Government affair data application management platform driven by big data

Abstract

The invention 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 scenarized label dynamic mapping module, a space-time dimension dynamic index module, an index library construction module and a user interaction module; according to the invention, a multi-module collaborative government affair data application management platform is constructed, the management bottleneck of traditional government affair data dispersion and splitting is broken through, and the data acquisition module realizes enterprise registration, project approval and credit supervision data total convergence for five types of government affair institutions; the data classification module is based on a three-dimensional classification system of a theme, a source and an access mode, and is combined with a temporary storage and dynamic reminding mechanism of data without theme division, so that the integrity and traceability of data classification are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of government data management, and specifically to a big data-driven government data application management platform. Background Art

[0002] With the rapid development of information technology, data has become a core resource driving social progress and economic development. Across all industries, the generation and accumulation of massive amounts of data provides a rich source of material for data analysis, mining, and application. Particularly in the fields of enterprise services, project management, and credit supervision, the integration and efficient use of data are key to improving management efficiency and optimizing resource allocation. Traditionally, data is dispersed across various departments and systems, forming information silos that make cross-domain and cross-departmental data sharing and comprehensive analysis difficult. The rise of big data technology has provided a possible solution to this problem. Through data collection, classification, labeling, and indexing, centralized data management, intelligent analysis, and efficient utilization are now a key direction of technological development.

[0003] Under traditional data management models, data collection often relies on manual entry or simple system integration, which is inefficient and prone to errors. Inconsistent data classification standards make data integration difficult and make it difficult to form a complete data view. Traditional methods for data labeling often use static labels that cannot be dynamically adjusted according to changes in business processes, resulting in a disconnect between labels and actual business needs. The lack of information in the spatiotemporal dimension means that data analysis lacks spatial and temporal depth and breadth, making it difficult to meet the analytical needs of complex business scenarios. Furthermore, traditional systems have limited user interaction functionality, limited query conditions, and insufficient data visualization, severely impacting user experience and the efficiency of data analysis. These issues collectively restrict the full realization of data value and have become a pressing challenge in the field of data management and application.

[0004] Therefore, developing a big data-driven government data application management platform will effectively promote innovative development in the field of data management and application, and provide powerful data support and analysis tools for various industries. Summary of the Invention

[0005] The purpose of this invention is to make up for the shortcomings of the existing technology and provide a big data-driven government data application management platform. Through the collaborative work of six major modules including data collection, classification, scenario-based label dynamic mapping, spatiotemporal dimension indexing, indicator library construction and user interaction, the automatic integration and dynamic management of multi-source heterogeneous data can be realized. The platform can establish a dynamic mapping mechanism between labels and business processes, support spatiotemporal dual-dimensional compound retrieval, and provide visual query and custom indicator management functions, effectively solving the problems of label rigidity, lack of spatiotemporal analysis, and low integration efficiency in traditional data management methods.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a big data-driven government data application management platform, which 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 collection module is used to connect with data sources of various government agencies such as market supervision, administrative approval, industry and information technology, environmental protection, and civil affairs, and collect various government data; The data classification module receives the original government data transmitted by the data collection module, classifies the data according to the data subject, data source, and data access method, simultaneously identifies the data that has not been divided into topics, and transmits it to the scenario tag dynamic mapping module and the user interaction module respectively; The scenario-based tag dynamic mapping module is used to generate scenario tags for classified data based on government business processes, establish a dynamic mapping relationship between tags and business links, adjust the tag system when the business process is updated, and transmit it to the user interaction module and the spatiotemporal dimension dynamic indexing module; The spatiotemporal dimension dynamic indexing module receives the labeled data transmitted by the scenario-based label dynamic mapping module, extracts the time attributes 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: constructs an indicator library based on the indicators of the spatiotemporal dual-dimensional index, configures the name, source, period, and filtering conditions of the indicators, and supports the addition, editing, and deletion of indicators; The user interaction module integrates the data of each module, provides a data viewing interface and query entry, and supports users to query data through various conditions.

