Space information layer generation and early warning method, device, equipment and medium

Abstract

The application relates to the technical field of data analysis, can be applied to business scenes such as financial technology, and discloses a spatial information layer generation and early warning method, device, equipment and medium, which comprises the following steps: collecting multi-source heterogeneous data and performing spatial alignment and attribute fusion to construct a unified digital space base; monitoring business and environmental events and extracting affected plots; searching for a layer generation strategy based on event and layer type mapping; calling a layer generation logic and an intelligent inference model to update related spatial data, generating a target thematic map layer; and comparing the target thematic map layer with an associated reference layer to generate abnormal early warning information. Through the unified space base and the event-driven mechanism, the plot recognition, layer generation, updating and comparison processes are automatically connected, the layer is quickly updated and the abnormality is automatically early warned, the manual participation is reduced, and the updating efficiency and the timeliness of abnormality discovery are improved.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method, apparatus, device, and medium for generating and issuing early warnings for spatial information layers. Background Technology

[0002] In fields such as agricultural insurance, which require refined management of geospatial information, it is often necessary to generate and maintain a series of thematic map layers to support business decisions. However, existing technical solutions have several limitations in achieving automatic layer generation and updating, restricting the system's intelligence level and application effectiveness. First, existing systems lack automated spatial alignment and attribute fusion mechanisms when processing multi-source heterogeneous data such as land ownership, transfer records, insurance business, remote sensing monitoring, meteorological monitoring, and field yield measurement. This results in inconsistent data benchmarks and spatial misalignment and attribute fragmentation between data from different sources. The inability to construct a unified digital spatial base indexed by globally unique land parcel identifiers directly affects the accuracy and data consistency of all subsequent layer generation, making collaborative and comparative analysis between different layers difficult.

[0003] Secondly, the layer generation process has a low degree of automation and relies heavily on manual intervention. The entire process, from data preparation and model invocation to result export, often requires manual coordination. The system lacks an integrated automated strategy matching and processing engine, making it impossible to achieve end-to-end automatic flow from event occurrence to layer output. This not only leads to low layer generation efficiency but also makes it difficult for the system to respond to diverse business scenario requirements at scale. Furthermore, existing layer update mechanisms are mostly periodic or manually triggered, lacking the ability to perceive and respond to dynamic events such as land transfer, crop growth stage transitions, and meteorological disasters in real time. The system cannot automatically monitor business change signals and environmental dynamic signals from multiple business nodes and accurately trigger incremental updates or full reconstructions of corresponding layers based on events, resulting in delayed layer information updates that fail to reflect real-time conditions.

[0004] Furthermore, existing technologies lack effective interpretability support for the layer generation process and results. The generated layers themselves typically lack traceable metadata regarding their data sources, processing logic, models used, and processing time, and also lack quantitative evaluation of the confidence level of their results. The system also fails to automatically and intelligently compare newly generated layers with historical or related reference layers to identify anomalies and generate alerts, making it difficult for business personnel to intuitively assess the credibility and reliability of the layers, increasing the understanding cost and uncertainty risk in the decision-making process. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, device, and storage medium for generating and providing early warning of spatial information layers. This invention aims to solve the technical problems of existing technologies, such as the lack of an event-driven automatic generation and update mechanism for layers based on a unified spatial foundation, and the lack of automatic comparison and verification of generated layers and associated reference layers, as well as the lack of early warning of anomalies, which leads to low update and verification efficiency and delayed anomaly detection.

[0006] To achieve the above objectives, the present invention provides a method for generating and issuing early warnings for spatial information layers, comprising: Collect multi-source heterogeneous datasets, perform spatial alignment and attribute fusion processing on the multi-source heterogeneous datasets, and use the processed data to construct a unified digital spatial base with a globally unique land parcel identifier as the index key. Utilize the deployed real-time event listening interface to monitor business change signals and environmental dynamic signals from external data sources and internal business nodes; Read the event attribute information carried by at least one of the monitored business change signals and the environmental dynamic signals, and extract the set of affected land parcel identifiers from the event attribute information; Based on the preset mapping relationship between events and layer types, retrieve the layer generation strategy that matches the event attribute information; The layer generation logic and intelligent reasoning model associated with the layer generation strategy are invoked to perform incremental update or full spatial reconstruction operations on the spatial data associated with the affected land parcel identifier set in the unified digital space base, generating a target thematic map layer containing source metadata information and confidence analysis labels; The target thematic map layer is compared with the associated reference layer stored in the unified digital space base, and an anomaly warning message is generated based on the comparison result.

[0007] Furthermore, to achieve the above objectives, the present invention provides a spatial information layer generation and early warning device, comprising: The spatial base construction module is used to collect multi-source heterogeneous datasets, perform spatial alignment and attribute fusion processing on the multi-source heterogeneous datasets, and use the processed data to construct a unified digital spatial base with a globally unique land parcel identifier as the index key. The event listening module is used to monitor business change signals and environmental dynamic signals from external data sources and internal business nodes using the deployed real-time event listening interface; The land parcel ... The strategy matching module is used to retrieve layer generation strategies that match the event attribute information based on a preset mapping relationship between events and layer types. The layer generation module is used to call the layer generation logic and intelligent reasoning model associated with the layer generation strategy, perform incremental update or full spatial reconstruction operations on the spatial data associated with the affected land parcel identifier set in the unified digital space base, and generate a target thematic map layer containing source metadata information and confidence analysis labels. The layer warning module is used to compare the target thematic map layer with the associated reference layer stored in the unified digital space base, and generate abnormal warning information based on the comparison results.

[0008] Furthermore, to achieve the above objectives, the present invention also provides a computer device, the computer device including a memory, a processor, and a spatial information layer generation and early warning program stored in the memory and executable on the processor, wherein when the spatial information layer generation and early warning program is executed by the processor, it implements the steps of the spatial information layer generation and early warning method as described above.

[0009] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing a spatial information layer generation and early warning program, wherein the spatial information layer generation and early warning program, when executed by a processor, implements the steps of the spatial information layer generation and early warning method as described above.

[0010] Beneficial Effects: This invention relates to the field of data analysis technology and can be applied to business scenarios such as fintech. It discloses a method, apparatus, device, and medium for generating and issuing early warnings for spatial information layers, including: collecting multi-source heterogeneous data and performing spatial alignment and attribute fusion processing to construct a unified digital spatial foundation using globally unique land parcel identifiers as index keys; monitoring business change signals and environmental dynamic signals and extracting the set of affected land parcel identifiers; retrieving layer generation strategies based on the mapping relationship between events and layer types; calling the corresponding layer generation logic and intelligent inference model to perform incremental updates or full spatial reconstruction on relevant spatial data, generating a target thematic map layer containing source metadata information and confidence analysis labels; comparing the target thematic map layer with associated reference layers and generating anomaly early warning information. This invention, through a unified spatial foundation and event-driven mechanism, automatically connects the processes of land parcel identification, layer generation, updating, and comparison, achieving rapid layer updates and automatic anomaly early warnings, reducing manual intervention, and improving update efficiency and the timeliness of anomaly detection. Attached Figure Description

[0011] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1This is a schematic diagram of an application environment for a spatial information layer generation and early warning method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating an embodiment of the spatial information layer generation and early warning method of the present invention; Figure 3 This is a schematic diagram of the functional modules of a preferred embodiment of the spatial information layer generation and early warning device of the present invention; Figure 4 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention; Figure 5 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0012] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0013] The spatial information layer generation and early warning method provided in this invention can be applied to, for example... Figure 1 In this application environment, the client communicates with the server via a network. The server can collect multi-source heterogeneous data from the client and perform spatial alignment and attribute fusion processing to construct a unified digital spatial foundation with globally unique plot identifiers as index keys; monitor business change signals and environmental dynamic signals and extract the set of affected plot identifiers; retrieve layer generation strategies based on the mapping relationship between events and layer types; call the corresponding layer generation logic and intelligent inference model to perform incremental updates or full spatial reconstruction on relevant spatial data, generating target thematic map layers containing traceability metadata information and confidence analysis labels; compare the target thematic map layers with associated reference layers and generate anomaly warning information. This invention automatically connects the processes of plot identification, layer generation, updating, and comparison through a unified spatial foundation and event-driven mechanism, achieving rapid layer updates and automatic anomaly warnings, reducing manual intervention, and improving update efficiency and the timeliness of anomaly detection. The client can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster composed of multiple servers. The invention will be described in detail below through specific embodiments.

[0014] Please see Figure 2 , Figure 2 This is a flowchart illustrating an embodiment of the spatial information layer generation and early warning method provided by the present invention. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0015] like Figure 2As shown, the spatial information layer generation and early warning method proposed in this invention includes the following steps: S10, collect multi-source heterogeneous datasets, perform spatial alignment and attribute fusion processing on the multi-source heterogeneous datasets, and use the processed data to construct a unified digital space base with a globally unique land parcel identifier as the index key. In this embodiment, multi-source heterogeneous datasets refer to data sets from different business systems, spatial acquisition systems, or observation methods, with differences in coordinate systems, spatial precision, attribute structures, and semantic definitions. When collecting this type of data, a unified data access rule and parsing method are used to incorporate the dispersed data into the same processing flow, providing a complete input foundation for subsequent consistency processing.

[0016] Spatial alignment is used to eliminate discrepancies in the spatial representation of different data. It is achieved by parsing and transforming the coordinate reference information of each data point, ensuring that all spatial data express the same geographical location within the same coordinate frame. This process can be accomplished through projection transformation, coordinate recalculation, or correction based on a spatial datum, making data from different sources comparable when spatially overlaid.

[0017] Attribute fusion processing is used to integrate attribute descriptions of the same geographic entity from different data. Through field semantic matching and attribute relationship integration, it unifies the expression of attributes from different sources but pointing to the same meaning, and eliminates duplicate or conflicting attribute values, so that the attribute information is logically consistent.

[0018] After completing spatial alignment and attribute fusion, a unified digital spatial foundation is constructed based on the processing results. This foundation uses a globally unique parcel identifier as the index key to associate and store spatial geometric information with the fused attribute information, enabling each geographic parcel to form a stable, locatable, and reusable spatial entity at the data level.

