A zero-code rapid spatialization system and method for government spatiotemporal data

Abstract

The present invention discloses a zero-code rapid spatialization system and method for government spatiotemporal data, which relates to the field of government data processing and geographic information systems, including: a data access module, which can connect to a variety of government data sources, automatically identify data structures, etc., and perform preliminary cleaning; a spatial mapping configuration module, which configures data and geographic space mapping through a graphical interface; a spatial processing module, which spatializes data using an interpolation algorithm; a visualization module, which displays data on a map and interacts; a data management and storage module, which manages data; and a system management module, which manages users, permissions, and system parameters. Each module has a corresponding optimization technology, such as decision tree algorithm optimization, layer management optimization, etc. to improve performance. The present invention can achieve zero-code rapid spatialization of government spatiotemporal data. It improves the efficiency and accuracy of data access and spatial processing, optimizes data management, visualization display, and system management, lowers the threshold for use, ensures security, and promotes the intelligence and efficiency of government work.

Description

Technical Field

[0001] The present invention relates to the technical field of cross-border e-commerce security systems, and particularly to a zero-code rapid spatialization system and method for government affairs spatio-temporal data. Background Art

[0002] At present, the processing and utilization of government affairs spatio-temporal data face many challenges.

[0003] On the one hand, the sources of government affairs data are extensive and the formats are diverse, including Excel tables, CSV files from different departments, and data in various databases, etc. These data vary greatly in structure and type. Traditional data access methods often require a large amount of manual configuration and code writing to achieve data import and preprocessing, which is not only inefficient but also error-prone. For example, when integrating population data and geographical information data, different formats of data may be involved, and the manual processing of format conversion and cleaning of these data is extremely cumbersome.

[0004] On the other hand, spatially processing government affairs data to combine it with geographical spatial information is a key step in realizing the value of government affairs spatio-temporal data. However, most of the existing spatialization methods require professional geographical information system (GIS) knowledge and programming skills, which have a relatively high threshold for non-professionals. Moreover, when dealing with large-scale government affairs spatio-temporal data, traditional methods have obvious deficiencies in terms of data processing speed, accuracy, and system scalability. For example, in spatial interpolation calculations, some algorithms may not be able to adapt to complex data distributions, resulting in inaccurate spatialization results.

[0005] At the same time, in terms of government affairs data management and system management, it is necessary to ensure the security, reliability, and high efficiency of multi-user collaborative work. There is room for improvement in the current system in terms of user permission management, data storage and backup mechanisms, system performance monitoring, etc. For example, there may be a risk of single-point failure in data storage, and insufficiently fine-grained user permission management may lead to data leakage or misoperation.

[0006] Visualization display is also an important part of the application of government affairs spatio-temporal data. Existing visualization tools often lack intuitiveness and interactivity when displaying government affairs spatio-temporal data, and cannot well meet the user's needs for data viewing and analysis. For example, the map operation is not smooth enough, and the layer management function is limited, making it inconvenient to operate and compare multiple layers.

[0007] In summary, there are many problems in the existing government affairs spatio-temporal data processing technologies in aspects such as data access, spatialization processing, data and system management, and visualization display. There is an urgent need for a zero-code rapid spatialization system and method to improve the processing efficiency and quality of government affairs spatio-temporal data, lower the usage threshold, and promote the effective utilization of government affairs data. Summary of the invention

[0008] The present invention proposes a zero-code rapid spatialization system and method for government spatiotemporal data to solve the problems mentioned in the above-mentioned prior art.

[0009] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a zero-code rapid spatialization system for government spatiotemporal data, comprising:

[0010] The data access module is used to connect to a variety of government data sources and supports the import of common government data formats, such as Excel, CSV, and database file formats (such as MySQL and Oracle common database formats). This module has the function of automatically identifying data structures and field types. It is implemented by analyzing data samples and applying classification algorithms in machine learning algorithms, such as decision tree algorithms. Specifically, the data samples are firstly extracted for features, including the numerical range, character length, and data distribution characteristics of the data. Then a decision tree model is constructed. Based on these features, the data fields are classified to determine whether they are numerical, character, or date types, thereby improving the automation and accuracy of data access. At the same time, the module can perform preliminary cleaning of the data, including removing duplicate data and processing missing values. It also provides a data preview function to facilitate users to confirm the accuracy of the data.