[0007] Furthermore, in the data collection module, the types of government data collected include enterprise registration information, project approval data, enterprise credit supervision data, a list of technological innovation demonstration enterprises, enterprise social security headcount and ranking information in online approval project management, and enterprise comprehensive evaluation related data.

[0008] Furthermore, in the classification dimensions of the data classification module, data subjects include enterprise information subjects, project approval subjects, credit supervision subjects, and technological innovation subjects; data sources include the Market Supervision Administration, the Administrative Approval Service Bureau, the Industry and Information Technology Bureau, the Ecological Environment Bureau, and the Private Economic Development Bureau; data access methods include direct database connection, API interface call, file upload, and stored procedure call.

[0009] Furthermore, in the data classification module, when identifying data that are not divided into topics, the data collection time, data format and data size information are recorded synchronously, and a temporary storage directory for the data that are not divided into topics is established to regularly remind managers to divide the topics.

[0010] Furthermore, in the scenario-based tag dynamic mapping module, the government business processes include: enterprise registration process, project approval process, and credit supervision process; the generated scenario tags include: enterprise establishment scenario, energy-saving review scenario, environmental impact assessment scenario, and credit supervision scenario.

[0011] Furthermore, in the scenario-based tag dynamic mapping module, the specific steps of establishing a dynamic mapping relationship between tags and business links are as follows: (1) First, extract the business attribute characteristics of the classified data, covering the government business types and business links associated with the data; (2) Then 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) Based on the matching relationship between the business attribute characteristics of the data and the elements of the business process, the corresponding scenario labels are assigned to the data; (4) Then record the corresponding relationship between the scene label and the business link to form an initial dynamic mapping relationship table; (5) Continuously monitor changes in government business processes in real time, and immediately trigger the mapping relationship update mechanism once any business link is increased, decreased, or adjusted; (6) According to the changes in the business process, adjust the mapping relationship between the scene label and the business link, update the mapping relationship table, and synchronize it to the user interaction module and the spatiotemporal dimension dynamic index module.

[0012] Furthermore, in the spatiotemporal dimension dynamic index module, the extracted time attributes include the acceptance time, completion time, and data update cycle of the approval process, and the spatial attributes include the company's registered address, project construction location, and local jurisdiction area.

[0013] Furthermore, in the spatiotemporal dimension dynamic index module, the specific steps of constructing the spatiotemporal dual-dimensional index are: (1) Receive the labeled data transmitted by the scenario-based label dynamic mapping module and parse the time-related information 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 the timestamp to the time axis, divide the data by time slices, establish time series associations, and form a time dimension index structure; (5) Mapping longitude and latitude to a spatial coordinate system, combining administrative divisions and spatial grids to perform regional divisions, constructing spatial topological relationships, and forming a spatial dimension index structure; (6) The time dimension index and the space dimension index are bound together through the unique data identifier to form a time-space dual-dimensional index, which is then constructed and associated with the user interaction module.

[0014] Furthermore, in the indicator library construction module, the specific steps of sorting out and constructing the indicator library based on the indicators of constructing the spatiotemporal dual-dimensional index are as follows: (1) Obtain government data from the spatiotemporal dynamic index module, which has built a spatiotemporal dual-dimensional index, and analyze the quantifiable indicator information contained in each piece of data 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 subject, and access method; (4) Clarify the statistical period for each indicator and set the screening conditions for the indicator; (5) Classify the configured indicators by data theme or business scenario, and build a hierarchical structure of 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.

[0015] Furthermore, the query conditions supported by the user interaction module include keywords, data themes, data source departments, scene tags, time ranges, spatial ranges and custom indicators, and data visualization is displayed in the form of lists and charts.

