A big data driven government affair data application management platform

Abstract

The application discloses a big data driven government affair data application management platform, and relates to the technical field of government affair data management.The platform comprises a data acquisition module, a data classification module, a scene-based label dynamic mapping module, a time-space dimension dynamic indexing module, an index library construction module and a user interaction module.The application breaks through the management bottleneck of traditional government affair data dispersion and fragmentation by constructing a multi-module collaborative government affair data application management platform.The data acquisition module realizes the full quantity convergence of enterprise registration, project approval and credit supervision data for five types of government agencies based on a three-dimensional classification system based on themes, sources and access modes, and combines temporary storage and dynamic reminding mechanism of unclassified theme data to ensure the integrity and traceability of data classification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of government data management, in particular to a big data driven government data application management platform. BACKGROUND

[0002] With the rapid development of information technology, data has become the core resource to promote social progress and economic development. In various industries, the generation and accumulation of massive data provide rich materials for data analysis, mining and application. Especially in the fields of enterprise service, project management and credit supervision, the integration and efficient use of data become the key to improving management efficiency and optimizing resource allocation. In the traditional mode, data is scattered in various departments and systems, forming information islands, making it difficult to realize cross-field and cross-department data sharing and comprehensive analysis. The rise of big data technology provides a possible solution to this problem. Through data collection, classification, tagging and index construction, centralized management, intelligent analysis and efficient use of data are realized, which has become an important direction of current technological development.

[0003] In the traditional data management mode, data collection often relies on manual input or simple system docking, which is inefficient and prone to errors. The data classification standard is not unified, which makes data integration difficult and makes it difficult to form a complete data view. In the aspect of data tagging, the traditional method mostly uses static tags, which cannot be dynamically adjusted according to the changes of business processes, resulting in the disconnection between tags and actual business needs. The lack of space-time dimension information makes data analysis lack depth and breadth in space and time, making it difficult to meet the analysis needs in complex business scenarios. In addition, the traditional system has single function in user interaction, limited query conditions and insufficient data visualization display, which seriously affects the user experience and the efficiency of data analysis. These problems jointly restrict the full play of data value and become a difficult problem to be solved in the field of data management and application.

[0004] Therefore, the development of a big data driven government data application management platform will effectively promote the innovative development of data management and application field and provide strong data support and analysis tools for various industries. SUMMARY

[0005] The purpose of the present application is to overcome the shortcomings of the prior art and provide a big data driven government data application management platform. Through the collaborative work of six modules including data collection, classification, scenario-based tag dynamic mapping, space-time dimension index, index library construction and user interaction, the automatic integration and dynamic management of multi-source heterogeneous data are realized. The platform can establish a dynamic mapping mechanism between tags and business processes, support space-time dual-dimension composite retrieval, and provide visual query and custom index management functions, effectively solving the problems of rigid tags, lack of space-time analysis and low integration efficiency in traditional data management methods.

[0006] The application provides the following technical solutions to solve the above technical problems: a big data driven government data application management platform, which comprises a data acquisition module, a data classification module, a scenario-based label dynamic mapping module, a time-space dimension dynamic indexing module, an index library construction module and a user interaction module;

[0007] The data acquisition module: interfaces with data sources of various government agencies such as market supervision, administrative approval, industrial and information technology, environmental protection and civil affairs, and collects various government data;

[0008] The data classification module: receives the original government data transmitted by the data acquisition module, classifies the data according to data theme, data source and data access method, identifies the data without theme division, and transmits them to the scenario-based label dynamic mapping module and the user interaction module respectively;

[0009] The scenario-based label dynamic mapping module: used for generating scenario labels for the classified data based on government business processes, establishing a dynamic mapping relationship between the labels and the business links, adjusting the label system when the business process is updated, and transmitting to the user interaction module and the time-space dimension dynamic indexing module;

[0010] The time-space dimension dynamic indexing module: receives the labeled data transmitted by the scenario-based label dynamic mapping module, extracts the time attribute and space attribute of the data, constructs a time-space two-dimensional index, and transmits it to the index library construction module;

[0011] The index library construction module: based on the index of constructing the time-space two-dimensional index, the index library is constructed, the name, source, period and screening condition of the index are configured, and the addition, editing and deletion operations of the index are supported;

[0012] The user interaction module: integrates the data of each module, provides a data viewing interface and a query entry, and supports users to query data through multiple conditions.

