Method and system for managing marine scientific data

Abstract

This application discloses a method and system for managing marine scientific data. The method includes: receiving raw marine scientific data submitted by data producers, processing and quality-controlled to obtain standardized data objects; determining access permissions based on the data user's role and credit rating, and distributing the corresponding data objects; obtaining usage information of the data objects after distribution, and receiving quality feedback information input by the data users after use; determining a unique identifier for the data objects to be published and generating a publication data package; monitoring the citation status of the data objects after use or publication; determining the value information of the data objects based on citation status, usage status, and quality feedback information; and determining the credit rating of the data users based on usage status and quality feedback information. Implementing the technical solution of this application can not only improve the intelligence, efficiency, standardization, and security of data management, but also fully release the scientific value of the data.

Description

Technical Field

[0001] This application relates to the field of computer data processing, and in particular to a method and system for managing marine scientific data. Background Technology

[0002] Marine science is a comprehensive discipline that relies on observation, experimentation, and numerical simulation. Its data has typical characteristics such as multi-source (e.g., satellites, buoys, ships, stations, underwater sensors, etc.), multimodal (e.g., hydrometeorology, marine remote sensing, marine chemistry, biological ecology, marine geology, etc.), massive volume, and strong spatiotemporal correlation. Moreover, the scale and complexity of marine science data are growing exponentially.

[0003] Currently, although marine scientific data management has achieved a leap from "nothing" to "something", it still faces problems such as an inefficient sharing mechanism, unintelligent service capabilities, and insufficient release of the value of data science. Therefore, it is necessary to build a marine scientific data management system that covers the entire data lifecycle and has intelligent processing capabilities. Summary of the Invention

[0004] The technical health problem to be solved by this application is to provide a method and system for managing marine scientific data, addressing the aforementioned technical deficiencies in the existing technology.

[0005] The technical solution adopted by this application to solve its technical health problem is: to construct a method for managing marine scientific data, including: Step S10: Receive raw marine science data submitted by the data producer and process the raw marine science data to obtain standardized data objects; Step S20: When there is a data distribution need, determine the access rights of the data user based on the credit rating of the data user, and distribute the corresponding data object to the data user according to the access rights; after distribution, obtain the usage status of the data object, and after the data object is used, receive the quality feedback information input by the data user. Step S30: When there is a data publishing requirement, determine the unique identifier of the data object to be published, and generate a publishing data package based on the unique identifier and the data object, so that the data producer can publish the publishing data package; after publication, monitor the citation status of the data object based on the unique identifier. Step S40: Determine the value information of the data object based on its citation status, usage status, and quality feedback information to provide corresponding incentives to the data producer; and determine the credit rating of the data user based on the usage status of the data object and the quality feedback information to dynamically adjust the access permissions of the data user.

[0006] Optionally, step S10 includes: Step S11: During the data acquisition process, the raw marine scientific data collected is verified in real time and subjected to initial quality control in order to obtain data acquisition information. Step S12: Receive data files submitted online or offline by data producers, the data files including data collection information and metadata; Step S13: Process the data file to obtain standardized data objects. The processing includes cleaning, hierarchical classification, and secondary quality control. Step S14: Store the data object in the database.

[0007] Optionally, in step S20, when there is a data distribution need, the access rights of the data user are determined based on the data user's credit rating, and the corresponding data object is distributed to the data user according to the access rights, including: Step S21: If a retrieval request is received from a data user, it is determined that there is a data distribution need, wherein the retrieval request includes retrieval conditions, purpose of use, and period of use; Step S22: Based on the search criteria, search for the corresponding data object in the database; Step S23: Based on the hierarchical classification of the data object and the role and credit rating of the data user, approve the purpose of use and the period of use, and generate and store an authorization record based on the approval result. The authorization record includes the scope of authorization and the period of authorization. Step S24: Based on the approval result, distribute the corresponding data objects to the data users; Following step S20, the method further includes: Step S50: Periodically scan the stored authorization records to determine whether the authorization period in the authorization record has expired, and when the authorization period expires, update the status of the authorization record to expired, and intercept and prompt when the data user accesses the data object again.

