A method for batch encapsulation of digital objects in a network

Abstract

The application discloses a batch encapsulation method of digital objects in a network, and belongs to the field of digital object encapsulation, and comprises the following steps: splitting a plurality of encapsulation tasks in an encapsulation task queue to obtain at least one encapsulation task element of each encapsulation task; classifying all the encapsulation task elements based on a preset encapsulation classification mode to obtain a plurality of element encapsulation queues; performing parallel encapsulation on the encapsulation task elements in each element encapsulation queue to obtain encapsulation sub-data corresponding to each encapsulation task element respectively; and fusing the encapsulation sub-data corresponding to all the encapsulation task elements of each encapsulation task respectively to obtain encapsulation data of each encapsulation task. Through the chain processing mechanism of “splitting-classification-parallel encapsulation-fusion”, the application improves the business fine granularity of batch encapsulation, makes the encapsulation process further efficient, accurate and stable, and improves the problems of encapsulation result delivery quality and insufficient efficiency.

Description

Technical Field

[0001] This application belongs to the field of digital object encapsulation, and specifically relates to a method, apparatus, device and storage medium for batch encapsulation of digital objects in a digital network. Background Technology

[0002] With the rapid development of the Internet of Things (IoT), digital objects are widely used in scenarios such as scientific research achievement registration, digital publishing and distribution, data asset storage, and cross-platform exchange. Digital objects typically consist of a digital object identifier, metadata, and a data entity; their encapsulation quality directly affects the object's uniqueness, integrity, and traceability. In scenarios involving large-scale data processing and multi-source data fusion, it is often necessary to encapsulate a large number of digital objects in batches to meet the requirements of efficient registration, accurate identification, and stable delivery.

[0003] Existing batch encapsulation methods typically treat a complete task as the basic unit, placing multiple encapsulation tasks sequentially into a queue for processing. These methods generally first parse the task as a whole, then uniformly perform identifier generation, metadata encapsulation, and data entity encapsulation, finally storing the encapsulation results in a repository system. Some methods further introduce task grouping mechanisms to support a certain degree of concurrent execution.

[0004] However, in the scenario of batch packaging of large-scale, heterogeneous data, the above methods are prone to result in duplicate digital object identifiers, missing or conflicting metadata, and incorrect or redundant data entities, which makes it difficult to guarantee the quality of the packaged results. Summary of the Invention

[0005] This application aims to provide a method, apparatus, device, and storage medium for batch packaging of digital objects within a data network, at least to solve the problem of insufficient batch packaging quality of digital objects.

[0006] In a first aspect, embodiments of this application disclose a method for batch encapsulating digital objects within a data network, including:

[0007] Split multiple packaging tasks in the packaging task queue to obtain at least one packaging task element for each packaging task;

[0008] Based on a preset encapsulation classification method, all the encapsulation task elements are classified to obtain multiple element encapsulation queues.

[0009] Each of the encapsulation task elements in the encapsulation queue is encapsulated in parallel to obtain encapsulated sub-data corresponding to each of the encapsulation task elements; each of the encapsulated sub-data has a corresponding digital object identifier, metadata, and data entity.

[0010] The encapsulation sub-data corresponding to all encapsulation task elements of each encapsulation task are merged to obtain the encapsulation data of each encapsulation task.

[0011] Secondly, embodiments of this application also disclose a batch packaging device for digital objects within a data network, comprising:

[0012] The task splitting module is used to split multiple encapsulation tasks in the encapsulation task queue to obtain at least one encapsulation task element for each encapsulation task.

[0013] The task classification module is used to classify all the encapsulated task elements based on a preset encapsulation classification method to obtain multiple element encapsulation queues.

[0014] The classification and encapsulation module is used to perform parallel encapsulation of the encapsulation task elements in each of the meta-encapsulation queues to obtain encapsulated sub-data corresponding to each of the encapsulation task elements; the encapsulated sub-data each has a corresponding digital object identifier, metadata, and data entity.

[0015] The encapsulation fusion module is used to merge the encapsulation sub-data corresponding to all encapsulation task elements of each encapsulation task to obtain the encapsulation data of each encapsulation task.

[0016] Thirdly, embodiments of this application also disclose an electronic device, including a processor and a memory, wherein the memory stores a program or instructions that can run on the processor, and the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0017] Fourthly, embodiments of this application also disclose a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the method described in the first aspect.

[0018] In summary, in this embodiment, by splitting multiple packaging tasks in the packaging task queue, the complex overall task is refined into multiple packaging task elements, making the processing units smaller and more flexible, reducing processing latency caused by the large scale of the overall task, and providing higher execution efficiency. Furthermore, based on task splitting, the packaging task elements are classified according to a preset packaging classification method, so that data from different sources, with different formats, or with different permissions are reasonably grouped in a fine-grained manner before packaging. This not only avoids conflicts that may occur when heterogeneous data is directly mixed and processed, but also allows each type of data to be packaged under more matching rules, improving the targeting and stability of the processing, and providing a basis for subsequent parallel packaging. Clear input boundaries are established, reducing cross-interference and mitigating the risks of metadata inconsistency and data entity redundancy. Then, based on classification, the encapsulation task elements in each encapsulation queue are encapsulated in parallel, allowing digital object identifiers, metadata, and data entities to be generated simultaneously in relatively independent channels. This not only improves the overall encapsulation speed but also reduces waiting and resource waste caused by sequential execution. Finally, after parallel encapsulation, the encapsulated sub-data corresponding to all encapsulation task elements of each encapsulation task are merged, ensuring consistency in metadata and data entities at the result level under the same task identifier. This improves the uniqueness, completeness, and traceability of the final encapsulated data. Therefore, the method based on this application, through a chain processing mechanism of "split-classification-parallel encapsulation-fusion," improves the fine-grained nature of batch encapsulation, further refining the efficiency, accuracy, and stability of the encapsulation process, and addressing the issues of insufficient quality and efficiency in encapsulation result delivery. Attached Figure Description

[0019] In the attached diagram:

[0020] Figure 1 This is a flowchart illustrating the steps of a batch encapsulation method for digital objects within a data network, as provided in an embodiment of this application.

[0021] Figure 2 This is a flowchart of another method for batch packaging digital objects within a data network provided in an embodiment of this application;

[0022] Figure 3 This is a batch packaging process for digital objects based on an embodiment of this application;

[0023] Figure 4 This is a batch encapsulated digital object verification process based on the embodiments of this application;

[0024] Figure 5 This is a process for batch encapsulating digital objects and pushing them to a message queue service, based on an embodiment of this application.

[0025] Figure 6 This is a batch encapsulated digital object consumption digital queue service process provided based on the embodiments of this application;

[0026] Figure 7 This is a batch encapsulation of digital objects execution task process provided by an embodiment of this application;

[0027] Figure 8 This is a block diagram of a batch packaging device for digital objects within a data network, provided in an embodiment of this application.

[0028] Figure 9 This is a block diagram of an electronic device provided in one embodiment of this application. Detailed Implementation

[0029] 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, 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.

[0030] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0031] like Figure 1 The image shows a method for batch encapsulating digital objects within a data network, as provided in an embodiment of this application.

[0032] The method may include the following steps:

[0033] Step 101: Split multiple packaging tasks in the packaging task queue to obtain at least one packaging task element for each packaging task.

[0034] In some embodiments of this application, to improve the processing efficiency and packaging quality of batch packaging of digital objects, the system first splits multiple packaging tasks in the packaging task queue to obtain at least one packaging task element for each packaging task. Specifically, each packaging task in the packaging task queue can be structured and parsed to identify its corresponding multiple packaging targets, and these packaging targets can be converted into independent packaging task elements. A packaging task element refers to the smallest task unit that can independently perform packaging operations in a packaging task. Its composition typically includes reference or location information of the target data entity, binding strategies related to digital object identifiers, metadata mapping and verification rules, and configuration parameters required for packaging execution. By splitting the overall task into multiple packaging task elements, the system can perform subsequent classification, parallel packaging, and fusion operations at a smaller granularity, thereby reducing processing latency and data errors caused by excessive task scale and improving the flexibility and controllability of the overall packaging process.

[0035] In a specific example, the data network platform receives a scientific research achievement registration task, which includes multiple experimental data files, corresponding metadata descriptions, and unified achievement identification information. The system breaks down this task into several encapsulated task elements. For example, an encapsulated task element is generated for the data entity reference information corresponding to the experimental data file paths, another for the achievement identification information, and yet another for the metadata description rules. Subsequently, these encapsulated task elements are sent to the subsequent classification and encapsulation processes. In this way, the complex scientific research achievement registration task submitted by the user is transformed into multiple independently executable encapsulated task elements, providing the foundation for subsequent classification and parallel encapsulation.

[0036] Step 102: Based on the preset encapsulation classification method, classify all encapsulation task elements to obtain multiple element encapsulation queues.