[0013] Further, in the data acquisition module, the types of government data collected include enterprise registration information, project approval data, enterprise credit supervision data, technology innovation demonstration enterprise list, enterprise social security number and ranking information in online approval project management, and enterprise comprehensive evaluation related data.

[0014] Further, in the classification dimension of the data classification module, the data theme includes enterprise information theme, project approval theme, credit supervision theme and technology innovation theme; the data source includes market supervision administration, administrative approval service bureau, industrial and information technology bureau, ecological environment bureau and private economy development bureau; the data access method includes database direct connection, API interface call, file upload and stored procedure call.

[0015] Further, in the data classification module, when identifying data without divided topics, the collection time, data format and data size information of the data are recorded synchronously, and a temporary storage directory of the data without divided topics is established to regularly remind the administrator to divide the topics.

[0016] Further, in the scenario-based label dynamic mapping module, the government affair business processes include: enterprise registration process, project approval process, credit supervision process; and the generated scenario labels include: enterprise opening scenario, energy saving review scenario, environmental impact assessment scenario, and credit supervision scenario.

[0017] Further, in the scenario-based label dynamic mapping module, the specific steps of establishing the dynamic mapping relationship between the label and the business link are:

[0018] (1) the business attribute features of the classified post-data are extracted, including the government affair business types related to the data and the business links involved;

[0019] (2) then, the elements of each link of the government affair business process are sorted out, and the core data requirements and features of each business link are clearly and explicitly determined;

[0020] (3) based on the matching relationship between the data business attribute features and the business link elements, the corresponding scenario labels are allocated to the data;

[0021] (4) then, the corresponding relationship between the scenario labels and the business links is recorded to form an initial dynamic mapping relationship table;

[0022] (5) the changes of the government affair business process are continuously and real-timely monitored, and once the business link is increased, decreased or adjusted, the mapping relationship updating mechanism is triggered immediately;

[0023] (6) according to the change content of the business process, the mapping relationship between the scenario label and the business link is adjusted, the mapping relationship table is updated, and is synchronously transmitted to the user interaction module and the time-space dimension dynamic indexing module.

[0024] Further, in the time-space dimension dynamic indexing module, the extracted time attributes include the acceptance time, the settlement time and the data update cycle of the approval handling, and the space attributes include the enterprise registration address, the project construction site and the jurisdictional region.

[0025] Further, in the time-space dimension dynamic indexing module, the specific steps of constructing the time-space double dimension index are:

[0026] (1) the labeled data transmitted by the scenario-based label dynamic mapping module is received, and the time-related information and the space-related information contained in the data are analyzed;

[0027] (2) the timestamp, the time interval and the update cycle are extracted from the data;

[0028] (3) Extracting longitude and latitude, administrative division, and spatial grid information from the data;

[0029] (4) Corresponding the timestamp to a time axis, slicing the data by time, establishing a time sequence correlation, and forming a time dimension index structure;

[0030] (5) Mapping the longitude and latitude to a spatial coordinate system, combining administrative division and spatial grid for regional division, constructing a spatial topology relationship, and forming a spatial dimension index structure;

[0031] (6) Binding the time dimension index and the spatial dimension index through the data unique identifier, forming a space-time two-dimensional index, and completing the construction and association to the user interaction module.

[0032] Further, in the index library construction module, the specific steps of constructing the index library based on the indexes of the constructed space-time two-dimensional index are:

[0033] (1) Obtaining the government data of the government data index information of the constructed space-time two-dimensional index from the space-time dimension dynamic index module, and analyzing the quantifiable index information contained in each piece of data one by one;

[0034] (2) Summarizing the parsed indexes, removing duplicate indexes, and initially forming an index list;

[0035] (3) Configuring basic information for each index in the list, including index name, data source department, data theme, and access method;

[0036] (4) Defining the statistical period of each index and setting the filtering conditions of the index;

[0037] (5) Classifying the configured indexes by data theme or business scenario, and building a hierarchical structure of the index library;

[0038] (6) Developing an index management function interface to support adding, editing, and deleting operations on the indexes in the index library, and completing the construction of the index library.

[0039] Further, the user interaction module supports query conditions including keywords, data theme, data source department, scene label, time range, space range, and custom indexes, and adopts list and chart forms for data visualization display.