[0016] Compared with existing technologies, this big data-driven government data application management platform has the following beneficial effects: 1. The present invention breaks through the management bottleneck of traditional government data dispersion and fragmentation by constructing a multi-module collaborative government data application management platform. The data collection module realizes the full aggregation of enterprise registration, project approval, and credit supervision data for five types of government agencies; the data classification module is based on a three-dimensional classification system of subject, source, and access method, combined with the temporary storage and dynamic reminder mechanism of unclassified subject data to ensure the integrity and traceability of data classification; the scenario-based label dynamic mapping module automatically generates business establishment and energy-saving review scenario labels by extracting the matching relationship between business attribute characteristics and government process links, and establishes a dynamic mapping relationship table. When the business process changes, the mapping relationship update is triggered in real time to ensure the synchronous iteration of the label system and business needs. This design improves the application flexibility of government data, avoids the problem of data label failure caused by business process adjustments, and provides a data foundation for the precision of government services.

[0017] 2. The present invention realizes the dual innovation of spatiotemporal correlation analysis and visual query of government data through the linkage design of spatiotemporal dimension dynamic index module and user interaction module. The spatiotemporal dimension dynamic index module extracts spatiotemporal attributes from labeled data, constructs a composite index structure of time series and spatial topology, and supports multi-dimensional retrieval. The user interaction module integrates the data of each module, provides seven types of query conditions, and supports list and chart visual display forms. It not only meets the needs of government personnel for in-depth data mining, but also gives users data configuration autonomy through custom indicator management functions, thereby improving the efficiency of government data analysis and the scientific nature of decision-making.

[0018] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0020] Figure 1 This is a workflow diagram for the big data-driven government data application management platform; Figure 2 A logical framework diagram for the classification and processing of government data; Figure 3 Dynamically map flow charts between scenario-based tags and business processes; Figure 4 Construct a flow chart for the spatiotemporal dual-dimensional indexing of government data; Figure 5 Build and manage a flowchart for the government data indicator database. DETAILED DESCRIPTION

[0021] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0022] Example 1: Government data application management in the enterprise credit supervision scenario.

[0023] In order to achieve accurate and dynamic supervision of the credit status of enterprises within its jurisdiction and improve supervision efficiency and pertinence, a regional market supervision and administration bureau relies on the big data-driven government data application management platform of the present invention to carry out its work. The specific implementation process and the functions of each link are as follows: The data collection module connects to multiple data sources, including the enterprise credit supervision system of the Market Supervision Administration, the project violation record database of the Administrative Approval Service Bureau, and the environmental protection penalty information database of the Ecology and Environment Bureau, to comprehensively collect various credit-related government data, including enterprise administrative penalty records, business abnormality lists, environmental protection violation data, and project approval violation information, providing a comprehensive and complete data foundation for subsequent regulatory analysis.

[0024] After receiving the above-mentioned 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 Approval Service Bureau) and the access method (such as direct connection to the database, API interface call). For some environmental monitoring raw data that are not clearly associated with the credit supervision theme, the module will record its collection time, data format and data size information, establish a temporary storage directory and regularly remind managers to supplement the theme division. The purpose of this classification processing is to make the messy raw data orderly, facilitate the subsequent modules to accurately process and call the data, and ensure that the data without the theme division is properly managed to avoid data omissions.

[0025] The scenario-based tag dynamic mapping module generates scenario tags for classified credit data, such as "Credit Warning Scenario" and "Joint Punishment for Dishonesty Scenario," based on the credit supervision business process. It also establishes a dynamic mapping relationship between these tags and various credit supervision business processes. For example, a company's repeated administrative penalty records are mapped to the "Credit Warning Scenario," and data on serious environmental violations that have not been rectified is mapped to the "Joint Punishment for Dishonesty Scenario." This process aims to closely integrate data with actual business scenarios, making the data more business-oriented, enabling staff to quickly identify business scenarios corresponding to different credit statuses, and improving the targeted nature of data applications.

[0026] When the credit supervision process adds a new "credit repair acceptance" link, the platform will automatically trigger the mapping relationship update mechanism, add a "credit repair scenario" label to the company's credit repair application materials and acceptance results data, and synchronously update it to the spatiotemporal dimension dynamic index module. The purpose of this dynamic adjustment is to ensure that the labeling system remains synchronized with the business process, ensure the timeliness and accuracy of data labels, and adapt to new demands brought about by business changes.