[0037] In some embodiments of this application, to improve the organization and parallel processing capabilities of the encapsulation process, the system classifies all encapsulation task elements based on a preset encapsulation classification method to obtain multiple meta-encapsulation queues. Specifically, all encapsulation task elements can be divided into different meta-encapsulation queues according to their source type, reference information format, permission attributes, or other predefined classification rules. The encapsulation classification method refers to a set of strategies used to guide the classification of encapsulation task elements, typically including classification criteria based on data structure, business attributes, or processing priority. Through this classification process, the system can aggregate encapsulation task elements with similar processing needs into the same queue, thereby reducing conflicts caused by heterogeneous data mixing in subsequent parallel encapsulation stages and improving the stability and consistency of the encapsulation process.

[0038] In a specific example, the data network platform receives multiple research achievement registration task elements, including citation information for image files, citation information for text files, and mapping rules for structured metadata. The system then categorizes these tasks according to a preset encapsulation and classification method: task elements corresponding to image file citation information are assigned to the image processing queue, task elements corresponding to text file citation information are assigned to the text processing queue, and task elements corresponding to structured metadata mapping rules are assigned to the metadata processing queue. After classification, task elements in each queue enter the corresponding encapsulation process based on their specific encapsulation attributes. This classification process ensures that image files are encapsulated in the image channel, text files in the text channel, and structured metadata in the metadata channel, avoiding encapsulation errors caused by cross-format mixed processing and providing clear input boundaries for subsequent parallel encapsulation.

[0039] Step 103: Perform parallel encapsulation on the encapsulation task elements in each encapsulation queue to obtain encapsulated sub-data corresponding to each encapsulation task element.

[0040] Each encapsulated sub-data has a corresponding digital object identifier, metadata, and data entity.

[0041] In some embodiments of this application, to improve the speed and quality of encapsulation processing, the system performs parallel encapsulation on encapsulation task elements in each meta-encapsulation queue to obtain encapsulated sub-data corresponding to each encapsulation task element. Specifically, after classification, the system can allocate encapsulation task elements in each meta-encapsulation queue to parallel processing channels, allowing them to perform encapsulation operations simultaneously, thereby generating encapsulated sub-data corresponding to each encapsulation task element. Encapsulated sub-data refers to the intermediate results formed during the encapsulation process, typically including three parts: a digital object identifier, metadata, and a data entity. The digital object identifier is used to uniquely identify the encapsulated object, the metadata is used to describe the object's attributes and structure, and the data entity is the actual content of the object. The encapsulation task element acts as a logical driving unit in this process, providing reference information, mapping rules, and configuration parameters to guide the generation of encapsulated sub-data. Through parallel encapsulation, the system can generate these encapsulated sub-data simultaneously in multiple channels, reducing the waiting time and resource consumption caused by sequential processing, improving overall encapsulation efficiency, and reducing the risk of unstable identifiers, missing metadata, or incorrect data entities that may occur due to serial processing.

[0042] In a specific example, the data network platform receives a batch publishing and distribution task, which includes several ebook files and their corresponding metadata descriptions. The system generates encapsulation task elements from the ebook file reference information and metadata mapping rules, and allocates them to different parallel channels for encapsulation. Each channel generates corresponding encapsulation sub-data during the encapsulation process. For example, the encapsulation sub-data for a particular ebook includes a unique digital object identifier, complete metadata fields, and the ebook's file content. Through this parallel encapsulation method, the encapsulation sub-data for multiple ebooks can be generated simultaneously, ultimately providing complete and consistent input conditions for subsequent fusion steps.

[0043] Step 104: Merge the encapsulation sub-data corresponding to all encapsulation task elements of each encapsulation task to obtain the encapsulation data of each encapsulation task.

[0044] In some embodiments of this application, to ensure the integrity and consistency of the encapsulation results, the system merges the encapsulation sub-data corresponding to all encapsulation task elements of each encapsulation task to obtain the encapsulation data for each encapsulation task. Specifically, after parallel encapsulation is completed, the system aggregates the encapsulation sub-data generated by multiple encapsulation task elements under the same encapsulation task, integrating metadata and data entities under a unified digital object identifier. Through the fusion operation, the system can recombine the scattered encapsulation sub-data into complete encapsulation data, ensuring that the identifiers, metadata, and data entities of the same task maintain consistency at the result level, thereby improving the uniqueness, integrity, and traceability of the encapsulation results.

[0045] In a specific example, the data network platform receives a scientific research achievement registration task. This task has been broken down into multiple encapsulation task elements in previous steps, generating corresponding encapsulation sub-data during the parallel encapsulation process. Examples include: identifier sub-data for a specific experimental result, metadata sub-data generated by metadata mapping rules, and data entity sub-data generated driven by experimental file reference information. The system can then merge these encapsulation sub-data to generate a complete encapsulation data, giving the experimental result a complete metadata description and data entity content under a unified digital object identifier. This fusion process yields a clearly structured, complete, and traceable encapsulation result, providing a stable foundation for subsequent storage and delivery.

[0046] In summary, in this embodiment, by splitting multiple packaging tasks in the packaging task queue, the complex overall task is refined into multiple packaging task elements, making the processing units smaller and more flexible, reducing processing latency caused by the large scale of the overall task, and providing higher execution efficiency. Furthermore, based on task splitting, the packaging task elements are classified according to a preset packaging classification method, so that data from different sources, with different formats, or with different permissions are reasonably grouped in a fine-grained manner before packaging. This not only avoids conflicts that may occur when heterogeneous data is directly mixed and processed, but also allows each type of data to be packaged under more matching rules, improving the targeting and stability of the processing, and providing a basis for subsequent parallel packaging. Clear input boundaries are established, reducing cross-interference and mitigating the risks of metadata inconsistency and data entity redundancy. Then, based on classification, the encapsulation task elements in each encapsulation queue are encapsulated in parallel, allowing digital object identifiers, metadata, and data entities to be generated simultaneously in relatively independent channels. This not only improves the overall encapsulation speed but also reduces waiting and resource waste caused by sequential execution. Finally, after parallel encapsulation, the encapsulated sub-data corresponding to all encapsulation task elements of each encapsulation task are merged, ensuring consistency in metadata and data entities at the result level under the same task identifier. This improves the uniqueness, completeness, and traceability of the final encapsulated data. Therefore, the method based on this application, through a chain processing mechanism of "split-classification-parallel encapsulation-fusion," improves the fine-grained nature of batch encapsulation, further refining the efficiency, accuracy, and stability of the encapsulation process, and addressing the issues of insufficient quality and efficiency in encapsulation result delivery.

[0047] Figure 2 This is another method for batch encapsulating digital objects within a data network, provided in the embodiments of this application.

[0048] The method may include the following steps:

[0049] Step 201: Split multiple packaging tasks in the packaging task queue to obtain at least one packaging task element for each packaging task.

[0050] The method shown in this step has been explained in step 101 and will not be repeated here.

[0051] Optionally, step 201 includes the following sub-steps:

[0052] Sub-step 2011: Based on the request parameters for each encapsulation task, perform structured analysis on each encapsulation task to obtain at least one encapsulation target for each encapsulation task.

[0053] In some embodiments of this application, to ensure that encapsulation tasks can be accurately decomposed and mapped to subsequent encapsulation processes, the system needs to perform structured analysis on each encapsulation task based on the request parameters for each task, in order to obtain at least one encapsulation target for each task. Specifically, after receiving the request parameters for the encapsulation task, the system can parse the parameters, identify the reference information, mapping rules, and permission requirements involved, and convert this information into structured analysis results to determine the encapsulation target corresponding to the encapsulation task. The encapsulation target refers to the specific processing object identified in the encapsulation task, typically including the generation constraints of digital object identifiers, metadata mapping rules, and reference information of data entities. Through structured analysis, the system can transform complex request parameters into a clear set of encapsulation targets, thereby improving the accuracy of task decomposition and providing clear input conditions for the subsequent generation of encapsulation task metadata.

[0054] In a specific example, the data network platform receives a scientific research achievement registration task. The request parameters for this task include the experimental data file path, achievement identification information, and metadata description rules. The system can then perform structured analysis on these request parameters, identifying the experimental data file path as the reference target for data entities, the achievement identification information as the generation target for digital object identifiers, and the metadata description rules as the metadata mapping target. This analysis process yields three clear encapsulation targets, each corresponding to a different metadata generation stage in the subsequent encapsulation task, thus ensuring that the scientific research achievement registration task has clear processing objects and complete input conditions in the subsequent encapsulation process.

[0055] Sub-step 2012: Generate a corresponding packaging task element for each packaging target, and establish the correspondence between the packaging task element and the packaging task based on the correspondence between each packaging target and the packaging task.

[0056] In some embodiments of this application, in order to transform the encapsulation targets obtained from structured analysis into executable minimum task units, the system needs to generate a corresponding encapsulation task element for each encapsulation target and establish a correspondence between the encapsulation task elements and the encapsulation tasks based on the correspondence between each encapsulation target and the encapsulation task. Specifically, the system will generate an independent encapsulation task element for each encapsulation target, giving it encapsulation-capable attributes, and record the mapping relationship between this task element and the original encapsulation task in the task management module. By generating encapsulation task elements and establishing correspondence, the system can ensure that each encapsulation target has a clear execution object in the subsequent encapsulation process, while maintaining the logical association with the original encapsulation task, thereby improving the integrity and traceability of the encapsulation process.