[0040] Compared with the prior art, the big data driven government data application management platform has the following beneficial effects:

[0041] I. This invention, by constructing a multi-module collaborative government data application management platform, breaks through the traditional bottleneck of fragmented and scattered government data management. The data collection module achieves full aggregation of data from five types of government agencies, including enterprise registration, project approval, and credit supervision. The data classification module, based on a three-dimensional classification system of theme, source, and access method, combined with a temporary storage and dynamic reminder mechanism for unclassified data, ensures the integrity and traceability of data classification. The scenario-based label dynamic mapping module automatically generates scenario labels for enterprise establishment and energy conservation review by extracting the matching relationship between business attribute features and government process links, and establishes a dynamic mapping relationship table. When business processes change, the mapping relationship is updated in real time, ensuring that the label system iterates synchronously with business needs. This design improves the application flexibility of government data, avoids the problem of data label failure due to business process adjustments, and provides a data foundation for the precision of government services.

[0042] Second, this invention achieves dual innovation in spatiotemporal correlation analysis and visual query of government data through the coordinated design of a spatiotemporal dynamic index module and a user interaction module. The spatiotemporal dynamic index module extracts spatiotemporal attributes from tagged data, constructs a composite index structure of time series and spatial topology, and supports multi-dimensional retrieval. The user interaction module integrates data from various modules, provides seven types of query conditions, and supports list and chart visualization formats. This not only meets the needs of government personnel for in-depth data mining, but also empowers users with autonomy in data configuration through the custom indicator management function, thereby improving the efficiency of government data analysis and the scientific nature of decision-making.

[0043] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0045] Figure 1 Workflow diagram for a big data-driven government data application management platform;

[0046] Figure 2 A logical framework diagram for classifying and processing government data;

[0047] Figure 3 A flowchart for dynamically mapping scenario-based tags to business processes;

[0048] Figure 4 A flow chart for constructing a spatio-temporal two-dimensional index of government data;

[0049] Figure 5 A flow chart for constructing and managing a government data index library. DETAILED DESCRIPTION

[0050] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object of the application, the specific embodiments, structures, features and effects thereof according to the present application are described in detail below in combination with the drawings and preferred embodiments.

[0051] Embodiment one: government data application management in the enterprise credit supervision scenario.

[0052] In order to realize accurate and dynamic supervision of the credit status of enterprises in the jurisdiction, improve the supervision efficiency and pertinence, and rely on the big data driven government data application management platform of the present application, the specific implementation process and the role of each link are as follows:

[0053] The data collection module connects the enterprise credit supervision system of the market supervision administration, the project violation record database of the administrative examination and approval service bureau, and the environmental protection penalty information base of the ecological environment bureau, and comprehensively collects various government data related to credit, such as enterprise administrative penalty records, abnormal business records, environmental protection violation data, and project approval violation information, to provide a comprehensive and complete data basis for subsequent supervision analysis.

[0054] After receiving the above raw data, the data classification module classifies the data according to the "credit supervision theme", and clearly marks the data source (such as the market supervision administration, the administrative examination and approval service bureau) and the access method (such as database direct connection, API interface call). For some environmental monitoring raw data that does not explicitly associate with the credit supervision theme, this module will record the information of the collection time, data format and data size, establish a temporary storage directory and regularly remind the management personnel to supplement the theme division. The role of this classification processing is to make the disordered raw data become orderly, facilitate the accurate processing and calling of the data by the subsequent module, and at the same time ensure that the data not divided into themes is properly managed, avoiding data omission.

[0055] The scenarioized label dynamic mapping module generates "credit warning scenario" and "loss of credit joint punishment scenario" labels for classified credit data based on credit supervision business processes, and establishes a dynamic mapping relationship between the labels and each business link of credit supervision. For example, the records of administrative penalties imposed on an enterprise for several consecutive times are mapped to the "credit warning scenario", and the data of serious environmental violations and non-reform are mapped to the "loss of credit joint punishment scenario". This link plays a role in closely combining data with actual business scenarios, making data more business-oriented, and facilitating staff to quickly identify the business scenarios corresponding to different credit conditions, thereby improving the pertinence of data application.

[0056] When the credit supervision process adds a "credit repair acceptance" link, the platform will automatically trigger the mapping relationship update mechanism, add a "credit repair scenario" label to the data of enterprise credit repair application materials and acceptance results, and synchronously update it to the space-time dimension dynamic indexing module. This dynamic adjustment ensures that the label system is synchronized with the business process, guarantees the timeliness and accuracy of data labels, and adapts to new demands brought by business changes.