[0019] In different application environments, multi-source heterogeneous data can be collected through batch import or interface access. Spatial alignment processing can be performed using pre-configured transformation parameters or dynamic parsing of coordinate information. During attribute fusion, field mapping and merging rules can be adjusted according to data quality or business needs. The unified digital spatial foundation can adjust its index structure and storage method according to data scale during construction to adapt to centralized or distributed operating environments.

[0020] This embodiment performs spatial alignment and attribute fusion on multi-source heterogeneous data, and constructs a unified digital spatial base with a globally unique land parcel identifier. This enables data from different sources to form a consistent geographic unit representation in terms of spatial location and attribute expression, avoiding spatial misalignment and attribute fragmentation problems, and providing a stable and consistent data foundation for subsequent land parcel-based data processing.

[0021] S20 utilizes the deployed real-time event listening interface to monitor business change signals and environmental dynamic signals from external data sources and internal business nodes; In this embodiment, a real-time event monitoring interface is used to continuously sense changes in the internal and external states of the system. Deployed between the data access layer and the business processing layer, it receives event push information from various sources. Signals from external data sources typically reflect changes in environmental conditions or updates to external systems, while signals from internal business nodes reflect state changes or data update behaviors within the business process. By using a unified monitoring interface to centrally receive signals from different sources, the event sensing process becomes independent of manual triggering.

[0022] The monitoring process operates in an event-driven manner, with the listening interface remaining constantly running, continuously monitoring signal channels between external data sources and internal business nodes. When a signal arrives, the interface receives and temporarily stores the signal content, ensuring that business change signals and dynamic environmental signals are captured promptly and incorporated into subsequent processing. This approach avoids reliance on periodic polling, thereby improving the timeliness of signal detection.

[0023] In different operating environments, real-time event listening interfaces can be implemented through message subscription, callback mechanisms, or streaming access. For scenarios with dense signals or high concurrency, multi-instance listening or parallel processing mechanisms can be configured to improve reception capacity. For signals from significantly different sources, signal channels can be grouped at the listening layer to adapt to the event publishing methods of different systems or business nodes.

[0024] This embodiment deploys a real-time event monitoring interface and continuously monitors business change signals and environmental dynamic signals, enabling the system to obtain corresponding information in a timely manner when state changes occur, reducing event response delays, and improving the efficiency and accuracy of overall data update triggering.

[0025] S30, read the event attribute information carried by at least one of the monitored business change signal and the environmental dynamic signal, and extract the set of affected land parcel identifiers from the event attribute information; In this embodiment, event attribute information is used to characterize key content associated with spatial objects in business change signals or environmental dynamic signals, and it originates from the signal payload that has been monitored and successfully received. The reading process is performed for at least one signal type, enabling the processing logic to adapt to operational scenarios where only business changes or only environmental changes exist. Event attribute information typically includes a spatial description of the event occurrence, a change type identifier, and constraints associated with the spatial object, providing a data foundation for identifying the scope of impact.

[0026] After reading the event attribute information, the spatial association fields are parsed to identify land parcels associated with the event's location or impact area. By mapping spatial description information to a unified land parcel index system, corresponding land parcel identifiers are matched one by one, and the matching results are aggregated to form a set of affected land parcel identifiers. This set is used to clarify the scope of spatial objects that need to be focused on in subsequent processing, so that the processing is no longer based on the entire dataset.

[0027] Under different data structure conditions, event attribute information can exist in the form of key-value pairs, structured records, or nested fields, and can be read using field parsing, rule matching, or template parsing methods respectively. For the parsing of spatial description information, coordinate parsing, range parsing, or identifier mapping methods can be used to adapt to point-like, area-like, or coded spatial descriptions. In scenarios with large-scale land parcels, spatial index structures can be combined to quickly retrieve and aggregate land parcel identifiers.

[0028] This embodiment extracts the set of affected land parcel identifiers from event attribute information, thus limiting the scope of subsequent processing to the actual land parcel objects that have changed, reducing the involvement of irrelevant data in the processing, and improving the targeting and processing efficiency of spatial data updates and analysis.

[0029] S40, based on the preset mapping relationship between events and layer types, retrieve the layer generation strategy that matches the event attribute information; In this embodiment, the mapping relationship between events and layer types describes the correspondence rules between different event attributes and spatial layer processing targets. This mapping relationship originates from business rule configurations or system operating parameter conventions and is used to convert abstract event attributes into executable layer processing directions. Using the event type identifier in the event attribute information as input, this mapping relationship limits the range of layer types that can be triggered, thereby avoiding arbitrary associations between events and layer processing.

[0030] During the retrieval process, event type elements for matching are first obtained from the event attribute information, and these elements are used as index conditions to locate the corresponding layer type identifier in the mapping relationship. Subsequently, based on the obtained layer type identifier, the corresponding layer generation strategy is selected from the set of layer generation strategies. This strategy describes the combination of processing logic and processing path that should be adopted for subsequent spatial data, giving the layer generation behavior a clear business orientation.

[0031] Under different business configuration conditions, mapping relationships can be maintained in the form of configuration tables, rule sets, or parameter files, and strategy positioning can be completed using key-value retrieval, rule matching, or condition filtering methods. When the event attribute information contains multiple event type elements, single or multiple layer generation strategies can be determined according to priority or combination rules to adapt to complex event scenarios.

[0032] This embodiment establishes a mapping relationship between event attributes and layer generation strategies, enabling layer generation behavior to automatically match processing strategies based on event characteristics, reducing manual intervention and improving the determinism and consistency of the layer generation process.

[0033] S50, invoke the layer generation logic and intelligent reasoning model associated with the layer generation strategy to perform incremental update or full spatial reconstruction operations on the spatial data associated with the affected land parcel identifier set in the unified digital space base, and generate a target thematic map layer containing source metadata information and confidence analysis labels; In this embodiment, the layer generation strategy carries the processing constraints and parameter set for a single layer generation task, the layer generation logic carries the data processing and graphics rendering instruction set corresponding to the layer generation strategy, and the intelligent inference model carries the inference capabilities and parameter set corresponding to the layer generation strategy. The unified digital spatial foundation organizes spatial and attribute data using globally unique land parcel identifiers as index keys, enabling the affected land parcel identifier set to be used as a filtering condition to locate spatial data associated with the affected land parcel identifier set. The associated spatial data includes spatial geometric representations and attribute field sets bound to globally unique land parcel identifiers. The actions of calling the layer generation logic and intelligent inference model execute parameter parsing and resource loading based on the strategy content of the layer generation strategy, allowing the layer generation logic and intelligent inference model to be used collaboratively within the same execution context. Parameter parsing is used to obtain data range, field selection, inference input format, and output structure constraints. Resource loading is used to load the layer generation logic and intelligent inference model into the computing process or service container and complete version consistency verification. Incremental updates or full spatial reconstructions of spatial data generate new spatial geometric representations and attribute field sets within the representation space of the target thematic map layer. Incremental updates limit the rewriting and replacement of spatial data to only the spatial data corresponding to the affected parcel identifier set, while full spatial reconstruction regenerates and reconstructs the spatial geometric representations and attribute field sets within the coverage area of ​​the affected parcel identifier set. The intelligent inference model's role in spatial data is manifested in generating inference outputs from inputs such as historical attribute data and remote sensing image feature data. These inference outputs drive the updates of spatial geometric representations and attribute fields. The layer generation logic's role in spatial data is manifested in performing filtering, aggregation, field derivation, and geometric generation constraints on the inference outputs and base data, ensuring that the spatial geometric representations and attribute field sets of the target thematic map layer satisfy the format and semantics defined by the layer generation strategy. The source metadata information is used to record the data source version, model version and processing timestamp involved in the generation process of the current target thematic map layer. The confidence analysis label is used to express the credibility description of the probability value or confidence score output by the intelligent inference model in the parcel dimension or feature dimension. Together with the spatial geometric expression and attribute field set, they constitute the output content of the target thematic map layer, so that the target thematic map layer has spatial expression, attribute expression and process traceability expression at the same time.

[0034] This embodiment collaboratively invokes layer generation logic and intelligent inference model based on layer generation strategy, and performs incremental update or full spatial reconstruction operations on spatial data associated with the affected land parcel identifier set from the unified digital space base. Under the premise of limiting the scope of influence, it can complete the generation and update of spatial geometric expression and attribute field set. When outputting the target thematic map layer, it simultaneously provides traceability metadata information and confidence analysis labels, so that the target thematic map layer has traceability and credibility expression, reduces the risk of erroneous modification of the scope during the update process and improves the verifiability of the results.

[0035] S60, compare the target thematic map layer with the associated reference layer stored in the unified digital space base, and generate anomaly warning information based on the comparison result.

[0036] In this embodiment, the target thematic map layer carries the set of spatial elements and their attribute information generated in the previous processing stage, while the unified digital spatial base is used to store basic spatial data associated with the spatial elements, historical records, and reference information bound to globally unique parcel identifiers. The associated reference layer represents a comparison layer selected from the unified digital spatial base that is related to the target thematic map layer in terms of business semantics or spatial extent. This association can originate from historical version correspondence, business type correspondence, or spatial coverage correspondence. Comparing the target thematic map layer with the associated reference layer involves verifying the consistency of spatial elements and attribute content in both types of layers under the same spatial benchmark and attribute structure. The comparison content includes at least spatial location, spatial morphology, and attribute fields bound to the spatial elements.

[0037] In the spatial dimension, the comparison process analyzes the spatial geometric features of the target thematic map layer and the associated reference layer by overlaying them to identify differences such as non-overlapping spatial boundaries, changes in coverage, or missing elements. The results of the difference identification are expressed in the form of spatial regions or sets of spatial elements. In the attribute dimension, the comparison process verifies the attribute fields of corresponding spatial elements in the target thematic map layer and the associated reference layer item by item to identify inconsistencies in the logical relationships, value ranges, or status markers of attribute field values. The anchor point for attribute comparison is derived from globally unique parcel identifiers or equivalent spatial element association identifiers.