[0027] The spatiotemporal dynamic indexing module extracts temporal attributes (such as the time of administrative penalty acceptance and the effective period of a record of dishonesty) and spatial attributes (such as the street where a company's registered address is located and the construction site of an environmental violation) from the data. It then constructs a dual-dimensional spatiotemporal index, creating a temporal index based on time slices and a spatial index based on administrative divisions. The two are then linked using the company's unified social credit code. This step significantly improves data query efficiency, allowing staff to quickly locate required credit supervision data based on temporal and spatial conditions, providing efficient data retrieval support for precise supervision.

[0028] The indicator library construction module organizes relevant indicators based on the construction of a spatiotemporal dual-dimensional index, constructing an indicator library that includes indicators such as "Annual Number of Enterprise Dishonesty" and "Number of Interdepartmental Joint Punishment Implementations," and configures the source, cycle, and filtering conditions for each indicator. At the same time, it supports managers to add, edit, or delete indicators through the interface, such as adding a "Credit Repair Completion Rate" indicator to evaluate the effectiveness of corporate credit repair. The purpose of this module is to provide quantitative analysis tools for credit supervision. Through a clear indicator system, it facilitates staff to conduct quantitative assessments and trend analysis of corporate credit status, thereby improving the scientific and objective nature of supervision.

[0029] The user interaction module integrates data processed by various modules, providing an intuitive data viewing interface and diverse query entry points. Market Supervision Bureau staff can access detailed information about relevant companies by entering the query criteria of "Enterprises with Credit Warnings in Specific Regions." The platform displays the company name, reason for default, and warning time in a list format, and visualizes the regional distribution of companies using a heat map. This component aims to provide users with a convenient and user-friendly way to access data, enabling staff to quickly obtain the information they need and gain an intuitive understanding of the credit supervision status, thereby assisting regulatory authorities in conducting precise interviews and urging rectification, thereby improving the efficiency and effectiveness of supervision.

[0030] In summary, in the enterprise credit supervision scenario, the big data-driven government data application management platform breaks down departmental barriers through the data collection module and aggregates multi-source credit data; the classification module realizes orderly data sorting and ensures data integrity; the scenario-based label dynamic mapping module allows data to be accurately associated with the supervision scenario and dynamically adjusted with the business process; the spatiotemporal dimension dynamic indexing module improves data retrieval efficiency, and the indicator library provides quantitative analysis tools; the user interaction module supports supervision decision-making in a visual way, such as Figure 1 As shown, a closed loop of "data aggregation-classification labeling-spatiotemporal indexing-quantitative analysis-precise application" is formed as a whole, which effectively improves the comprehensiveness, pertinence and scientific nature of credit supervision, and helps regulatory authorities achieve precise supervision and efficient governance.

[0031] Example 2: Government data application management in the project approval efficiency analysis scenario.

[0032] In order to analyze the efficiency of project approval, identify problems in the approval process and optimize them, an administrative approval service bureau uses the big data-driven government data application management platform of the present invention to advance its work. The specific implementation process and the functions of each link are as follows: The data collection module connects to the online approval system of the Administrative Approval Service Bureau and the data source of the technical transformation project library of the Industry and Information Technology Bureau to collect project approval data (such as acceptance time, time spent in the approval process), number of corporate social security personnel, project ranking information and data related to project approval. The role of this link is to integrate project approval data scattered in different systems to form a comprehensive approval data resource, providing data support for subsequent efficiency analysis.

[0033] The data classification module classifies the collected data according to the "project approval theme", marking the data source (such as the Administrative Approval Service Bureau, the Industry and Information Technology Bureau) and the access method (such as API interface call, file upload). The purpose of this classification is to organize the project approval data, facilitate the subsequent modules to carry out special processing and analysis on the approval theme, and improve the accuracy of data processing, such as Figure 2 shown.