[0057] In a specific example, when the data network platform performs the scientific research achievement registration task, the system has already identified three encapsulation targets in previous steps: experimental data file paths, achievement identification information, and metadata description rules. The system can then generate corresponding encapsulation task elements for each of these targets: data entity reference task elements for experimental data file paths, identifier binding task elements for achievement identification information, and metadata mapping task elements for metadata description rules. Simultaneously, the system establishes a correspondence between these three encapsulation task elements and the scientific research achievement registration task in the task management module. This allows for independent operation of each task element in subsequent classification, parallel encapsulation, and fusion stages, while maintaining the overall association with the original scientific research achievement registration task, ultimately ensuring the structural and content integrity of the encapsulation result.

[0058] Step 202: Based on the preset encapsulation classification method, classify all encapsulation task elements to obtain multiple element encapsulation queues.

[0059] The method shown in this step has been explained in step 102 and will not be repeated here.

[0060] Optionally, step 202 includes at least one of the following steps:

[0061] Sub-step 2021: Classify the encapsulation task elements according to the data source type corresponding to the encapsulation task elements to obtain the element encapsulation queues corresponding to different data source types.

[0062] In some embodiments of this application, to ensure a clearer processing path for encapsulated task elements in subsequent encapsulation processes, the system needs to classify them according to the data source type corresponding to each encapsulated task element to obtain encapsulation queues corresponding to different data source types. Specifically, after generating the encapsulated task elements, the data source type corresponding to each task element can be identified and assigned to the corresponding encapsulation queue. The data source type refers to the category of data source referenced or depended upon by the encapsulated task element, typically including file systems, databases, sensor acquisition terminals, or external interfaces. Through this classification method, the system can allocate encapsulated task elements with consistent sources to the same queue, thereby reducing conflicts caused by cross-source data processing in subsequent parallel encapsulation stages and ensuring improved data consistency and processing stability in the encapsulation process.

[0063] In a specific example, the data network platform receives a scientific research achievement registration task. This task has already been broken down into multiple packaged task elements in previous steps. Some task elements correspond to experimental data files stored in the file system, some to experimental parameters stored in the database, and others to real-time monitoring data acquired by sensors. The system can then categorize these packaged task elements according to their data source type: file system task elements into a file queue, database task elements into a database queue, and sensor acquisition task elements into a sensor queue. Through this classification process, the user-submitted scientific research achievement registration task can enter the corresponding processing channel according to its data source type in the subsequent packaging process, ultimately ensuring that data from different sources maintains a clear structure and logical consistency in the packaged result.

[0064] Sub-step 2022: Classify the encapsulation task elements according to the data volume corresponding to the encapsulation task elements to obtain the element encapsulation queues corresponding to different data volume levels.

[0065] In some embodiments of this application, to ensure the stability and resource utilization balance of the encapsulation process when processing data of different scales, the system needs to classify the data volume corresponding to the encapsulation task element to obtain encapsulation queues corresponding to different data volume levels. Specifically, after generating the encapsulation task element, the system can detect the data volume referenced by each task element and classify it into the corresponding data volume level queue. The data volume refers to the scale range of the data entities or referenced information involved in the encapsulation task element, which can usually be divided into small-scale, medium-scale, and large-scale according to a predefined data scale. Through this classification method, the system can aggregate encapsulation task elements with similar data volumes into the same queue, thereby reasonably allocating computing resources in the subsequent parallel encapsulation stage, avoiding processing delays or resource waste caused by excessive differences in data volume, and ultimately improving the overall efficiency and stability of the encapsulation process.

[0066] In a specific example, the data network platform receives a scientific research achievement registration task. This task has already been broken down into multiple packaged task elements in previous steps. Some task elements correspond to small data files of only a few hundred KB, some to medium-sized experimental log files of tens of MB, and others to large experimental image files of several GB. The system can then categorize these packaged task elements into small-scale queues, medium-scale queues, and large-scale queues based on the data size. Through this classification process, the user-submitted scientific research achievement registration task can enter the corresponding processing channel according to its data volume in the subsequent packaging process, ultimately ensuring that data of different sizes are processed appropriately and with consistency in the packaging results.

[0067] Sub-step 2023: Classify the encapsulation task elements according to the data format corresponding to the encapsulation task elements to obtain the element encapsulation queues corresponding to different data formats.

[0068] In some embodiments of this application, to ensure that the encapsulation process maintains clear structure and consistent processing paths when handling different types of data, the system needs to classify the data elements according to their corresponding data formats to obtain meta-encapsulation queues corresponding to different data formats. Specifically, after generating encapsulation task elements, the system can identify the data format corresponding to each task element and assign it to the corresponding meta-encapsulation queue. Data format refers to the organization of the data referenced or depended upon by the encapsulation task element during storage or transmission, typically including text, image, audio, or structured table formats. Through this classification method, the system can aggregate encapsulation task elements with the same data format into the same queue, thereby avoiding parsing conflicts caused by cross-format mixed processing in the subsequent parallel encapsulation stage and ensuring improved data structure processing and result consistency in the encapsulation process.

[0069] In a specific example, the data network platform receives a scientific research achievement registration task. This task has already been broken down into multiple packaged task elements in previous steps. Some task elements correspond to experimental report files in text format, some to experimental image files in image format, and others to experimental parameter tables in structured table format. The system can then categorize these packaged task elements according to their data format, grouping text-formatted task elements into a text queue, image-formatted task elements into an image queue, and structured table task elements into a table queue. Through this classification process, the user-submitted scientific research achievement registration task can enter the corresponding processing channel according to its data format in the subsequent packaging process, ultimately ensuring that data of different formats maintains a clear structure and logical consistency in the packaged result.

[0070] Sub-step 2024: Based on the permission configuration corresponding to the encapsulation task element, classify the encapsulation task elements to obtain the element encapsulation queues corresponding to different permission levels.

[0071] In some embodiments of this application, to ensure the security and access control rationality of the encapsulation process when processing data with different permission requirements, the system needs to classify the encapsulation task elements according to their corresponding permission configurations to obtain meta-encapsulation queues corresponding to different permission levels. Specifically, after generating encapsulation task elements, the system can identify the permission configuration corresponding to each task element and assign it to the corresponding meta-encapsulation queue. Permission configuration refers to the permission level required by the encapsulation task element during access or encapsulation, typically including public, restricted, and confidential levels. Through this classification method, the system can aggregate encapsulation task elements with the same permission requirements into the same queue, thereby avoiding the security risks caused by cross-permission mixed processing in the subsequent parallel encapsulation stage and ensuring improved data access control and result compliance in the encapsulation process.

[0072] In a specific example, the data network platform receives a scientific research achievement registration task. This task has already been broken down into multiple packaged task elements in previous steps. Some task elements correspond to experimental data files that are public-level, some to experimental parameter files that are restricted-level, and others to experimental image files that are confidential-level. The system can then, based on the permission configurations corresponding to these packaged task elements, categorize public-level task elements into a public queue, restricted-level task elements into a restricted queue, and confidential-level task elements into a confidential queue. Through this classification process, the user-submitted scientific research achievement registration task can enter the corresponding processing channel according to the permission level in the subsequent packaging process, ultimately ensuring that data with different permissions is properly processed in the packaging results while maintaining security and compliance.

[0073] Step 203: Perform parallel encapsulation on the encapsulation task elements in each encapsulation queue to obtain encapsulated sub-data corresponding to each encapsulation task element.

[0074] Each encapsulated sub-data has a corresponding digital object identifier, metadata, and data entity.

[0075] The method shown in this step has been explained in step 103 and will not be repeated here.

[0076] Optionally, when multiple encapsulation task elements are each assigned to a corresponding element encapsulation queue, step 203 includes the following sub-steps:

[0077] Sub-step 2031: Encapsulate each encapsulation task element with a digital object identifier to obtain the digital object identifier for each encapsulation task element.

[0078] In some embodiments of this application, to ensure that the encapsulation task elements can be uniquely identified in subsequent encapsulation processes, the system needs to encapsulate each encapsulation task element with a digital object identifier (DOI) to obtain a DOI for each task element. Specifically, during the parallel encapsulation phase, the system generates a corresponding DOI for each encapsulation task element and establishes a binding relationship between this identifier and the task element. A Digital Object Identifier (DOI) is an encoded sequence used to uniquely identify a digital object. Its design aims to ensure that objects from different sources and of different types can be accurately distinguished and referenced in the encapsulation results through a unified identification system. For multiple encapsulation task elements belonging to the same encapsulation task, the system assigns them the same DOI to ensure that these task elements can be aggregated based on a unified identifier in subsequent fusion steps. Through this encapsulation method, the system can maintain the consistency of identifiers in subsequent metadata encapsulation and data entity encapsulation processes, thereby improving the uniqueness, integrity, and traceability of the encapsulation results.