[0038] The generation of anomaly warning information based on the comparison results involves structuring spatial and attribute differences. Anomaly warning information includes at least the location identifier of the anomaly, the anomaly category, and the anomaly severity marker. The anomaly category distinguishes between spatial and attribute anomalies, while the anomaly severity reflects the scope or severity of the difference. The anomaly warning information is generated from the difference description results formed during the comparison process. The anomaly warning information is output as an independent data object for subsequent display, notification, or verification processing and does not participate in the rewriting of the spatial data itself.

[0039] After generating the target thematic map layer and constructing the anomaly warning information, the target thematic map layer and the anomaly warning information are pushed to the visualization decision terminal for display. The visualization decision terminal is used to carry the graphic rendering results of the spatial layer and the visual representation of the anomaly information. Its display content comes from the spatial geometric elements, attribute information, and source identifiers associated with the spatial elements contained in the target thematic map layer, while overlaying the anomaly location and anomaly status described by the anomaly warning information.

[0040] The push process involves converting the target thematic map layer into a layer data format that can be parsed by a visualization decision-making terminal, and establishing a correlation between the anomaly warning information and the target thematic map layer in a structured data form, enabling the anomaly information to be transmitted along with the corresponding spatial elements. When displayed, the anomaly warning information maintains its spatial location within the target thematic map layer, presented through graphic highlighting, symbol markings, or status prompts, thus making spatial differences or attribute anomalies intuitively identifiable on the map interface.

[0041] Through this push and display process, the visualization decision terminal can simultaneously present the overall spatial distribution of the target thematic map layer and the abnormal areas or elements indicated by the abnormal warning information, enabling decision-makers to obtain spatial data results and abnormal prompts on the same interface.

[0042] This embodiment compares the target thematic map layer with the associated reference layer in the unified digital space base, and generates anomaly warning information based on spatial and attribute differences. This enables the layer update results to be automatically verified and form structured anomaly prompts, thereby improving the efficiency of layer data consistency checks, reducing manual verification costs, and reducing business risks caused by data deviations.

[0043] In one embodiment, step S10 includes: S101, acquire land ownership vector layer data, land transfer record form data, agricultural insurance business form data, multi-temporal remote sensing image data, meteorological monitoring grid data and on-site yield measurement sample data; S102, extract spatial geometric features and non-spatial attribute fields from the land ownership vector layer data, the land transfer record form data and the agricultural insurance business form data, and perform geographic coordinate transformation processing on the spatial geometric features to generate spatial geometric features with unified coordinates; S103, perform radiometric calibration and atmospheric correction processing on the multi-temporal remote sensing image data, and use image segmentation technology to extract the vector boundary data of crop planting patches from the corrected multi-temporal remote sensing image data; S104, based on spatial location relationships, the meteorological monitoring grid data is associated with the spatial location corresponding to the vector boundary data of the crop planting patch through spatial grid mapping, and the field yield measurement sample data is associated with the corresponding crop planting patch through spatial connection operation; S105, perform spatial overlay analysis on the vector boundary data of the crop planting patch and the spatial geometric features after the unified coordinates, and determine the crop planting patches contained or intersecting within each geographic plot unit defined by the spatial geometric features after the unified coordinates, so as to establish the attribution relationship between the geographic plot unit and the crop planting patch. S106, Based on the geographic parcel unit defined by the spatial geometric features after the unified coordinates, the geographic coordinates and the key identification features in the non-spatial attribute field are integrated to generate a globally unique string as a globally unique parcel identifier. S107, the spatial geometric features after unifying coordinates, the associated meteorological monitoring grid data, the associated field yield measurement sample data, and the non-spatial attribute fields are mounted as comprehensive attribute fields under the corresponding globally unique land parcel identifier according to the attribution relationship, and stored to construct a unified digital spatial base.

[0044] In this embodiment, the multi-source heterogeneous dataset is used to describe the dataset formed by the same geographic object under different business links and different observation carriers. It includes at least land ownership vector layer data, land transfer record form data, agricultural insurance business form data, multi-temporal remote sensing image data, meteorological monitoring grid data, and field yield measurement sample data. The land ownership vector layer data provides vector representations of land parcel boundaries and ownership-related attribute fields; the land transfer record form data provides time-dimensional records of land parcel management relationships; the agricultural insurance business form data provides business fields such as insured objects, scope of the insured object, and insured object attributes; the multi-temporal remote sensing image data provides pixel-level spectral and texture information from time-series observations; the meteorological monitoring grid data provides environmental observation fields organized by grid units; and the field yield measurement sample data provides ground sampling fields with spatial location constraints. The acquisition process not only covers data retrieval and reception but also includes data integrity checks and field structure alignment, ensuring that subsequent spatial alignment and attribute fusion processing have the prerequisite of being associated with the same object.

[0045] Spatial alignment and attribute fusion processing revolves around spatial geometric features and non-spatial attribute fields. Spatial geometric features originate from geometric elements that express geographical scope in land ownership vector layer data, land transfer record form data, and agricultural insurance business form data. Spatial geometric features can be polygonal boundaries, linear boundaries, or point positioning information. Non-spatial attribute fields come from the business field set of the corresponding records, including ownership fields, transfer fields, and insurance fields. The extraction process performs field parsing and feature extraction for different data structures, separating geometric features and attribute fields into two independently processable objects to avoid the spread of alignment errors caused by the mixing of geometric and attribute data.

[0046] Geographic coordinate transformation is used to unify spatial geometric features under a common coordinate reference system. Differences in coordinate reference systems may stem from inconsistencies in data production systems, mapping datums, or image projection methods. Coordinate transformation achieves coordinate unification by identifying the source coordinate reference system, selecting the target coordinate reference system, and performing projection and datum transformations, generating spatial geometric features with unified coordinates. These unified spatial geometric features serve as the spatial reference for subsequent spatial overlay analysis. Their geometric vertex sequences, boundary closure relationships, and topological consistency must remain usable after transformation to avoid issues such as boundary self-intersections, duplicate points, and gaps that could interfere with the overlay analysis.

[0047] After undergoing radiometric calibration and atmospheric correction, multi-temporal remote sensing image data is transformed from sensor observations into comparable surface reflectance or equivalent representations. Radiometric calibration eliminates differences in gain and exposure between different sensors, while atmospheric correction reduces biases caused by atmospheric factors such as aerosols and water vapor, ensuring a consistent and comparable basis across different temporal phases. The corrected multi-temporal remote sensing image data serves as input for image segmentation techniques. Image segmentation is used to define the spatial extent of crop planting patches at the pixel level, outputting vector boundary data for these patches. This vector boundary data transforms continuous pixel regions in the image domain into vector boundary representations, facilitating spatial overlay analysis with unified coordinate spatial geometric features and providing computationally achievable geometric objects for subsequent attribution construction.

[0048] The association between meteorological monitoring grid data and the vector boundary data of crop planting patches depends on spatial location. Spatial grid mapping establishes a correspondence between the grid cells of meteorological monitoring grid data and the coverage area of ​​the vector boundary data of crop planting patches. The mapping method can be based on inclusion relationship, intersection area weight, or nearest neighbor grid selection, so that each crop planting patch obtains a corresponding set of meteorological fields. The correspondence between field yield measurement sample data and crop planting patches is established through spatial connection operations. Spatial connection operations match the spatial relationship between sample point location or sample range and the boundary of crop planting patches to obtain the binding result of samples and patches, enabling patches to carry the yield or quality fields of ground samples. The association action transforms the gridded environmental fields and point or small-scale sample fields into attributes that can be carried by area patches, supplementing the information sources of environmental and measured dimensions for the construction of comprehensive attribute fields.

[0049] Spatial overlay analysis is used to establish the attribution relationship between geographic parcel units and crop planting patches. Geographic parcel units are defined by spatial geometric features with unified coordinates, while crop planting patches are defined by their vector boundary data. Spatial overlay analysis takes two types of areal geometric objects as input, performs inclusion and intersection determinations, and outputs a set of crop planting patches that are contained or intersecting within the geographic parcel unit. The attribution relationship describes the correspondence structure between geographic parcel units and crop planting patches, supporting one-to-one, one-to-many, or many-to-one scenarios, adapting to spatial forms such as inconsistent boundaries, parcel splitting, and patch cross-boundary situations. Establishing the attribution relationship allows subsequent attribute fusion to be performed under a clear object mapping, avoiding spatial mismatches caused by directly matching attribute fields.

[0050] Globally unique parcel identifiers are used to enable indexing and referencing centered on geographic parcel units within a unified digital space framework. The generation process is based on geographic parcel units, integrating geographic coordinates with key identifying features from non-spatial attribute fields to form a globally unique string. Geographic coordinates provide spatial location stability, while key identifying features distinguish different business objects or ownership units within the same spatial location. These key identifying features can originate from stable identifying fields within ownership, transfer, and underwriting fields. The globally unique string emphasizes unique location and repeatable generation, ensuring consistent identification of the same geographic parcel unit across different data batches and business update cycles, thereby supporting cross-data source associations within the unified digital space framework.

[0051] The unified digital spatial foundation's storage is organized around globally unique land parcel identifiers. Spatial geometric features with unified coordinates, correlated meteorological monitoring grid data, correlated field yield measurement sample data, and non-spatial attribute fields are all mounted to their corresponding globally unique land parcel identifiers according to their affiliation, forming an object aggregation structure centered on index keys. Comprehensive attribute fields are used to uniformly represent sets of fields from different data sources under the same index key. These comprehensive attribute fields include business fields, environmental fields, and sample fields, maintaining the distinguishability of field sources and time dimensions to support subsequent updates. As a structured storage entity carrying object sets, the unified digital spatial foundation includes at least the mapping relationship from index keys to spatial geometric elements and comprehensive attribute fields, as well as an index structure for spatial retrieval and correlation operations, thus providing a stable underlying data foundation for subsequent extraction, updating, and comparison of land parcel objects.

