Marking method and system on block chain for distributed storage data

Abstract

The invention discloses a marking method and system on a block chain of distributed storage data, and relates to the technical field of big data management.The marking method comprises the steps that when a distributed storage layer completes data archiving and fragmentation, a static semantic capsule is automatically generated for each data fragment through a semantic vocabulary and an anchoring entry on the chain; writing a static mark chain through an anchoring entrance; and in a preset management period, summarizing the static semantic capsules and the use situation capsules through an on-chain analysis contract to form a semantic situation capsule atlas, and generating a high-order on-chain mark for each data fragment according to the semantic situation capsule atlas. According to the method, the semantic situation capsule atlas is recombined by utilizing the on-chain analysis contract in the preset management period, and the high-order on-chain mark is generated for each data fragment, so that the data forms a traceable and reasonable association structure among the business semantics, the use situation and the storage strategy.

Description

Technical Field

[0001] This invention relates to the field of big data management technology, and in particular to a method and system for marking data on a blockchain for distributed storage. Background Technology

[0002] In big data management scenarios, distributed storage and blockchain technology are gradually being integrated to record the hash values, storage locations, version information, and simple metadata of data shards, ensuring data immutability and traceability. Some solutions register dataset identifiers, time ranges, node information, and other details on the blockchain to achieve on-chain filing of distributed storage processes. Combined with access logs and audit records, data access behavior is registered on the blockchain for post-event tracking and compliance documentation. With the expansion of multi-service and multi-tenant scenarios, blockchain-based metadata management is evolving from single key-value tags to a data resource registration method that supports multi-field and multi-dimensional descriptions.

[0003] However, existing technologies are generally based on storage structure-oriented labeling, lacking a unified semantic expression mechanism for business meaning and usage context. On-chain records are mostly associated with low-level identifiers such as hashes and addresses, making it difficult to integrate data content, access subjects, and operating scenarios at the semantic level. This leads to a reliance on extensive offline rule configuration and manual sorting for cross-dataset retrieval, refined hierarchical control, and compliance analysis in big data management. At the same time, static storage attributes and dynamic usage behaviors are often scattered across different logs and on-chain records, lacking a mechanism to associate and aggregate static semantic information with real-time contextual information to form high-level labels, which is not conducive to intelligent governance of distributed storage data throughout its entire lifecycle. Summary of the Invention

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

[0005] Therefore, this invention provides a tagging method on a blockchain for distributed storage data to solve the problem of difficulties in refined governance of big data management caused by the lack of unified semantic tags and usage context associations for distributed storage data on the blockchain.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for marking on a blockchain chain for distributed storage of data, comprising, Based on the business domain and data type, a semantic vocabulary is created, each word is assigned a unique semantic identifier and association, static tag chains and contextual tag chains are divided, and anchor entries are assigned to each semantic identifier on the chain. When data archiving and sharding are completed in the distributed storage layer, a static semantic capsule is automatically generated for each data shard using a semantic vocabulary and on-chain anchor entry, and written into a static tag chain through the anchor entry. During real-time access, for each access request, a usage context capsule is generated using a semantic vocabulary and static semantic capsules, and the corresponding context metadata is recorded and written into the context tagging chain; Within a preset management period, static semantic capsules and usage context capsules are aggregated through on-chain analytics contracts to form a semantic context capsule graph, and high-order on-chain tags are generated for each data shard based on the semantic context capsule graph.

[0007] As a preferred embodiment of the tagging method on the blockchain for distributed storage data according to the present invention, the specific steps for creating the semantic vocabulary are as follows: Collect data type descriptions, metadata field descriptions, and industry classifications used in business scenarios to form a candidate set of semantic terms; Based on the business scenarios and data sources in which each word appears in the semantic vocabulary candidate set, the words are categorized into data domain categories and data granularity levels, and each word is assigned a sensitivity level, compliance requirements, and typical use case labels to form a semantic vocabulary list.

[0008] As a preferred embodiment of the marking method on the blockchain chain for distributed storage data according to the present invention, the specific steps for dividing the static marking chain and the context marking chain are as follows: Read words one by one from the semantic vocabulary, generate a unique semantic identifier for each word, and establish a mapping relationship between words and semantic identifiers; Based on the data domain category and usage scenario to which the words belong, semantic identifiers related to data content and storage location are assigned to the static tagging chain, and semantic identifiers related to access behavior and operation scenario are assigned to the context tagging chain. A unique on-chain anchor entry address is generated for each semantic identifier.