[0079] In a specific example, the data network platform receives a scientific research achievement registration task. This task has been broken down into multiple encapsulated task elements in previous steps, including experimental data file citation task elements, achievement identification information task elements, and metadata mapping rule task elements. The system will assign the same DOI to these task elements, such as "10.1234 / abc123". Subsequently, these task elements are bound to this unified DOI in the subsequent metadata encapsulation and data entity encapsulation processes. Finally, in the fusion step, the system can aggregate these encapsulated sub-data into a complete scientific research achievement encapsulation data, ensuring that the achievement remains consistent at the identification level and can be accurately cited.

[0080] Sub-step 2032: Perform metadata encapsulation on each encapsulation task element to obtain the metadata of each encapsulation task element.

[0081] In some embodiments of this application, to ensure that the encapsulated task elements have complete attribute descriptions in subsequent encapsulation processes, the system needs to encapsulate metadata for each encapsulated task element to obtain its metadata. Specifically, during the parallel encapsulation phase, the system can extract corresponding metadata information from the data source referenced by the encapsulated task element and establish a binding relationship with it. If the data source corresponding to the task element does not contain available metadata information, new metadata can be automatically generated according to preset rules to ensure that the encapsulated task elements have complete attribute descriptions in the encapsulation results. Metadata refers to data information used to describe the attributes, structure, and semantics of digital objects, typically including the object's name, creation time, author information, data type, and structured fields. Through this extraction or generation encapsulation method, the system can maintain the consistency of object attributes during subsequent data entity encapsulation and fusion processes, thereby improving the integrity and traceability of the encapsulation results.

[0082] In a specific example, the data network platform receives a scientific research achievement registration task. This task has been broken down into multiple encapsulation task elements in previous steps, including experimental data file reference task elements, achievement identification information task elements, and metadata mapping rule task elements. The system first attempts to extract existing metadata information from the experimental data files, such as file name, size, collection time, and data type. If some files lack complete metadata information, the system generates new metadata fields according to preset rules, such as automatically generating a collection timestamp or supplementing data type descriptions. The achievement identification information task element generates metadata containing the achievement title, author, affiliated institution, and registration time; the metadata mapping rule task element generates metadata containing field mapping relationships and verification rules. Through this extraction or generation encapsulation process, the user-submitted scientific research achievement registration task can rely on this metadata for unified description and management in subsequent encapsulation processes, ultimately ensuring the integrity and consistency of the encapsulation results at the attribute level.

[0083] Sub-step 2033: Perform data entity encapsulation on each encapsulation task element to obtain the data entity of each encapsulation task element.

[0084] In some embodiments of this application, to ensure that the encapsulation task element has a complete content payload in subsequent encapsulation processes, the system needs to encapsulate each encapsulation task element into a data entity to obtain the data entity for each encapsulation task element. Specifically, during the parallel encapsulation phase, the system extracts the actual data content from the data source referenced by the encapsulation task element and performs format conversion or consistency verification on the data as necessary to ensure that the data entity can be correctly encapsulated. A data entity refers to the actual content of a digital object, typically including file content, experimental data, image information, or structured records. Through this extraction and conversion encapsulation method, the system can maintain the authenticity and integrity of the object content during subsequent fusion processes, thereby improving the usability and traceability of the encapsulation results.

[0085] In a specific example, the data network platform receives a scientific research achievement registration task. The system can extract corresponding data entities from experimental data files, such as the actual content of experimental data tables or image files; for achievement identification information task elements, it will extract the corresponding identification description file as a data entity. If the data source for some task elements is missing or unavailable, the system will mark the data entity of that task element as empty or abnormal in the encapsulation result and prompt the user to supplement it. Through this encapsulation process of extraction, transformation, and verification, the scientific research achievement registration task submitted by the user can be uniformly managed and integrated based on these data entities in the subsequent encapsulation process, ultimately ensuring that the encapsulation result maintains authenticity and consistency at the content level.

[0086] Sub-step 2034: The digital object identifier, metadata, and data entity of the encapsulated task element are determined as the encapsulated sub-data of the encapsulated task element.

[0087] In some embodiments of this application, to ensure that the encapsulation task elements can form complete and uniformly manageable intermediate results in subsequent encapsulation processes, the system needs to define the digital object identifier, metadata, and data entity of the encapsulation task elements as encapsulation sub-data of the encapsulation task elements. Specifically, after completing the encapsulation of digital object identifiers, metadata, and data entities, the system can uniformly bind the three according to a preset data storage structure and store them as encapsulation sub-data. Encapsulation sub-data refers to the intermediate results formed during the encapsulation process. Its preset data storage structure typically includes three parts: identifier field, metadata field, and data entity field. The three are associated through a unified index relationship and stored in a structured manner to ensure that each encapsulation task element has complete input conditions in subsequent fusion steps and can achieve fast retrieval and consistency verification in a database or storage system, thereby improving the uniqueness, completeness, and traceability of the encapsulation results.

[0088] In a specific example, the data network platform receives a scientific research achievement registration task. This task has already been broken down into multiple encapsulated task elements in previous steps, with digital object identifier (DOI), metadata, and data entity encapsulation completed separately. The system can then uniformly bind the DOI "10.1234 / abc123" of a specific experimental data file referencing the task element, the corresponding metadata (such as file name, size, collection time, and data type), and the corresponding data entity (such as the actual content of the experimental data table) according to a preset data storage structure, forming an encapsulated sub-data record. This record contains three fields in the storage system: a DOI field, a metadata field, and a data entity field, and an indexing mechanism ensures the correlation between the three. Through this processing, the user-submitted scientific research achievement registration task can be aggregated in subsequent fusion steps based on these encapsulated sub-data, ultimately ensuring the consistency and integrity of the scientific research achievements at the levels of identification, attributes, and content.

[0089] Step 204: Merge the encapsulation sub-data corresponding to all encapsulation task elements of each encapsulation task to obtain the encapsulation data of each encapsulation task.

[0090] The method shown in this step has been explained in step 104 and will not be repeated here.

[0091] Optionally, step 204 includes the following sub-steps:

[0092] Sub-step 2041 involves matching all the encapsulated sub-data based on the numeric object identifier to obtain the encapsulated sub-dataset corresponding to the encapsulation task for each numeric object identifier.

[0093] In some embodiments of this application, to ensure accurate aggregation of encapsulation tasks during subsequent fusion, the system needs to match all encapsulated sub-data based on digital object identifiers to obtain encapsulated sub-data sets corresponding to the encapsulation tasks associated with each digital object identifier. Specifically, after the encapsulated sub-data is generated, the system matches all encapsulated sub-data based on digital object identifiers and groups encapsulated sub-data with the same identifier into the same set. Through this matching and grouping method, the system can maintain the integrity of the encapsulation tasks during subsequent metadata fusion and data entity fusion, thereby improving the uniqueness and traceability of the encapsulation results.

[0094] In a specific example, the data network platform receives a scientific research achievement registration task. This task has already been broken down into multiple encapsulated task elements in previous steps, with digital object identifier encapsulation, metadata encapsulation, and data entity encapsulation completed separately. The system can now match these encapsulated sub-data. For instance, the experimental data file reference task element, the achievement identification information task element, and the metadata mapping rule task element are all bound to the same digital object identifier "10.1234 / abc123". The system will group these three encapsulated sub-data into the same encapsulated sub-dataset. Through this matching process, the user-submitted scientific research achievement registration task can rely on this encapsulated sub-dataset for metadata fusion and data entity fusion in subsequent encapsulation processes, ultimately ensuring that the scientific research achievement remains consistent at the identifier level and complete at the content level.

[0095] Sub-step 2042 involves fusing the metadata of each encapsulated subdata in the encapsulation subdata set of each encapsulation task to obtain the metadata of the encapsulation task.

[0096] In some embodiments of this application, to ensure that the packaging task has a unified and complete attribute description in subsequent packaging processes, the system needs to perform metadata fusion on the metadata of each packaging sub-data in the packaging sub-dataset of each packaging task to obtain the metadata of the packaging task. Specifically, after completing the matching of the packaging sub-datasets, the system will perform fusion processing on the metadata of each packaging sub-data in the same set. The fusion process typically includes two stages: information verification and information merging. Information verification is used to check the consistency and completeness of the information expressed by the metadata, such as whether the time information expressed by the fields matches, whether the author information is consistent, and whether the data type description is unified. Information merging is used to integrate the information expressed by multiple metadata into a unified metadata set. Through this fusion method based on information verification and information merging, the system can maintain the consistency of attribute descriptions in subsequent data entity fusion and structured packaging processes, thereby improving the completeness and traceability of the packaging results.

[0097] In a specific example, the data network platform receives a scientific research achievement registration task. This task has been broken down into multiple encapsulated task elements in previous steps, generating corresponding encapsulated sub-data. The system can then perform metadata fusion on the encapsulated sub-datasets corresponding to the same digital object identifier. For example, the metadata of the experimental data file referencing task element includes the file name and collection time; the metadata of the achievement identification information task element includes the achievement title and author information; and the metadata of the metadata mapping rules task element includes field mapping relationships and verification rules. The system first verifies the information expressed by these metadata elements, ensuring consistency of the time field, matching of author information, and correctness of field mapping rules. Then, it merges the information expressed by these metadata elements into a complete set of scientific research achievement metadata. Through this fusion process, the scientific research achievement registration task submitted by the user can be managed and referenced in subsequent encapsulation processes using this unified metadata, ultimately ensuring the consistency and integrity of the scientific research achievements at the attribute level.