[0057] The space-time dimension dynamic indexing module extracts the time attributes (such as the acceptance time of administrative penalties and the effective period of loss of credit records) and spatial attributes (such as the street where the enterprise is registered and the construction site of environmental violation projects) from the data, and constructs a space-time two-dimensional index. The time dimension index is established by time slicing, the space dimension index is divided by administrative division, and the two are bound through the enterprise unified social credit code. This link plays a role in significantly improving data query efficiency, and staff can quickly locate the required credit supervision data according to time and space conditions, providing efficient data retrieval support for accurate supervision.

[0058] The index library construction module sorts related indexes based on the construction of space-time two-dimensional indexes, constructs an index library containing "enterprise annual loss of credit times" and "cross-department joint punishment implementation times", and configures the sources, periods and screening conditions of each index. At the same time, it supports management personnel to add, edit or delete indexes through an interface, such as adding a "credit repair completion rate" index to evaluate the effectiveness of enterprise credit repair. This module plays a role in providing quantitative analysis tools for credit supervision work, facilitating staff to quantitatively evaluate and trend analyze the credit status of enterprises through a clear index system, thereby improving the scientificity and objectivity of supervision.

[0059] The user interaction module integrates the data processed by each module, provides an intuitive data viewing interface and diversified query entrances, and market supervision bureau staff can obtain detailed information of relevant enterprises by inputting a query condition of "credit warning enterprises in a specific region", the platform displays the enterprise name, credit loss reason, and warning time in a list form, and visualizes the regional distribution of the enterprises by a heat map. The role of this link is to provide a convenient and friendly data usage mode for users, so that the staff can quickly obtain the required information and intuitively understand the credit supervision status, thereby assisting the supervision department in accurately carrying out the interview and rectification supervision work, and improving the efficiency and effect of the supervision work.

[0060] In summary, in the enterprise credit supervision scenario, the big data driven government data application management platform breaks down department barriers through the data collection module, gathers multi-source credit data, the classification module realizes orderly data sorting and guarantees data integrity, the scenario-based label dynamic mapping module accurately associates data with the supervision scenario and dynamically adjusts with the business process, the time and space dimension dynamic indexing module improves data retrieval efficiency, the index library provides quantitative analysis tools, and the user interaction module supports supervision decision-making in a visual manner, as shown in Figure 1 , and overall forms a closed loop of "data gathering-classification label-time and space indexing-quantitative analysis-precise application", effectively improving the comprehensiveness, pertinence and scientificity of credit supervision, and helping the supervision department to realize accurate supervision and efficient governance.

[0061] Embodiment two: government data application management in the project approval efficiency analysis scenario.

[0062] An administrative examination and approval service bureau wants to analyze the project approval efficiency, find out the problems existing in the approval process and optimize them, and promotes the work by means of the big data driven government data application management platform of the application, and the specific implementation process and the role of each link are as follows:

[0063] The data collection module interfaces the online examination and approval system of the administrative examination and approval service bureau and the data source of the technical transformation project library of the industrial and information technology bureau, collects project approval case data (such as acceptance time, approval link time consumption), enterprise social security number, project ranking information and data related to project approval, and the role of this link is to integrate the project approval data scattered in different systems, form a comprehensive approval data resource, and provide data support for subsequent efficiency analysis.

[0064] The data classification module classifies the collected data according to "project approval theme", marks the data source (such as the administrative examination and approval service bureau and the industrial and information technology bureau) and the access mode (such as API interface calling and file uploading), and the role of this classification is to make the project approval data systematic, facilitate the subsequent modules to carry out special processing and analysis according to the approval theme, and improve the accuracy of data processing, as shown in Figure 2 .

[0065] The scenarioized label dynamic mapping module generates "energy saving review scene" and "environmental impact assessment approval scene" labels for the classified data based on the project approval process (project establishment, energy saving review, environmental impact assessment, and completion acceptance), and establishes a dynamic mapping relationship between the labels and each approval link. For example, the energy saving review opinion and expert review opinion data are mapped to the "energy saving review scene". The role of this link is to make the data correspond to the specific approval link, so that the staff can clearly understand the data situation of different approval links, and provide clear data direction for analyzing the efficiency of each link.