[0008] Optionally, between step S23 and step S24, the method further includes: Step S25: Based on the approval result, embed a digital watermark in the corresponding data object, wherein the digital watermark includes the data user's identity information, download timestamp, and authorization scope; Following step S20, the method further includes: Step S60: Periodically monitor whether unauthorized use events occur on the data object, and when such events occur, determine the data user corresponding to the unauthorized use event based on the digital watermark.

[0009] Optionally, in step S30, generating a publication data package based on the unique identifier and the data object includes: Step S31: Bind the unique identifier and the metadata of the data object, and establish a reference link; Step S32: Generate reference text according to a preset standard template, wherein the reference text includes: data owner, title, unit, production date, publishing institution, release date, and unique identifier; Step S33: Package the data object, the reference link, and the reference text into a publication data package so that the data producer can publish it.

[0010] Optionally, in step S20, obtaining the usage status of the data object includes: Based on the log information, the usage of the data object is obtained, including: download volume, number of views, and user distribution.

[0011] Optionally, in step S30, monitoring the reference status of the data object based on the unique identifier includes: Based on the unique identifier, the citation status of academic papers for the data object is monitored periodically through an academic search engine. Based on the unique identifier, the non-academic paper citations of the data object are tracked from non-academic channels, wherein the non-academic channels include at least one of the following: social media, policy documents, and news media.

[0012] Optionally, in step S30, the quality feedback information includes: data quality evaluation information and data problem information; Following step S30, the method further includes: The quality feedback information is notified to the data owner so that they can revise the data object in response to the data problem information and obtain the version number; Re-execute steps S20 and S30 on the revised data object.

[0013] Optionally, in step S40, the value information of the data object is determined based on its citation status, usage, and quality feedback information, including: The influence index of the data object is determined based on its reference and usage. The value information of the data object is determined based on the data quality evaluation information and the influence.

[0014] The present invention also constructs a marine scientific data management system, including a processor and a memory storing a computer program, wherein the processor implements the steps of the marine scientific data management method described above when executing the computer program.

[0015] The technical solution of this application constructs a comprehensive intelligent management system for marine scientific data throughout its entire lifecycle. This system integrates controlled data distribution, multi-dimensional tracking, dynamic feedback, and corresponding reward and punishment mechanisms, forming a complete closed loop from data generation to value regeneration. Therefore, it not only enhances the intelligence, efficiency, standardization, and security of data management but also fully releases the scientific value of data, stimulates the sustainable vitality of the data ecosystem, and lays a solid data foundation for theoretical innovation and applied breakthroughs in marine scientific research. Attached Figure Description

[0016] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart of a method for managing marine scientific data in one embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] Figure 1 This is a flowchart of a method for managing marine scientific data according to an embodiment of the present invention. The management method of this embodiment includes: Step S10: Receive raw marine science data submitted by the data producer and process the raw marine science data to obtain standardized data objects; Step S20: When there is a data distribution need, determine the access permissions of the data user based on the data user's role and credit rating, and distribute the corresponding data object to the data user according to the access permissions; after distribution, obtain the usage status of the data object, and after the data object is used, receive quality feedback information input by the data user. Step S30: When there is a data publishing requirement, determine the unique identifier of the data object to be published, and generate a publishing data package based on the unique identifier and the data object, so that the data producer can publish the publishing data package; after publication, monitor the citation status of the data object based on the unique identifier. In this step, the unique identifier is a globally permanent and unique identifier, for example, by submitting metadata to a DOI / CSTR registry (such as DataCite) to permanently register and obtain a Digital Object Identifier (DOI) or a China Science and Technology Resource Identifier (CSTR) number.