[0009] As a preferred embodiment of the marking method on the blockchain for distributed data storage according to the present invention, the specific steps for automatically generating static semantic capsules are as follows: After the historical data of the block is archived, the dataset identifier, the time range covered, the source of generation, the storage node identifier where the fragment is located, and the cross-data center and cross-regional distribution information are collected for each data fragment to obtain the data fragment description information. Based on the data shard description information, semantic identifiers that match the industry, granularity, sensitivity level, and compliance requirements of the data are selected from the semantic vocabulary. Combined with the storage node identifier where the shard is located and cross-regional distribution information, a static semantic capsule is constructed for each data shard.

[0010] As a preferred embodiment of the tagging method on the blockchain for distributed storage data described in this invention, the specific steps for generating usage context capsules using a semantic vocabulary and static semantic capsules are as follows: When business users and analysis tasks initiate access requests to data shards, the caller identifier, business process name, access time range, access location information and access operation type are extracted from the access request from the entry gateway to obtain the access request attribute set. Based on the access request attribute set, select semantic identifiers that match the caller identifier, business process, access time range, and access location from the semantic vocabulary, query the static semantic capsules corresponding to the target data shards of the access request, and combine them with the contextual metadata in the access request attribute set to construct usage context capsules.

[0011] As a preferred embodiment of the marking method on the blockchain chain for distributed storage data according to the present invention, the contextual metadata includes a calling entity identifier, a scene tag, a time tag, a location tag, a frequency level, and an operation type tag.

[0012] As a preferred embodiment of the tagging method on the blockchain for distributed storage data according to the present invention, the specific steps for forming a semantic context capsule graph are as follows: The on-chain analysis contract is triggered within a preset management cycle. Static semantic capsules are read from the static tag chain, and usage context capsules are read from the context tag chain to obtain capsule data for the current cycle. Based on the capsule data of the current period, using semantic identifiers and on-chain anchor entry addresses as indexes, static semantic capsules and use case capsules are grouped by semantic identifiers. The number of static semantic capsules under each semantic identifier, the data shard categories involved, and the use case categories covered are counted to generate semantic grouping statistics. Based on the semantic grouping statistics, the association between common data fragments and common usage contexts of semantic identifiers is established, semantic context clusters are divided, and the set of semantic identifiers contained in the semantic context clusters and the association between cross-clusters are recorded to obtain a semantic context capsule map.

[0013] As a preferred embodiment of the marking method on the blockchain of distributed storage data described in this invention, the preset management period is a fixed time length set by the big data manager based on the business statistics period and resource scheduling period.

[0014] As a preferred embodiment of the blockchain-based tagging method for distributed storage data according to the present invention, the specific steps for generating high-order on-chain tags for each data shard based on the semantic context capsule graph are as follows: In the semantic context capsule graph, find the semantic context cluster where the static semantic capsule corresponding to each data piece belongs, and obtain the semantic context cluster identifier; Based on the semantic context cluster identifier and the typical use case categories and compliance attributes recorded in the semantic context capsule graph, a storage management strategy label is selected for each data shard to generate a higher-order on-chain tag.

[0015] Secondly, this invention provides a tagging system on a blockchain for distributed data storage, comprising: The semantic configuration module creates a semantic vocabulary based on business domains and data types, assigns a unique semantic identifier and association to each word, divides static tag chains and contextual tag chains, and assigns an anchor entry point on the chain to each semantic identifier. The static tagging module automatically generates static semantic capsules for each data shard when data archiving and sharding are completed in the distributed storage layer, using a semantic vocabulary and on-chain anchor entry points, and writes them into the static tagging chain through the anchor entry points. The context tagging module generates usage context capsules using a semantic vocabulary and static semantic capsules for each access request during real-time access, records the corresponding context metadata, and writes it into the context tagging chain. The graph tagging module, within a preset management period, aggregates static semantic capsules and usage context capsules through on-chain analysis contracts to form a semantic context capsule graph, and generates high-order on-chain tags for each data shard based on the semantic context capsule graph.

