A seismic remote sensing data management method and system

Abstract

The application discloses a kind of seismic remote sensing data management method and system, it is related to earth science information technology field, including, access multi-source seismic remote sensing data, define the standardized metadata description format to each type of multi-source seismic remote sensing data, store multi-source seismic remote sensing data and corresponding standardized metadata description into data resource library;Real-time monitoring of earthquake event information, when target earthquake event is captured, the epicenter of target earthquake event is generated as dynamic space-time range, according to dynamic space-time range, aggregate multi-source seismic remote sensing data from data resource library, and construct dynamic data cube for target earthquake event;Based on dynamic data cube, generate data product through data processing workflow, record the complete processing process log of data product.The application forms integrated management scheme from data intelligent aggregation, product quality controllable to problem accurate traceability and closed-loop correction.

Description

Technical Field

[0001] This invention relates to the field of earth science information technology, and in particular to a method and system for earthquake remote sensing data management. Background Technology

[0002] In recent years, earthquake remote sensing data management technology has gradually developed into a data storage model based on standardized metadata. By defining unified metadata specifications for multi-source data such as synthetic aperture radar and optical images, it enables systematic archiving and retrieval of data. Combined with predefined data processing workflows, it can automatically generate data products such as deformation fields and damage maps, improving the standardization and efficiency of data processing. This technology provides important data foundation support for earthquake scientific research.

[0003] When faced with the high timeliness requirement of earthquake emergency response, the limitations of the existing static data management model are quite prominent. The data organization method is independent of the earthquake event itself, making it difficult to quickly and automatically aggregate complete datasets related to specific events after the disaster, which affects the timeliness of emergency analysis. Since the data forms complex derivation relationships through multi-level processing, when there are quality defects in the underlying raw data, the existing technology lacks effective means to accurately trace all affected advanced data products, which poses a potential challenge to the reliability of scientific conclusions. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for managing earthquake remote sensing data to solve the problems of insufficient timeliness of emergency response and difficulty in accurately tracing the global impact of data quality issues caused by the static data organization mode in existing technologies.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for managing earthquake remote sensing data, which includes: accessing multi-source earthquake remote sensing data, defining a standardized metadata description format for each type of multi-source earthquake remote sensing data, and storing the multi-source earthquake remote sensing data and the corresponding standardized metadata description in a data resource library.

[0008] Real-time monitoring of seismic event information; when a target seismic event is captured, a dynamic spatiotemporal range is generated with the epicenter of the target seismic event as the basis. Based on the dynamic spatiotemporal range, multi-source seismic remote sensing data is aggregated from the data resource library to construct a dynamic data cube oriented towards the target seismic event.

[0009] Based on a dynamic data cube, data products are generated through a data processing workflow, and a complete processing log of the data products is recorded.

[0010] Based on the complete processing log, a data lineage map is constructed for the data products. When multi-source seismic remote sensing data in the data resource library is identified as having quality problems, reverse tracing is performed based on the data lineage map to obtain the data products and dynamic data cubes affected by the multi-source seismic remote sensing data.

[0011] The multi-source seismic remote sensing data is updated. Based on the updated multi-source seismic remote sensing data and the complete processing log, the data processing workflow is re-executed to generate updated data products.

[0012] As a preferred embodiment of the earthquake remote sensing data management method of the present invention, the method includes the following steps: accessing multi-source earthquake remote sensing data, defining a standardized metadata description format for each type of multi-source earthquake remote sensing data, and storing the multi-source earthquake remote sensing data and its corresponding standardized metadata description in a data resource library:

[0013] A standardized metadata description format is defined for synthetic aperture radar imagery, optical satellite imagery, and multi-source seismic remote sensing data.

[0014] Using a standardized metadata description format, newly acquired synthetic aperture radar imagery, optical satellite imagery, and multi-source seismic remote sensing data are parsed to extract standardized metadata descriptions.

[0015] The extracted standardized metadata descriptions are associated with the multi-source seismic remote sensing data, and the associated multi-source seismic remote sensing data and standardized metadata descriptions are stored in the data resource repository.

