Configurable system generation method for geospatial data combined with workflow engine

Abstract

This invention belongs to the field of computer software technology. To address the technical problems of insufficient support for spatial data and map services in existing low-code platforms and their inability to effectively integrate with workflow engines, this invention proposes a configurable system generation method for combining geospatial data with workflow engines. The method configures system interface components, serializing the configuration information of each component into a structured JSON object in real time and saving it to a non-relational database. For spatial data that has been connected to a spatial data source, a spatial map service is published, binding the process to a specific spatial data source. This enables spatial data instances to undergo state transitions along predefined workflow nodes. The system and business data are stored using a non-relational database, and the workflow engine can be integrated.

Description

Technical Field

[0001] This invention belongs to the field of computer software technology, specifically relating to a method for generating spatially configurable systems, which is particularly suitable for rapidly building application systems that integrate geospatial data interaction and business process management through low-code, visual configuration methods. Background Technology

[0002] Configurable systems refer to software / hardware systems that allow users or administrators to change system functions or behaviors by adjusting parameters, selecting components, or modifying configuration files without modifying the underlying source code. Existing configurable systems typically manifest as low-code development platforms, allowing users to customize applications through graphical interfaces. However, when facing complex business scenarios, especially those involving geospatial information, the following technical bottlenecks exist: 1. Insufficient support for complex business logic: Existing configurable systems are good at building single-page demos or simple applications, but for systems with complex functions, close logical relationships and data interactions between pages, custom coding development is often still required, and they cannot be fully implemented through configuration.

[0003] 2. Weak Spatial Data Processing Capabilities: Existing configurable systems offer good support for structured data operations, but because spatial data has geographic coordinate attributes, it needs to be deeply integrated with map services. Furthermore, spatial data fields from different data sources are often non-fixed, requiring corresponding deployment as callable layer services during processing. Current low-code platforms lack effective compatibility processing methods for spatial data, making it difficult to achieve spatial data visualization and analysis within the platform.

[0004] 3. Lack of professional map service components: Existing configurable systems still have shortcomings in map service functions, lacking core components for geospatial analysis, such as roll-up tools that support layer comparison, spatial measurement (distance, area), custom geographic feature drawing, and time-series comparison of multi-period remote sensing images, making it difficult to support professional application scenarios that require deep integration of spatial data and map services.

[0005] 4. Disconnect between workflow engines and spatial data: Workflow engines are widely used in various management systems to automate business processes. However, in the realm of configurable systems, few solutions can integrate workflow engines with the state flow of spatial data. Business scenarios involving spatial data, such as task distribution, approval, and state changes, still require custom development, which presents a high technical barrier.

[0006] In summary, existing technologies lack support for complex business logic and are still insufficient in terms of map services and functions related to spatial data. Therefore, there is an urgent need for a spatially configurable system generation method that can effectively integrate spatial data storage, map service publishing, and workflow engines to reduce the development difficulty and cost of such systems. Summary of the Invention

[0007] This invention aims to address the technical problems of insufficient support for spatial data and map services in existing low-code platforms, and their inability to effectively integrate with workflow engines. It proposes a configurable system generation method for combining geospatial data with workflow engines, specifically including the following steps: S1. System initialization: Initiate a request to create a configurable system and generate corresponding initialization information based on permissions; S2. Configure system interface components. The configuration information of each component is serialized into a structured JSON object in real time and saved to a non-relational database. S3. Configure and connect to the spatial data source; S4. For spatial data that has been connected to a spatial data source, publish a spatial map service. If a workflow needs to be configured, execute S5; otherwise, publish the configurable system directly. S5 provides a workflow designer, which receives workflow definitions arranged by users in a visual manner, and binds the workflow to the spatial data source specified in S3, so that the data records in the spatial data source can be used as process instances and perform state transitions according to the workflow definition, thereby realizing task-oriented operations on spatial data.

[0008] Technical effects: This invention provides a spatially configurable system generation method that supports drag-and-drop visual arrangement, utilizes a non-relational database storage system and business data, and can integrate a workflow engine and handle multiple heterogeneous data sources, thereby reducing the technical threshold for generating enterprise-level remote sensing application systems containing geographic information spatial data.