[0016] The beneficial effects of this invention are as follows: By constructing a semantic vocabulary covering business domains and data types, and assigning a unique semantic identifier to each term, distributed storage data is uniformly mapped onto the blockchain as a computable and scalable semantic tagging system, realizing a structured expression of data content attributes, sensitivity levels, and compliance requirements; by generating static semantic capsules for each data shard through a static tagging chain, the storage topology is bound to semantic information; by constructing usage context capsules for access requests through a context tagging chain, contextual metadata such as the calling entity, business process, time and location, and access frequency are associated with the static semantic capsules, enabling static attributes and dynamic behaviors to achieve a two-dimensional closed expression on the chain; within a preset management cycle, the semantic context capsule graph is reorganized using on-chain analysis contracts, and high-order on-chain tags are generated for each data shard, enabling data to form a traceable and inferable association structure between business semantics, usage context, and storage strategy, thereby improving the automation and refinement of cross-dataset retrieval, hierarchical storage strategy formulation, and compliance auditing in big data management scenarios. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of the tagging method on a blockchain for distributed data storage.

[0019] Figure 2 Flowchart for creating and partitioning the semantic vocabulary.

[0020] Figure 3 A flowchart for generating and writing static semantic capsules.

[0021] Figure 4 A flowchart for generating semantic context capsule graphs. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a marking method on a blockchain for distributed storage data, comprising the following steps: S1. Based on the business domain and data type, create a semantic vocabulary, assign a unique semantic identifier and association to each word, divide the static tag chain and context tag chain, and assign an anchor entry on the chain to each semantic identifier.

[0026] S1.1. In big data management scenarios, based on business domains and data types, collect descriptive information for each type of data, including data type descriptions, metadata field descriptions, and industry classifications. Organize the key terms appearing in the data type descriptions, metadata field descriptions, and industry classifications into a semantic vocabulary candidate set. Each word in the semantic vocabulary candidate set carries three attribute labels: source data type, source field, and industry.

[0027] For each word in the semantic vocabulary candidate set, we iterate through them one by one, and count the number of times each word appears in different business scenarios in the big data management requirement document, operation log and configuration instructions. We record the number of data source types and the number of data granularity levels involved for each word, and organize the number of times each word appears in business scenarios, the number of data source types and the number of data granularity levels into a semantic vocabulary attribute record set.

[0028] S1.2. In the semantic lexical attribute record set, establish a relevance score for each word, calculated using the following expression: ; in, Indicates the first The comprehensive relevance score of each word in a big data management scenario. Indicates the first The normalized value of the frequency of occurrence of the term in the description of big data management related business scenarios Indicates the first The data sources for each word are normalized numerical values. Indicates the first The normalized numerical value of the number of data granularity levels involved in each word.

[0029] It should be noted that the normalized values ​​can be obtained using a linear normalization method based on the maximum value of the sample.

[0030] The words in the semantic vocabulary attribute record set are sorted according to the relevance score, and words with higher relevance scores are retained first. For each retained word, sensitivity level, compliance requirements and typical use scenario tags are added to form a semantic vocabulary that includes data domain category, data granularity level, sensitivity level, compliance requirements and typical use scenario tags.

[0031] S1.3. Generate a unique semantic identifier for each word in the semantic vocabulary, establish a one-to-one mapping relationship between the semantic identifier and the word content, as well as the corresponding data domain category, data granularity level, sensitivity level, compliance requirements, and typical use scenario labels, establish a hierarchical relationship between semantic identifiers based on data domain category and data granularity level, and establish an association relationship between semantic identifiers based on typical use scenarios, forming a semantic identifier relationship table.

[0032] Read each semantic identifier and its corresponding lexical attribute from the semantic identifier relation table. Semantic identifiers related to the description of distributed storage data content, data location, and data structure are assigned to the semantic identifier set of the static tag chain. Semantic identifiers related to the description of the access subject, access scenario, and running process status are assigned to the semantic identifier set of the context tag chain.

[0033] In the set of semantic identifiers for static marker chains and the set of semantic identifiers for context marker chains, a unique on-chain anchor entry address is assigned to each semantic identifier. In the blockchain network, a logical marker space is reserved for recording static semantic capsules and storage location information for static marker chains, and a logical marker space is reserved for recording the use of context capsules for context marker chains. Each semantic identifier, its corresponding on-chain anchor entry, and its logical marker space type are registered as an on-chain anchor entry mapping relationship.