[0016] As a preferred embodiment of the earthquake remote sensing data management method of the present invention, the method includes the following steps: real-time monitoring of earthquake event information; when a target earthquake event is captured, generating a dynamic spatiotemporal range with the epicenter of the target earthquake event as the reference point:

[0017] Obtain event information of the target earthquake event, calculate the initial boundary radius of the dynamic spatiotemporal range based on the magnitude of the target earthquake event and statistical analysis of historical earthquake cases;

[0018] By combining the information in the target earthquake event, the epicenter location of the target earthquake event, and the initial boundary radius of the dynamic spatiotemporal range, the spatial boundary of the dynamic spatiotemporal range is obtained.

[0019] Based on the occurrence time of the target earthquake event, the time boundary of the dynamic spatiotemporal range is obtained. The time boundary includes the pre-earthquake period and the post-earthquake duration, forming the dynamic spatiotemporal range of the epicenter of the target earthquake event.

[0020] As a preferred embodiment of the earthquake remote sensing data management method of the present invention, the following steps are included: aggregating multi-source earthquake remote sensing data from a data resource database according to a dynamic spatiotemporal range to construct a dynamic data cube oriented towards a target earthquake event:

[0021] Using dynamic spatiotemporal range as query criteria, spatiotemporal matching retrieval is performed in the standardized metadata description of the data resource repository;

[0022] Retrieve multi-source earthquake remote sensing data and standardized metadata descriptions that are matched in the spatiotemporal matching search results;

[0023] The hit multi-source seismic remote sensing data and standardized metadata descriptions are logically organized into a unified data view;

[0024] Assign a unique identifier to the target seismic event to a unified data view, and construct a dynamic data cube for the target seismic event.

[0025] As a preferred embodiment of the earthquake remote sensing data management method of the present invention, the method includes the following steps: Based on a dynamic data cube, data products are generated through a data processing workflow, and a complete processing log of the data products is recorded.

[0026] The multi-source seismic remote sensing data in the dynamic data cube is input into a data processing workflow defined based on the data processing algorithms, execution order, and parameter configuration required for coseismic deformation field extraction. The multi-source seismic remote sensing data is then processed to output data products.

[0027] Based on the execution of the data processing workflow, the algorithms called, parameters, input data versions, and output data product storage paths are recorded to form a complete data product processing log.

[0028] As a preferred embodiment of the earthquake remote sensing data management method described in this invention, the following steps are included: Based on the complete processing log, a data lineage map is constructed for the data products. When multi-source earthquake remote sensing data in the data resource library is identified as having quality problems, reverse tracing is performed based on the data lineage map to obtain the data products and dynamic data cubes affected by the multi-source earthquake remote sensing data:

[0029] Analyze the complete processing log of the data product and construct an initial directed acyclic graph of data lineage by connecting the data objects recorded in the log with the processing operations.

[0030] Calculate the dependency weight for each node in the initial directed acyclic graph of data lineage to form a data lineage graph;

[0031] When multi-source seismic remote sensing data in the data resource library is identified as having quality problems, the nodes representing the multi-source seismic remote sensing data are located from the data lineage map.

[0032] Starting from the nodes representing multi-source earthquake remote sensing data, a reverse traversal is performed in the data lineage map, and the scope of influence is determined based on the dependency weights to obtain data products and dynamic data cubes reflecting the impact of multi-source earthquake remote sensing data.

[0033] As a preferred embodiment of the earthquake remote sensing data management method of the present invention, the method includes the following steps: updating multi-source earthquake remote sensing data, and re-executing the data processing workflow based on the updated multi-source earthquake remote sensing data and complete processing logs to generate updated data products:

[0034] Replace the multi-source seismic remote sensing data in the data repository that was identified as having quality issues with the new version of data that has undergone quality correction.

[0035] Parse the list of input data identifiers required by the data processing workflow from the complete processing log;

[0036] Based on the input data identifier list, retrieve the updated multi-source seismic remote sensing data and the required input data that has not been updated from the data resource library;

[0037] The updated multi-source seismic remote sensing data and the required input data that have not been updated, along with the processing algorithms and parameters recorded in the complete processing log, are submitted to the data processing workflow engine to re-execute the data processing workflow and output the updated data product.

[0038] Secondly, the present invention provides an earthquake remote sensing data management system, including a data access module for accessing multi-source earthquake remote sensing data, defining a standardized metadata description format for each type of multi-source earthquake remote sensing data, and storing the multi-source earthquake remote sensing data and the corresponding standardized metadata description in a data resource library.

