A data relationship network generation method and system based on data standards

Abstract

The application provides a data relationship network generation method and system based on data standards, wherein the method comprises the following steps: determining the data type to which the data belongs, and establishing a respective data standard according to the data type; analyzing the logical relationship of data elements in different data standards, identifying data elements without relationship, and storing the coordinate-relation value pair of data elements with relationship as a sparse storage engine; taking a data standard as the core, forming an exclusive relationship matrix based on the logical relationship between each data element of the data standard and the data elements of other data standards; and generating a data relationship network based on the exclusive relationship matrices of multiple data standards, wherein the data relationship network utilizes the sparse storage engine for path indexing. The application realizes "one standard one view" business direct access, and for the first time realizes semantic accuracy, high efficiency of calculation and convenience of use in the field of data standards.

Description

A method and system for generating data relationship networks based on data standards Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for generating data relationship networks based on data standards. Background Technology

[0002] Data standardization management is a core aspect of enterprise data governance. Its goal is to eliminate semantic heterogeneity between systems and improve data interoperability by unifying data definitions, formats, and semantic specifications (i.e., "data standards"). As enterprise data scales up, the number of cross-system and cross-domain data standards surges, and traditional methods of manually identifying logical relationships between standards face bottlenecks such as inefficiency and maintenance delays. In recent years, the industry has attempted to introduce automation technologies to assist in data standard management, but existing methods still have significant shortcomings in the systematic modeling and efficient expression of cross-standard relationships, specifically manifested in the following technical limitations:

[0003] 1.1 Static Management Method Based on Manual Documents

[0004] Early data standards relied on Excel or document storage, with relationships between standards described in text (e.g., "equivalent to field in standard XX"). This method required manual comparison of field definitions, which was time-consuming and difficult to maintain consistency. When standards changed, the relationship descriptions were prone to becoming invalid, making it unable to support dynamically changing system environments.

[0005] 1.2 The semantic missing problem of statistical correlation matrix

[0006] The existing correlation matrix is ​​widely used to analyze the numerical correlation of structured data. Its limitations are: (1) it is only applicable to numerical data analysis and cannot express the semantic logical relationship between data standards; (2) the output result is a dense matrix and does not distinguish between "unrelated" states, resulting in redundant storage; (3) it lacks symbolic annotation of relation types (such as "z" to indicate no relation), making it difficult to support upper-level applications.

[0007] 1.3 Computational Efficiency Bottleneck of Knowledge Graph Technology

[0008] In recent years, standard relation modeling methods based on knowledge graphs have emerged, such as: the State Energy Group patent CN119129555A "Method, Device, Equipment and Medium for Constructing Knowledge Graph of Table Data Relationships", which constructs a dependency graph of table data by parsing cell formulas; and Kingsoft Cloud patent CN119378546A "Method, Device, Equipment and Medium for Generating Entity Relationship Graphs", which generates entity relationship graphs using "data collision". These solutions all have the following drawbacks: (1) The graph structure query complexity is high, making it difficult to respond to large-scale relation retrieval requests in real time; (2) The relation visualization complexity increases exponentially with the number of nodes, and the readability of the graph decreases sharply when the number of standards increases; (3) No explicit mapping with data standards is established, resulting in a high understanding threshold for business personnel.

[0009] 1.4 Limitations of the Applicability of Relational Database Join Matrices

[0010] The patent CN118377838A of Zhejiang Mechanical and Electrical Vocational Technical College, entitled "An Automatic Generation Method and System for Relational Database Connection Queries," proposes to automatically generate SQL queries based on a "relational table attribute connection matrix." Its design only targets physical table connections and has the following defects: (1) The matrix elements only represent foreign key relationships between tables and cannot carry the rich semantics between data standards (such as partial matching and historical version compatibility); (2) The logical layer (standard) and the physical layer (table structure) are not separated, resulting in strong coupling between business semantics and implementation.