[0034] S2. When data archiving and sharding are completed in the distributed storage layer, a static semantic capsule is automatically generated for each data shard using a semantic vocabulary and on-chain anchor entry, and written into the static tag chain through the anchor entry.

[0035] S2.1. After the semantic vocabulary, semantic identifier relationship table, static tag chain semantic identifier set, context tag chain semantic identifier set, and on-chain anchor entry mapping relationship have been established, in the big data management scenario, when the distributed storage layer completes the archiving of block historical data, it performs block historical data sharding processing, and collects the dataset identifier, the time range or block range covered, the generation source identifier, the storage node identifier where the shard is located, and the data center identifier and region identifier to which the storage node where the shard is located belong. The dataset identifier, time or block range, generation source identifier, storage node identifier, data center identifier, and region identifier are combined into data shard description information.

[0036] S2.2. In the data shard description information set, for each data shard, read the storage node identifier, the corresponding data center identifier, and the region identifier. Count the number of storage nodes and the number of regions involved in each data shard. Organize the number of storage nodes and the number of regions involved into a storage topology statistical record. Calculate the cross-regional distribution coverage score for each data shard. The cross-regional distribution coverage score is calculated using the following expression: ; in, Indicates the first The cross-regional distribution coverage score of the data shard in a big data management scenario is used to represent the coverage score of the data shard. The degree of distribution of data shards across different regions Indicates the first The number of regions involved in each data shard. Indicates the first The number of storage nodes for each data shard.

[0037] The data segment description information corresponding to each data segment is combined with the cross-regional distribution coverage score to form a set of data segment feature records.

[0038] S2.3. In the data shard feature record set, for each data shard, read the dataset identifier, the time or block range covered, the generation source identifier, the storage node identifier, the data center identifier, the region identifier, and the cross-regional distribution coverage score. Based on the data industry, data granularity information, sensitivity level information, and compliance requirement information contained in the data shard feature record, retrieve semantic terms matching the data domain category, data granularity level, sensitivity level, and compliance requirement information from the semantic terminology table.

[0039] Semantic identifiers corresponding to semantic words are read from the semantic identifier relation table, and semantic identifiers related to data content description, data location description and data structure description are selected from the set of semantic identifiers in the static tag chain. The data fragmentation feature records and the selected semantic identifiers are combined to construct a static semantic capsule.

[0040] S3. During real-time access, for each access request, a usage context capsule is generated using a semantic vocabulary and a static semantic capsule, and the corresponding context metadata is recorded and written into the context tagging chain.

[0041] S3.1. In big data management scenarios, when a business party or analysis task initiates a read, write, scan, or batch processing access request to any data shard, the caller identifier, business process name, access time range, access location information, access operation type, and target data shard identifier of the access request are extracted from the entry gateway; the caller identifier, business process name, access time range, access location information, access operation type, and target data shard identifier of the access request are combined into an access request attribute set.

[0042] S3.2. In the access request attribute set, for each access request, aggregate the number of access requests within the preset statistical time window according to the data fragment identifier, and record the length of the preset statistical time window as... It will target the first [item] within the preset statistical time window. The number of access requests for each data shard is denoted as Use the following expression to determine the first... Access frequency rating for each data shard: ; in, Indicates the first The access frequency level value of each data shard in a big data management scenario is used to represent the access frequency level of the first data shard. The frequency with which each data shard is accessed per unit of time. This indicates that within the preset statistical time window, for the first... The number of access requests per data shard. This indicates the length of the preset statistical time window.

[0043] It should be noted that the preset statistical time window is a fixed time length set by the big data management party based on the access monitoring granularity and business statistical cycle in the big data management scenario, such as setting a continuous time interval of 10 minutes.

[0044] The access frequency level value is combined with the caller identifier, business process name, access time range, access location information, and access operation type to form a set of contextual metadata records containing the caller identifier, scenario tag, time tag, location tag, frequency level, and operation type tag.