[0039] The dynamic aggregation module monitors seismic event information in real time. When a target seismic event is captured, it generates a dynamic spatiotemporal range with the epicenter of the target seismic event as the reference. Based on the dynamic spatiotemporal range, it aggregates multi-source seismic remote sensing data from the data resource library to construct a dynamic data cube oriented towards the target seismic event.

[0040] The data processing workflow module, based on a dynamic data cube, generates data products through the data processing workflow and records a complete processing log of the data products.

[0041] The data lineage management module constructs a data lineage map for data products based on the complete processing log. When multi-source seismic remote sensing data in the data resource library is identified as having quality problems, it performs reverse tracing based on the data lineage map to obtain data products and dynamic data cubes affected by multi-source seismic remote sensing data.

[0042] The reprocessing module updates the multi-source seismic remote sensing data. Based on the updated multi-source seismic remote sensing data and the complete processing log, it re-executes the data processing workflow to generate updated data products.

[0043] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein the computer program, when executed by the processor, implements any step of the earthquake remote sensing data management method as described in the first aspect of the present invention.

[0044] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the earthquake remote sensing data management method as described in the first aspect of the present invention.

[0045] The beneficial effects of this invention are as follows: by constructing a standardized data resource library and dynamically generating a spatiotemporal cube of aggregated multi-source data for earthquake events, it enables rapid organization and efficient access to key data in emergency scenarios; furthermore, by recording complete data processing logs to construct a data lineage map with quantifiable node dependency weights, when quality problems occur in the underlying data, it can accurately trace the scope of impact and trigger targeted updates and reprocessing, forming an integrated management solution from intelligent data aggregation and controllable product quality to accurate problem tracing and closed-loop correction. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart of earthquake remote sensing data management methods.

[0048] Figure 2 This is a schematic diagram of an earthquake remote sensing data management system.

[0049] Figure 3 This is a schematic diagram of the dynamic spatiotemporal range generation process. Detailed Implementation

[0050] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0051] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0052] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0053] Reference Figures 1-3 As one embodiment of the present invention, this embodiment provides a method for managing earthquake remote sensing data, including the following steps:

[0054] S1. Access multi-source seismic remote sensing data, define a standardized metadata description format for each type of multi-source seismic remote sensing data, and store the multi-source seismic remote sensing data and the corresponding standardized metadata description in the data resource library.

[0055] S1.1 Define a standardized metadata description format for synthetic aperture radar imagery, optical satellite imagery, and multi-source seismic remote sensing data.

[0056] Furthermore, for synthetic aperture radar imagery, the defined standardized metadata description format includes satellite platform name, imaging mode, polarization, incident angle, orbit number, start and end points of acquisition time, and product level; for optical satellite imagery, the defined standardized metadata description format includes satellite platform name, sensor type, band information, spatial resolution, cloud cover percentage, start and end points of acquisition time, and product level; for Global Navigation Satellite System observation data, the defined standardized metadata description format includes receiver number, station coordinates, observation type, sampling rate, and start and end points of data acquisition time.

[0057] S1.2. Using a standardized metadata description format, parse the newly accessed synthetic aperture radar imagery, optical satellite imagery, and multi-source seismic remote sensing data, and extract the standardized metadata description.

[0058] Furthermore, using the standardized metadata description format defined for synthetic aperture radar (SAR) imagery, the header information file or auxiliary data file of newly acquired SAR imagery is parsed to extract fields such as satellite platform name, imaging mode, orbital number, and product level that conform to the standardized metadata description format; using the standardized metadata description format defined for optical satellite imagery, the metadata file of newly acquired optical satellite imagery is parsed to extract fields such as satellite platform name, band information, spatial resolution, and cloud cover percentage that conform to the standardized metadata description format; using the standardized metadata description format defined for Global Navigation Satellite System (GNSS) observation data, the newly acquired GNSS observation data file is parsed to extract fields such as receiver number, station coordinates, and sampling rate that conform to the standardized metadata description format.

[0059] S1.3. Establish a correlation between the extracted standardized metadata description and the multi-source seismic remote sensing data, and store the correlated multi-source seismic remote sensing data and standardized metadata description in the data resource library.