[0009] 1. Use a non-relational database to store spatial data: If the existing relational database used by the platform is used for data storage, the number of data fields used in the system is uncertain, and it is impossible to pre-create database tables and set fields. Different data can only be stored in JSON format, which cannot handle conditional queries or aggregation analysis of specific attributes. When it is published as a callable service, the corresponding attribute information cannot be read. In order to meet the above scenarios, and considering fast, near real-time search and complex retrieval of massive data, the non-relational database ElasticSearch is used to store the data, and then the spatial data is published as a service.

[0010] 2. Adapt to multiple heterogeneous data sources: The system design supports access to four types of data sources: static data, external files, API interfaces, and database connections. For static data and files, the data is read, parsed, and then saved to Elasticsearch. For API interfaces and database connections, the connection parameters are saved to enable dynamic loading and interaction of data, ensuring the flexibility of data access.

[0011] 3. Integrated Workflow Engine: For applications in the field of remote sensing satellites, spatial data is often combined with geographic information to perform specific operational tasks, such as... Figure 2 As shown, users can design business processes (including start, approval, and end nodes) visually and bind the processes to specific spatial data sources. This enables spatial data instances (such as a verification task) to flow through predefined workflow nodes, thereby integrating geospatial information processing into business management processes. Attached Figure Description

[0012] Figure 1 This is an overall flowchart of an embodiment of the present invention.

[0013] Figure 2 This is a schematic diagram of the visual configuration interface of the workflow engine in an embodiment of the present invention. Detailed Implementation

[0014] This embodiment provides a method for generating a spatially configurable system using a non-relational database and a workflow engine. It employs the distributed search and analytics engine ElasticSearch as the core storage medium to store system component configuration information and geospatial data with non-fixed attributes. The system generated by this embodiment supports access to various heterogeneous data sources, including static data, files, API interfaces, and databases. It also publishes spatial data as a callable map service through the GeoServer geospatial server, enabling data and map linkage. In terms of business logic control, workflow orchestration is performed through a visual drag-and-drop interface, lowering the development threshold for combining spatial data with the workflow engine to generate a configurable system. This enhances the integration and construction capabilities of business scenarios and is suitable for enterprise-level applications in the SaaS model.

[0015] like Figure 1As shown, S1 involves system initialization, initiating a request to create a configurable system. After logging into the platform, the user initiates a request to create a configurable business system. The platform first obtains the user's login information and verifies whether the user has permission to create a system. Based on the permissions, it generates the system's initialization information, including a unique system identifier (systemId), the owner user, a default name, and a background image. Simultaneously, it creates the system's initial page, assigning a unique page identifier, name, route address, component path, and owner system. Subsequently, it initializes the menu tree, roles, users, and other information. Upon successful initialization, the system identifier is returned, serving as the unique identifier for this system within the entire platform, and initialization is complete.

[0016] S2. Configure system interface components. Users can add various components (such as text, charts, tables, map containers, etc.) to the canvas by dragging and dropping. The configuration information of each component is serialized into a structured JSON object in real time and saved to a non-relational Elasticsearch database. Elasticsearch, as an open-source distributed search and analysis engine, is primarily designed for search and analysis scenarios.

[0017] Furthermore, the component's JSON structure includes: (1) Basic attributes: Position information: top (distance from top boundary), left (distance from left boundary), zIndex (display level); Dimensions: width, height; Identifiers: id (unique identifier), key (component identifier), name (name); Status: visible (whether it is visible), focus (whether it can get focus), alignCenter (whether the text is centered); (2) Functional attributes: Component style and property configuration: props; Data model binding configuration: modelOption; Associated dataset information: dataSet; Component binding events: events; Actions executed after an event is triggered; Component access permission control rule: permission; S3. Configure and connect to the spatial data source: The system supports four types of spatial data source configurations: static data, external files, API interfaces, and databases. External files support multiple formats including JSON, ZIP, XLSX, XLS, and CSV. For static data and files, the system parses the data and stores it in an Elasticsearch database. For data transmission via API interfaces and database connections, the connection address and related parameters must be saved, and the connection is dynamically loaded to retrieve data on each call.

[0018] Furthermore, to facilitate the storage and retrieval of spatial data, corresponding data storage fields are established in the ElasticSearch database, including id (unique identifier), systemId (system to which it belongs), createTime (creation time), shape (spatial information), and extra (extended field). Among them, shape is a spatial information field used to store geographical location information, and extra is an extended attribute field. When data is uploaded, if there is data that is not in a field reserved by the system, it is stored in the extra field in JSON format. When accessing data, conditional queries or aggregation encapsulation are performed.