[0045] S3.3. In the context metadata record set, for each context metadata record, read the calling subject identifier, scene tag, time tag, location tag, frequency level, and operation type tag. Based on the calling subject category, business process category, access time feature, access location region, and access operation type contained in the context metadata record, retrieve semantic terms that match the calling subject category, business process category, access time feature, access location region, and access operation type from the semantic vocabulary table. Read the set of semantic identifiers corresponding to the semantic terms from the semantic identifier relation table.

[0046] By leveraging the one-to-one correspondence between access requests and static semantic capsules, static semantic capsule identifiers are associated with contextual metadata records. The static semantic capsule identifiers, semantic identifier sets, and contextual metadata records are then combined to construct usage contextual capsules.

[0047] S4. Within the preset management period, static semantic capsules and usage context capsules are aggregated through on-chain analytics contracts to form a semantic context capsule graph, and high-order on-chain tags are generated for each data shard based on the semantic context capsule graph.

[0048] S4.1. The big data manager sets a preset management cycle based on the business statistics cycle and resource scheduling cycle. The preset management cycle is set to a fixed time length (e.g., 24 hours). At the end of each preset management cycle, the manager initiates a contract call transaction to the blockchain network, triggering the on-chain analysis contract. The on-chain analysis contract reads the static semantic capsule based on the on-chain position information of the static semantic capsule on the static tag chain, and reads the usage context capsule based on the on-chain position information of the usage context capsule on the context tag chain. The static semantic capsule and the usage context capsule are combined to form the capsule data for the current preset management cycle.

[0049] S4.2. In the capsule data of the current preset management period, using each semantic identifier in the semantic identifier relationship table as an index, count the number of static semantic capsules under each semantic identifier, the number of usage context capsules under each semantic identifier, and the number of data fragment categories associated with each semantic identifier. Calculate a semantic context aggregation score for each semantic identifier. The semantic context aggregation score is calculated using the following expression: ; in, Indicates the first The semantic context aggregation score of the first semantic identifier in a big data management scenario is used to represent the semantic context of the first identifier. The degree of concentration of semantic identifiers in static semantic capsules and use case capsules Indicates the first in the static tag chain Number of static semantic capsules under each semantic identifier Indicates the first in the context labeling chain Number of use case capsules under each semantic identifier Indicates by the first The number of data fragment categories associated with each semantic identifier; The number of static semantic capsules, the number of usage context capsules, the number of data shard categories, and the semantic context aggregation score for each semantic identifier are combined to form the semantic grouping statistics.

[0050] S4.3. In the semantic grouping statistical result set, for any two semantic identifiers, compare the data fragment identifiers and usage context categories contained in the static semantic capsule records and usage context capsule records corresponding to the two semantic identifiers. When the two semantic identifiers have at least one common data fragment identifier and at least one common usage context category, the two semantic identifiers are grouped into the same semantic context cluster. All semantic identifiers are merged multiple times according to the common data fragment identifier and the common usage context category to obtain multiple semantic context clusters.

[0051] Assign a semantic context cluster identifier to each semantic context cluster, and record the semantic context cluster identifier, the set of semantic identifiers contained in the semantic context cluster, the set of data fragment categories corresponding to the semantic context cluster, and the set of usage context categories corresponding to the semantic context cluster. At the same time, record the association relationship between different semantic context clusters through common data fragment identifiers or common usage context categories to form a semantic context capsule map.

[0052] S4.4. In the semantic context capsule graph, for each data segment, read the static semantic capsule identifier of the data segment in the static semantic capsule, find the semantic identifier of the static semantic capsule based on the static semantic capsule identifier, and find the corresponding semantic context cluster identifier based on the semantic identifier to determine the semantic context cluster to which each data segment belongs.

[0053] For each data shard, the typical use case category recorded in the semantic context capsule graph of its corresponding semantic context cluster is read, along with the sensitivity level and compliance requirements determined by the semantic vocabulary and semantic identifier relationship table. The semantic context cluster identifier, typical use case category, sensitivity level, and compliance requirements are combined. Storage management policy tags are selected for each data shard using storage management rules pre-configured by the big data management party (decision rules pre-defined by the big data management party that map semantic context cluster identifier, typical use case category, sensitivity level, and compliance requirements to four types of storage parameters: number of replicas, storage media level, geographical distribution, and retention period). A higher-order on-chain tag record is constructed using the semantic context cluster identifier, typical use case category, sensitivity level, compliance requirements, and storage management policy tag. The higher-order on-chain tag record is written to the static tag chain by using the on-chain anchor entry address in the on-chain anchor entry mapping relationship of the corresponding semantic identifier in the static tag chain. This is associated with the static semantic capsule record in the form of version updates, so that the higher-order on-chain tag provides semantic context-oriented on-chain tag information for each data shard in the big data management scenario.