[0060] Furthermore, the standardized metadata descriptions extracted from synthetic aperture radar (SAR) imagery are associated with the newly acquired SAR image files by assigning them the same unique data identifier; the standardized metadata descriptions extracted from optical satellite imagery are associated with the newly acquired optical satellite image files by assigning them the same unique data identifier; the standardized metadata descriptions extracted from Global Navigation Satellite System (GNSS) observation data are associated with the newly acquired GNSS observation data files by assigning them the same unique data identifier; finally, the associated SAR imagery and its corresponding standardized metadata descriptions, optical satellite imagery and its corresponding standardized metadata descriptions, and GNSS observation data and its corresponding standardized metadata descriptions are persistently stored in the data resource repository.

[0061] S2. Real-time monitoring of earthquake event information; when a target earthquake event is captured, the epicenter of the target earthquake event is used as the dynamic spatiotemporal range for generation.

[0062] S2.1 Obtain event information of the target earthquake event, calculate the initial boundary radius of the dynamic spatiotemporal range based on the magnitude of the target earthquake event and statistical analysis of historical earthquake cases.

[0063] Furthermore, obtain event information about the target earthquake event, including its magnitude. Regional geological characteristic coefficients corresponding to the area where the target seismic event occurred Determining the reference radius based on statistical analysis of historical earthquake cases. In the magnitude-frequency relationship Value, reference magnitude and the function of prediction bias The specific form; the magnitude of the target earthquake event. With regional geological characteristic coefficient Substituting the values ​​into the expression and performing calculations, we obtain the initial boundary radius of the dynamic spatiotemporal range. , The confidence level factor, The actual radius of influence for an earthquake of magnitude M, obtained from historical earthquake case statistics, is compared with that obtained from... The standard deviation between the predicted values.

[0064] The expression for the initial boundary radius of the spatiotemporal range is:

[0065] ;

[0066] in, The initial boundary radius of the dynamic spatiotemporal range. The magnitude of the target earthquake event. Based on regional geological characteristics, As a reference radius, For the magnitude-frequency relationship value, For reference magnitude, This is a function of the prediction bias.

[0067] S2.2. Combining the information in the target earthquake event, the epicenter location of the target earthquake event and the initial boundary radius of the dynamic spatiotemporal range, the spatial boundary of the dynamic spatiotemporal range is obtained.

[0068] Furthermore, by combining the epicenter location of the target earthquake event with the initial boundary radius of the dynamic spatiotemporal range from the event information of the target earthquake event, a circular area is determined in geographic space with the epicenter location of the target earthquake event as the center and the initial boundary radius of the dynamic spatiotemporal range as the radius. The boundary of the circular area is the spatial boundary of the dynamic spatiotemporal range.

[0069] S2.3. Based on the occurrence time of the target earthquake event, obtain the time boundary of the dynamic spatiotemporal range. The time boundary includes the pre-earthquake period and the post-earthquake duration, forming the dynamic spatiotemporal range of the epicenter of the target earthquake event.

[0070] Furthermore, based on the occurrence time of the target earthquake event in the event information of the target earthquake event, the time boundary of the dynamic spatiotemporal range is determined. The time boundary is based on the occurrence time of the target earthquake event, extending forward by a fixed pre-earthquake period and backward by a continuous post-earthquake period. The four-dimensional range formed by the spatial circular boundary and the time interval is the dynamic spatiotemporal range with the epicenter of the target earthquake event as the core.

[0071] S3. Aggregate multi-source seismic remote sensing data from the data resource library according to the dynamic spatiotemporal range, and construct a dynamic data cube oriented towards the target seismic event.

[0072] S3.1 Use dynamic spatiotemporal range as query conditions to perform spatiotemporal matching retrieval in the standardized metadata description of the data resource database.

[0073] Furthermore, the spatial boundary (i.e., the geographic coordinate range of the circular area) and the temporal boundary (i.e., the start and end time points) of the dynamic spatiotemporal range are used as query conditions to perform matching and retrieval within the standardized metadata description fields stored in the data resource library. The retrieval conditions are to determine whether the spatial coverage of the data recorded in the standardized metadata description intersects with the spatial boundary of the dynamic spatiotemporal range, and whether the start and end points of the data acquisition time fall within the temporal boundary of the dynamic spatiotemporal range.