[0011] The spatial mapping configuration module allows users to intuitively configure the mapping relationship between government data and geospatial information through a graphical interface. This graphical interface uses a drag-and-drop operation method. Users can directly drag the government data field to the corresponding geospatial information field to complete the establishment of the mapping relationship. For complex mapping relationships, a visual mapping rule editing tool is provided to display and edit the mapping logic in the form of a flowchart or expression, which is convenient for users to understand and operate. At the same time, the module provides a variety of preset mapping templates, which are suitable for common government data types, such as population data, enterprise registration data, and public facility data, and supports users to customize mapping rules to adapt to special data structures. In addition, it has a mapping relationship verification function to ensure the accuracy of the mapping.

[0012] The spatial processing module performs spatial processing on government data according to the mapping relationship determined by the spatial mapping configuration module. It uses efficient spatial interpolation algorithms, such as Kriging interpolation and inverse distance weighted interpolation. When using the Kriging interpolation method, interpolation calculations are performed based on the spatial autocorrelation and variation function model of the data. The specific formula is: ,in is the estimated value of the point to be interpolated, is the value of a known data point, The weight coefficient is determined by solving the variogram model to improve the accuracy of interpolation and adapt to the spatialization needs of government spatiotemporal data with different data distribution characteristics. This module supports multiple spatial reference systems, which can be selected according to actual needs. It has data quality monitoring function during processing, and displays the processing progress and possible error information in real time.

[0013] The visualization display module visualizes the spatially processed government data in the form of a map. It provides a variety of map styles and layer management functions, and users can customize the map display effects according to their needs, such as color themes and symbol styles. It supports zooming, panning, and eagle-eye map operation functions. When zooming, it uses tile map-based technology to dynamically load map tiles of different resolutions according to the user's zoom level to improve the smoothness of map zooming; the panning operation uses real-time data update technology to ensure the timely display of data during the panning process; the eagle-eye function provides a thumbnail view to facilitate users to quickly locate and browse areas of interest, and supports adaptive display on mobile devices, so that users can view government spatiotemporal data anytime and anywhere. In addition, this module has data interaction functions, and users can click on data points on the map to view detailed information.

[0014] The data management and storage module is responsible for managing spatialized government data, including data storage, query, update, and deletion operations. It adopts a distributed storage architecture, such as the Hadoop distributed file system (HDFS) or the Ceph distributed storage system, to ensure data reliability and high availability through data block storage and redundant backup mechanisms. In data query operations, it adopts index-based query optimization technology, such as establishing spatial indexes (such as R-tree indexes) and attribute indexes, to improve query speed and meet the needs of fast query of large-scale government spatiotemporal data. At the same time, it supports data backup and recovery functions, backs up data regularly to prevent data loss, provides data security management mechanisms, sets user permissions, and ensures data confidentiality and integrity.

[0015] The system management module is used to manage the user accounts, role permissions, and system configuration parameters of the system. It supports multi-user collaboration and assigns different operating permissions according to user roles. For example, administrators have system configuration and user management permissions, and ordinary users have data viewing and partial configuration permissions. Its user permission management adopts a role-based access control (RBAC) model to define different role sets, such as super administrators, ordinary administrators, data analysts, and ordinary users. Each role is assigned a corresponding set of operating permissions, such as system settings, user management, data management, and data viewing. Through the mapping relationship between users, roles, and permissions, precise control of user operations is achieved to ensure the security of the system. In addition, it has a system log recording function to record user operations and system operating status, which is convenient for system maintenance and problem troubleshooting.

[0016] Furthermore, the automatic identification of data structure and field type function in the data access module, when using the decision tree algorithm, in order to improve the accuracy and efficiency of the algorithm, adopts the information gain ratio as the feature selection criterion, performs multiple random sampling and training on the data samples to avoid overfitting, and at the same time combines the regularization technology to limit the growth of the decision tree to ensure that the generated decision tree model has good generalization ability.

[0017] Furthermore, the visual mapping rule editing tool in the spatial mapping configuration module provides syntax checking and intelligent prompting functions when editing complex mapping logic. Syntax checking can detect in real time whether there are syntax errors in the expressions or flowcharts entered by the user, and intelligent prompting provides users with possible next operations or parameter selection suggestions based on the content entered by the user and common mapping logic patterns, thereby improving the efficiency and accuracy of user configuration of mapping relationships.