[0052] This embodiment reduces association errors caused by spatial misalignment and field fragmentation across data sources through the above steps, shortens the data preparation chain at the parcel level, improves the efficiency of subsequent data retrieval, fusion and reuse for parcel objects, and provides a stable index foundation for incremental data supplementation and state consistency maintenance of the same parcel in different periods.

[0053] In one embodiment, step S20 above includes: S201, Configure the connection parameters of the real-time event listening interface to subscribe to the event publishing channel provided by the external data source and the status change notification channel issued by the internal business node; S202, start multiple concurrent listening threads of the real-time event listening interface, so that the multiple concurrent listening threads continuously listen to the subscribed event publishing channel and the state change notification channel, and receive the raw signal stream transmitted through the event publishing channel and the state change notification channel; S203, perform data cleaning and validity verification on the original signal stream, and remove the original signals with invalid payloads or incorrect formats to generate a filtered valid signal stream; S204, based on the data source identifier and content topic keywords carried in the metadata of the filtered effective signal stream, the filtered effective signal stream is classified and identified as business change signals and environmental dynamic signals. S205, the business change signal and the environmental dynamic signal are packaged into standard event objects with a unified data structure, and a timestamp and a globally incrementing sequence number are attached to each standard event object.

[0054] In this embodiment, the real-time event monitoring interface is used to establish a continuous data reception path between the data production end and the processing end, enabling state changes generated by external data sources and internal business nodes to be captured in the form of events and incorporated into subsequent links. Deployment emphasizes the interface's residency and availability in the operating environment. The deployment location can be at the data access layer, message middleware layer, or business service bypass layer, allowing the interface to simultaneously access the event publishing channel provided by the external data source and the state change notification channel issued by the internal business nodes. Monitoring is conducted on two types of signal sets: business change signals, which express event payloads such as changes in the state of business objects, record changes, or process changes; and environmental dynamic signals, which express event payloads of changes in external environmental elements such as weather, disasters, crop growth, and remote sensing updates. These two types of signals differ in source structure, transmission frequency, and data quality constraints; therefore, a unified reception and subsequent reclassification processing organization is formed at the monitoring entry stage.

[0055] The connection parameters of the real-time event listening interface are used to configure and maintain the connection to the event channel. These parameters must cover at least the channel address, authentication information, subscription scope, and transmission reliability parameters. The channel address is used to locate the event publishing channel and the status change notification channel. The authentication information restricts the access permissions of subscribers. The subscription scope limits the received event topics, business domains, or data source identifier sets. Transmission reliability parameters control mechanisms such as reconnection after disconnection, heartbeat detection, offset retrieval, and acknowledgment. The subscription action is triggered by the connection parameters. Subscription objects include event publishing channels provided by external data sources and status change notification channels issued by internal business nodes. The subscription result is that the listening interface gains continuous read and receive permissions for the specified channel, enabling subsequent listening threads to obtain the raw signal stream from the subscribed channel.

[0056] Concurrent listening threads are used to improve event reception throughput and reduce the impact of single-thread blocking on overall monitoring. The number of concurrent listening threads and the thread scheduling strategy are related to the number of channels, the peak event rate, and the cost of parsing a single event. Threads can be allocated by channel, by topic, or by using a shared queue to pull channel data from a thread pool. Continuous listening is manifested as a loop reception under long-connection or long-polling mode. The listening objects are limited to subscribed event publishing channels and status change notification channels, and the reception results form a raw signal stream. The raw signal stream represents the unstructured data sequence obtained from the channel. The data carrier can be a message body, log entry, or streaming record. The raw signal stream contains two parts: event payload and metadata. The metadata carries fields such as data source identifier, topic keywords, and generation time, providing a basis for subsequent classification and identification.

[0057] Data cleaning is used to standardize the format of the raw signal stream, remove noise, and complete fields, ensuring stable input for subsequent verification processing. Data cleaning processes individual raw signal records within the raw signal stream, and cleaning actions can include character set unification, escape character restoration, field separator standardization, filling in missing field default values, and compressing duplicate records. Validity verification determines whether the raw signal records meet the minimum constraints for subsequent classification and recognition. Validity constraints can be implemented based on message signature verification, necessary field existence verification, field value range verification, timestamp validity verification, and structural pattern verification. Invalid payloads indicate that the event payload is missing key fields or that field values ​​do not meet constraints; format errors indicate that it cannot be parsed according to the expected structure or that field types do not match. The removal process removes raw signal records with invalid payloads or format errors from the raw signal stream, outputting a filtered, valid signal stream. The filtered, valid signal stream meets the minimum usability requirements in terms of data quality and retains the metadata fields required for classification and recognition.

[0058] Classification and identification are used to distinguish between business change signals and environmental dynamic signals in a uniformly received, filtered, and valid signal stream, preventing signals with different semantics from being mixed into subsequent links during the monitoring phase. Classification is based on the data source identifier and content theme keywords carried in the metadata of the filtered valid signal stream. The data source identifier expresses the signal's source system or source domain, while the content theme keywords express the signal's semantic theme or business theme. The classification process can construct a mapping table from data source identifiers to signal types, or a matching set from content theme keywords to signal types, or combine both for joint determination to improve differentiation accuracy. The classification result divides the filtered valid signal stream into a set of business change signals and a set of environmental dynamic signals, maintaining the integrity of the payload and metadata of each individual signal record after the division, so that necessary fields can be retained during subsequent encapsulation.

[0059] Standard event objects unify business change signals and environmental dynamic signals into a consistent data structure, facilitating subsequent parsing, extraction, and routing based on this unified structure. The packaging action takes business change signals and environmental dynamic signals as input, mapping the signal's payload fields to the standard event object's field set and mapping the signal's metadata to the standard event object's header fields or extended fields, ensuring consistent field naming and types across different source signals at the object level. The unified data structure emphasizes cross-source consistency, including at least the event generation time field, event type field, data source identifier field, subject field, and payload field set. Timestamps represent the temporal semantics of standard event objects; they can be taken from the signal generation time or from the reception time, retaining the source time as an extended field. Globally incrementing sequence numbers provide strictly ordered sequence identification for standard event objects within the same access domain. Global incrementing emphasizes the monotonically increasing attribute within the same deployment instance or access cluster. Sequence numbers can be generated by atomic counters or distributed incrementing number segments, enabling subsequent links to perform deduplication, sorting, compensation, and replay control based on sequence numbers. The additional action writes a timestamp and a globally incrementing sequence number to each standard event object, enabling the standard event objects to have time alignment and sequence alignment capabilities, thereby providing a stable input for subsequent extraction of event attribute information and location of affected objects.

[0060] Through the above steps, this embodiment can reduce the risk of structural inconsistencies and quality fluctuations caused by heterogeneous source signals directly entering subsequent links, improve the throughput and continuity of event reception, enhance the controllability of signal classification and the ability to align event objects in sequence, thereby improving the response efficiency and consistency maintenance capability of subsequent processing to changing events.

[0061] In one embodiment, step S30 above includes: S301, Obtain a standard event object encapsulated from the service change signal and the environmental dynamic signal; S302, parse the payload of the standard event object and extract event attribute information including event type code, event occurrence time and spatial location descriptor; S303, based on the event type encoding, convert the spatial location descriptor into a standard format geographic coordinate point or geographic polygon range; S304, using the geographic coordinate point or the geographic polygon range as the query condition, perform a spatial range query in the spatial index of the unified digital spatial base to retrieve all geographic plot units whose spatial locations fall within the geographic polygon range or contain the geographic coordinate point. S305, extract the globally unique parcel identifiers corresponding to all retrieved geographic parcel units, and aggregate all the globally unique parcel identifiers into a set of affected parcel identifiers.

[0062] In this embodiment, the acquisition action is directed at at least one of the monitored business change signals and environmental dynamic signals. The acquisition result is a set of standard event objects. Each standard event object in the set of standard event objects corresponds to a business state change or an environmental state change. The standard event object set can be organized as a queue, a buffer, or a streaming batch, which facilitates the parsing process in batch or streaming mode when computing resources are limited.

[0063] The payload carries the main semantic content of the standard event object. The payload field set must include at least three types of fields: event type code, event occurrence time, and spatial location descriptor. The parsing process performs a structured reading of the payload of the standard event object. This structured reading includes field location, field type validation, and field value extraction, ensuring that the event type code, event occurrence time, and spatial location descriptor are explicitly extracted and combined to form event attribute information. Event attribute information represents the minimum usable description set for a single event. The event type code characterizes the event type and serves as the basis for selecting subsequent conversion rules. The event occurrence time characterizes the temporal semantics of the event and supports subsequent time-series alignment or expiration filtering. The spatial location descriptor characterizes the spatial location semantics associated with the event and serves as the entry parameter for spatial queries. Event attribute information can be organized into structured records. Each field in the structured record retains both raw and normalized values. Raw values ​​are used for audit trails, while normalized values ​​are used for query matching.

[0064] Event type encoding participates in the transformation process of spatial location descriptors. This transformation maps spatial location descriptors to standard-format geographic coordinate points or geographic polygon ranges, transforming them from semantic representations into computable spatial geometric representations. Spatial location descriptors can be represented as administrative division codes, place name text, image raster indexes, spatial references associated with business object numbers, vector boundary text representations, or point coordinate strings, etc. The representation of spatial location descriptors is related to event type encoding; therefore, the corresponding transformation path is selected based on the event type encoding. Transformation actions may include coordinate system identification and normalization, boundary point sequence rearrangement, closure verification, geometric validity verification, and precision truncation control, ensuring that the output geographic coordinate points or geographic polygon ranges meet unified coordinate and geometric expression rules. The standard format emphasizes consistency between coordinate dimensions, coordinate order, and coordinate reference system. Geographic coordinate points are used to represent single-point location events or events that represent affected locations as points, while geographic polygon ranges are used to represent regional coverage events or events that represent affected ranges as polygons. These two types of complementary query inputs provide subsequent spatial index queries.