[0074] S3.2 Obtain the multi-source earthquake remote sensing data and standardized metadata descriptions that are matched in the spatiotemporal matching search results.

[0075] Furthermore, acquire all multi-source seismic remote sensing data files that meet the spatiotemporal matching retrieval conditions, as well as the standardized metadata descriptions corresponding to the multi-source seismic remote sensing data files; the data files include synthetic aperture radar images, optical satellite images, global navigation satellite system observation data, and all multi-source seismic remote sensing data that hit within the dynamic spatiotemporal range.

[0076] S3.3 Logically organize the hit multi-source seismic remote sensing data and standardized metadata descriptions into a unified data view.

[0077] Furthermore, all the hit multi-source seismic remote sensing data and their corresponding standardized metadata descriptions are logically organized according to data type and data acquisition time to form a unified data view oriented towards the target seismic event; the unified data view encapsulates a transparent access interface for distributed, heterogeneous multi-source seismic remote sensing data.

[0078] S3.4 Assign a unique identifier to the target seismic event to the unified data view and construct a dynamic data cube of the target seismic event.

[0079] Furthermore, a unique identifier is assigned to the unified data view formed, which is usually composed of the target seismic event's number or feature code; once this unique identifier is assigned, the construction of a dynamic data cube oriented towards a specific target seismic event is declared complete.

[0080] S4. Generate data products through data processing workflow and record the complete processing log of the data products.

[0081] S4.1 Input the multi-source seismic remote sensing data in the dynamic data cube into the data processing workflow defined based on the data processing algorithm, execution order and parameter configuration required for coseismic deformation field extraction, and perform processing on the multi-source seismic remote sensing data to output data products.

[0082] Furthermore, multi-source seismic remote sensing data from the dynamic data cube, including synthetic aperture radar image pairs and optical satellite imagery, are submitted as input data to a predefined data processing workflow for coseismic deformation field extraction. The data processing workflow explicitly specifies the algorithm execution order, such as sequentially executing image registration, interferogram generation, phase unwrapping, and geocoding, and pre-configuring parameters for each algorithm step, such as the matching window size for the registration algorithm and the unwrapping method for the unwrapping algorithm. Once the data processing workflow is triggered, the input multi-source seismic remote sensing data is processed step by step according to the algorithm execution sequence and parameters determined based on professional knowledge in the field of seismic remote sensing data processing, which can ensure the correctness of the data processing logic and the reliability of the results for a specific target (coseismic deformation field extraction), and the coseismic deformation field data product is output.

[0083] S4.2 Based on the execution of the data processing workflow, record the algorithms called, parameters, input data versions, and output data product storage paths to form a complete data product processing log.

[0084] Furthermore, during the execution of the data processing workflow, the specific algorithm name and version number called in each step of the process, all parameter key-value pairs used in each step, the specific version identifier of the input data consumed in each step, and the storage path of the output data product generated by each step are recorded synchronously. The recorded information is organized in the order of execution time of the processing steps and formed into a complete and traceable data processing log in a structured format.

[0085] S5. Based on the complete processing log, construct a data lineage map for the data products. When multi-source seismic remote sensing data in the data resource library is identified as having quality problems, perform reverse tracing based on the data lineage map to obtain the data products and dynamic data cubes affected by the multi-source seismic remote sensing data.

[0086] S5.1 Analyze the complete processing log of the data product and construct an initial directed acyclic graph of data lineage by connecting the data objects recorded in the log with the processing operations.

[0087] Furthermore, the complete processing log of the data product is analyzed, and each data object recorded in the log (including input data, intermediate results, and final data product) is created as a graph node. Each data processing operation is created as a directed edge, with the starting point of the directed edge being all input data nodes of the processing operation and the ending point of the directed edge being the output data node generated by the processing operation. This constructs an initial data lineage directed acyclic graph that reflects the data derivation relationship.

[0088] S5.2 Calculate the dependency weight for each node in the initial directed acyclic graph of data lineage to form a data lineage map.

[0089] Furthermore, dependency weights are calculated for each node in the initial directed acyclic graph of data lineage. The calculation process begins with all nodes with an in-degree of zero (i.e., source data nodes), assigning initial dependency weights to the source data nodes. Then, following the topological sorting order, dependency weights are applied to each output data node. Calculate the dependency weights, sum and iterate to generate output data nodes. All input data nodes , It is a processing operation For input data nodes The preset sensitivity coefficient, It is the input data node The dependency weights are used to form a weighted data lineage map.