[0018] Furthermore, the inverse distance weighted interpolation method in the spatial processing module introduces a distance decay function when calculating the weight coefficient, and adjusts the weight according to the distance between the data point and the point to be interpolated. The closer the distance, the greater the weight, and the parameters of the distance decay function can be adaptively adjusted according to the distribution density and spatial correlation of the data, thereby further improving the accuracy of interpolation. It is particularly suitable for the spatialization of government spatiotemporal data with uneven distribution of data points.

[0019] Furthermore, the layer management function in the visualization module supports layer group management, and users can group related layers together to facilitate unified operations on multiple layers, such as displaying or hiding a group of layers at the same time. At the same time, a batch setting function for layer styles is provided, and users can apply the same style settings to multiple layers at one time, thereby improving the efficiency of map visualization configuration.

[0020] Furthermore, the distributed storage architecture in the data management and storage module adopts a dynamic block strategy when storing data in blocks, dynamically adjusting the size of the data block according to the access frequency and amount of data, thereby improving the efficiency of data storage and reading. At the same time, in the redundant backup mechanism, the erasure coding technology is used to replace the traditional multi-copy backup method, thereby reducing the storage space occupied while ensuring data reliability.

[0021] Furthermore, the system performance monitoring function in the system management module, in addition to real-time monitoring of the system's CPU usage, memory usage, and network traffic key performance indicators, also uses anomaly detection algorithms in machine learning algorithms, such as the isolation forest algorithm, to analyze system performance data and promptly discover potential performance anomalies. When system performance is abnormal, an alarm is automatically issued and optimization suggestions are provided, such as adjusting system configuration parameters and increasing hardware resources to ensure the stable operation of the system.

[0022] Furthermore, a zero-code rapid spatialization method for government spatiotemporal data includes the following steps:

[0023] The data access step is to connect to the government data source, import government data, automatically identify data structure and field type, and perform preliminary data cleaning and preview. When encountering incompatible data formats or data errors, the system automatically prompts the user and provides possible solutions, such as data format conversion tool recommendations and data repair suggestions, to ensure that data can be smoothly connected to the system and improve the system's fault tolerance and ease of use. When specifically identifying data structures and field types, a decision tree algorithm and related optimization techniques are used.

[0024] The spatial mapping configuration step uses a graphical interface, preset templates or custom rules to configure and verify the mapping relationship between government data and geographic spatial information. If the mapping relationship configured by the user has logical conflicts or is incomplete, the system automatically detects and gives detailed error prompts and improvement suggestions to guide the user to correctly configure the mapping relationship and ensure the accuracy of spatial processing. During the configuration process, use a visual mapping rule editing tool with syntax checking and smart prompting functions.

[0025] In the spatial processing step, according to the mapping relationship, the appropriate spatial interpolation algorithm is used to spatialize the government data and monitor the quality of the processing process. According to the characteristics and spatial distribution of government data, the optimal spatial interpolation algorithm is automatically selected, or the user is allowed to manually select the algorithm and adjust the algorithm parameters to adapt to different data scenarios and improve the accuracy and efficiency of spatial processing. Among them, if the Kriging interpolation method or the inverse distance weighted interpolation algorithm is used, the calculation is performed according to the corresponding optimization technology.

[0026] The visualization step displays spatialized data in the form of a map, providing a variety of map operations and interactive functions. It supports the overlay display of multiple different types of government spatiotemporal data layers. Users can adjust the display order and transparency attributes of each layer through the layer control function to facilitate comprehensive analysis and comparison. At the same time, it provides a dynamic data update function. When the underlying data changes, the map display can reflect the changes in a timely manner to ensure the timeliness of the data.

[0027] Data management and storage steps: manage and store spatialized data, including operation, backup, recovery and security management. Regularly perform consistency checks and optimizations on stored data, such as integrity checks on data blocks and defragmentation of storage space, to ensure data reliability and efficient operation of the storage system. At the same time, data migration functions are provided to facilitate users to migrate data to other storage systems or platforms. In the process of data management and storage, a distributed storage architecture and related optimization technologies are used.

[0028] System management steps: manage system users, permissions and configuration parameters, and record system logs. It has system performance monitoring function, real-time monitoring of the system's CPU usage, memory usage, network traffic key performance indicators, and automatically issues alarms and provides optimization suggestions when system performance is abnormal, such as adjusting system configuration parameters and increasing hardware resources to ensure stable operation of the system. In the system management process, role-based access control models and performance monitoring related technologies are used.