[0019] Step S40: Determine the value information of the data object based on its citation status, usage status, and quality feedback information to provide corresponding incentives to the data producer; and determine the credit rating of the data user based on the usage status of the data object and the quality feedback information to dynamically adjust the access permissions of the data user.

[0020] In this step, after determining the value information of the data object, data producers can be incentivized based on this value information. For example, they can be included in the performance evaluation, professional title assessment, and project completion acceptance of researchers, and those who make outstanding contributions can be recognized with honors or given preferential resources. Furthermore, the standardization of data citation, the value of the data quality feedback provided, and the integrity of data use (e.g., whether it exceeds the permitted scope) should be incorporated into the user credit system, and user access permissions should be dynamically adjusted. For example, users with positive and standardized behavior can be given priority or higher-level data access permissions when applying for data; users who abuse data, cite data improperly, or make malicious evaluations can be warned, have their data application qualifications suspended, or even have their misconduct publicized. It is preferable to use blockchain smart contracts to automate the execution of some reward and punishment clauses, such as the automatic distribution of data usage points.

[0021] The technical solution of this embodiment constructs a comprehensive intelligent management system for marine scientific data throughout its entire lifecycle. This system integrates controlled data distribution, multi-dimensional tracking, dynamic feedback, and corresponding reward and punishment mechanisms, forming a complete closed loop from data generation to value regeneration. Therefore, it not only enhances the intelligence, efficiency, standardization, and security of data management but also fully releases the scientific value of data, stimulates the continuous vitality of the data ecosystem, and lays a solid data foundation for theoretical innovation and applied breakthroughs in marine scientific research.

[0022] Further, in an optional embodiment, step S10 includes: Step S11: During the data acquisition process, the raw marine scientific data collected is verified in real time and subjected to initial quality control in order to obtain data acquisition information. Step S12: Receive data files submitted online or offline by data producers, the data files including data collection information and metadata; Step S13: Process the data file to obtain standardized data objects. The processing includes cleaning, hierarchical classification, and secondary quality control. Step S14: Store the data object in the database.

[0023] In this embodiment, since data preparation and storage are the starting point of the data lifecycle, data needs to undergo standardization and quality control before entering the system to ensure that only high-quality data can be formally stored in the data resource library, laying the foundation for subsequent data applications.

[0024] In one specific embodiment, data preparation and storage includes: data generation (standard specifications and intelligent sensing), data aggregation (online and offline collaboration), data processing and quality control (AI-enhanced standardization and automation), and data storage (hybrid storage and intelligent backup). Specifically, Regarding data generation, since data production is the source of the data value chain, data producers are required to strictly adhere to unified standards during the collection and recording stages to ensure the scientific validity, consistency, and machine readability of the data. By combining intelligent sensing devices and edge computing technology, real-time verification and preliminary quality control of the data collection process can be achieved, improving data quality from the source.

[0025] Regarding data submission, since data submission is the entry point for data management, and to adapt to complex scenarios such as marine outdoor scientific expeditions and long-term station observations, as well as different data security requirements, a combination of online and offline submission modes can be adopted. For online submission, a data submission portal based on a B / S architecture can be built to support researchers (data producers) in submitting data files online. SSL / TLS encryption is used during transmission to ensure data security, and hash values ​​(e.g., MD5, SHA-256) are calculated for uploaded files for integrity verification. Disk arrays are used to provide highly available storage space. For situations such as oceanographic expeditions, poor network conditions, extremely large data volumes (e.g., high-frequency ADCP, seabed video), and the management needs of classified data, offline submission services based on offline storage media (e.g., pre-configured hard drives, optical discs, etc.) can be provided. During offline submission, data, metadata, and verification information are packaged using data encapsulation standards (e.g., Bagit format). Introducing blockchain technology during data handover records key information (such as hash values, timestamps, and responsible persons) to ensure the immutability and full traceability of the offline data exchange process.