[0065] The unified digital spatial foundation comprises a spatial index and a set of geographic parcel units. The spatial index maps spatial geometric representations to a searchable index structure, enabling spatial extent queries to be performed on large-scale sets of geographic parcel units with controllable overhead. Using geographic coordinates or geographic polygon extents as query conditions reflects the determinism of spatial query input, providing clear spatial constraints for subsequent retrieval actions. Spatial extent queries perform spatial matching within the spatial index, covering at least two types of determinations: point inclusion and area coverage. Point inclusion determines geographic parcel units containing geographic coordinates, while area coverage determines geographic parcel units whose spatial location falls within a geographic polygon extent. The retrieval action outputs a set of geographic parcel units. Each geographic parcel unit in the set has a spatial location field and an attribute field. The spatial location field expresses the spatial extent of the geographic parcel unit, while the attribute field expresses its ownership, transfer, insurance, or other business attributes. The set of geographic parcel units in the search results can be further filtered and deduplicated based on the event occurrence time, event type code, or spatial coverage ratio, so that geographic parcel units that are highly related to the spatial range of the event are retained first, thereby reducing the scale of subsequent processing and improving the stability of the link.

[0066] Globally unique parcel identifiers (GUIDs) serve as index keys for a unified digital spatial foundation, ensuring stable pointing to the same geographic parcel unit across data sources and processing stages. The extraction process reads the corresponding GUIDs from the retrieved geographic parcel units, guaranteeing their integrity and consistency through null value checks, format checks, and duplicate checks. The aggregation process combines all GUIDs into a set of affected parcel identifiers. This set represents the target parcel set within the impact range of the event. The affected parcel identifier set can employ a set structure to eliminate duplicate identifiers, or it can be appended with coverage ratios, hit types, or spatial hit evidence associated with each GUID, providing stronger constraints when subsequently used to locate spatial data. At least one selection constraint ensures that the link can still form an affected parcel identifier set even when only business change signals or only environmental dynamic signals exist, avoiding trigger gaps caused by the simultaneous occurrence of two signals.

[0067] This embodiment can quickly map the event space range to the set of land parcel objects after the signal is triggered, reduce the parsing cost caused by inconsistent event description methods from different sources, improve the determinism and reusability of land parcel location, reduce the indiscriminate scanning of the entire spatial data in subsequent processing, thereby improving event response efficiency and reducing processing resource overhead.

[0068] In one embodiment, step S40 above includes: S401, Extract the event type code from the event attribute information; S402, query the configuration table, which stores a preset mapping relationship between events and layer types, and obtain the associated layer type code from the configuration table according to the event type code; S403, based on the layer type encoding, retrieve the corresponding layer generation strategy identifier from the preset layer generation strategy library; S404, Based on the layer generation strategy identifier, load the layer generation strategy containing the data processing flow identifier and model call parameters from the layer generation strategy library.

[0069] In this embodiment, event attribute information serves as a structured description carrier for events, containing at least fields such as event type code, event occurrence time, and spatial location descriptor. The event type code performs type routing, directing events of the same type to a consistent layer generation path in subsequent processing. The extraction of the event type code involves locating and reading field values ​​from the event attribute information's field set. Field location can be based on fixed field names, field numbers, or field mapping tables. Field value reading simultaneously performs format validation and null value validation. Format validation ensures consistency in the event type code's encoding system, while null value validation prevents subsequent mapping link interruptions due to missing event type codes. The event type code can be string encoding, integer encoding, or hierarchical encoding. Hierarchical encoding expresses event subclasses and supports parent-class encoding overriding subclass encoding in mapping relationships, thereby enhancing the scalability of the mapping relationship.

[0070] The configuration table stores the predefined mapping relationships between events and layer types. Predefined mappings emphasize that these relationships are directly referenced at runtime rather than inferred temporarily, and that a defined association rule exists between event type codes and layer type codes. The action of querying the configuration table locates records that match the event type code. These records can contain an event type code field, a layer type code field, an applicable condition field, and a version field. The applicable condition field allows for finer selection when multiple layer type codes correspond to the same event type code. This finer selection can be achieved by combining event occurrence time, spatial location descriptor, data source identifier, or business type identifier. The version field constrains the effective range of the mapping relationship and supports canary releases and rollbacks of the configuration table. The action of retrieving the associated layer type code from the configuration table based on the event type code outputs the layer type code. This layer type code describes the category semantics of the target layer and serves as a key index for subsequent policy library retrieval. Layer type codes can be enumerated codes, string codes, or hierarchical codes. Hierarchical codes support subclass extensions within the same major layer category, allowing policy retrieval to choose between major and subclass policies.

[0071] The layer generation strategy library centrally stores various layer generation strategies. These strategies are organized as searchable entries, each containing at least a field linking the layer generation strategy identifier and the layer type code. It may also include an applicability field, a strategy version field, and a compatibility field. The action of retrieving the corresponding layer generation strategy identifier from the library based on the layer type code aims to map the layer type code to a unique identifier for each strategy entry. Retrieval can be achieved through index queries, key-value searches, or conditional filtering. Index queries prioritize low latency, while conditional filtering focuses on precise matching when strategy entries have multiple versions or applicability ranges. The applicability field describes the spatial scale, data source type, or business stage to which the strategy entry applies. The strategy version field supports multiple versions of the same layer type code and uses an activation flag to determine the currently used version. The compatibility field constrains the available combinations of data processing flow identifiers and model call parameters for each strategy entry.

[0072] The action of loading layer generation strategies from the layer generation strategy library based on layer generation strategy identifiers aims to parse the strategy entries pointed to by the identifiers into executable strategy content. The loading action includes reading the strategy entry content, verifying the integrity of the strategy entry, and parsing the strategy entry field set. Data processing flow identifiers are used to indicate the organization method or execution path of subsequent data processing flows. Data processing flow identifiers can be flow names, flow numbers, or flow chain identifiers. Flow chain identifiers express the combination relationship of multiple processing steps and support the selection of different processing chains at runtime based on the identifier. Model call parameters describe the calling constraints of intelligent inference models or other inference components. Model call parameters can include a set of parameters such as model identifier references, input feature selection, inference threshold, batch size, and resource quota. Input feature selection constrains the set of data fields subsequently extracted from the unified digital space base; the inference threshold constrains the inference result filtering rules; the batch size constrains the inference throughput and latency balance; and the resource quota constrains computational resource consumption and avoids resource contention under concurrent events. The loaded layer generation strategy serves as upstream control information for subsequent calls to layer generation logic and intelligent inference models, enabling the mapping relationship driven by event type encoding to be stably implemented into specific executable strategy content.

[0073] This embodiment can complete the layer type selection and strategy entry location through a defined mapping link after the event is triggered, reducing the dependence on manual strategy selection and manual configuration of call parameters, reducing the matching deviation between event type and layer output, improving the consistency and traceability of strategy selection, and maintaining the stability and controllability of strategy loading in the case of multiple concurrent events and multiple version strategies.

[0074] In one embodiment, step S50 above includes: S501, the layer generation logic identifier and the intelligent reasoning model identifier are parsed from the layer generation strategy; S502, based on the layer generation logic identifier, load the corresponding data processing and graphics rendering instruction sequence from the preset layer generation logic library as the layer generation logic; S503, Based on the intelligent reasoning model identifier, load the corresponding pre-trained neural network model from the preset intelligent reasoning model library as the intelligent reasoning model; S504, Based on the set of affected land parcel identifiers, extract the corresponding historical attribute data and the latest periodic remote sensing image feature data from the unified digital space base as spatial data to be processed; S505, input the historical attribute data and the latest period remote sensing image feature data into the intelligent reasoning model to perform feature reasoning and state prediction, and generate reasoning result data; S506, Based on the inference result data and the layer generation logic, drive the geographic information system rendering engine to perform incremental update or full spatial reconstruction operation on the spatial data to be processed, and obtain the processed spatial graphics and the updated attribute table. S507 records the data source version, model version and processing timestamp used in this operation, and combines them to generate traceability metadata information; S508, Generate confidence analysis labels based on the probability value or confidence score output by the intelligent reasoning model; S509, the processed spatial graphics, the updated attribute table, the source metadata information, and the confidence analysis labels are integrated to generate a target thematic map layer.

[0075] In this embodiment, the layer generation strategy carries a set of control information for layer generation. This set of control information at least covers the selection criteria for the layer generation logic and the selection criteria for the intelligent inference model, ensuring consistency in the layer generation path for the same event type under different operating environments and data states. The actions of parsing the layer generation strategy involve performing field location and field value reading on the field set of the layer generation strategy. Field location can be based on field name mapping, field index mapping, or strategy templates. Field value reading simultaneously performs type verification and existence verification. Type verification is used to constrain the consistency of the encoding form of the layer generation logic identifier and the intelligent inference model identifier, while existence verification is used to prevent subsequent loading chain interruptions due to missing identifiers. The layer generation logic identifier uniquely points to a logical entry in the layer generation logic library, and the intelligent inference model identifier uniquely points to a model entry in the intelligent inference model library. Both the layer generation logic identifier and the intelligent inference model identifier can be string identifiers, integer identifiers, or hierarchical identifiers. Hierarchical identifiers express the inheritance relationship between the logical version and the model version and support compatibility matching within the library.

[0076] The layer generation logic library stores reusable data processing and graphics rendering instruction sequences. These sequences express the sets of instructions required for processing, filtering, fusing, and rendering spatial data. The actions of loading data processing and graphics rendering instruction sequences based on layer generation logic identifiers include locating logic entries, reading instruction sequence content, and verifying instruction sequence integrity. Locating logic entries can be done based on index retrieval or conditional matching. Reading instruction sequence content can be done using structured scripts, instruction lists, or intermediate representations. Verifying instruction sequence integrity confirms that the input and output field sets referenced by the instructions in the sequence can be consumed by subsequent processes. As the carrier of layer generation logic, the data processing and graphics rendering instruction sequences not only limit the processing order but also the scope and granularity of the processed objects. The scope of the processed objects can be constrained by the filtering results of the affected parcel identifier set, and the granularity can be expressed through parcel-level, patch-level, or raster-level data organization. An executable link is formed between the layer generation logic and the subsequent geographic information system rendering engine. The rendering engine can process spatial data according to the instruction sequences and output spatial graphics and attribute tables.