[0029] Furthermore, in the data access step, for cases where data formats are incompatible, the system has built-in multiple data format conversion libraries, such as the data conversion function in the Pandas library, which can automatically convert data in incompatible formats into a processable format, thereby improving the system's compatibility with different data sources.

[0030] Furthermore, in the spatial mapping configuration step, the design of the preset templates is based on the analysis of a large number of common government data types and geospatial information association patterns, covering a variety of typical mapping scenarios. Users can quickly apply them to actual data through simple parameter adjustments, saving configuration time.

[0031] Furthermore, in the spatial processing step, when the user manually selects a spatial interpolation algorithm, the system provides an intelligent recommendation function for algorithm parameters. Based on the statistical characteristics and spatial distribution of the data, it recommends appropriate initial parameter values ​​to the user, helping the user find the optimal parameter settings more quickly.

[0032] Furthermore, in the visualization step, the data dynamic update function adopts incremental update technology to update only the changed data part instead of reloading the entire map data, which improves the efficiency of data update and reduces network transmission and system resource consumption.

[0033] Furthermore, in the data management and storage steps, the data migration function supports a variety of target storage systems, including cloud storage services (such as Alibaba Cloud OSS and Tencent Cloud COS), and during the migration process, data is encrypted for transmission to ensure data security.

[0034] Furthermore, in the system management step, the system log recording function not only records user operations and system operating status, but also audits and tracks important operations, recording the time, location, and detailed information of the executor of the operation to facilitate security audits and problem tracing.

[0035] Furthermore, a computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the function of the zero-code rapid spatialization method of government spatiotemporal data is implemented.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] First, in terms of data access, the automatic identification of data structure and field type functions and the use of advanced machine learning algorithms have greatly improved the automation of data access. Government data in both common and complex formats can be quickly and accurately imported into the system, reducing manual intervention and errors caused by manual processing, improving data access efficiency, and saving a lot of time and labor costs. For example, report data from a large number of different departments can be quickly imported and initially processed.

[0038] In terms of spatial mapping and spatial processing, the zero-code graphical interface and multiple preset templates allow non-professionals to easily complete the mapping configuration of government data and geographic spatial information. Users can establish mapping relationships through simple drag and drop operations or using visual editing tools, and the system can automatically verify its accuracy. Efficient spatial interpolation algorithms and their optimization measures can adapt to different data distributions, improve the accuracy of spatial processing, and enable government data to be accurately combined with geographic space, providing a reliable basis for subsequent analysis and decision-making.

[0039] For data management and storage, the distributed storage architecture combines dynamic block strategy, erasure coding technology, etc. to ensure high reliability and high availability of data while reducing storage space usage. Optimized query technology improves data query speed and meets large-scale data query needs. Perfect data security management mechanism and backup and recovery functions ensure data security.

[0040] In the visualization stage, rich map styles, convenient layer management functions, and smooth map operations such as zooming, panning, and eagle eye, as well as data interaction functions and data dynamic update technology, enable users to view and analyze government spatiotemporal data more intuitively and efficiently. Users can easily overlay different types of layers for comparative analysis and obtain data changes in a timely manner.

[0041] In terms of system management, the role-based access control model realizes fine user rights management and ensures system security. The system performance monitoring function can detect anomalies in a timely manner and provide optimization suggestions to ensure stable operation of the system. At the same time, the log audit function facilitates problem tracing and security auditing. Overall, this patent improves the efficiency, accuracy and security of government spatiotemporal data processing, and promotes the intelligence and efficiency of government work. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic block diagram of a zero-code rapid spatialization system for government spatiotemporal data proposed by the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise" and "counterclockwise" indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0045] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0046] Reference Figure 1:A zero-code rapid spatialization system for government spatiotemporal data, including:

[0047] The data access module is used to connect to a variety of government data sources and supports the import of common government data formats, such as Excel, CSV, and database file formats (such as MySQL and Oracle common database formats). This module has the function of automatically identifying data structures and field types. It is implemented by analyzing data samples and applying classification algorithms in machine learning algorithms, such as decision tree algorithms. Specifically, the data samples are firstly extracted for features, including the numerical range, character length, and data distribution characteristics of the data. Then a decision tree model is constructed. Based on these features, the data fields are classified to determine whether they are numerical, character, or date types, thereby improving the automation and accuracy of data access. At the same time, the module can perform preliminary cleaning of the data, including removing duplicate data and processing missing values. It also provides a data preview function to facilitate users to confirm the accuracy of the data.