[0098] Sub-step 2043 involves fusing the data entities of each encapsulated sub-data in the encapsulated sub-data set of each encapsulation task to obtain the data entity of the encapsulation task.

[0099] In some embodiments of this application, to ensure that the encapsulation task has a unified and complete content payload in subsequent encapsulation processes, the system needs to fuse the data entities of each encapsulated sub-data in the encapsulation subset of each encapsulation task to obtain the data entity of the encapsulation task. Specifically, after completing the matching of encapsulation subsets, the system performs fusion processing on the data entities of each encapsulated sub-data in the same set. The fusion process is typically completed by retaining different data content and deduplicating identical data content. Through this fusion method based on difference retention and duplication removal, the system can maintain the authenticity and integrity of the data content in the subsequent structured encapsulation process, thereby improving the usability and traceability of the encapsulation results.

[0100] In a specific example, the data network platform receives a scientific research achievement registration task. This task has already been broken down into multiple encapsulated task elements in previous steps, generating corresponding encapsulated sub-data. The system can then perform data entity fusion on the encapsulated sub-data sets corresponding to the same digital object identifier. For example, the data entity for the experimental data file referencing task element includes experimental table content, the data entity for the achievement identification information task element includes the achievement description file, and the data entity for the metadata mapping rules task element includes the mapping configuration file. During the fusion process, the system retains these different data entities and removes duplicate experimental table entries or duplicate description fields, ultimately forming a complete set of scientific research achievement data entities. Through this fusion process, the user-submitted scientific research achievement registration task can be managed and referenced in subsequent encapsulation processes based on this unified data entity, ultimately ensuring the authenticity and consistency of the scientific research achievements at the content level.

[0101] Sub-step 2044 involves structurally encapsulating the digital object identifier, metadata, and data entity for each encapsulation task to obtain the encapsulation data for the encapsulation task.

[0102] In some embodiments of this application, to ensure that the encapsulation task forms a complete and uniformly manageable data object in the final encapsulation result, the system needs to structurally encapsulate the digital object identifier, metadata, and data entity of each encapsulation task to obtain the encapsulated data of the encapsulation task. Specifically, after completing digital object identifier matching, metadata fusion, and data entity fusion, the system will organize the three in a unified manner according to a preset data storage structure to form structured encapsulated data. Structured encapsulation refers to combining digital object identifiers, metadata, and data entities into a complete data object through fielding, hierarchical organization, and indexing, enabling unified management and rapid retrieval in a database or storage system. Through this structured encapsulation method, the system can ensure that the encapsulated data of the encapsulation task maintains consistency in terms of identification, attributes, and content, thereby improving the integrity, traceability, and operability of the encapsulation result.

[0103] In a specific example, the data network platform receives a scientific research achievement registration task. This task has already completed the matching of encapsulated sub-data, metadata fusion, and data entity fusion in previous steps. At this point, the system will organize the scientific research achievement's digital object identifier "10.1234 / abc123", the fused metadata (such as achievement title, author, affiliated institution, collection time, and field mapping rules), and the fused data entities (such as experimental table content, image files, and explanatory documents) according to a preset data storage structure, forming an encapsulated data object. This encapsulated data object contains three main parts in the storage system: a DOI field, a metadata field, and a data entity field, and an indexing mechanism ensures the relationship between these three. Through this structured encapsulation process, the user-submitted scientific research achievement registration task can maintain integrity and consistency in subsequent management, citation, and retrieval processes, ultimately ensuring that the scientific research achievement has a unified identifier, attributes, and content in the encapsulated result.

[0104] Step 205: Store the obtained encapsulation data to the data network warehouse respectively, and generate the association between each stored encapsulation data and the corresponding encapsulation task.

[0105] In some embodiments of this application, to ensure the long-term preservation and subsequent retrieval of the encapsulation results, the system stores the obtained encapsulation data separately in a data network warehouse and generates an association between each stored encapsulation data and its corresponding encapsulation task. Specifically, the fused encapsulation data can be written into the storage unit of the data network warehouse, and an index or mapping relationship can be established in the warehouse to identify the correspondence between the encapsulation data and its source encapsulation task. A data network warehouse is a system platform used for centralized management and storage of digital objects, capable of supporting unique data identification, metadata retrieval, and object traceability. Through this storage and association process, the system not only achieves persistent storage of encapsulation data but also ensures that the corresponding encapsulation task can be accurately located during subsequent queries or retrievals, thereby improving the integrity and traceability of data management.

[0106] In a specific example, the DataNet platform completes the encapsulation process for a digital publishing and distribution task, generating encapsulated data containing ebook files, metadata descriptions, and unique identifiers. The system can then store this encapsulated data in the DataNet repository and establish a link between the encapsulated data and the original publishing and distribution task. Subsequently, when the publishing task needs to be retrieved, the encapsulated data in the repository can be quickly located using the task identifier, ensuring consistency and integrity of the published results during storage, retrieval, and traceability.

[0107] Step 206: Determine the encapsulation execution status of each encapsulation task element.

[0108] The encapsulation execution status is used to characterize the success of the encapsulation task element execution in step 203.

[0109] In some embodiments of this application, in order to monitor the task execution status in real time during the encapsulation process, the system needs to determine the encapsulation execution status of each encapsulation task element. Specifically, during parallel encapsulation, the execution status of each encapsulation task element can be monitored in real time, and a corresponding encapsulation execution status can be generated upon completion of the encapsulation operation. The encapsulation execution status is used to characterize whether the encapsulation task element successfully generates encapsulated sub-data in a step, and typically includes status information such as success, failure, or exception. By determining the encapsulation execution status in real time, the system can promptly identify problematic task elements during the encapsulation process and provide immediate basis for subsequent error recovery or task reassignment, thereby improving the stability and continuity of the encapsulation process.

[0110] In a specific example, when the data network platform performs the task of registering scientific research results, the encapsulation task element of a certain experimental data file is monitored in real time during the parallel encapsulation process. When the system executes this step, if the task element successfully generates encapsulated sub-data containing digital object identifiers, metadata, and data entities, its encapsulation execution status is recorded as successful in real time. If data loss or processing abnormalities occur during the encapsulation process, the system immediately marks the encapsulation execution status of the task element as failed. This real-time status determination allows the system to know the execution status of task elements while the encapsulation process is still in progress, and to reallocate or handle failed task elements in subsequent steps, ensuring the integrity and consistency of the final encapsulated data.

[0111] Step 207: If the encapsulation execution status of the first target encapsulation task element is characterized as a failure, the first target encapsulation task element is added back to the meta-encapsulation queue where the target encapsulation task element is located.

[0112] In some embodiments of this application, to ensure the continuity and fault tolerance of the encapsulation process, when the encapsulation execution status of the first target encapsulation task element is characterized as a failure in real-time monitoring, the system can re-add the task element to its original encapsulation queue. That is, during parallel encapsulation, when the system detects that an encapsulation task element has failed to successfully generate the corresponding encapsulated sub-data, it immediately triggers a rollback mechanism to put the task element back into the original encapsulation queue for processing again in subsequent encapsulation processes. Through this rollback mechanism, the system can promptly repair failed task elements during the encapsulation process, avoiding overall task interruption due to a single failure, thereby improving the stability and continuity of the encapsulation process.

[0113] In a specific example, when the data network platform is performing a scientific research achievement registration task, the packaging task element of a certain experimental data file fails to generate complete packaged sub-data during the parallel packaging process due to data format mismatch. Based on the real-time monitoring of the packaging failure status of this task element, the system immediately adds it back to the text processing queue, allowing it to attempt packaging again in subsequent packaging processes. This real-time rollback mechanism enables the reprocessing of failed task elements before the packaging process is completed, ensuring that the final scientific research achievement registration task still generates complete packaged data and avoiding the impact of a single task element failure on the overall quality of the packaged deliverables.

[0114] Step 208: If the encapsulation execution status of the first target encapsulation task element is characterized as a failure, determine all second target encapsulation task elements corresponding to the first target encapsulation task element.

[0115] The second target packaging task element is the same target packaging task as the first target packaging task element.

[0116] In some embodiments of this application, to provide more comprehensive handling of failed task elements during the encapsulation process, when the encapsulation execution status of the first target encapsulation task element is characterized as failed, the system can also identify all second target encapsulation task elements corresponding to that task element. Specifically, when real-time monitoring detects that the first target encapsulation task element has failed to successfully generate encapsulated sub-data, the system will retrieve the target encapsulation task to which that task element belongs and identify other encapsulation task elements under that target encapsulation task, i.e., second target encapsulation task elements. Second target encapsulation task elements refer to other task elements corresponding to the same target encapsulation task as the first target encapsulation task element, and typically contain metadata fields or data entity fragments related to that target task. By identifying these second target encapsulation task elements, the system can perform unified error repair or rollback operations on the entire target encapsulation task in subsequent processing stages, thereby avoiding inconsistencies caused by repeatedly encapsulating only a single task element.