[0034] The scenario-based label dynamic mapping module generates "energy-saving review scenario" and "environmental impact assessment approval scenario" 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 scenario". 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 guidance for analyzing the efficiency of each link.

[0035] When the approval process is simplified and the "project establishment and land use planning permit" links are merged, the platform automatically adjusts the label mapping relationship and integrates the data originally belonging to the two links into the "project establishment and land use planning scenario". The role of this dynamic adjustment is to ensure that the label system is consistent with the changed approval process, ensure that the data can accurately reflect the new approval link situation, and provide an accurate data basis for efficiency analysis. Figure 3 shown.

[0036] The spatiotemporal dynamic indexing module extracts the time attributes (such as the acceptance time and completion time of energy-saving review) and spatial attributes (such as the park to which the project construction site belongs) of the data, constructs a spatiotemporal dual-dimensional index, establishes a time dimension index by time slice, divides the spatial dimension index by administrative division or park, and binds the two through the project's unique identifier. The role of this link is to achieve rapid positioning and retrieval of project approval data. Staff can quickly obtain corresponding approval data based on time range and spatial area, and provide efficient data query support for analyzing the approval efficiency of different time periods and different areas. Figure 4 shown.

[0037] The indicator library construction module, based on the data transmitted by the spatiotemporal dynamic index module, sorts out the indicators related to approval efficiency such as "average approval time" and "link timeout rate", configures the source, cycle (such as weekly update) and screening conditions (such as for key investment projects) of each indicator, and staff can also edit indicators through the interface, such as adding the "time consumption ratio of energy-saving review link" indicator to analyze the impact of this link on the overall approval efficiency. The role of this module is to provide quantitative analysis indicators to make the approval efficiency analysis more specific and measurable, so that staff can find bottleneck problems in the approval process, such as Figure 5 shown.

[0038] The user interaction module integrates data from each module, providing a data viewing interface and query entry. Administrative Approval Service Bureau staff enter the query criteria for "timeouts for energy-saving reviews of key projects in a specific time period." The platform displays the number of timeout projects in each time period in a bar chart, lists the reasons for the timeouts (such as incomplete materials and delayed expert reviews), and uses spatial indexing to locate industrial parks with concentrated timeout projects. This step provides staff with intuitive and easy-to-understand data displays and convenient query methods, enabling them to quickly grasp the efficiency of approvals and existing problems, and provide data support for targeted optimization of the approval process (such as adding a pre-examination step for industrial parks), thereby improving overall project approval efficiency.

[0039] To sum up, in the project approval efficiency analysis scenario, the platform integrates approval data from multiple departments through the data collection module, laying the foundation for efficiency analysis; the classification module sorts data according to approval themes to ensure the accuracy of data processing; the scenario-based label dynamic mapping module realizes dynamic binding of data and approval links to adapt to process changes; the spatiotemporal dimension dynamic indexing module supports rapid data retrieval to meet analysis needs in different spatiotemporal ranges; the indicator library provides quantitative indicators to help identify approval bottlenecks; the user interaction module presents analysis results in an intuitive form, and the platform enables approval efficiency analysis throughout the entire process, from data integration to problem location to process optimization suggestions, forming a complete support system to effectively promote approval process optimization and efficiency improvement.