[0048] The spatial mapping configuration module allows users to intuitively configure the mapping relationship between government data and geospatial information through a graphical interface. This graphical interface uses a drag-and-drop operation method. Users can directly drag the government data field to the corresponding geospatial information field to complete the establishment of the mapping relationship. For complex mapping relationships, a visual mapping rule editing tool is provided to display and edit the mapping logic in the form of a flowchart or expression, which is convenient for users to understand and operate. At the same time, the module provides a variety of preset mapping templates, which are suitable for common government data types, such as population data, enterprise registration data, and public facility data, and supports users to customize mapping rules to adapt to special data structures. In addition, it has a mapping relationship verification function to ensure the accuracy of the mapping.

[0049] The spatial processing module performs spatial processing on government data according to the mapping relationship determined by the spatial mapping configuration module. It uses efficient spatial interpolation algorithms, such as Kriging interpolation and inverse distance weighted interpolation. When using the Kriging interpolation method, interpolation calculations are performed based on the spatial autocorrelation and variation function model of the data. The specific formula is: ,in is the estimated value of the point to be interpolated, is the value of a known data point, The weight coefficient is determined by solving the variogram model to improve the accuracy of interpolation and adapt to the spatialization needs of government spatiotemporal data with different data distribution characteristics. This module supports multiple spatial reference systems, which can be selected according to actual needs. It has data quality monitoring function during processing, and displays the processing progress and possible error information in real time.

[0050] The visualization display module visualizes the spatially processed government data in the form of a map. It provides a variety of map styles and layer management functions, and users can customize the map display effects according to their needs, such as color themes and symbol styles. It supports zooming, panning, and eagle-eye map operation functions. When zooming, it uses tile map-based technology to dynamically load map tiles of different resolutions according to the user's zoom level to improve the smoothness of map zooming; the panning operation uses real-time data update technology to ensure the timely display of data during the panning process; the eagle-eye function provides a thumbnail view to facilitate users to quickly locate and browse areas of interest, and supports adaptive display on mobile devices, so that users can view government spatiotemporal data anytime and anywhere. In addition, this module has data interaction functions, and users can click on data points on the map to view detailed information.

[0051] The data management and storage module is responsible for managing spatialized government data, including data storage, query, update, and deletion operations. It adopts a distributed storage architecture, such as the Hadoop distributed file system (HDFS) or the Ceph distributed storage system, to ensure data reliability and high availability through data block storage and redundant backup mechanisms. In data query operations, it adopts index-based query optimization technology, such as establishing spatial indexes (such as R-tree indexes) and attribute indexes, to improve query speed and meet the needs of fast query of large-scale government spatiotemporal data. At the same time, it supports data backup and recovery functions, backs up data regularly to prevent data loss, provides data security management mechanisms, sets user permissions, and ensures data confidentiality and integrity.

[0052] The system management module is used to manage the user accounts, role permissions, and system configuration parameters of the system. It supports multi-user collaboration and assigns different operating permissions according to user roles. For example, administrators have system configuration and user management permissions, and ordinary users have data viewing and partial configuration permissions. Its user permission management adopts a role-based access control (RBAC) model to define different role sets, such as super administrators, ordinary administrators, data analysts, and ordinary users. Each role is assigned a corresponding set of operating permissions, such as system settings, user management, data management, and data viewing. Through the mapping relationship between users, roles, and permissions, precise control of user operations is achieved to ensure the security of the system. In addition, it has a system log recording function to record user operations and system operating status, which is convenient for system maintenance and problem troubleshooting.

[0053] In the present invention, the automatic identification of data structure and field type function in the data access module, when using the decision tree algorithm, in order to improve the accuracy and efficiency of the algorithm, adopts the information gain ratio as the feature selection standard, performs multiple random sampling and training on the data samples to avoid overfitting, and at the same time combines the regularization technology to limit the growth of the decision tree to ensure that the generated decision tree model has good generalization ability.

[0054] In the present invention, the visual mapping rule editing tool in the spatial mapping configuration module provides grammar checking and intelligent prompting functions when editing complex mapping logic. The grammar checking can detect in real time whether there are grammatical errors in the expression or flowchart entered by the user, and the intelligent prompting provides the user with possible next operations or parameter selection suggestions based on the content entered by the user and the common mapping logic mode, thereby improving the efficiency and accuracy of the user's configuration of the mapping relationship.