[0077] The intelligent inference model library stores pre-trained neural network models that can be invoked. These pre-trained neural network models are used to perform inference on historical attribute data and the latest periodic remote sensing image feature data to generate inference result data. The actions of loading pre-trained neural network models based on intelligent inference model identifiers include model entry localization, model entity loading, and runtime dependency verification. Model entry localization determines the location identifiers of model files, model weights, or model server endpoints. Model entity loading loads model weights into the inference runtime environment. Runtime dependency verification confirms that the shape of the model input tensor, the specifications of the input fields, and the inference computational resources meet the constraints. Pre-trained neural network models can correspond to different inference task objectives, such as state prediction, category determination, or change recognition. State prediction corresponds to outputting continuous or discrete state quantities, category determination corresponds to outputting a category probability distribution, and change recognition corresponds to outputting a change mask or change intensity value. The inference task objective is constrained by the model invocation parameters in the layer generation strategy. These parameters can constrain input feature selection, inference batch size, and output thresholds, ensuring that the inference result data remains consistent with the subsequent consumption of the layer generation logic.

[0078] The affected land parcel identifier set is used to define the scope of data to be processed. The unified digital spatial base provides a spatial data organization method using globally unique land parcel identifiers as index keys, enabling spatial data to be directly associated with the land parcel identifier set at the index level. The actions of extracting historical attribute data and the latest period's remote sensing image feature data from the unified digital spatial base based on the affected land parcel identifier set include index hitting, field pruning, and time slice selection. Index hitting maps each identifier in the affected land parcel identifier set to a record unit in the unified digital spatial base. Field pruning selects the set of fields relevant to subsequent inference and rendering from the record unit. Time slice selection selects the latest period's remote sensing image feature data from multiple periods and forms an aligned input pair with the historical attribute data. Historical attribute data expresses the long-term and business attributes of the land parcels. Long-term attributes may include land parcel boundary stability attributes, land type attributes, or ownership attributes. Business attributes may include insurance attributes, transfer attributes, or management attributes. The latest period's remote sensing image feature data expresses recent observation characteristics, which may be spectral features, texture features, temporal variation features, or spatial morphological features. Historical attribute data and the latest periodic remote sensing image feature data are used as the actions of spatial data to be processed to form a unified data processing entry point. The spatial data to be processed can be organized using record lists, feature matrices, or key-value mappings. Key-value mappings use globally unique parcel identifiers as keys to maintain consistent index semantics with the unified digital spatial base.

[0079] The process of inputting historical attribute data and the latest period's remote sensing image feature data into an intelligent inference model for feature inference and state prediction includes input alignment, inference execution, and output encapsulation. Input alignment involves aligning historical attribute data and the latest period's remote sensing image feature data using globally unique parcel identifiers and quantifying or encoding them according to model input specifications. Inference execution involves calling a pre-trained neural network model in the inference runtime environment and obtaining the model's output tensor. Output encapsulation involves converting the output tensor into inference result data that can be consumed by subsequent layer generation logic. The inference result data can contain parcel-level state fields, patch-level determination fields, or pixel-level change fields. The field organization and naming of the inference result data need to be consistent with the input field constraints of the data processing and graphics rendering instruction sequence so that the inference result data can participate in spatial data processing in subsequent rendering stages. Feature inference maps input features to high-dimensional semantic representations or intermediate representations, while state prediction outputs the target state or target label based on the intermediate representation. These two can be represented as different output heads or different post-processing paths in the inference process. The post-processing path can include threshold pruning, confidence interval mapping, or class selection. Threshold pruning and class selection are constrained by the model's call parameters and are consistent with the generation of confidence analysis labels.

[0080] The inference result data is combined with the layer generation logic to form the rendering-driven input. This combination can manifest as field merging, associative mounting, or spatial association. Field merging adds the inference fields of the inference result data to the record structure of the spatial data to be processed. Associative mounting uses a globally unique parcel identifier as the key to attach the inference fields to the corresponding record unit. Spatial association maps the output to the spatial location of spatial geometric features or vector boundary data when there is patch-level or pixel-level output. The actions that drive the GIS rendering engine to perform incremental updates or full spatial reconstruction operations generate processed spatial graphics and updated attribute tables at the output level. Incremental updates update only the spatial graphic fragments and attribute table records associated with the spatial data set of affected parcel identifiers. Full spatial reconstruction reconstructs a larger range of data when it is necessary to rebuild spatial topological relationships, rebuild attribute derived fields, or rebuild the layer expression structure. The choice between incremental updates and full spatial reconstruction can be constrained by the instruction branches in the layer generation logic or the processing flow identifiers in the layer generation strategy. The instruction branches can be determined based on event type encoding, data source version differences, or the magnitude of changes in the inference result data. The processed spatial graphics are used to express the geometric shape and visual elements of spatial entities. Spatial entities can be plot units, crop patches, or risk areas. The updated attribute table is used to express the set of attribute fields associated with spatial entities. The set of attribute fields can include original business fields, inference derived fields, and rendering derived fields. Rendering derived fields can include hierarchical labels, display weights, or symbolic parameters. Symbolic parameters are used to control visual encoding such as color, line type, fill style, or transparency.

[0081] The data source version identifies the snapshot of the data source used to generate the target thematic map layer; the model version identifies the version of the pre-trained neural network model used to generate the inference result data; and the processing timestamp identifies the time point or time interval in which the layer generation process occurred. The actions of recording the data source version, model version, and processing timestamp and combining them to generate traceability metadata information include version acquisition, timestamp acquisition, and structured encapsulation. Version acquisition can be read from the metadata area of ​​the unified digital space foundation, the version field of external data sources, or the version field of the layer generation strategy. Timestamp acquisition can be read from the system time service and encapsulated in a unified format. Structured encapsulation organizes the data source version, model version, and processing timestamp into a searchable metadata structure and establishes a connection with the target thematic map layer. The traceability metadata information supports subsequent tracing and comparison. Tracing can pinpoint the specific data source version and model version, and supports determining the source of anomalies and updates when anomaly warning information is generated.

[0082] The probability values ​​or confidence scores output by the intelligent inference model are used to characterize the uncertainty of the inference result data. The probability values ​​can be category probabilities, event probabilities, or risk probabilities, while the confidence scores can be calibration confidence, marginal confidence, or consistency confidence. The process of generating confidence analysis labels based on the probability values ​​or confidence scores output by the intelligent inference model includes score extraction, hierarchical mapping, and label encapsulation. Score extraction extracts the probability values ​​or confidence scores corresponding to land parcels or patches from the inference result data or model output tensors. Hierarchical mapping maps the probability values ​​or confidence scores to discrete levels or label sets. Discrete levels can be high, medium, and low semantic levels or multi-level interval labels, while label sets can be text labels, enumerated labels, or combinations of structured fields. Confidence analysis labels form a consistent index granularity with the inference result data. This index granularity can be the granularity of globally unique land parcel identifiers or patch identifiers, ensuring that confidence analysis labels are consistently bound to spatial entities within the target thematic map layer.

[0083] The process of integrating processed spatial graphics, updated attribute tables, source metadata information, and confidence analysis tags to generate a target thematic map layer is used to form a deliverable layer representation structure. Integration includes entity alignment between spatial graphics and attribute tables, association mounting of attribute tables and source metadata information, and field merging of attribute tables and confidence analysis tags. Entity alignment confirms that each spatial entity in the spatial graphics has a corresponding record in the attribute table and is consistently associated using globally unique parcel identifiers or spatial entity identifiers. Association mounting binds source metadata information to the target thematic map layer in the form of layer-level metadata or record-level metadata. Field merging adds confidence analysis tags as attribute fields to the updated attribute table, which can be used for symbolization or filtering in the rendering engine. The target thematic map layer, as the final output object, internally contains at least a spatial graphic data structure, an attribute table data structure, and a metadata structure. The spatial graphic data structure can be a set of vector features or a set of raster layers; the attribute table data structure can be a relational table structure or a key-value table structure; and the metadata structure can be key-value metadata or hierarchical metadata. This enables the target thematic map layer to support the input requirements for subsequent comparison and anomaly warning information generation.

[0084] This embodiment reduces the reliance on manual sequential processing steps for layer generation through the above steps, reduces output deviations caused by inconsistent layer generation paths triggered by different events, and enables the generated results to have traceability and credibility supported by source metadata information and confidence analysis labels, making it easier to reuse the same output structure in subsequent layer comparison and anomaly warning information generation steps.

[0085] In one embodiment, step S60 above includes: S601, parse the business type identifier and spatial range parameter of the target thematic map layer, and use the business type identifier and spatial range parameter to retrieve the matching historical version layer or layer with business logic association in the unified digital space base, and use the matching historical version layer or layer with business logic association as the associated reference layer. S602, extract the spatial geometric features of the target thematic map layer and the associated reference layer, and perform spatial overlay analysis on the spatial geometric features to identify spatial difference areas where the target thematic map layer and the associated reference layer have non-overlapping boundaries or conflicting coverage at the same geographical location; S603, extract the attribute field values ​​corresponding to the same globally unique parcel identifier in the target thematic map layer and the associated reference layer, and perform an attribute consistency comparison operation to identify attribute difference records where the attribute field values ​​have logical contradictions; S604, aggregate the spatial difference region and the attribute difference record, and attach an anomaly type code and an anomaly severity level label to generate an anomaly warning information.