[0090] The dependency weight expression is:

[0091] ;

[0092] in, For output data nodes Dependency weights To generate output data nodes This data processing operation The total number of input data nodes required For input data nodes, For the first input data nodes Dependency weights For processing operations For the first input data nodes The sensitivity coefficient, For the first One input data node, For data processing operations, For output data nodes;

[0093] S5.3 When multi-source seismic remote sensing data in the data resource library is identified as having quality problems, locate the node representing the multi-source seismic remote sensing data from the data lineage map.

[0094] Furthermore, when multi-source seismic remote sensing data in the data resource library is identified as having quality problems, the node representing the multi-source seismic remote sensing data can be accurately located in the data lineage map by comparing the identification information of the nodes in the data lineage map with the unique identifier of the problematic data.

[0095] S5.4 Starting from the nodes representing multi-source earthquake remote sensing data, perform a reverse traversal in the data lineage map, and determine the scope of influence based on the dependency weights to obtain data products and dynamic data cubes affected by multi-source earthquake remote sensing data.

[0096] Furthermore, starting from the nodes representing the multi-source seismic remote sensing data identified in step S5.3, a reverse depth-first or breadth-first traversal is performed along the directed edges in the data lineage map. For each node visited during the traversal, it is determined whether the dependency weight is affected by the problematic node. The data products and dynamic data cubes corresponding to nodes whose cumulative impact weight exceeds the threshold are identified as affected objects, thus obtaining the data products and dynamic data cubes affected by the multi-source seismic remote sensing data.

[0097] S6. Update the multi-source seismic remote sensing data. Based on the updated multi-source seismic remote sensing data and the complete processing log, re-execute the data processing workflow to generate the updated data product.

[0098] S6.1 Replace the multi-source seismic remote sensing data in the data resource library that is marked as having quality problems with the new version of data that has been quality corrected.

[0099] Furthermore, by using new, quality-corrected versions of data, such as recalibrated and orbit-refined synthetic aperture radar (SAR) imagery data, the original SAR imagery data in the data repository that was flagged as having quality issues due to calibration parameter errors is replaced, thus completing the update of the multi-source seismic remote sensing data.

[0100] S6.22. Parse the list of input data identifiers required by the data processing workflow from the complete processing log.

[0101] Furthermore, the complete processing log is parsed to extract a list of all input data identifiers required by the data processing workflow when generating the raw data product, including unique identifiers for all input data such as synthetic aperture radar imagery and optical satellite imagery involved in the processing.

[0102] S6.3. Based on the input data identifier list, retrieve the updated multi-source seismic remote sensing data and the required input data that has not been updated from the data resource library.

[0103] Furthermore, based on the input data identifier list, the updated multi-source seismic remote sensing data (i.e., the new version of synthetic aperture radar image data) is retrieved from the data resource library, while the required input data that is not identified as having quality problems and does not need to be updated is also retrieved from the list.

[0104] S6.4 Submit the updated multi-source seismic remote sensing data and the required input data that has not been updated, along with the processing algorithms and parameters recorded in the complete processing log, to the data processing workflow engine to re-execute the data processing workflow and output the updated data product.

[0105] Furthermore, the updated multi-source seismic remote sensing data and the required input data that have not been updated, along with the processing algorithm name, version number, and parameter configuration recorded in the complete processing log, are submitted to the data processing workflow engine. The data processing workflow engine re-executes the data processing workflow based on the same algorithm and parameters, and finally outputs the updated data product.

[0106] This embodiment also provides an earthquake remote sensing data management system, including: a data access module, which accesses multi-source earthquake remote sensing data, defines a standardized metadata description format for each type of multi-source earthquake remote sensing data, and stores the multi-source earthquake remote sensing data and the corresponding standardized metadata description in a data resource library.

[0107] The dynamic aggregation module monitors seismic event information in real time. When a target seismic event is captured, it generates a dynamic spatiotemporal range with the epicenter of the target seismic event as the reference. Based on the dynamic spatiotemporal range, it aggregates multi-source seismic remote sensing data from the data resource library to construct a dynamic data cube oriented towards the target seismic event.