[0055] In the present invention, the inverse distance weighted interpolation method in the spatial processing module introduces a distance decay function when calculating the weight coefficient, and adjusts the weight according to the distance between the data point and the point to be interpolated. The closer the distance, the greater the weight. The parameters of the distance decay function can be adaptively adjusted according to the distribution density and spatial correlation of the data, so as to further improve the accuracy of interpolation. It is particularly suitable for the spatialization of government spatiotemporal data with uneven distribution of data points.

[0056] In the present invention, the layer management function in the visualization display module supports layer group management, and users can group related layers together to facilitate unified operations on multiple layers, such as displaying or hiding a group of layers at the same time. At the same time, a batch setting function of layer styles is provided, and users can apply the same style settings to multiple layers at one time, thereby improving the efficiency of map visualization configuration.

[0057] In the present invention, the distributed storage architecture in the data management and storage module adopts a dynamic block strategy when storing data in blocks, dynamically adjusts the size of the data block according to the access frequency and data volume of the data, and improves the efficiency of data storage and reading. At the same time, in the redundant backup mechanism, the erasure code technology is used to replace the traditional multi-copy backup method, reducing the storage space occupied while ensuring data reliability.

[0058] In the present invention, the system performance monitoring function in the system management module, in addition to real-time monitoring of the system's CPU usage, memory usage, and network traffic key performance indicators, also uses anomaly detection algorithms in machine learning algorithms, such as the isolation forest algorithm, to analyze system performance data and promptly discover potential performance anomalies. When system performance is abnormal, an alarm is automatically issued and optimization suggestions are provided, such as adjusting system configuration parameters and increasing hardware resources, to ensure the stable operation of the system.

[0059] In the present invention, a zero-code rapid spatialization method for government spatiotemporal data includes the following steps:

[0060] The data access step is to connect to the government data source, import government data, automatically identify data structure and field type, and perform preliminary data cleaning and preview. When encountering incompatible data formats or data errors, the system automatically prompts the user and provides possible solutions, such as data format conversion tool recommendations and data repair suggestions, to ensure that data can be smoothly connected to the system and improve the system's fault tolerance and ease of use. When specifically identifying data structures and field types, a decision tree algorithm and related optimization techniques are used.

[0061] The spatial mapping configuration step uses a graphical interface, preset templates or custom rules to configure and verify the mapping relationship between government data and geographic spatial information. If the mapping relationship configured by the user has logical conflicts or is incomplete, the system automatically detects and gives detailed error prompts and improvement suggestions to guide the user to correctly configure the mapping relationship and ensure the accuracy of spatial processing. During the configuration process, a visual mapping rule editing tool with syntax checking and intelligent prompting functions is used.

[0062] In the spatial processing step, according to the mapping relationship, the appropriate spatial interpolation algorithm is used to spatialize the government data and monitor the quality of the processing process. According to the characteristics and spatial distribution of government data, the optimal spatial interpolation algorithm is automatically selected, or the user is allowed to manually select the algorithm and adjust the algorithm parameters to adapt to different data scenarios and improve the accuracy and efficiency of spatial processing. Among them, if the Kriging interpolation method or the inverse distance weighted interpolation algorithm is used, the calculation is performed according to the corresponding optimization technology.

[0063] The visualization step displays spatialized data in the form of a map, providing a variety of map operations and interactive functions. It supports the overlay display of multiple different types of government spatiotemporal data layers. Users can adjust the display order and transparency attributes of each layer through the layer control function to facilitate comprehensive analysis and comparison. At the same time, it provides a dynamic data update function. When the underlying data changes, the map display can reflect the changes in a timely manner to ensure the timeliness of the data.

[0064] Data management and storage steps: manage and store spatialized data, including operation, backup, recovery and security management. Regularly perform consistency checks and optimizations on stored data, such as integrity checks on data blocks and defragmentation of storage space, to ensure data reliability and efficient operation of the storage system. At the same time, data migration functions are provided to facilitate users to migrate data to other storage systems or platforms. In the process of data management and storage, a distributed storage architecture and related optimization technologies are used.

[0065] System management steps: manage system users, permissions and configuration parameters, and record system logs. It has system performance monitoring function, real-time monitoring of the system's CPU usage, memory usage, network traffic key performance indicators, and automatically issues alarms and provides optimization suggestions when system performance is abnormal, such as adjusting system configuration parameters and increasing hardware resources to ensure stable operation of the system. In the system management process, role-based access control models and performance monitoring related technologies are used.