[0117] In a specific example, when the data network platform is performing a scientific research achievement registration task, the encapsulation task of a certain experimental data file fails during parallel encapsulation due to data corruption. The system will first determine the scientific research achievement registration task to which the experimental data file belongs and identify other encapsulation task elements under that task, such as the corresponding metadata field task elements and achievement identifier task elements. The system will mark these task elements as second target encapsulation task elements so that they can be deleted or reassigned together with the first target encapsulation task elements in subsequent processing steps. In this way, researchers can perform unified error handling for the entire scientific research achievement registration task in the encapsulation process, ensuring the consistency and integrity of the final encapsulation result.

[0118] Step 209: Remove the first target encapsulation task element and all the second target encapsulation task elements from their respective element encapsulation queues.

[0119] In some embodiments of this application, to prevent failed task elements from continuing to participate in subsequent encapsulation processes, when the encapsulation execution status of the first target encapsulation task element is characterized as failed, the system removes that task element and all its corresponding second target encapsulation task elements from their respective meta-encapsulation queues. That is, after the system identifies the first target encapsulation task element and its corresponding second target encapsulation task elements, it performs a cleanup operation on the meta-encapsulation queues containing these task elements, removing them all to prevent them from being called again in subsequent encapsulation processes. By deleting these task elements, the system can ensure that all related task elements of the same target encapsulation task are processed uniformly after failure, avoiding inconsistencies or omissions in the encapsulation results caused by some task elements continuing to execute, thereby improving the reliability and consistency of the encapsulation process.

[0120] In a specific example, when the data network platform is performing a scientific research achievement registration task, the encapsulation task element of a certain experimental data file fails during the parallel encapsulation process. The system has already identified the other task elements corresponding to this experimental data file during the previous step, including metadata field task elements and achievement identifier task elements. Therefore, the system will remove the task element of this experimental data file, along with its corresponding metadata field task elements and achievement identifier task elements, from their respective encapsulation queues. This deletion operation ensures that all relevant task elements of the scientific research achievement registration task will no longer participate in encapsulation after failure, thereby avoiding the generation of incomplete or conflicting encapsulated data and providing clear boundaries for subsequent error handling or task rollback.

[0121] Step 210: Add the target packaging task corresponding to the first target packaging task element and / or the second target packaging task element to the packaging task queue, and return to step 201, or generate error information based on the target packaging task.

[0122] In some embodiments of this application, to perform closed-loop processing of failed target packaging tasks during the packaging process, the system adds the target packaging tasks corresponding to the first target packaging task element and / or the second target packaging task element to the packaging task queue and returns to the previous steps for re-execution, or generates error information based on the target packaging task. Specifically, after deleting the failed first target packaging task element and its corresponding second target packaging task element, the system can choose to re-add it to the packaging task queue based on the overall situation of the target packaging task, so that packaging can start again from task splitting; if the target packaging task has unrecoverable errors in data or structure, the system will generate error information to prompt users or experimenters to intervene manually. In this way, the system can provide at least two processing paths in the event of failure: automated re-packaging by re-adding to the queue, or manual intervention by error information, thereby improving the fault tolerance and controllability of the packaging process.

[0123] In a specific example, when the data network platform is executing a scientific research achievement registration task, the encapsulation task of a certain experimental data file fails during parallel encapsulation, and its corresponding metadata field task element is also deleted. The system will then re-add the scientific research achievement registration task to the encapsulation task queue, allowing it to be executed again from the task split in subsequent processes. If the system detects that the source file of the scientific research achievement registration task is corrupted and cannot be repaired, it will generate an error message, prompting the task initiator to repair or replace the data. Through this dual-path processing mechanism, the scientific research achievement registration task can obtain an automated re-encapsulation opportunity after failure, and can also receive manual intervention when necessary, thereby ensuring the integrity and consistency of the final encapsulation result.

[0124] To more intuitively demonstrate the technical solutions of the embodiments of this application, Figures 3 to 7 The overall process is explained in detail from the perspective of program execution, covering the entire process from submitting the encapsulated request, validating its validity, processing the task, pushing and consuming messages, to executing the encapsulated task and updating the results. These flowcharts clearly illustrate the system's operational mechanisms and functional divisions at different stages, thus allowing for a grasp of the overall execution logic of the solution. It should be noted that these diagrams are merely illustrative of the embodiments, used to explain the system's working principle, and do not constitute a limitation on the embodiments of this application.

[0125] refer to Figure 3 This is a batch encapsulation process for digital objects based on the embodiments of this application. The entire process starts with a user submission request and ends with the push of the encapsulation result, forming a complete encapsulation closed loop. Specifically, it includes the following steps:

[0126] P1. Submit a batch packaging request: The user initiates a batch packaging request through the system interface. The request contains multiple packaging tasks to be processed, which serves as the starting point for the process.

[0127] P2. Batch Packaging Task Processing: The system parses and splits batch requests, generates multiple packaging task elements, and distributes them to the packaging service module, laying the foundation for subsequent execution.

[0128] P3, Encapsulation Service: The encapsulation service module receives task elements and pushes them to the process execution service through the message queue service. It is also responsible for receiving, managing and providing feedback on the encapsulation results.

[0129] P4. Message Queue Service: As an asynchronous communication bridge between the encapsulation service and the process execution service, it is responsible for pushing task instructions and pulling encapsulation results, ensuring scheduling decoupling and efficient transmission.

[0130] P5. Encapsulated Process Execution Service: Utilizing the task scheduling submodule, it is responsible for pulling tasks from the message queue and performing digital object identifier encapsulation, metadata encapsulation, and data entity encapsulation, and finally returning the results to the encapsulation service to form a closed loop.

[0131] refer to Figure 4 This is a batch encapsulation digital object verification process provided by the embodiments of this application. The core objective of this process is to ensure that the encapsulation requests submitted by users are fully verified in terms of legality, data integrity, and permission compliance, and finally enter the encapsulation service for execution.

[0132] Q1. Submitting a batch encapsulation request: Users submit a batch encapsulation request through the interface, and the system enters the verification stage after receiving it.

[0133] Q2. Batch Packaging Request Validation: The system performs parameter validation, user identity validation, and permission information validation to ensure that the request is legal and compliant.

[0134] Q3. Batch packaging and processing of tasks: The system configures the data source, metadata and access control list for requests that pass the verification to ensure that the tasks have the conditions for execution at the data, description and permission levels.

[0135] Q4. Packaging Service: The system submits the completed configuration and verification tasks to the packaging service module, performs the packaging operation, and generates the results.

[0136] refer to Figure 5 This is a batch encapsulation of digital objects and their push to a message queue service process provided by the embodiments of this application. The entire process forms a standardized encapsulation task distribution chain:

[0137] R1, Encapsulation Service: Before pushing, complete message queue service configuration verification, connectivity verification, topic existence confirmation and connection initialization to ensure stable pushing;

[0138] R2, Batch Encapsulation Request Formatting: The system splits batch requests into sub-requests and performs structured transformation to make them conform to the message queue service's receiving specifications;

[0139] R3, Message Queue Service: Receives formatted sub-requests, is responsible for subsequent task distribution and scheduling, and completes the asynchronous transmission entry point.

[0140] refer to Figure 6 This application provides a batch encapsulation of digital objects and a digital queue service process. The process demonstrates how the system consumes encapsulated task messages from a message queue service and distributes the tasks to clients for execution using a load balancing strategy, forming a consumption chain driven by verification, strategy selection, and task push.

[0141] S1, Message Queue Service: Verifies the client connection whitelist, tests network connectivity, confirms message topic validity, and ensures secure and reachable consumption process;

[0142] S2, Load Balancing: After receiving the compliance message, the load balancing phase begins, preparing to select an appropriate distribution strategy;

[0143] S3, Strategy Push: Push task messages to target clients according to preset strategies (such as polling, minimum load, priority distribution);

[0144] S4. Client Execution: The client receives the task message and performs the corresponding encapsulation operation to complete the task landing.

[0145] refer to Figure 7 This is a batch encapsulation task execution process based on the embodiments of this application. The process describes how the system schedules and executes various encapsulation operations after receiving an encapsulation task message, and pushes the results and updates the status:

[0146] T1, Message Queue Service: Receives and encapsulates task messages, serving as the trigger entry point for task execution;

[0147] T2. Task Scheduling: The system arranges the execution order and resource allocation according to the task type and resource status;

[0148] T3. Encapsulated Process Execution Service: Generates task plans and executes three types of encapsulated processes: digital object identifier encapsulation (used to connect to the identifier service and register the identifier), metadata encapsulation (used to process descriptive information, execute technical metadata, business metadata and discovery metadata encapsulation, and complete permission data and user information encapsulation), and data entity encapsulation (used to connect to the data source, pull multi-type, multi-format, large-volume data and complete encapsulation).

[0149] T4. Push the encapsulation result: The system will push the encapsulation result to the specified module or server for subsequent use or archiving;

[0150] T5. Update the encapsulation service status: Update the data status of digital objects in the encapsulation service, mark the task completion status and record logs to ensure that the data status is consistent with the execution records.