[0040] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A big data driven government data application management platform, characterized by: The platform includes a data collection module, a data classification module, a scenario-based tag dynamic mapping module, a spatiotemporal dimension dynamic indexing module, an indicator library construction module, and a user interaction module; The data collection module is used to connect with data sources of various government agencies such as market supervision, administrative approval, industry and information technology, environmental protection, and civil affairs, and collect various government data; The data classification module receives the original government data transmitted by the data acquisition module, classifies the data according to the data subject, data source, and data access method, simultaneously identifies the data that has not been divided into topics, and transmits them to the scenario-based label dynamic mapping module and the user interaction module respectively; The scenario-based tag dynamic mapping module is used to generate scenario tags for classified data based on government business processes, establish a dynamic mapping relationship between tags and business links, adjust the tag system when the business process is updated, and transmit it to the user interaction module and the spatiotemporal dimension dynamic indexing module; The spatiotemporal dimension dynamic indexing module receives the labeled data transmitted by the scenario-based label dynamic mapping module, extracts the time and space attributes of the data, constructs a spatiotemporal dual-dimensional index, and transmits it to the indicator library construction module; The indicator library construction module: constructs an indicator library based on the indicators of the spatiotemporal dual-dimensional index, configures the name, source, period, and filtering conditions of the indicators, and supports the addition, editing, and deletion of indicators; The user interaction module integrates the data of each module, provides a data viewing interface and query entry, and supports users to query data through various conditions.

2. A big data driven government data application management platform according to claim 1, characterized in that: In the data collection module, the types of government affairs data collected include enterprise registration information, project approval data, enterprise credit supervision data, a list of technological innovation demonstration enterprises, enterprise social security personnel and ranking information in online approval project management, and enterprise comprehensive evaluation related data.

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

4. A big data driven government data application management platform according to claim 1, characterized in that: In the data classification module, when the data that has not been divided into topics is identified, the data collection time, data format and data size information are recorded synchronously, and a temporary storage directory for the data that has not been divided into topics is established to regularly remind managers to divide the topics.

5. A big data driven government data application management platform according to claim 1, characterized in that: In the scenario-based tag dynamic mapping module, government business processes include: enterprise registration process, project approval process, and credit supervision process; the generated scenario tags include: enterprise establishment scenario, energy-saving review scenario, environmental impact assessment scenario, and credit supervision scenario.

6. A big data driven government data application management platform according to claim 1, characterized in that: In the scenario-based tag dynamic mapping module, the specific steps for establishing a dynamic mapping relationship between tags and business links are as follows: (1) First, extract the business attribute characteristics of the classified data, covering the government business types and business links associated with the data; (2) Then 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) Based on the matching relationship between the business attribute characteristics of the data and the elements of the business process, the corresponding scenario labels are assigned to the data; (4) Then record the corresponding relationship between the scene label and the business link to form an initial dynamic mapping relationship table; (5) Continuously monitor changes in government business processes in real time, and immediately trigger the mapping relationship update mechanism once any business link is increased, decreased, or adjusted; (6) According to the changes in the business process, adjust the mapping relationship between the scene label and the business link, update the mapping relationship table, and synchronize it to the user interaction module and the spatiotemporal dimension dynamic index module.

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

8. The big data driven government data application management platform according to claim 1, characterized in that: In the spatiotemporal dimension dynamic index module, the specific steps of constructing the spatiotemporal dual-dimensional index are as follows: (1) Receive the labeled data transmitted by the scenario-based label dynamic mapping module and parse the time-related information 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 the timestamp to the time axis, divide the data by time slices, establish time series associations, and form a time dimension index structure; (5) Mapping longitude and latitude to a spatial coordinate system, combining administrative divisions and spatial grids to perform regional divisions, constructing spatial topological relationships, and forming a spatial dimension index structure; (6) The time dimension index and the space dimension index are bound together through the unique data identifier to form a time-space dual-dimensional index, which is then constructed and associated with the user interaction module.

9. 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 constructing the indicator library based on the indicators of the spatiotemporal dual-dimensional index are as follows: (1) Obtain government data from the spatiotemporal dynamic index module, which has built a spatiotemporal dual-dimensional index, and analyze the quantifiable indicator information contained in each piece of data 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 subject, and access method; (4) Clarify the statistical period for each indicator and set the screening conditions for the indicator; (5) Classify the configured indicators by data theme or business scenario, and build a hierarchical structure of 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.

10. A big data driven government data application management platform according to claim 1, characterized in that: The query conditions supported by the user interaction module include keywords, data themes, data source departments, scene tags, time ranges, spatial ranges and custom indicators, and data visualization is displayed in the form of lists and charts.

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