201] The above merely describes some embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation, direct / indirect application in other related technical fields, or the like, which is made based on the technical concept of the present application and the content of the specification and drawings, is included in the patent protection scope of the present application.

Claims

1. A method for monitoring multi-level land space planning indicators, characterized in that, The method comprises: After completing the table creation of the database and the input of the initial index basic information and spatial coordinate data, an association relationship between the index and the spatial unit is established, and an initialized database and associated data are obtained; Based on the data synchronization parameters, the initialized database and the associated data are configured, the index values are calculated and updated, and index business data and historical record data are generated; According to a multi-dimensional interaction request, index data corresponding to the level, year, and type in the index business data and the historical record data and index change related records are retrieved, and target retrieval data are obtained; The index planning values and implementation values of different spatial levels and corresponding years in the target retrieval data and index change information are extracted, and index traceability analysis core data sets are obtained; According to the index traceability analysis core data sets, the changes in planning values are sorted according to the full-dimensional time record, and index preliminary analysis results are generated; The index preliminary analysis results are integrated according to the structure of the level comparison data and the historical change data, the data source and the calculation logic are determined, and index traceability analysis results are outputted; After integrating the target retrieval data and the index traceability analysis results, the results are displayed in three forms of map annotation, list presentation, and card highlighting, and index multi-dimensional comprehensive achievement sets are outputted; According to a verification request and a verification rule, the index multi-dimensional comprehensive achievement sets are verified, and land space planning index monitoring results are integrated and generated.

2. The method of claim 1, wherein the multi-level national spatial planning index is monitored by a plurality of monitoring devices. After completing the table creation of the database and the input of the initial index basic information and spatial coordinate data, an association relationship between the index and the spatial unit is established, and an initialized database and associated data are obtained; According to business requirements, table creation is completed in the database, and a double-database framework containing various empty table structures is outputted; Based on the double-database framework, the initial index basic information and the spatial coordinate data are inputted, and a double-database filled with initial data is generated; Through corresponding matching of index codes and spatial unit codes, an association relationship between the index and the spatial unit is established and stored in the double-database filled with initial data, and the initialized database and the associated data are obtained.

3. The method of claim 1, wherein the multi-level national spatial planning index is monitored by a plurality of monitoring devices. Based on the initialized database and the associated data, the corresponding data synchronization parameters are called, and a data synchronization service is started to obtain original index data from an external data source interface; According to preset data cleaning rules and operation logic, the preprocessing of the original index data and the calculation of derived data are completed, and standardized index data are outputted; The standardized index data are compared with the existing data of the index value table in the initialized database, and the index business data and the historical record data are outputted according to the data comparison rule. The step of verifying the index multi-dimensional comprehensive achievement sets according to a verification request and a verification rule, and integrating and generating land space planning index monitoring results comprises:

4. The method of claim 1, wherein the multi-level national land space planning index is monitored by a plurality of levels of government. ​ Based on the verification request, the index multi-dimensional comprehensive result set is verified according to the verification rule, and the land space planning index monitoring result is generated. The difference and proportion of cross-level indicators in the index connection verification data source are calculated, the connection state of each index is judged by comparing the verification rule, and the abnormal reason and calculation basis are marked, and the verification result is output. After the completion of the database table creation and the initial index basic information and spatial coordinate data input, the index and spatial unit association relationship is established, and the initialization database and the associated data are obtained.

5. The method of claim 1, wherein the multi-level national land space planning index is monitored by a plurality of levels of government. Based on the business data storage requirements, the table types and table core fields that the index database and the graph database need to carry are determined, and the double-database table structure scheme is output. According to the double-database table structure scheme, the corresponding table structure is created in the index database and the graph database, and the double-database initial framework is obtained. The table structure adaptation information of the double-database initial framework is checked, the field exception problem is corrected, and the double-database empty table framework is output. Before the step of configuring the initialization database and the associated data based on the data synchronization parameters, calculating and updating the index value, and generating the index business data and historical record data, the multi-level land space planning index monitoring method further includes:

6. The method of claim 1, wherein the multi-level national land space planning index is monitored by a plurality of levels of government. Based on the initialization database and the associated data, combined with the business scene requirements, the parameter types to be configured and the business constraint conditions of each parameter are determined, and the data synchronization parameter configuration requirement list is output. According to the data synchronization parameter configuration requirement list, the legal and effective data source interface address is obtained by connecting the external data source provider, and the synchronization frequency is determined according to the index type, and the preliminary data synchronization parameter scheme is obtained. The preliminary data synchronization parameter scheme is checked, and the parameter details are adjusted for test abnormal conditions, and the data synchronization parameters are generated. After the step of verifying the index multi-dimensional comprehensive result set according to the verification request and the verification rule, and integrating the land space planning index monitoring result, the multi-level land space planning index monitoring method further includes:

7. The method of claim 1, wherein the multi-level national spatial planning index is monitored by a plurality of sensors. According to the preset table structure classification of the double-database, the verification conclusion, the abnormal index list, the spatial position information and the historical trace data in the land space planning index monitoring result are respectively stored in the corresponding data table, and the monitoring result data set is obtained. According to the preset achievement delivery requirement, the core information in the monitoring result data set is extracted and arranged in a standard format, and the index monitoring successful delivery data is obtained. Based on the index monitoring successful delivery data, the table data and dynamic association information of the initialization database are updated, and the updated database is output. The multi-level land space planning index monitoring system includes:

8. A multi-level land space planning index monitoring system, characterized in that, ​ A database planning module establishes an association between indexes and spatial units after completing table creation of the database and inputting initial index basic information and spatial coordinate data, obtaining an initialized database and associated data; A document management module configures the initialized database and the associated data based on data synchronization parameters, calculates and updates index values, and generates index business data and historical record data; A historical tracking module retrieves index data and index change related records corresponding to the level, year, and type in the index business data and the historical record data according to a multi-dimensional interaction request, obtains target search data, extracts index planning values and implementation values of different spatial levels and corresponding years and index change information in the target search data, obtains an index tracking analysis core data set, sorts out a time context according to the index tracking analysis core data set to generate an index preliminary analysis result, integrates the index preliminary analysis result according to the structure of level comparison data and historical change data, determines data sources and calculation logic, and outputs an index tracking analysis result, and integrates the target search data and the index tracking analysis result, and displays the result through three forms of map annotation, list presentation, and card highlighting, and outputs an index multi-dimensional comprehensive result set; A report generation module verifies the index multi-dimensional comprehensive result set according to a verification request and a verification rule, and integrates and generates a land space planning index monitoring result.

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