[0011] In summary, existing matrix techniques lack a relation classification system (such as equivalence, derivation, and irrelevance) designed for data standard scenarios, while graph techniques are difficult to implement in enterprise-level standard management platforms due to computational overhead. Currently, there is a lack of a large-scale data standard relation modeling method that can both carry rich semantic relationships and support efficient querying and visualization. Summary of the Invention

[0012] This invention provides a data relationship network generation method and system based on data standards to solve the problems of lack of semantic relationship modeling capabilities in the prior art and the computational and storage efficiency problems of large-scale relationship networks.

[0013] This invention provides a data relationship network generation method based on data standards, the method comprising:

[0014] Determine the data type to which the data belongs, and establish respective data standards based on the data type. The data standards include data elements, standardized relation symbol sets, and relation determination rules.

[0015] Analyze the logical relationships between data elements in different data standards, identify unrelated data elements, and store the coordinate-relationship value pairs of related data elements as a sparse storage engine.

[0016] Based on a data standard, a unique relationship matrix is ​​formed by the logical relationships between each data element of the data standard and data elements of other data standards.

[0017] A data relationship network is generated based on the proprietary relationship matrix of multiple data standards, and the data relationship network uses the sparse storage engine for path indexing.

[0018] Preferably, the standardized relation symbol set covers all relation types in the business scenario and can be parsed by machines.

[0019] Preferably, establishing respective data standards based on the data types includes: using the standardized symbol set to achieve lossless conversion from business rules to machine-resolvable relationships.

[0020] Preferably, the analysis of the logical relationships of data elements in different data standards includes: performing name similarity calculation, syntax tree parsing, and / or value range inclusion detection based on the standardized relation symbol set and relation determination rules, wherein the standardized relation symbol set includes equivalence relation symbols, derived relation symbols, value range constraint symbols, similarity symbols, and no-relationship symbols.

[0021] Preferably, the method further includes: when processing the data, using an identifier indicating that there is no relationship between data elements, skipping data elements that are irrelevant to the data.

[0022] Preferably, processing the data includes storing, compressing, and computing the data.

[0023] This invention provides a data relationship network formation system based on data standards. The system includes a data standard management layer, a relationship analysis engine layer, and a matrix generation and directory service layer. The data standard management layer determines the data type and establishes respective data standards based on the data type. Each data standard includes data elements, a standardized set of relational symbols, and relation determination rules. The relationship analysis engine layer analyzes the logical relationships between data elements in different data standards, identifies unrelated data elements, and stores the coordinate-relationship values ​​of related data elements as a sparse storage engine. The matrix generation and directory service layer uses a data standard as the core to form a dedicated relationship matrix based on the logical relationships between each data element of that data standard and data elements of other data standards. It then generates a data relationship network based on the dedicated relationship matrices of multiple data standards. The data relationship network utilizes the sparse storage engine for path indexing.

[0024] Preferably, the relation analysis engine layer performs name similarity calculation, syntax tree parsing, and / or value range inclusion detection based on the standardized relation symbol set and relation determination rules. The standardized relation symbol set includes equivalent relation symbols, derived relation symbols, value range constraint symbols, similarity symbols, and no-relation symbols.

[0025] Preferably, the system further includes a data processing layer, which, when processing the data, uses an identifier indicating that there is no relationship between data elements to skip data elements that are irrelevant to the data.

[0026] Preferably, the data processing layer includes a data storage unit, a data compression unit, and a data calculation unit, which are used to store data, compress data, and perform calculations on data, respectively.

[0027] The present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the data relationship network generation method based on data standards described above.

[0028] The present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the aforementioned data relationship network generation method based on data standards.

[0029] This invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned data relationship network generation method based on data standards.