[0026] Regarding data processing and quality control, since these are core aspects of ensuring data usability and compliance, the following steps are taken: First, using AI-enhanced ETL (Extraction Transformation Loading) tools or custom-designed preprocessing programs, common data problems are automatically identified and corrected in the submitted raw data, such as inconsistent formats, unit confusion, and incompatible coordinate systems. Then, based on relevant laws and industry standards, and combined with domain ontology and deep learning models, the data is automatically semantically labeled and scientifically classified (e.g., public, restricted, confidential) and categorized (e.g., by subject, element, region). When processing data that may involve personal privacy, sensitive geographical locations, or information on endangered species, data ethics reviews are implemented, and anonymization or desensitization is performed when necessary to ensure that data activities comply with ethical norms. Finally, following the QARTOD standard (Quality Assurance of Real-Time Oceanographic Data), the core framework for quality assurance of current real-time oceanographic data, a learnable and configurable quality control rule base is constructed, covering range checks (e.g., whether seawater temperature is within a reasonable range), logical consistency checks (e.g., whether water depth and pressure match), and mutation detection (to prevent data mutations). By leveraging AI to automate quality control processes and anomaly detection algorithms, potentially problematic data is automatically identified, suspicious or erroneous data is clearly identified and marked, and quality control reports are generated and fed back to the data producer for data review. Data that fails quality control is returned to the submission stage for reprocessing.

[0027] Regarding data storage, a hybrid storage architecture can be adopted to store both structured and unstructured data, depending on the degree of data structuring, and a robust backup mechanism can be implemented to achieve efficient and secure management. Specifically, for structured data such as well-formatted tables and time series (e.g., CTD profile data, station observation time series), relational databases (e.g., PostgreSQL, combined with PostGIS to extend spatial data management) are used to support complex queries and statistical analysis. For scientific datasets (unstructured data) in self-describing formats such as images, videos, documents, and NetCDF / HDF, NoSQL databases or object storage systems (e.g., MinIO, Ceph) are used for storage. Furthermore, key metadata (e.g., data name, time, location, keywords, etc.) is extracted and stored in relational databases or search engines (e.g., Elasticsearch) to enable rapid discovery and retrieval of unstructured data. When performing data security and backup, data heat analysis can be combined to develop differentiated backup strategies, thereby establishing regular, multi-copy, off-site data backups. AI can be used to predict storage load and failure risks, automating and intelligentizing the backup process. Daily incremental backups and weekly full backups are performed on the core database and object storage, with backup data stored in a physically isolated off-site disaster recovery center. Data recovery drills are conducted regularly to ensure data recoverability in the event of hardware failures, natural disasters, or cyberattacks.

[0028] Further, in an optional embodiment, in step S20, when there is a data distribution need, the access permissions of the data user are determined according to the data user's role and credit rating, and the corresponding data object is distributed to the data user according to the access permissions, including: Step S21: If a retrieval request is received from a data user, it is determined that there is a data distribution need, wherein the retrieval request includes retrieval conditions, purpose of use, and period of use; Step S22: Based on the search criteria, search for the corresponding data object in the database; In this step, a one-stop data retrieval portal based on natural language search and knowledge graphs can be established, providing conventional data retrieval functions based on keywords, spatiotemporal range, subject classification, observation instruments, and other dimensions. After locating the data objects to be distributed, map visualization and online data preview functions can be provided to data users to help them quickly locate and evaluate the required data, improving data discovery efficiency. Therefore, spatial visualization and spatial querying of marine data are realized.

[0029] Step S23: Based on the hierarchical classification of the data object and the role and credit rating of the data user, approve the purpose of use and the period of use, and generate and store an authorization record based on the approval result. The authorization record includes the scope of authorization and the period of authorization. Step S24: Based on the approval result, distribute the corresponding data objects to the data users; Following step S20, the following is also included: Step S50: Periodically scan the stored authorization records to determine whether the authorization period in the authorization record has expired, and when the authorization period expires, update the status of the authorization record to expired, and intercept and prompt when the data user accesses the data object again.