[0019] S4. For spatial data already connected to a spatial data source, publish a spatial map service. If workflow configuration is required, execute S5; otherwise, directly publish to the configurable system. For spatial data already connected to an ElasticSearch database and containing a shape field, it needs to be published as a map service (such as WMS or WFS) for front-end map components to call. The specific execution steps are as follows: Create a new data store in the GeoServer and select the ElasticSearch database; Configure connection parameters, including ElasticSearch server address, index name, search type, etc. Publish the data as a layer, and configure the coordinate reference system, boundaries, styles, etc. Service publishing and access: OpenLayers is used to load and call published services to achieve spatial data visualization and interaction.

[0020] S5. To configure a workflow, first define a custom workflow by selecting the data source for which you want to configure the workflow. Configure the workflow in the workflow designer by dragging and dropping work nodes, defining the flow paths between nodes with connecting lines, and setting branch conditions for the mutual exclusion gateway. Then, bind the workflow by associating the designed workflow with a specific spatial data source. Each data record in this data source (such as a verification plot) becomes a process instance. The initial data state is the start node. When the user performs the "approval" operation, the process status field of that data will be updated, driving it to the next node, thus enabling task-based operations on spatial data.

[0021] Furthermore, workflow nodes include start nodes, intermediate nodes, mutual exclusion gateways, and end nodes. Intermediate nodes can be configured with attributes, including display and query items, and each node can be assigned a corresponding operation role. Mutual exclusion gateways can be configured with conditions, allowing data to flow to the corresponding node based on different conditions, thus achieving parallel branching. Users can configure business processes for specific spatial data sources, enabling state-driven data flow.

[0022] After completing all the above configurations, a spatially configurable system with rich components and geographic information services is built, and the system can be published as an externally accessible link.

[0023] All content not described in detail in this specification belongs to the prior art known to those skilled in the art. Furthermore, for those skilled in the art, there will be changes in specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for generating a configurable system that combines geospatial data with a workflow engine, characterized in that: Specifically, the steps include the following: S1. System initialization: Initiate a request to create a configurable system and generate corresponding initialization information based on permissions; S2. Configure system interface components. The configuration information of each component is serialized into a structured JSON object in real time and saved to a non-relational database. S3. Configure and connect to the spatial data source; S4. For spatial data that has been connected to a spatial data source, publish a spatial map service. If a workflow needs to be configured, execute S5; otherwise, publish the configurable system directly. S5 provides a workflow designer, which receives workflow definitions arranged by users in a visual manner, and binds the workflow to the spatial data source specified in S3, so that the data records in the spatial data source can be used as process instances and perform state transitions according to the workflow definition, thereby realizing task-oriented operations on spatial data.

2. The configurable system generation method for combining geospatial data and a workflow engine according to claim 1, characterized in that, The initialization information described in S1 includes a unique system identifier, the user to which the system belongs, a default name, and a background image. At the same time, the system's initial page is created, and a unique page identifier, name, routing address, component path, and system to which the page belongs are assigned. Subsequently, the menu tree, roles, and user information are initialized.

3. The configurable system generation method for combining geospatial data and a workflow engine according to claim 1, characterized in that, The non-relational database is ElasticSearch.

4. The configurable system generation method for combining geospatial data and a workflow engine according to claim 3, characterized in that, The spatial data source includes static data, external files, API interfaces, and databases. For static data and files, the system parses the data and stores it in the ElasticSearch database. When transmitting data via API interfaces and database connections, the connection address and related parameters are saved, and the connection is dynamically loaded to obtain data each time it is called.

5. The method for generating a configurable system combining geospatial data and a workflow engine according to claim 3, characterized in that, Publishing a spatial map service in S4 involves: creating a new data store in the geospatial server, selecting the ElasticSearch database; configuring connection parameters, including the ElasticSearch server address, index name, and search type; publishing the data store as a layer, configuring the coordinate reference system, boundaries, and styles; and then publishing and accessing the service by loading and calling the published service to achieve visualization and interaction of spatial data.

6. The configurable system generation method for combining geospatial data with a workflow engine according to claim 1, characterized in that, Configure the workflow in the workflow designer by dragging and dropping to add work nodes, define the flow path between nodes with connecting lines, and set branch conditions for the mutual exclusion gateway. Binding a workflow involves associating a designed workflow with a specific spatial data source, where each data record in the data source becomes a workflow instance.

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