[0151] In summary, in this embodiment, by splitting multiple packaging tasks in the packaging task queue, the complex overall task is refined into multiple packaging task elements, making the processing units smaller and more flexible, reducing processing latency caused by the large scale of the overall task, and providing higher execution efficiency. Furthermore, based on task splitting, the packaging task elements are classified according to a preset packaging classification method, so that data from different sources, with different formats, or with different permissions are reasonably grouped in a fine-grained manner before packaging. This not only avoids conflicts that may occur when heterogeneous data is directly mixed and processed, but also allows each type of data to be packaged under more matching rules, improving the targeting and stability of the processing, and providing a basis for subsequent parallel packaging. Clear input boundaries are established, reducing cross-interference and mitigating the risks of metadata inconsistency and data entity redundancy. Then, based on classification, the encapsulation task elements in each encapsulation queue are encapsulated in parallel, allowing digital object identifiers, metadata, and data entities to be generated simultaneously in relatively independent channels. This not only improves the overall encapsulation speed but also reduces waiting and resource waste caused by sequential execution. Finally, after parallel encapsulation, the encapsulated sub-data corresponding to all encapsulation task elements of each encapsulation task are merged, ensuring consistency in metadata and data entities at the result level under the same task identifier. This improves the uniqueness, completeness, and traceability of the final encapsulated data. Therefore, the method based on this application, through a chain processing mechanism of "split-classification-parallel encapsulation-fusion," improves the fine-grained nature of batch encapsulation, further refining the efficiency, accuracy, and stability of the encapsulation process, and addressing the issues of insufficient quality and efficiency in encapsulation result delivery.

[0152] refer to Figure 8 It illustrates a batch packaging device 30 for digital objects within a data network according to an embodiment of this application, comprising:

[0153] The task splitting module 301 is used to split multiple encapsulation tasks in the encapsulation task queue to obtain at least one encapsulation task element for each encapsulation task.

[0154] The task classification module 302 is used to classify all encapsulated task elements based on a preset encapsulation classification method to obtain multiple element encapsulation queues.

[0155] The classification and encapsulation module 303 is used to encapsulate the encapsulation task elements in each encapsulation queue in parallel to obtain encapsulation sub-data corresponding to each encapsulation task element; the encapsulation sub-data has a corresponding digital object identifier, metadata, and data entity.

[0156] The encapsulation fusion module 304 is used to merge the encapsulation sub-data corresponding to all encapsulation task elements of each encapsulation task to obtain the encapsulation data of each encapsulation task.

[0157] Optionally, the task splitting module 301 includes:

[0158] The target confirmation submodule is used to perform structured analysis on each encapsulation task based on the request parameters for each encapsulation task, so as to obtain at least one encapsulation target for each encapsulation task.

[0159] The task splitting submodule is used to generate a corresponding encapsulation task element for each encapsulation target, and to establish a correspondence between the encapsulation task element and the encapsulation task based on the correspondence between each encapsulation target and the encapsulation task.

[0160] Optionally, the task classification module 302 includes at least one of the following sub-modules:

[0161] The type classification submodule is used to classify the encapsulated task elements according to the data source type corresponding to the encapsulated task elements, so as to obtain the element encapsulation queues corresponding to different data source types.

[0162] The data volume classification submodule is used to classify the encapsulation task elements according to the data volume size corresponding to the encapsulation task elements, so as to obtain the element encapsulation queues corresponding to different data volume levels.

[0163] The format classification submodule is used to classify the encapsulation task elements according to the data format corresponding to the encapsulation task elements, so as to obtain the element encapsulation queues corresponding to different data formats.

[0164] The permission classification submodule is used to classify the encapsulated task elements according to the permission configuration corresponding to the encapsulated task elements, so as to obtain the element encapsulation queues corresponding to different permission levels.

[0165] Optionally, multiple encapsulation task elements are each assigned to a corresponding element encapsulation queue. The classification encapsulation module 303 includes:

[0166] The identifier submodule is used to encapsulate each encapsulation task element with a digital object identifier to obtain the digital object identifier of each encapsulation task element.

[0167] The metadata submodule is used to encapsulate the metadata of each encapsulation task element in order to obtain the metadata of each encapsulation task element.

[0168] The entity submodule is used to encapsulate each encapsulation task element into a data entity to obtain the data entity of each encapsulation task element.

[0169] The sub-data encapsulation submodule is used to identify the digital object identifier, metadata, and data entity of the encapsulation task element as the encapsulation sub-data of the encapsulation task element.

[0170] Optionally, the encapsulation fusion module 304 includes:

[0171] The identifier matching submodule is used to match all the encapsulated subdata according to the numeric object identifier to obtain the encapsulated subdata set corresponding to the encapsulation task corresponding to each numeric object identifier.

[0172] The metadata fusion submodule is used to fuse the metadata of each encapsulated subdata in the encapsulated subdata set of each encapsulation task to obtain the metadata of the encapsulation task.

[0173] The entity fusion submodule is used to fuse the data entities of each encapsulated subdata in the encapsulated subdata set of each encapsulation task to obtain the data entity of the encapsulation task.

[0174] The data encapsulation submodule is used to structurally encapsulate the digital object identifier, metadata, and data entity of each encapsulation task to obtain the encapsulation data of the encapsulation task.

[0175] Optionally, the batch packaging device 30 for digital objects within the data network further includes:

[0176] The storage module is used to store the obtained encapsulated data into the data network warehouse and generate the association between each stored encapsulated data and the corresponding encapsulation task.

[0177] Optionally, the batch packaging device 30 for digital objects within the data network further includes:

[0178] The first state module is used to determine the encapsulation execution state of each encapsulation task element. The encapsulation execution state is used to characterize the success of the encapsulation task element in the process of performing parallel encapsulation of the encapsulation task elements in each element encapsulation queue to obtain the encapsulated sub-data corresponding to each encapsulation task element.

[0179] The callback module is used to re-add the first target encapsulation task element to the meta-encapsulation queue where the target encapsulation task element is located if the encapsulation execution status of the first target encapsulation task element is characterized as a failure.

[0180] Optionally, the batch packaging device 30 for digital objects within the data network further includes:

[0181] The second state module is used to determine the encapsulation execution state of each encapsulation task element. The encapsulation execution state is used to characterize the success of the encapsulation task element in the process of performing parallel encapsulation of the encapsulation task elements in each element encapsulation queue to obtain the encapsulated sub-data corresponding to each encapsulation task element.

[0182] The task element determination module is used to determine all second target encapsulation task elements corresponding to the first target encapsulation task element when the encapsulation execution status of the first target encapsulation task element is characterized as a failure; the second target encapsulation task elements are the same target encapsulation tasks corresponding to the first target encapsulation task element.

[0183] The task element deletion module is used to delete the first target encapsulated task element and all the second target encapsulated task elements from their respective element encapsulation queues.

[0184] The task rollback module is used to add the target packaging task corresponding to the first target packaging task element and / or the second target packaging task element to the packaging task queue, and return to the step of splitting multiple packaging tasks in the packaging task queue to obtain at least one packaging task element of each packaging task, or generate error information based on the target packaging task.

[0185] In summary, in this embodiment, by splitting multiple packaging tasks in the packaging task queue, the complex overall task is refined into multiple packaging task elements, making the processing units smaller and more flexible, reducing processing latency caused by the large scale of the overall task, and providing higher execution efficiency. Furthermore, based on task splitting, the packaging task elements are classified according to a preset packaging classification method, so that data from different sources, with different formats, or with different permissions are reasonably grouped in a fine-grained manner before packaging. This not only avoids conflicts that may occur when heterogeneous data is directly mixed and processed, but also allows each type of data to be packaged under more matching rules, improving the targeting and stability of the processing, and providing a basis for subsequent parallel packaging. Clear input boundaries are established, reducing cross-interference and mitigating the risks of metadata inconsistency and data entity redundancy. Then, based on classification, the encapsulation task elements in each encapsulation queue are encapsulated in parallel, allowing digital object identifiers, metadata, and data entities to be generated simultaneously in relatively independent channels. This not only improves the overall encapsulation speed but also reduces waiting and resource waste caused by sequential execution. Finally, after parallel encapsulation, the encapsulated sub-data corresponding to all encapsulation task elements of each encapsulation task are merged, ensuring consistency in metadata and data entities at the result level under the same task identifier. This improves the uniqueness, completeness, and traceability of the final encapsulated data. Therefore, the method based on this application, through a chain processing mechanism of "split-classification-parallel encapsulation-fusion," improves the fine-grained nature of batch encapsulation, further refining the efficiency, accuracy, and stability of the encapsulation process, and addressing the issues of insufficient quality and efficiency in encapsulation result delivery.

[0186] Reference Figure 9 The electronic device 500 may include one or more of the following components: processing component 502, memory 504, power supply component 506, multimedia component 508, audio component 510, input / output (I / O) interface 512, sensor component 514, and communication component 516.