[0030] In this embodiment, a refined access control and permission management mechanism is established to distribute data in a controlled manner, thereby promoting the release of data value and the confirmation of rights. Specifically, user behavior is deeply integrated with credit rating and dynamic permission management to achieve more granular data security control. When the authorization period of a data object expires, it automatically becomes invalid. If continued use is required, a new application and approval must be submitted to prevent long-term data retention and unauthorized use, thus achieving refined management of the data lifecycle.

[0031] Furthermore, in an optional embodiment, between step S23 and step S24, the following step is further included: Step S25: Based on the approval result, embed a digital watermark in the corresponding data object, wherein the digital watermark includes the data user's identity information, download timestamp, and authorization scope; Following step S20, the following is also included: Step S60: Periodically monitor whether unauthorized use events occur on the data object, and when such events occur, determine the data user corresponding to the unauthorized use event based on the digital watermark.

[0032] In this embodiment, digital watermarking and traceability are used to enhance ownership protection and traceability during data distribution. An invisible or robust digital watermark is embedded within the distributed data files. In the event of unauthorized use (dissemination or misuse) of data, the source of responsibility can be accurately traced by extracting the digital watermark, effectively curbing data infringement.

[0033] Further, in an optional embodiment, step S30, generating a publication data package based on the unique identifier and the data object, includes: Step S31: Bind the unique identifier and the metadata of the data object, and establish a reference link; Step S32: Generate reference text according to a preset standard template, wherein the reference text includes: data owner, title, unit, production date, publishing institution, release date, and unique identifier; In this step, a standard template can be developed based on national standards and includes information such as: data owner, title, affiliated unit, production date, publishing institution, publication date, DOI and CSTR.

[0034] Step S33: Package the data object, the reference link, and the reference text into a publication data package so that the data producer can publish it.

[0035] In this embodiment, by setting fixed, permanent citation links, the data object can be reliably located and accessed regardless of changes in its storage location (e.g., server migration, URL change), avoiding link failures. Furthermore, based on this traceable citation link, the number of times the object is cited and the users who cite it can be accurately counted, thereby quantifying its academic influence. Additionally, by providing a standard citation format for the data object, it becomes possible to cite data in academic works, which is the basis for acknowledging the contributions of data producers and conducting scientific attribution.

[0036] It should be noted that a standardized data publication process should be established, including the review of data and metadata, peer review of the data paper, and the formal release of data objects and unique identifiers. The published data package is considered an unalterable version, possessing complete, authoritative, and standardized citation information. Furthermore, data publication must clearly define the data owner's initial publication rights, ownership, and contributor role, ensuring that it enjoys the same academic status as traditional academic papers in academic evaluation.

[0037] Furthermore, in an optional embodiment, step S20, obtaining the usage information of the data object, includes: obtaining the usage information of the data object based on log information, wherein the usage information includes: download volume, number of views, and user distribution.

[0038] Further, in an optional embodiment, step S30, monitoring the reference status of the data object based on the unique identifier, includes: Based on the unique identifier, the citation status of academic papers for the data object is monitored periodically through an academic search engine. Based on the unique identifier, the non-academic paper citations of the data object are tracked from non-academic channels, wherein the non-academic channels include at least one of the following: social media, policy documents, and news media.

[0039] In this embodiment, for academic paper citations, the unique identifiers of data objects can be regularly monitored for citations in academic papers using APIs such as CrossRef and DataCite, or academic search engines such as Google Scholar and Web of Science. For non-academic paper citations, APIs of commercial tools such as Altmetric, or self-monitoring of APIs of news aggregation websites and policy databases, can be used to regularly query the unique identifiers or keywords of data objects. Additionally, APIs of social media platforms (such as Twitter and Weibo) or authorized crawlers can be used to crawl tweets and posts containing citation links or data object titles. Web crawling and natural language processing technologies can be used to identify paragraphs mentioning data object names, unique identifiers, or related research projects from news and policy documents. Finally, through deduplication algorithms and statistics, the number of mentions, evaluations, and dissemination scope of each data object across various non-academic channels are summarized.