[0086] In this embodiment, the target thematic map layer carries the spatial representation results to be verified, the unified digital spatial base provides data organization and historical traces consistent with the land parcel index, and the associated reference layer provides a comparison benchmark to determine the degree of abnormal deviation of the target thematic map layer. The business type identifier expresses the layer category semantics of the target thematic map layer, and the spatial extent parameter expresses the spatial boundary conditions covered by the target thematic map layer. Together, they define the retrieval scope and retrieval intent of the associated reference layer. The actions of parsing the business type identifier and spatial extent parameter of the target thematic map layer perform field location, field reading, and field normalization processing on the layer metadata field set of the target thematic map layer. Field normalization processing is used to unify the encoding form of the business type identifier and the coordinate reference and boundary representation form of the spatial extent parameter. The spatial extent parameter can be represented as a geographic polygon range, a bounding rectangle range, or an administrative grid set, and the business type identifier can be represented as string encoding, enumeration encoding, or hierarchical encoding. The process of retrieving matching historical version layers or layers with business logic relationships within a unified digital space base using business type identifiers and spatial range parameters includes search condition construction, candidate layer filtering, and candidate layer confirmation. Search condition construction maps business type identifiers to layer category index fields and spatial range parameters to spatial index filtering conditions. Candidate layer filtering eliminates layer entries with non-overlapping spatial ranges or inconsistent business types. Candidate layer confirmation determines a single associated reference layer among multiple candidate layers, and the confirmation criteria can include the principle of most recent version time, data source version consistency, or highest business logic relevance. Historical version layers provide a time series benchmark under the same business type, while layers with business logic relationships provide a cross-layer logical verification benchmark. This cross-layer logical verification benchmark can be reflected in boundary consistency constraints, coverage relationship constraints, or state linkage constraints.

[0087] Spatial geometric features are used to express the geometric shape and spatial location of spatial entities. These features can originate from the point, line, and surface geometry of vector elements, vector boundary data, or spatial mapping results of raster cell ranges. The process of extracting spatial geometric features from the target thematic map layer and associated reference layers includes spatial entity enumeration, geometric field reading, and geometric normalization. Spatial entity enumeration determines the set of spatial entities to be compared; geometric field reading retrieves geometric objects from the layer's feature structure; and geometric normalization unifies coordinate references, geometric accuracy, and topology repair rules, avoiding comparison errors caused by differences in coordinate systems and geometric accuracy. Spatial overlay analysis projects the spatial geometric features of the target thematic map layer and associated reference layers onto the same spatial coordinate reference for spatial relationship calculation. Spatial relationship calculations can cover relationship types such as intersection, containment, disjointness, and overlap. Boundary non-coincidence characterizes the offset differences of the same spatial entity on boundary segments or vertices. Coverage conflict characterizes the mutually exclusive or abnormally overlapping coverage of spatial ranges by the target thematic map layer and associated reference layers at the same geographical location. The identification of spatially disparate regions can be accomplished through geometric difference operations, symmetric difference operations, or conflict coverage region extraction. Spatially disparate regions are expressed in the form of planar geometry or grid sets, and their association with the spatial entity identifiers of the target thematic map layer is preserved for subsequent aggregation and encoding.

[0088] Globally unique parcel identifiers (GUIDs) provide alignment anchors for attribute comparison, ensuring a one-to-one correspondence between attribute field values ​​from different layers within the same parcel index dimension. The process of extracting attribute field values ​​corresponding to the same GUID from the target thematic map layer and associated reference layers includes identifier alignment, field set determination, and field value reading. Identifier alignment locates records corresponding to the same GUID in both layers. Field set determination identifies the set of attribute fields participating in the consistency comparison; this set can include key business fields, status fields, and derived fields. Field value reading retrieves the field values ​​for the same attribute field from records in the target thematic map layer and associated reference layer. Attribute consistency comparison operations determine the consistency of field values ​​and output discrepancies. Consistency determination can cover equality determination, interval tolerance determination, enumeration validity determination, and cross-field logical constraint determination. Equality determination handles fields requiring complete consistency; interval tolerance determination handles fields with measurement errors or statistical fluctuations; enumeration validity determination handles the value range constraints of category fields; and cross-field logical constraint determination handles logical consistency relationships between fields. The action of identifying attribute difference records with logical contradictions in attribute field values ​​encapsulates the difference results into structured records. The structured records contain at least a globally unique parcel identifier, difference field name, target thematic map layer field value, associated reference layer field value, and contradiction type identifier. The contradiction type identifier is used to distinguish between missing contradictions, conflicting contradictions, boundary-crossing contradictions, or linked contradictions, which facilitates the generation of subsequent anomaly type codes.

[0089] Aggregating spatial difference regions and attribute difference records is used to converge spatial anomalies and attribute anomalies into a unified anomaly representation object. This unified anomaly representation object supports organization by plot, region, or layer range. Aggregation actions include association alignment, deduplication and merging, and summary statistics. Association alignment establishes a link between spatial difference regions and attribute difference records through globally unique plot identifiers or spatial coverage relationships. Deduplication and merging merges duplicate differences and eliminates duplicate outputs caused by multiple detections. Summary statistics calculate anomaly scale, anomaly density, or anomaly impact range, providing a basis for anomaly severity level labels. Anomaly type encoding provides machine-recognizable representations of anomaly categories, and anomaly severity level labels are used to grade the severity of anomalies. Anomaly severity level labels can be jointly determined by the area ratio of spatial difference regions, boundary offset distance, coverage conflict ratio, and the field importance weight and contradiction type weight of attribute difference records. The action of generating anomaly warning information encapsulates the aggregation results, anomaly type codes, and anomaly severity level labels into a transmittable data structure. The transmittable data structure includes at least anomaly location information, anomaly description information, and anomaly classification information. Anomaly location information may include a globally unique set of parcel identifiers and spatial difference region geometry. Anomaly description information may include attribute difference record details and contradiction type identifiers. Anomaly classification information includes anomaly type codes and anomaly severity level labels, thereby enabling anomaly warning information to be directly consumed by subsequent display or processing links.

[0090] This embodiment can output layer anomalies in a structured manner from both spatial and attribute dimensions, reducing missed detections and false judgments caused by relying on only a single dimension for verification. It also expresses the location and degree of anomalies in a calculable code and level form, improving the efficiency of anomaly detection and the feasibility of anomaly handling.

[0091] In one embodiment, a spatial information layer generation and early warning device is provided, which corresponds one-to-one with the spatial information layer generation and early warning method described in the above embodiments. (Refer to...) Figure 3 , Figure 3 This is a schematic diagram of the functional modules of a preferred embodiment of the spatial information layer generation and early warning device of the present invention. The modules include a spatial base construction module 10, an event listening module 20, a plot parsing module 30, a strategy matching module 40, a layer generation module 50, and a layer early warning module 60. Detailed descriptions of each functional module are as follows: The spatial base construction module 10 is used to collect multi-source heterogeneous datasets, perform spatial alignment and attribute fusion processing on the multi-source heterogeneous datasets, and use the processed data to construct a unified digital spatial base with a globally unique land parcel identifier as the index key. The event listening module 20 is used to monitor business change signals and environmental dynamic signals from external data sources and internal business nodes using the deployed real-time event listening interface; The land parcel parsing module 30 is used to read the event attribute information carried by at least one of the monitored business change signals and the environmental dynamic signals, and extract the set of affected land parcel identifiers from the event attribute information. The strategy matching module 40 is used to retrieve a layer generation strategy that matches the event attribute information based on a preset mapping relationship between events and layer types. The layer generation module 50 is used to call the layer generation logic and intelligent reasoning model associated with the layer generation strategy, perform incremental update or full spatial reconstruction operations on the spatial data associated with the affected land parcel identifier set in the unified digital space base, and generate a target thematic map layer containing source metadata information and confidence analysis labels. The layer warning module 60 is used to compare the target thematic map layer with the associated reference layer stored in the unified digital space base, and generate abnormal warning information based on the comparison result.

[0092] Specific limitations regarding the spatial information layer generation and early warning device can be found in the aforementioned limitations on the spatial information layer generation and early warning method, and will not be repeated here. Each module in the aforementioned spatial information layer generation and early warning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0093] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides determination and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When executed by the processor, the computer program implements the functions or steps of a spatial information layer generation and early warning method on the server side.

[0094] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 5As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides determination and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When executed by the processor, the computer program implements the client-side functions or steps of a spatial information layer generation and early warning method.

[0095] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Collect multi-source heterogeneous datasets, perform spatial alignment and attribute fusion processing on the multi-source heterogeneous datasets, and use the processed data to construct a unified digital spatial base with a globally unique land parcel identifier as the index key. Utilize the deployed real-time event listening interface to monitor business change signals and environmental dynamic signals from external data sources and internal business nodes; Read the event attribute information carried by at least one of the monitored business change signals and the environmental dynamic signals, and extract the set of affected land parcel identifiers from the event attribute information; Based on the preset mapping relationship between events and layer types, retrieve the layer generation strategy that matches the event attribute information; The layer generation logic and intelligent reasoning model associated with the layer generation strategy are invoked to perform incremental update or full spatial reconstruction operations on the spatial data associated with the affected land parcel identifier set in the unified digital space base, generating a target thematic map layer containing source metadata information and confidence analysis labels; The target thematic map layer is compared with the associated reference layer stored in the unified digital space base, and an anomaly warning message is generated based on the comparison result.

[0096] In one embodiment, a computer-readable storage medium is provided, which may be non-volatile or volatile, and a computer program is stored thereon, which, when executed by a processor, performs the following steps: Collect multi-source heterogeneous datasets, perform spatial alignment and attribute fusion processing on the multi-source heterogeneous datasets, and use the processed data to construct a unified digital spatial base with a globally unique land parcel identifier as the index key. Utilize the deployed real-time event listening interface to monitor business change signals and environmental dynamic signals from external data sources and internal business nodes; Read the event attribute information carried by at least one of the monitored business change signals and the environmental dynamic signals, and extract the set of affected land parcel identifiers from the event attribute information; Based on the preset mapping relationship between events and layer types, retrieve the layer generation strategy that matches the event attribute information; The layer generation logic and intelligent reasoning model associated with the layer generation strategy are invoked to perform incremental update or full spatial reconstruction operations on the spatial data associated with the affected land parcel identifier set in the unified digital space base, generating a target thematic map layer containing source metadata information and confidence analysis labels; The target thematic map layer is compared with the associated reference layer stored in the unified digital space base, and an anomaly warning message is generated based on the comparison result.