[0066] When the approval process is simplified and the "project establishment and land use planning permission" link is combined, the platform automatically adjusts the label mapping relationship, and integrates the data originally belonging to two links into the "project establishment and land use planning scene". The role of this dynamic adjustment is to ensure that the label system is consistent with the changed approval process, and to ensure that the data can accurately reflect the situation of the new approval link, and provide accurate data basis for efficiency analysis, as shown in Figure 3 .

[0067] The space-time dimension dynamic indexing module extracts the time attribute (such as the acceptance time and completion time of energy saving review) and space attribute (such as the park to which the project construction location belongs) of the data, constructs a space-time two-dimensional index, establishes a time dimension index according to time slicing, establishes a space dimension index according to administrative division or park division, and binds the two through the project unique identifier. The role of this link is to realize the quick positioning and retrieval of project approval data, and the staff can quickly obtain the corresponding approval data according to the time range and space area, and provide efficient data query support for analyzing the approval efficiency of different time periods and different areas, as shown in Figure 4 .

[0068] The index library construction module, based on the data transmitted by the space-time dimension dynamic indexing module, sorts out the "average approval time" and "link overtime rate" related to the approval efficiency, configures the source, period (such as weekly update) and filtering conditions (such as for key investment projects) of each index, and the staff can also edit the index through the interface, such as adding the "energy saving review link time consumption proportion" index to analyze the influence of this link on the overall approval efficiency. The role of this module is to provide quantitative analysis indexes, making the approval efficiency analysis more specific and measurable, and facilitating the staff to find the bottleneck problems in the approval process, as shown in Figure 5 .

[0069] The user interaction module integrates data of each module, provides a data viewing interface and a query entry. An administrative examination and approval service bureau staff member inputs a "specific time period key project energy saving review overtime condition" query condition, and the platform displays the number of overtime projects in each time period in a column chart, presents overtime reasons (such as incomplete materials and expert review delay) in a list, and locates the park where the overtime projects are concentrated through spatial indexing. The role of this link is to provide the staff member with intuitive and easy-to-understand data display and convenient query methods, so that the staff member can quickly master the examination and approval efficiency and existing problems, provide data support for targeted optimization of the examination and approval process (such as adding a park pre-examination link), and thus improve the overall project examination and approval efficiency.

[0070] To sum up, in the project examination and approval efficiency analysis scene, the platform integrates multi-department examination and approval data through the data collection module, lays a foundation for efficiency analysis; the classification module sorts data according to examination and approval themes, ensures data processing accuracy; the scenario-based label dynamic mapping module realizes dynamic binding of data and examination and approval links, adapts to process changes; the space-time dimension dynamic indexing module supports fast data retrieval, meets analysis needs in different space-time ranges; the index library provides quantitative indexes, helps identify examination and approval bottlenecks; and the user interaction module presents analysis results in an intuitive form. The platform enables examination and approval efficiency analysis in the whole process, from data integration to problem positioning to process optimization suggestions, forms a complete support system, and effectively promotes examination and approval process optimization and efficiency improvement.

[0071] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above with a preferred embodiment, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, without departing from the scope of the technical solution of the present application. Any simplification, modification, equivalent change and modification of the above embodiments made according to the technical essence of the present application are still within the scope of the technical solution of the present application.