[0040] Further, in an optional embodiment, in step S30, the quality feedback information includes: data quality evaluation information and data problem information. Moreover, after step S30, the method further includes: notifying the data owner of the quality feedback information so that they can revise the data object in response to the data problem information and obtain a version number; Re-execute steps S20 and S30 on the revised data object.

[0041] In this embodiment, a user-driven continuous improvement mechanism for data quality can be established, supporting multi-dimensional scoring and text feedback. Text mining methods are used to perform content analysis and value assessment on the feedback text to automatically identify key issues and high-quality improvement suggestions.

[0042] In one specific embodiment, after a user downloads and uses the data, they can evaluate its quality from multiple dimensions, including data completeness, accuracy, timeliness, document completeness, and usability. For example, a combination of star ratings and text comments can be used for quality evaluation. Users can also describe any data issues they discover (such as discrepancies or errors) and upload supporting evidence. When the system receives high-quality feedback from the data user, it automatically notifies the data owner (data producer or designated contact person) and sets a timeframe to facilitate timely revisions. Once the data owner revises the data and submits a new version, the system uses version control technology (e.g., based on Git principles) to fully record the data's revision history. Each new version is assigned a new unique identifier and version number, while retaining access permissions for older versions to ensure the reproducibility of cited research. The revised, higher-quality data version re-enters the data distribution and publication process, attracting more users and citations, forming a virtuous cycle of "use-feedback-revision-better use," continuously improving the scientific rigor and practicality of the data resources.

[0043] Further, in an optional embodiment, in step S40, determining the value information of the data object based on its citation status, usage status, and quality feedback information includes: determining the influence index of the data object based on its citation status and usage status; and determining the value information of the data object based on its data quality evaluation information and influence.

[0044] In this embodiment, firstly, an influence index is determined by comprehensively considering the data object's citations outside the system and its usage within the system (e.g., downloads, pageviews, user distribution, etc.), providing data support for scientific research performance evaluation and resource allocation. Then, the value information of the data object is determined by combining it with quality feedback information, incentivizing positive behavior from data producers and users.

[0045] The present invention also constructs a marine scientific data management system, which includes a processor and a memory storing a computer program. When the processor executes the computer program, it implements the steps of the marine scientific data management method described above.

[0046] In summary, this application constructs a comprehensive intelligent management system for marine scientific data throughout its entire lifecycle. This system integrates online / offline data submission, AI-enhanced ETL processes and automated quality control mechanisms, efficient storage and intelligent backup via a hybrid storage architecture, controlled distribution, standardized citation, formal publication, multi-dimensional tracking, dynamic feedback, collaborative revision, and a corresponding reward and punishment mechanism. This forms a dynamic, clearly defined, continuously optimized, and human-machine collaborative data governance ecosystem with a virtuous cycle. This system not only enhances the intelligence, efficiency, standardization, and security of data management but also, through technological empowerment and mechanism design, stimulates the continuous vitality of the data ecosystem, laying a solid data foundation for theoretical innovation and applied breakthroughs in marine scientific research.

[0047] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for managing marine scientific data, characterized in that, include: Step S10: Receive raw marine science data submitted by the data producer and process the raw marine science data to obtain standardized data objects; Step S20: When there is a data distribution need, determine the access permissions of the data user based on the data user's role and credit rating, and distribute the corresponding data object to the data user according to the access permissions; after distribution, obtain the usage status of the data object, and after the data object is used, receive quality feedback information input by the data user. Step S30: When there is a data publishing requirement, determine the unique identifier of the data object to be published, and generate a publishing data package based on the unique identifier and the data object, so that the data producer can publish the publishing data package; After publication, the reference status of the data object is monitored based on the unique identifier; Step S40: Determine the value information of the data object based on its citation status, usage status, and quality feedback information to provide corresponding incentives to the data producer; and determine the credit rating of the data user based on the usage status of the data object and the quality feedback information to dynamically adjust the access permissions of the data user.