[0097] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0098] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0099] It should be noted that if any AI models, software tools, or components not belonging to this company appear in the embodiments of this application, they are merely illustrative examples and do not represent actual use. The above-described embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

[0100] The user personal information involved in this application embodiment is all authorized (knowing and consenting) by the relevant parties or fully authorized by all parties, and the executing entity can obtain it through various open, legal and compliant means. The collection, storage, use, processing, transmission, provision and disclosure of the information, data and signals involved all comply with the relevant laws and regulations of the relevant countries and regions, and do not violate public order and good morals.

Claims

1. A method for generating and issuing early warnings for spatial information layers, characterized in that, Includes the following steps: Collect multi-source heterogeneous datasets, perform spatial alignment and attribute fusion processing on the multi-source heterogeneous datasets, and use the processed data to construct a unified digital spatial base with a globally unique land parcel identifier as the index key. Utilize the deployed real-time event listening interface to monitor business change signals and environmental dynamic signals from external data sources and internal business nodes; Read the event attribute information carried by at least one of the monitored business change signals and the environmental dynamic signals, and extract the set of affected land parcel identifiers from the event attribute information; Based on the preset mapping relationship between events and layer types, retrieve the layer generation strategy that matches the event attribute information; The layer generation logic and intelligent reasoning model associated with the layer generation strategy are invoked to perform incremental update or full spatial reconstruction operations on the spatial data associated with the affected land parcel identifier set in the unified digital space base, generating a target thematic map layer containing source metadata information and confidence analysis labels; The target thematic map layer is compared with the associated reference layer stored in the unified digital space base, and an anomaly warning message is generated based on the comparison result.

2. The spatial information layer generation and early warning method as described in claim 1, characterized in that, Collect multi-source heterogeneous datasets, perform spatial alignment and attribute fusion processing on the datasets, and construct a unified digital spatial base using globally unique land parcel identifiers as index keys, including: Acquire land ownership vector layer data, land transfer record form data, agricultural insurance business form data, multi-temporal remote sensing image data, meteorological monitoring grid data, and on-site yield measurement sample data; Spatial geometric features and non-spatial attribute fields are extracted from the land ownership vector layer data, the land transfer record form data, and the agricultural insurance business form data. Geographic coordinate transformation is performed on the spatial geometric features to generate spatial geometric features with unified coordinates. Radiometric calibration and atmospheric correction are performed on the multi-temporal remote sensing image data, and vector boundary data of crop planting patches are extracted from the corrected multi-temporal remote sensing image data using image segmentation technology. Based on spatial location relationships, the meteorological monitoring grid data is associated with the spatial location corresponding to the vector boundary data of the crop planting patch through spatial grid mapping, and the field yield measurement sample data is associated with the corresponding crop planting patch through spatial connection operation; The vector boundary data of the crop planting patches are spatially overlaid with the spatial geometric features after the unified coordinates. The crop planting patches contained or intersecting within each geographic plot unit defined by the spatial geometric features after the unified coordinates are determined to establish the attribution relationship between geographic plot units and crop planting patches. Based on the geographic parcel unit defined by the spatial geometric features after the unified coordinates, a globally unique string is generated by integrating the geographic coordinates with the key identification features in the non-spatial attribute fields as a globally unique parcel identifier. The spatial geometric features after unifying coordinates, the associated meteorological monitoring grid data, and the associated field yield measurement sample data, as well as the non-spatial attribute fields, are mounted as comprehensive attribute fields under the corresponding globally unique land parcel identifier based on the attribution relationship, and stored to construct a unified digital spatial base.

3. The spatial information layer generation and early warning method as described in claim 1, characterized in that, Utilize the deployed real-time event listening interface to monitor business change signals and environmental dynamic signals from external data sources and internal business nodes, including: Configure the connection parameters of the real-time event listening interface to subscribe to the event publishing channel provided by the external data source and the status change notification channel issued by the internal business node; Multiple concurrent listening threads of the real-time event listening interface are started, so that the multiple concurrent listening threads continuously listen to the subscribed event publishing channel and the state change notification channel, and receive the raw signal stream transmitted through the event publishing channel and the state change notification channel; The original signal stream is cleaned and validated, and invalid or incorrectly formatted original signals are removed to generate a filtered valid signal stream. Based on the data source identifier and content topic keywords carried in the metadata of the filtered valid signal stream, the filtered valid signal stream is classified and identified as business change signals and environmental dynamic signals. The business change signal and the environmental dynamic signal are packaged into standard event objects with a unified data structure, and a timestamp and a globally incrementing sequence number are attached to each standard event object.

4. The spatial information layer generation and early warning method as described in claim 1, characterized in that, Read the event attribute information carried by at least one of the monitored business change signals and the environmental dynamic signals, and extract the set of affected land parcel identifiers from the event attribute information, including: Obtain a standard event object encapsulated from the business change signal and the environmental dynamic signal; Parse the payload of the standard event object and extract event attribute information including event type code, event occurrence time and spatial location descriptor; Based on the event type encoding, the spatial location descriptor is converted into a standard format geographic coordinate point or geographic polygon range; Using the geographic coordinates or the geographic polygon range as the query conditions, perform a spatial range query in the spatial index of the unified digital spatial base to retrieve all geographic parcel units whose spatial locations fall within the geographic polygon range or contain the geographic coordinates. Extract the globally unique parcel identifiers corresponding to all retrieved geographic parcel units, and aggregate all the globally unique parcel identifiers into a set of affected parcel identifiers.

5. The spatial information layer generation and early warning method as described in claim 1, characterized in that, Based on a preset mapping relationship between events and layer types, a layer generation strategy matching the event attribute information is retrieved, including: Extract the event type code from the event attribute information; The configuration table is queried, which stores a preset mapping relationship between events and layer types. The associated layer type code is obtained from the configuration table according to the event type code. Based on the layer type encoding, retrieve the corresponding layer generation strategy identifier from the preset layer generation strategy library; Based on the layer generation strategy identifier, load the layer generation strategy containing the data processing flow identifier and model call parameters from the layer generation strategy library.

6. The spatial information layer generation and early warning method as described in claim 1, characterized in that, The layer generation logic and intelligent inference model associated with the layer generation strategy are invoked to perform incremental updates or full spatial reconstruction operations on the spatial data associated with the affected land parcel identifier set in the unified digital spatial base, generating a target thematic map layer containing source metadata information and confidence analysis labels, including: The layer generation logic identifier and intelligent reasoning model identifier are parsed from the layer generation strategy; Based on the layer generation logic identifier, the corresponding data processing and graphics rendering instruction sequence is loaded from the preset layer generation logic library as the layer generation logic; Based on the intelligent reasoning model identifier, the corresponding pre-trained neural network model is loaded from the preset intelligent reasoning model library as the intelligent reasoning model; Based on the set of affected land parcel identifiers, the corresponding historical attribute data and the latest periodic remote sensing image feature data are extracted from the unified digital space base as spatial data to be processed. The historical attribute data and the latest periodic remote sensing image feature data are input into the intelligent reasoning model to perform feature reasoning and state prediction, and generate reasoning result data. Based on the inference result data and the layer generation logic, the geographic information system rendering engine is driven to perform incremental update or full spatial reconstruction operations on the spatial data to be processed, so as to obtain the processed spatial graphics and the updated attribute table. Record the data source version, model version, and processing timestamp used in this operation, and combine them to generate traceability metadata information; Based on the probability value or confidence score output by the intelligent reasoning model, generate confidence analysis labels; The processed spatial graphics, the updated attribute table, the source metadata information, and the confidence analysis labels are integrated to generate a target thematic map layer.

7. The spatial information layer generation and early warning method as described in claim 1, characterized in that, The target thematic map layer is compared with the associated reference layer stored in the unified digital space base, and anomaly warning information is generated based on the comparison results, including: The business type identifier and spatial range parameter of the target thematic map layer are analyzed, and the business type identifier and spatial range parameter are used to retrieve the matching historical version layer or the layer with business logic association in the unified digital space base. The matching historical version layer or the layer with business logic association is used as the associated reference layer. Extract the spatial geometric features from the target thematic map layer and the associated reference layer, and perform spatial overlay analysis on the spatial geometric features to identify spatial difference areas where the target thematic map layer and the associated reference layer have non-overlapping boundaries or conflicting coverage at the same geographical location; Extract the attribute field values ​​corresponding to the same globally unique parcel identifier from the target thematic map layer and the associated reference layer, and perform an attribute consistency comparison operation to identify attribute difference records where the attribute field values ​​have logical contradictions; Aggregate the spatial difference regions and the attribute difference records, and attach anomaly type codes and anomaly severity level labels to generate anomaly warning information.

8. A spatial information layer generation and early warning device, characterized in that, The spatial information layer generation and early warning device includes: The spatial base construction module is used to collect multi-source heterogeneous datasets, perform spatial alignment and attribute fusion processing on the multi-source heterogeneous datasets, and use the processed data to construct a unified digital spatial base with a globally unique land parcel identifier as the index key. The event listening module is used to monitor business change signals and environmental dynamic signals from external data sources and internal business nodes using the deployed real-time event listening interface; The land parcel ... The strategy matching module is used to retrieve layer generation strategies that match the event attribute information based on a preset mapping relationship between events and layer types. The layer generation module is used to call the layer generation logic and intelligent reasoning model associated with the layer generation strategy, perform incremental update or full spatial reconstruction operations on the spatial data associated with the affected land parcel identifier set in the unified digital space base, and generate a target thematic map layer containing source metadata information and confidence analysis labels. The layer warning module is used to compare the target thematic map layer with the associated reference layer stored in the unified digital space base, and generate abnormal warning information based on the comparison results.

9. A computer device, characterized in that, The computer device includes a memory, a processor, and a spatial information layer generation and early warning program stored in the memory and executable on the processor. When the spatial information layer generation and early warning program is executed by the processor, it implements the steps of the spatial information layer generation and early warning method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a spatial information layer generation and early warning program, which, when executed by a processor, implements the steps of the spatial information layer generation and early warning method as described in any one of claims 1-7.

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