[0030] The data relationship network generation method and system based on data standards provided by this invention solves the technical contradictions between semantic expression, computational efficiency, and cross-role adaptation through a three-layer innovative design of semantic relationship symbolization, data matrix structure sparsity, and data relationship network cataloging. It realizes machine-parseable encoding of business logic and decouples business views driven by matrix catalogs, that is, it achieves direct access to business with "one standard, one view" through the catalog mechanism. For the first time, it uniformly achieves semantic accuracy, computational efficiency, and ease of use in the field of data standards. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0032] Figure 1 is a flowchart illustrating the data relationship network generation method based on data standards provided by the present invention.

[0033] Figure 2 is an example of a matrix index in the data relationship network generation method based on data standards provided by the present invention, which uses data standard SK1 as the core standard.

[0034] Figure 3 is a schematic diagram of the association between matrix blocks in the data relationship network generation method based on data standards provided by the present invention.

[0035] Figure 4 is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0037] This invention provides a data relationship network generation method based on data standards, as shown in Figure 1. The method includes:

[0038] S1. Determine the data type to which the data belongs, and establish respective data standards based on the data type. The data standards include data elements, standardized relation symbol sets, and relation determination rules.

[0039] S2. Analyze the logical relationships between data elements in different data standards, identify unrelated data elements, and store the coordinate-relationship value pairs of related data elements as a sparse storage engine.

[0040] S3. Taking a data standard as the core, a dedicated relationship matrix is ​​formed based on the logical relationship between each data element of the data standard and data elements of other data standards;

[0041] S4. A data relationship network is generated based on the dedicated relationship matrix of multiple data standards, and the data relationship network uses the sparse storage engine for path indexing.

[0042] This data relationship network generation method based on data standards encodes discrete business semantics into machine instructions through symbolic encoding, uses sparse compression to store star-shaped relationship matrices, and finally achieves direct access to business through a directory mechanism with "one standard, one view". This is the first time that semantic accuracy, computational efficiency and ease of use have been unified in the field of data standards.

[0043] In step S1, the data types to which the business semantic data belongs are identified, and their respective data standards are established. For example, the data types to which the business semantic data belongs include: data k1, data k2...data kn, and the corresponding data standards are Sk1, Sk2...Skn. Within these data standards, a machine-resolvable standardized set of relation symbols is defined, and a relation determination rule base is established. This standardized set of relation symbols includes equivalence relations (=), derived relations (→), foreign key references (FK→), and value range constraints. Similarity relations (≈), no association (Z), etc. The determination rule for equivalence relations (=) is "data element name, definition, and value range consistency ≥ threshold (e.g., 95%)"; the determination rule for derived relations (→) is through syntax tree parsing of formula dependencies (e.g., order amount → (unit price × quantity)); the determination rule for foreign key references (FK→) is based on primary key-foreign key declarations or value range determination and inclusion verification. This is achieved by adopting a standard relation symbol set, i.e., a unique one. The symbol system of this invention can cover all types of relationships across all scenarios, that is, it can accurately express equivalence relationships (such as customer ID = user number), derived relationships (such as order amount → unit price × quantity), and value range constraints (such as document type). This invention replaces manual descriptions with business logic such as the complete set of certificates, achieving for the first time a lossless conversion of real-world business rules into machine-resolvable relationships. The invention employs an explicit unrelated labeling mechanism, using the z symbol to clearly mark unrelated data elements, which can eliminate storage redundancy and semantic ambiguity in traditional dense matrices.

[0044] For example, using the equivalence relation "SK1.CustomerID = SK2.UserID", the manual description of the data element "CustomerID" in data standard SK1 and the data element "UserID" in data standard SK2 is accurately expressed. By using z to explicitly identify irrelevant states, only 1% of the elements in the 10k*10k matrix are non-z elements, thus greatly eliminating redundancy in dense matrices.

[0045] The standardized relation symbol set can be parsed by machines, enabling rule-driven high-precision determination. That is, based on rule bases such as name similarity calculation, syntax tree parsing, and value range inclusion detection, the accuracy of relation determination is improved.