2. The method for managing marine scientific data according to claim 1, characterized in that, Step S10 includes: Step S11: During the data acquisition process, the raw marine scientific data collected is verified in real time and subjected to initial quality control in order to obtain data acquisition information. Step S12: Receive data files submitted online or offline by data producers, the data files including data collection information and metadata; Step S13: Process the data file to obtain standardized data objects. The processing includes cleaning, hierarchical classification, and secondary quality control. Step S14: Store the data object in the database.

3. The method for managing marine scientific data according to claim 2, characterized in that, In step S20, when there is a data distribution need, the access permissions of the data user are determined based on the data user's role and credit rating, and the corresponding data object is distributed to the data user according to the access permissions, including: Step S21: If a retrieval request is received from a data user, it is determined that there is a data distribution need, wherein the retrieval request includes retrieval conditions, purpose of use, and period of use; Step S22: Based on the search criteria, search for the corresponding data object in the database; Step S23: Based on the hierarchical classification of the data object and the role and credit rating of the data user, approve the purpose of use and the period of use, and generate and store an authorization record based on the approval result. The authorization record includes the scope of authorization and the period of authorization. Step S24: Based on the approval result, distribute the corresponding data objects to the data users; Following step S20, the method further includes: Step S50: Periodically scan the stored authorization records to determine whether the authorization period in the authorization record has expired, and when the authorization period expires, update the status of the authorization record to expired, and intercept and prompt when the data user accesses the data object again.

4. The method for managing marine scientific data according to claim 3, characterized in that, Between step S23 and step S24, the following is also included: Step S25: Based on the approval result, embed a digital watermark in the corresponding data object, wherein the digital watermark includes the data user's identity information, download timestamp, and authorization scope; Following step S20, the method further includes: Step S60: Periodically monitor whether unauthorized use events occur on the data object, and when such events occur, determine the data user corresponding to the unauthorized use event based on the digital watermark.

5. The method for managing marine scientific data according to claim 1, characterized in that, In step S30, generating a publication data package based on the unique identifier and the data object includes: Step S31: Bind the unique identifier and the metadata of the data object, and establish a reference link; Step S32: Generate reference text according to a preset standard template, wherein the reference text includes: data owner, title, unit, production date, publishing institution, release date, and unique identifier; Step S33: Package the data object, the reference link, and the reference text into a publication data package so that the data producer can publish it.

6. The method for managing marine scientific data according to claim 1, characterized in that, In step S20, obtaining the usage status of the data object includes: Based on the log information, the usage of the data object is obtained, including: download volume, number of views, and user distribution.

7. The method for managing marine scientific data according to claim 1, characterized in that, In step S30, monitoring the reference status of the data object based on the unique identifier includes: Based on the unique identifier, the citation status of academic papers for the data object is monitored periodically through an academic search engine. Based on the unique identifier, the non-academic paper citations of the data object are tracked from non-academic channels, wherein the non-academic channels include at least one of the following: social media, policy documents, and news media.

8. The method for managing marine scientific data according to claim 1, characterized in that, In step S30, the quality feedback information includes: data quality evaluation information and data problem information; Following step S30, the method further includes: The quality feedback information is notified to the data owner so that they can revise the data object in response to the data problem information and obtain the version number; Re-execute steps S20 and S30 on the revised data object.

9. The method for managing marine scientific data according to claim 1, characterized in that, In step S40, the value information of the data object is determined based on its citation status, usage, and quality feedback information, including: The influence index of the data object is determined based on its reference and usage. The value information of the data object is determined based on the data quality evaluation information and the influence.

10. A management system for marine scientific data, comprising a processor and a memory storing computer programs, characterized in that, When the processor executes the computer program, it implements the steps of the marine scientific data management method according to any one of claims 1-9.

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