[0066] In the present invention, in the data access step, for the case of incompatible data formats, the system has built-in multiple data format conversion libraries, such as the data conversion function in the Pandas library, which can automatically convert data in incompatible formats into a processable format, thereby improving the system's compatibility with different data sources.

[0067] In the present invention, in the spatial mapping configuration step, the design of the preset template is based on the analysis of a large number of common government data types and geographic spatial information association patterns, covering a variety of typical mapping scenarios. Users can quickly apply it to actual data through simple parameter adjustments, saving configuration time.

[0068] In the present invention, in the spatial processing step, when the user manually selects the spatial interpolation algorithm, the system provides an intelligent recommendation function for the algorithm parameters, and recommends appropriate initial parameter values ​​to the user based on the statistical characteristics and spatial distribution of the data, helping the user to find the optimal parameter settings more quickly.

[0069] In the present invention, in the visualization step, the data dynamic update function adopts incremental update technology to update only the changed data part instead of reloading the entire map data, thereby improving the efficiency of data update and reducing network transmission and system resource consumption.

[0070] In the present invention, in the data management and storage steps, the data migration function supports multiple target storage systems, including cloud storage services (such as Alibaba Cloud OSS and Tencent Cloud COS), and during the migration process, the data is encrypted and transmitted to ensure the security of the data.

[0071] In the present invention, in the system management step, the system log recording function not only records user operations and system operating status, but also audits and tracks important operations, recording the time, location, and detailed information of the executor of the operation, which is convenient for security auditing and problem tracing.

[0072] In the present invention, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the function of the zero-code rapid spatialization method for government spatiotemporal data.

[0073] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can replace or change the technical solution and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A zero-code rapid spatialization system for government spatiotemporal data, characterized in that: include: The data access module is used to connect to various government data sources. By analyzing data samples and applying the decision tree algorithm in the machine learning algorithm, the module first extracts features from the data samples, then builds a decision tree model, classifies the data fields, determines whether they are numerical, character, or date types, and performs preliminary data cleaning. Spatial mapping configuration module: users configure the mapping relationship between government data and geographic spatial information through a graphical interface. Users directly drag and drop government data fields to corresponding geographic spatial information fields to complete the establishment of mapping relationships. The mapping logic is displayed and edited in the form of flowcharts or expressions, and preset mapping templates are provided. The spatial processing module performs spatial processing on government data according to the mapping relationship determined by the spatial mapping configuration module, and uses the Kriging interpolation method in the spatial interpolation algorithm to perform interpolation calculations based on the spatial autocorrelation and variation function model of the data. The specific formula is: ,in is the estimated value of the point to be interpolated, is the value of a known data point, is the weight coefficient, which is determined by solving the variogram model; The visualization module visualizes the spatially processed government data in the form of a map; The data management and storage module is responsible for managing spatialized government data. It adopts a distributed storage architecture and ensures data reliability through data block storage and redundant backup mechanisms. The system management module assigns different operation permissions according to user roles, including administrators with system configuration and user management permissions, and ordinary users with data viewing and partial configuration permissions. Its user permission management adopts the role-based access control RBAC model, defines different role sets, and assigns corresponding operation permission sets to each role; The inverse distance weighted interpolation method in the spatial processing module introduces a distance decay function when calculating the weight coefficient, and adjusts the weight according to the distance between the data point and the point to be interpolated. The closer the distance, the greater the weight. The parameters of the distance decay function can be adaptively adjusted according to the distribution density and spatial correlation of the data, so as to further improve the accuracy of interpolation. It is suitable for the spatialization of government spatiotemporal data with uneven distribution of data points. According to the mapping relationship, a suitable spatial interpolation algorithm is used to spatialize the government data and monitor the quality of the processing process.

2. According to claim 1, a zero-code rapid spatialization system for government spatiotemporal data is characterized in that: The automatic identification of data structure and field type function in the data access module adopts information gain ratio as the feature selection standard when applying the decision tree algorithm, performs multiple random sampling and training on data samples to avoid overfitting, and combines regularization technology to limit the growth of the decision tree to ensure that the generated decision tree model has good generalization ability.