[0054] This embodiment also provides a tagging system on a blockchain for distributed storage data, including: a semantic configuration module, which creates a semantic vocabulary based on business domain and data type, assigns a unique semantic identifier and association to each word, divides the static tagging chain and the contextual tagging chain, and assigns an on-chain anchor entry to each semantic identifier; The static tagging module automatically generates static semantic capsules for each data shard when data archiving and sharding are completed in the distributed storage layer, using a semantic vocabulary and on-chain anchor entry points, and writes them into the static tagging chain through the anchor entry points. The context tagging module generates usage context capsules using a semantic vocabulary and static semantic capsules for each access request during real-time access, records the corresponding context metadata, and writes it into the context tagging chain. The graph tagging module, within a preset management period, aggregates static semantic capsules and usage context capsules through on-chain analysis contracts to form a semantic context capsule graph, and generates high-order on-chain tags for each data shard based on the semantic context capsule graph.

[0055] This embodiment also provides a computer device applicable to the marking method on a blockchain chain for distributed storage data, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the marking method on a blockchain chain for distributed storage data as proposed in the above embodiment.

[0056] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0057] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the tagging method on a blockchain chain for distributed storage data as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0058] In summary, this invention constructs a semantic vocabulary covering business domains and data types, assigning a unique semantic identifier to each term. This maps distributed storage data onto the blockchain in a unified manner into a computable and scalable semantic tagging system, achieving a structured expression of data content attributes, sensitivity levels, and compliance requirements. A static semantic capsule is generated for each data shard through a static tagging chain, binding storage topology with semantic information. A contextual tagging chain constructs usage context capsules for access requests, associating contextual metadata such as the calling entity, business process, time and location, and access frequency with the static semantic capsules, enabling a two-dimensional closed expression of static attributes and dynamic behaviors on the chain. Within a preset management cycle, on-chain analysis contracts reconstruct the semantic context capsule graph, generating high-order on-chain tags for each data shard. This creates a traceable and inferable association structure between data in terms of business semantics, usage context, and storage strategy, thereby improving the automation and refinement of cross-dataset retrieval, hierarchical storage strategy formulation, and compliance auditing in big data management scenarios.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for marking data on a distributed storage blockchain, characterized in that: include, Based on the business domain and data type, a semantic vocabulary is created, each word is assigned a unique semantic identifier and association, static tag chains and contextual tag chains are divided, and anchor entries are assigned to each semantic identifier on the chain. When data archiving and sharding are completed in the distributed storage layer, a static semantic capsule is automatically generated for each data shard using a semantic vocabulary and on-chain anchor entry, and written into the static tag chain through the anchor entry. During real-time access, for each access request, a usage context capsule is generated using a semantic vocabulary and static semantic capsules, and the corresponding context metadata is recorded and written into the context tagging chain; Within a preset management period, static semantic capsules and usage context capsules are aggregated through on-chain analytics contracts to form a semantic context capsule graph, and high-order on-chain tags are generated for each data shard based on the semantic context capsule graph.

2. The marking method on a blockchain chain for distributed data storage as described in claim 1, characterized in that: The specific steps for creating the semantic vocabulary are as follows: Collect data type descriptions, metadata field descriptions, and industry classifications used in business scenarios to form a candidate set of semantic terms; Based on the business scenarios and data sources in which each word appears in the semantic vocabulary candidate set, the words are categorized into data domain categories and data granularity levels, and each word is assigned a sensitivity level, compliance requirements, and typical use case labels to form a semantic vocabulary list.

3. The marking method on a blockchain for distributed data storage as described in claim 1, characterized in that: The specific steps for dividing the static tag chain and the context tag chain are as follows: Read words one by one from the semantic vocabulary, generate a unique semantic identifier for each word, and establish a mapping relationship between words and semantic identifiers; Based on the data domain category and usage scenario to which the words belong, semantic identifiers related to data content and storage location are assigned to the static tagging chain, and semantic identifiers related to access behavior and operation scenario are assigned to the context tagging chain. A unique on-chain anchor entry address is generated for each semantic identifier.