[0187] Processing component 502 typically controls the overall operation of electronic device 500, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 502 may include one or more processors 520 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 502 may include one or more modules to facilitate interaction between processing component 502 and other components. For example, processing component 502 may include a multimedia module to facilitate interaction between multimedia component 508 and processing component 502.

[0188] Memory 504 is used to store various types of data to support the operation of electronic device 500. Examples of this data include instructions for any application or method operating on electronic device 500, contact data, phonebook data, messages, pictures, multimedia, etc. Memory 504 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 read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0189] Power supply component 506 provides power to various components of electronic device 500. Power supply component 506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 500.

[0190] Multimedia component 508 includes an interface that provides an output interface between electronic device 500 and user. In some embodiments, the interface may include a liquid crystal display (LCD) and a touch panel (TP). If the interface includes a touch panel, the interface may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may not only sense the boundaries of touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 508 includes a front-facing camera and / or a rear-facing camera. When electronic device 500 is in an operating mode, such as shooting mode or multimedia mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0191] Audio component 510 is used to output and / or input audio signals. For example, audio component 510 includes a microphone (MIC) used to receive external audio signals when electronic device 500 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 504 or transmitted via communication component 516. In some embodiments, audio component 510 also includes a speaker for outputting audio signals.

[0192] Input / output (I / O) interface 512 provides an interface between processing component 502 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0193] Sensor assembly 514 includes one or more sensors for providing state assessments of various aspects of electronic device 500. For example, sensor assembly 514 may detect the on / off state of electronic device 500, the relative positioning of components such as the display and keypad of electronic device 500, changes in position of electronic device 500 or a component of electronic device 500, the presence or absence of user contact with electronic device 500, orientation or acceleration / deceleration of electronic device 500, and temperature changes of electronic device 500. Sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 514 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 514 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0194] Communication component 516 facilitates wired or wireless communication between electronic device 500 and other devices. Electronic device 500 can access wireless networks based on communication standards, such as WiFi, carrier networks (such as 2G, 3G, 4G, or 5G), or combinations thereof. In one exemplary embodiment, communication component 516 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 516 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0195] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to implement the methods provided in the embodiments of this application.

[0196] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 504 including instructions, which can be executed by a processor 520 of an electronic device 500 to perform the above-described method. For example, the non-transitory storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0197] In an exemplary embodiment, the electronic device 500 may also be provided as a server, including a processing component 502, which further includes one or more processors, and memory resources represented by memory 504 for storing instructions, such as applications, that can be executed by the processing component 502. The applications stored in memory 504 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 502 is configured to execute instructions to perform the methods provided in the embodiments of this application.

[0198] Electronic device 500 may also include a power supply component 506 configured to perform power management of electronic device 500, a wired or wireless communication component 516 configured to connect electronic device 500 to a network, and an input / output (I / O) interface 512. Electronic device 500 may operate on an operating system stored in memory 504, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0199] It should be noted that, for the sake of simplicity, the method embodiments of this application are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to the embodiments of this application.

[0200] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0201] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for batch encapsulating digital objects within a data network, characterized in that, include: Split multiple packaging tasks in the packaging task queue to obtain at least one packaging task element for each packaging task; The encapsulation task element includes at least one of the following data: reference information of the target data entity, location information of the target data entity, binding strategy related to the digital object identifier, metadata mapping rules, metadata verification rules, and configuration parameters required for encapsulation execution. Based on a preset encapsulation classification method, all the encapsulation task elements are classified to obtain multiple element encapsulation queues. Parallel encapsulation is performed on each of the encapsulation task elements in the encapsulation queue to obtain encapsulated sub-data corresponding to each of the encapsulation task elements. Each of the encapsulated sub-data items has a corresponding digital object identifier, metadata, and data entity; The encapsulation sub-data corresponding to all encapsulation task elements of each encapsulation task are merged to obtain the encapsulation data of each encapsulation task.

2. The batch encapsulation method for digital objects within a data network as described in claim 1, characterized in that, The process of splitting multiple packaging tasks in the packaging task queue to obtain at least one packaging task element for each packaging task includes: Based on the request parameters for each of the encapsulation tasks, a structured analysis is performed on each of the encapsulation tasks to obtain at least one encapsulation target for each of the encapsulation tasks. A corresponding packaging task element is generated for each of the packaging targets, and a correspondence between the packaging task element and the packaging task is established based on the correspondence between each of the packaging targets and the packaging task.

3. The batch encapsulation method for digital objects within a data network as described in claim 1, characterized in that, Based on a preset encapsulation classification method, all the encapsulation task elements are classified to obtain multiple element encapsulation queues. Includes at least one of the following steps: Based on the data source type corresponding to the encapsulation task element, the encapsulation task elements are classified to obtain the element encapsulation queues corresponding to different data source types. Based on the data volume corresponding to the encapsulation task element, the encapsulation task elements are classified to obtain the element encapsulation queues corresponding to different data volume levels. Based on the data format corresponding to the encapsulation task element, the encapsulation task elements are classified to obtain the element encapsulation queues corresponding to different data formats. Based on the permission configuration corresponding to the encapsulation task element, the encapsulation task elements are classified to obtain the element encapsulation queues corresponding to different permission levels.

4. The batch encapsulation method for digital objects within a data network as described in claim 1, characterized in that, Multiple encapsulation task elements are respectively assigned to a corresponding meta-encapsulation queue. The step of performing parallel encapsulation on the encapsulation task elements in each meta-encapsulation queue to obtain encapsulated sub-data corresponding to each encapsulation task element includes: Each of the encapsulation task elements is encapsulated with a digital object identifier to obtain a digital object identifier for each of the encapsulation task elements; Metadata encapsulation is performed on each of the encapsulation task elements to obtain the metadata of each of the encapsulation task elements; Each of the encapsulation task elements is encapsulated into a data entity to obtain the data entity of each of the encapsulation task elements; The digital object identifier, metadata, and data entity of the encapsulation task element are determined as the encapsulation sub-data of the encapsulation task element.

5. The batch encapsulation method for digital objects within a data network as described in claim 1, characterized in that, The step of fusing the encapsulation sub-data corresponding to all encapsulation task elements of each encapsulation task to obtain the encapsulation data of each encapsulation task includes: All the encapsulated sub-data are matched according to the digital object identifier to obtain encapsulated sub-data sets corresponding to the encapsulation task corresponding to each digital object identifier. The metadata of each encapsulated sub-data in the encapsulation sub-dataset of each encapsulation task is fused to obtain the metadata of the encapsulation task. The data entities of each encapsulated sub-data in the encapsulated sub-data set of each encapsulation task are merged to obtain the data entity of the encapsulation task. The digital object identifier, metadata, and data entity of each encapsulation task are structured and encapsulated to obtain the encapsulation data of the encapsulation task.

6. The batch encapsulation method for digital objects within a data network as described in claim 1, characterized in that, The batch encapsulation method for digital objects within the data network also includes: The obtained encapsulation data is stored in the data network warehouse, and the association between each stored encapsulation data and the corresponding encapsulation task is generated.

7. The batch encapsulation method for digital objects within a data network as described in claim 1, characterized in that, The batch encapsulation method for digital objects within the data network also includes: Determine the encapsulation execution status of each encapsulation task element; the encapsulation execution status is used to characterize the success of the execution of the encapsulation task element during the parallel encapsulation of each encapsulation task element in the encapsulation queue to obtain encapsulated sub-data corresponding to each encapsulation task element. If the encapsulation execution status of the first target encapsulation task element is characterized as a failure, all second target encapsulation task elements corresponding to the first target encapsulation task element are identified; the second target encapsulation task elements are the same target encapsulation tasks corresponding to the first target encapsulation task element. Remove the first target encapsulation task element and all the second target encapsulation task elements from their respective element encapsulation queues; The steps of adding the target packaging task corresponding to the first target packaging task element and / or the second target packaging task element to the packaging task queue, and returning to the multiple packaging tasks in the split packaging task queue to obtain at least one packaging task element for each packaging task, or generating error information based on the target packaging task.

8. A batch packaging device for digital objects within a data network, characterized in that, include: The task splitting module is used to split multiple encapsulation tasks in the encapsulation task queue to obtain at least one encapsulation task element for each encapsulation task. The encapsulation task element includes at least one of the following data: reference information of the target data entity, location information of the target data entity, binding strategy related to the digital object identifier, metadata mapping rules, metadata verification rules, and configuration parameters required for encapsulation execution. The task classification module is used to classify all the encapsulated task elements based on a preset encapsulation classification method to obtain multiple element encapsulation queues. The classification and encapsulation module is used to perform parallel encapsulation of the encapsulation task elements in each of the encapsulation queues to obtain encapsulated sub-data corresponding to each of the encapsulation task elements. Each of the encapsulated sub-data items has a corresponding digital object identifier, metadata, and data entity; The encapsulation fusion module is used to merge the encapsulation sub-data corresponding to all encapsulation task elements of each encapsulation task to obtain the encapsulation data of each encapsulation task.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the batch encapsulation method for digital objects within a data network as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the batch packaging method for digital objects within a digital network as described in any one of claims 1 to 7.

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