[0108] The data processing workflow module, based on a dynamic data cube, generates data products through the data processing workflow and records a complete processing log of the data products.

[0109] The data lineage management module constructs a data lineage map for data products based on the complete processing log. When multi-source seismic remote sensing data in the data resource library is identified as having quality problems, it performs reverse tracing based on the data lineage map to obtain data products and dynamic data cubes affected by multi-source seismic remote sensing data.

[0110] The reprocessing module updates the multi-source seismic remote sensing data. Based on the updated multi-source seismic remote sensing data and the complete processing log, it re-executes the data processing workflow to generate updated data products.

[0111] This embodiment also provides a computer device applicable to the earthquake remote sensing data management method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the earthquake remote sensing data management method proposed in the above embodiment.

[0112] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing 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 stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0113] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the earthquake remote sensing data management method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0114] In summary, this invention achieves rapid organization and efficient access to critical data in emergency scenarios by constructing a standardized data resource library and dynamically generating a spatiotemporal cube of aggregated multi-source data for earthquake events. Furthermore, by recording data processing logs to construct a data lineage graph with quantifiable node dependency weights, it can accurately trace the scope of impact and trigger targeted updates and reprocessing when quality issues arise in the underlying data. This forms an integrated management solution from intelligent data aggregation and controllable product quality to precise problem tracing and closed-loop correction.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for managing earthquake remote sensing data, characterized in that: include, Access multi-source seismic remote sensing data, define a standardized metadata description format for each type of multi-source seismic remote sensing data, and store the multi-source seismic remote sensing data and the corresponding standardized metadata description in the data resource library; Real-time monitoring of seismic event information; upon capturing a target seismic event, generating a dynamic spatiotemporal extent based on the epicenter of the target seismic event, including the following steps: Obtain event information of the target earthquake event, calculate the initial boundary radius of the dynamic spatiotemporal range based on the magnitude of the target earthquake event and statistical analysis of historical earthquake cases; The expression for the initial boundary radius of the spatiotemporal range is: ; in, The initial boundary radius of the dynamic spatiotemporal range. The magnitude of the target earthquake event. Based on regional geological characteristics, As a reference radius, For the magnitude-frequency relationship value, For reference magnitude, This is a function of the prediction bias; Using the epicenter of the target earthquake event as the center and the initial boundary radius of the dynamic spatiotemporal range as the radius, the spatial boundary of the dynamic spatiotemporal range is obtained; Based on the occurrence time of the target earthquake event, the time boundary of the dynamic spatiotemporal range is obtained. The time boundary includes the pre-earthquake period and the post-earthquake duration, forming the dynamic spatiotemporal range of the epicenter of the target earthquake event. Based on the dynamic spatiotemporal range, multi-source seismic remote sensing data are aggregated from the data resource library to construct a dynamic data cube oriented towards the target seismic event; Based on a dynamic data cube, data products are generated through a data processing workflow, and a complete processing log of the data products is recorded. Based on the complete processing log, a data lineage map is constructed for the data products. When multi-source seismic remote sensing data in the data resource library is identified as having quality problems, reverse tracing is performed based on the data lineage map to obtain the data products and dynamic data cubes affected by the multi-source seismic remote sensing data. The multi-source seismic remote sensing data is updated. Based on the updated multi-source seismic remote sensing data and the complete processing log, the data processing workflow is re-executed to generate updated data products.

2. The earthquake remote sensing data management method as described in claim 1, characterized in that: Accessing multi-source seismic remote sensing data, defining a standardized metadata description format for each type of multi-source seismic remote sensing data, and storing the multi-source seismic remote sensing data and its corresponding standardized metadata description in a data resource repository includes the following steps: A standardized metadata description format is defined for synthetic aperture radar imagery, optical satellite imagery, and multi-source seismic remote sensing data. Using a standardized metadata description format, newly acquired synthetic aperture radar imagery, optical satellite imagery, and multi-source seismic remote sensing data are parsed to extract standardized metadata descriptions. The extracted standardized metadata descriptions are associated with the multi-source seismic remote sensing data, and the associated multi-source seismic remote sensing data and standardized metadata descriptions are stored in the data resource repository.