3. According to claim 1, a zero-code rapid spatialization system for government spatiotemporal data is characterized in that: The visual mapping rule editing tool in the spatial mapping configuration module provides syntax checking and intelligent prompting functions when editing complex mapping logic. The syntax checking detects in real time whether there are syntax errors in the expressions or flowcharts entered by the user. The intelligent prompting provides the user with suggestions for the next operation or parameter selection based on the content entered by the user and common mapping logic patterns.

4. According to claim 1, a zero-code rapid spatialization system for government spatiotemporal data is characterized in that: The layer management function in the visualization display module supports layer grouping management. Users can group related layers together to facilitate unified operations on multiple layers, including displaying or hiding a group of layers at the same time. It provides a batch setting function for layer styles. Users can apply the same style settings to multiple layers at one time, thereby improving the efficiency of map visualization configuration.

5. According to claim 1, a zero-code rapid spatialization system for government spatiotemporal data is characterized in that: The distributed storage architecture in the data management and storage module adopts a dynamic block strategy when storing data in blocks, and dynamically adjusts the size of the data block according to the access frequency and data volume of the data. At the same time, in the redundant backup mechanism, the erasure code technology is used to replace the traditional multi-copy backup method, thereby reducing the storage space occupied while ensuring data reliability.

6. According to claim 1, a zero-code rapid spatialization system for government spatiotemporal data is characterized in that: The system performance monitoring function in the system management module, in addition to real-time monitoring of the system's CPU usage, memory usage, and network traffic key performance indicators, also uses anomaly detection algorithms in machine learning algorithms, including the isolation forest algorithm, to analyze system performance data and promptly discover potential performance anomalies. When system performance is abnormal, it automatically issues an alarm and provides optimization suggestions, including adjusting system configuration parameters and increasing hardware resources.

7. A method for applying a zero-code rapid spatialization system for government spatiotemporal data as described in any one of claims 1 to 6, characterized in that: The following steps are involved: Data access steps: connect to government data sources, import government data, automatically identify data structure and field types, perform preliminary data cleaning and preview, and when encountering incompatible data formats or data errors, the system automatically prompts users and provides solutions; Spatial mapping configuration step: Through the graphical interface, use preset templates or custom rules to configure the mapping relationship between government data and geographic spatial information, and perform verification. If the mapping relationship configured by the user has logical conflicts or is incomplete, the system will automatically detect it and give detailed error prompts and improvement suggestions to guide the user to correctly configure the mapping relationship; Spatial processing step: Based on the mapping relationship, the government data is spatialized using a suitable spatial interpolation algorithm, and the quality of the processing process is monitored. According to the characteristics and spatial distribution of the government data, the optimal spatial interpolation algorithm is automatically selected, or the user is allowed to manually select the algorithm and adjust the algorithm parameters; Visual display step: display spatial data in the form of maps, provide a variety of map operations and interactive functions, support the overlay display of multiple different types of government spatiotemporal data layers, and users can adjust the display order and transparency attributes of each layer through the layer control function. When the underlying data changes, the map display can reflect the changes in a timely manner; Data management and storage steps: manage and store spatialized data, including operation, backup, recovery and security management, and regularly check and optimize the consistency of stored data; System management steps: manage system users, permissions and configuration parameters, record system logs, and have system performance monitoring functions. Real-time monitoring of the system's CPU usage, memory usage, and network traffic key performance indicators. When system performance is abnormal, it automatically issues an alarm and provides optimization suggestions.

8. The method of a zero-code rapid spatialization system for government spatiotemporal data according to claim 7 is characterized in that: In the data access step, if the data format is incompatible, the system has built-in multiple data format conversion libraries, including the data conversion function in the Pandas library, which automatically converts the incompatible data into a processable format. In the spatial mapping configuration step, the design of the preset templates is based on the analysis of a large number of common government data types and geospatial information association patterns, including a variety of typical mapping scenarios, which users can quickly apply to actual data through simple parameter adjustments; In the spatial processing step, when the user manually selects the spatial interpolation algorithm, the system provides an intelligent recommendation function for algorithm parameters, and recommends appropriate initial parameter values ​​to the user based on the statistical characteristics and spatial distribution of the data; In the data management and storage steps, the data migration function supports multiple target storage systems, including cloud storage services, including Alibaba Cloud OSS and Tencent Cloud COS, and during the migration process, the data is encrypted for transmission.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the function of the zero-code rapid spatialization method for government spatiotemporal data as described in any one of claims 7-8.

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