4. The marking method on a blockchain for distributed data storage as described in claim 1, characterized in that: The specific steps for automatically generating static semantic capsules are as follows. After the historical data of the block is archived, the dataset identifier, the time range covered, the source of generation, the storage node identifier where the fragment is located, and the cross-data center and cross-regional distribution information are collected for each data fragment to obtain the data fragment description information. Based on the data shard description information, semantic identifiers that match the industry, data granularity, sensitivity level, and compliance requirements of the data are selected from the semantic vocabulary. Combined with the storage node identifier where the shard is located and cross-regional distribution information, a static semantic capsule is constructed for each data shard.

5. The marking method on a blockchain for distributed data storage as described in claim 1, characterized in that: The specific steps for generating usage context capsules using a semantic vocabulary and static semantic capsules are as follows. When business users and analysis tasks initiate access requests to data shards, the caller identifier, business process name, access time range, access location information and access operation type are extracted from the access request from the entry gateway to obtain the access request attribute set. Based on the access request attribute set, select semantic identifiers that match the caller identifier, business process, access time range, and access location from the semantic vocabulary, query the static semantic capsules corresponding to the target data shards of the access request, and combine them with the contextual metadata in the access request attribute set to construct usage context capsules.

6. The marking method on a blockchain chain for distributed storage data as described in claim 5, characterized in that: The contextual metadata includes the calling entity identifier, scene tag, time tag, location tag, frequency level, and operation type tag.

7. The marking method on a blockchain chain for distributed data storage as described in claim 1, characterized in that: The specific steps for forming the semantic context capsule map are as follows: The on-chain analysis contract is triggered within a preset management cycle. Static semantic capsules are read from the static tag chain, and usage context capsules are read from the context tag chain to obtain capsule data for the current cycle. Based on the capsule data of the current period, using semantic identifiers and on-chain anchor entry addresses as indexes, static semantic capsules and use case capsules are grouped by semantic identifiers. The number of static semantic capsules under each semantic identifier, the data shard categories involved, and the use case categories covered are counted to generate semantic grouping statistics. Based on the semantic grouping statistics, the association between common data fragments and common usage contexts of semantic identifiers is established, semantic context clusters are divided, and the set of semantic identifiers contained in the semantic context clusters and the association between cross-clusters are recorded to obtain a semantic context capsule map.

8. The marking method on a blockchain chain for distributed storage data as described in claim 7, characterized in that: The preset management cycle is a fixed time length set by the big data management party based on the business statistics cycle and resource scheduling cycle.

9. The marking method on a blockchain chain for distributed data storage as described in claim 1, characterized in that: The specific steps for generating higher-order chain labels for each data fragment based on the semantic context capsule graph are as follows: In the semantic context capsule graph, find the semantic context cluster where the static semantic capsule corresponding to each data piece belongs, and obtain the semantic context cluster identifier; Based on the semantic context cluster identifier and the typical use case categories and compliance attributes recorded in the semantic context capsule graph, a storage management strategy label is selected for each data shard to generate a higher-order on-chain tag.

10. A tagging system on a blockchain for distributed data storage, based on the tagging method on a blockchain for distributed data storage according to any one of claims 1 to 9, characterized in that: include, The semantic configuration module creates a semantic vocabulary based on business domains and data types, assigns a unique semantic identifier and association to each word, divides static tag chains and contextual tag chains, and assigns an anchor entry point on the chain to each semantic identifier. The static tagging module automatically generates static semantic capsules for each data shard when data archiving and sharding are completed in the distributed storage layer, using a semantic vocabulary and on-chain anchor entry points, and writes them into the static tagging chain through the anchor entry points. The context tagging module generates usage context capsules using a semantic vocabulary and static semantic capsules for each access request during real-time access, records the corresponding context metadata, and writes it into the context tagging chain. The graph tagging module, within a preset management period, aggregates static semantic capsules and usage context capsules through on-chain analysis contracts to form a semantic context capsule graph, and generates high-order on-chain tags for each data shard based on the semantic context capsule graph.

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