3. The earthquake remote sensing data management method as described in claim 1, characterized in that: Based on the dynamic spatiotemporal range, multi-source seismic remote sensing data is aggregated from the data resource repository to construct a dynamic data cube oriented towards the target seismic event, including the following steps: Using dynamic spatiotemporal range as query criteria, spatiotemporal matching retrieval is performed in the standardized metadata description of the data resource repository; Retrieve multi-source earthquake remote sensing data and standardized metadata descriptions that are matched in the spatiotemporal matching search results; The hit multi-source seismic remote sensing data and standardized metadata descriptions are logically organized into a unified data view; Assign a unique identifier to the target seismic event to a unified data view, and construct a dynamic data cube for the target seismic event.

4. The earthquake remote sensing data management method as described in claim 3, characterized in that: Based on a dynamic data cube, data products are generated through a data processing workflow, and a complete processing log of the data products is recorded, including the following steps: The multi-source seismic remote sensing data in the dynamic data cube is input into a data processing workflow based on the data processing algorithms, execution order, and parameter configuration defined for coseismic deformation field extraction. The multi-source seismic remote sensing data is then processed to output data products. Based on the execution of the data processing workflow, the algorithms called, parameters, input data versions, and output data product storage paths are recorded to form a complete data product processing log.

5. The earthquake remote sensing data management method as described in claim 4, characterized in that: Based on the complete processing log, a data lineage map is constructed for the data products. When multi-source seismic remote sensing data in the data resource repository is identified as having quality issues, reverse tracing is performed based on the data lineage map to obtain the data products and dynamic data cubes affected by the multi-source seismic remote sensing data. This includes the following steps: Analyze the complete processing log of the data product and construct an initial directed acyclic graph of data lineage by connecting the data objects recorded in the log with the processing operations. Calculate the dependency weight for each node in the initial directed acyclic graph of data lineage to form a data lineage graph; When multi-source seismic remote sensing data in the data resource library is identified as having quality problems, the nodes representing the multi-source seismic remote sensing data are located from the data lineage map. Starting from the nodes representing multi-source earthquake remote sensing data, a reverse traversal is performed in the data lineage map, and the scope of influence is determined based on the dependency weights to obtain data products and dynamic data cubes reflecting the impact of multi-source earthquake remote sensing data.

6. The earthquake remote sensing data management method as described in claim 1, characterized in that: The multi-source seismic remote sensing data is updated. Based on the updated multi-source seismic remote sensing data and the complete processing log, the data processing workflow is re-executed to generate updated data products, including the following steps: Replace the multi-source seismic remote sensing data in the data repository that was identified as having quality issues with the new version of data that has undergone quality correction. Parse the list of input data identifiers required by the data processing workflow from the complete processing log; Based on the input data identifier list, retrieve the updated multi-source seismic remote sensing data and the required input data that has not been updated from the data resource library; The updated multi-source seismic remote sensing data and the required input data that have not been updated, along with the processing algorithms and parameters recorded in the complete processing log, are submitted to the data processing workflow engine to re-execute the data processing workflow and output the updated data product.

7. An earthquake remote sensing data management system, based on the earthquake remote sensing data management method according to any one of claims 1 to 6, characterized in that: This includes a data access module, which accesses multi-source seismic remote sensing data, defines a standardized metadata description format for each type of multi-source seismic remote sensing data, and stores the multi-source seismic remote sensing data and its corresponding standardized metadata description in a data resource library. The dynamic aggregation module monitors seismic event information in real time. When a target seismic event is captured, it generates a dynamic spatiotemporal range based on the epicenter of the target seismic event. Based on the dynamic spatiotemporal range, it aggregates multi-source seismic remote sensing data from the data resource library to construct a dynamic data cube oriented towards the target seismic event. The data processing workflow module, based on a dynamic data cube, generates data products through the data processing workflow and records a complete processing log of the data products. The data lineage management module constructs a data lineage map for data products based on the complete processing log. When multi-source seismic remote sensing data in the data resource library is identified as having quality problems, it performs reverse tracing based on the data lineage map to obtain data products and dynamic data cubes affected by multi-source seismic remote sensing data. The reprocessing module updates the multi-source seismic remote sensing data. Based on the updated multi-source seismic remote sensing data and the complete processing log, it re-executes the data processing workflow to generate updated data products.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the earthquake remote sensing data management method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the earthquake remote sensing data management method according to any one of claims 1 to 6.

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