Claims

1. A big data-driven government data application management platform, characterized in that, The platform includes a data acquisition module, a data classification module, a scenario-based label dynamic mapping module, a spatiotemporal dimension dynamic indexing module, an indicator library construction module, and a user interaction module. The data acquisition module connects to data sources from various government agencies, including market supervision, administrative approval, industry and information technology, environmental protection, and civil affairs, and aggregates various government data. The data classification module receives raw government data transmitted from the data acquisition module, classifies the data according to data theme, data source, and data access method, and simultaneously identifies data without a theme, and transmits it to the scenario-based label dynamic mapping module and the user interaction module respectively. When identifying data that has not been assigned a topic, the data collection time, data format, and data size information are recorded simultaneously, and a temporary storage directory for the unassigned data is established to periodically remind administrators to assign topics. The government service processes include: business registration process, project approval process, and credit supervision process; the generated scenario tags include: business establishment scenario, energy conservation review scenario, environmental impact assessment scenario, and credit supervision scenario. The scenario-based label dynamic mapping module is used to generate scenario labels based on the classified data of government business processes, establish a dynamic mapping relationship between labels and business processes, adjust the label system when the business processes are updated, and transmit the data to the user interaction module and the spatiotemporal dimension dynamic index module. The specific steps for establishing a dynamic mapping relationship between tags and business processes are as follows: (1) First, extract the business attribute features of the classified data, covering the types of government affairs related to the data and the business links involved; (2) Next, sort out the elements of each link in the government business process and clearly define the core data requirements and characteristics of each business link; (3) Assign corresponding scenario labels to the data based on the matching relationship between the data business attribute characteristics and the business process elements; (4) Then record the correspondence between scene tags and business links to form an initial dynamic mapping table; (5) Continuously monitor changes in government business processes in real time. Once a business process is added, reduced, or adjusted, the mapping relationship update mechanism will be triggered immediately. (6) Adjust the mapping relationship between scene tags and business links according to the changes in business processes, update the mapping relationship table, and synchronize it to the user interaction module and the spatiotemporal dimension dynamic index module. The spatiotemporal dynamic indexing module receives tagged data transmitted by the scenario-based label dynamic mapping module, extracts the time and spatial attributes of the data, constructs a spatiotemporal dual-dimensional index, and transmits it to the indicator library construction module. The indicator library construction module: Based on the indicators for constructing a spatiotemporal dual-dimensional index, it organizes and constructs an indicator library, configures the indicator name, source, period, and filtering conditions, and supports the addition, editing, and deletion of indicators; The user interaction module integrates data from various modules, provides a data viewing interface and query entry point, and supports users to query data based on multiple conditions.

2. The big data-driven government data application management platform according to claim 1, characterized in that, The data collection module collects government data including enterprise registration information, project approval data, enterprise credit supervision data, a list of technology innovation demonstration enterprises, information on the number of employees and rankings of enterprises in online approval project management, and relevant data on comprehensive enterprise evaluation.

3. The big data-driven government data application management platform according to claim 1, characterized in that, The data classification module includes the following classification dimensions: data themes include enterprise information, project approval, credit supervision, and technological innovation; data sources include the Market Supervision Administration, Administrative Approval Service Bureau, Industry and Information Technology Bureau, Ecology and Environment Bureau, and Private Economy Development Bureau; and data access methods include direct database connection, API interface call, file upload, and stored procedure call.

4. The big data-driven government data application management platform according to claim 1, characterized in that, In the spatiotemporal dynamic index module, the extracted time attributes include the acceptance time, completion time, and data update cycle of the approval process, while the spatial attributes include the company's registered address, project construction location, and local jurisdiction.

5. The big data-driven government data application management platform according to claim 1, characterized in that, The specific steps for constructing the spatiotemporal dual-dimensional index in the spatiotemporal dimension dynamic index module are as follows: (1) Receive tagged data transmitted by the contextualized tag dynamic mapping module and parse the time-related and space-related information contained in the data; (2) Extract timestamps, time intervals, and update cycles from the data; (3) Extract latitude and longitude, administrative divisions, and spatial grid information from the data; (4) Map timestamps to timelines, divide data by time slices, establish time series relationships, and form a time dimension index structure; (5) Map latitude and longitude to a spatial coordinate system, combine administrative divisions and spatial grids to divide regions, construct spatial topological relationships, and form a spatial dimension index structure; (6) Bind the time dimension index and the spatial dimension index through the unique data identifier to form a spatiotemporal dual-dimensional index, complete the construction and associate it with the user interaction module.

6. The big data-driven government data application management platform according to claim 1, characterized in that, In the indicator library construction module, the specific steps for organizing and constructing the indicator library based on indicators with a spatiotemporal dual-dimensional index are as follows: (1) Obtain government data index information with constructed spatiotemporal dual-dimensional index from the spatiotemporal dynamic index module, and analyze the quantifiable indicator information contained in each data item one by one; (2) Summarize the analyzed indicators, remove duplicate indicators, and form a preliminary indicator list; (3) Configure basic information for each indicator in the list, including indicator name, data source department, data theme, and access method; (4) Define the statistical period for each indicator and set the screening criteria for the indicators; (5) Classify the configured indicators according to data themes or business scenarios, and build a hierarchical structure for the indicator library; (6) Develop an indicator management function interface to support adding, editing and deleting indicators in the indicator library and complete the construction of the indicator library.

7. The big data-driven government data application management platform according to claim 1, characterized in that, The user interaction module supports query conditions including keywords, data themes, data source departments, scene tags, time ranges, spatial ranges, and custom indicators, and uses lists and charts to visualize the data.

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