[0046] In step S2, the logical relationships between data elements in each data standard are analyzed. These logical relationships are mapped from the relationships between data elements. For example, in data standard 1, there is a data element called "average monthly income." This value is calculated by adding up the monthly income for 12 months in data standard 2 and then dividing by 12. Therefore, these two data elements have a derivative relationship; that is, the data element "average monthly income" in data standard 1 is a derivative of the data element "monthly income" in data standard 2. For another example, the logical relationships in data standard Sk1 include MSk11, MSk12, etc., the logical relationships in data standard Sk2 include MSk21, MSk22, etc., and so on. The logical relationships in data standard Skn include MSkn1, MSkn2, MSkn3, etc. Among these, there may be a relationship a between MSk21 and MSkn1, a relationship b between MSkn1 and MSk22, and so on. If there is no relationship, it is represented by z.

[0047] In the sparse storage engine, only coordinate-relationship value pairs (SKi, SKj, DEp, DEq, rel) of non-z elements are stored. DEp and DEq are the core coordinate identifiers in the sparse storage engine, used to precisely locate the relationships between data elements. In the relational submatrix composed of data standards STi and SKj, they uniquely identify the position of a logical relationship (as a unit). Through this sparse storage engine, relationships are directly located using (i,j,p,q) coordinates, reducing the query complexity of data elements and decreasing storage space, further overcoming the computational efficiency bottleneck. This invention, through z-identification and block storage, can solve the computational and storage bottleneck of relational networks with tens of thousands of data elements.

[0048] Preferably, the analysis of the logical relationships between data elements in different data standards includes: performing name similarity calculation, syntax tree parsing, and / or value range inclusion detection based on the standardized relation symbol set and relation determination rules.

[0049] In step S3, starting from any data standard, a matrix is ​​formed showing the relationships between that data standard and other data element standards. For example, P1 is the matrix showing the relationships between data standard SK1 and other standards. If a dataset contains 5 types of data, then 5 matrices can be formed starting from the 5 types of data standards.

[0050] In step S4, a data relationship network is formed, which is represented by a matrix and ultimately forms a matrix directory. This matrix directory is an index storing all star-shaped matrix paths and is accessed through an API service interface.

[0051] Figure 2 shows an example of a matrix index with SK1 as the core standard. In Figure 2, SK1 (purple) is the core standard, and the associated standards are other data standards such as SK2-SK5 (light green, pink, light purple, and yellow). P1 (purple) is the relationship matrix between SK1 and other standards. The matrix directory (orange) is the index storing all star matrix paths, and the API service (green) is the matrix access interface. The final matrix directory (orange) covers the data relationship network and reflects the relationships between the various data standard matrices. Figure 3 shows a schematic diagram of the data relationship network. In Figure 3, the P1 matrix is ​​a relationship matrix centered on the data standard SK1, containing the relationship between SK1 and SK2 / SK3; the P2 matrix is ​​a relationship matrix centered on the data standard SK2, containing the relationship between SK2 and SK1 / SK3; the P3 matrix is ​​a relationship matrix centered on the data standard SK3, containing the relationship between SK3 and SK1 / SK2. There are associations between P1 and P3, and between P2 and P3, but no association between P1 and P2. This invention constructs a "one standard, one panorama" matrix data hub, forming a "one standard, one panorama" business view. A dedicated relationship matrix Pi is generated for each standard SKI (e.g., P1 = SK1 vs All). When a standard SKI is updated, the affected matrix blocks are automatically marked as needing recalculation. That is, when a single data standard changes, the change propagation engine only recalculates the affected sub-matrix blocks (e.g., an SK1 update only affects its associated n matrices). In one specific embodiment, the 6-hour time required for full reconstruction can be compressed to 10 minutes. Moreover, because the change propagation only recalculates the affected sub-matrix blocks, it can proactively push image analysis reports based on the recalculation results (e.g., modifying a customer ID will cascade and affect order amount calculations), which is beneficial for risk assessment and can reduce risk assessment time from man-days to minutes.

[0052] This invention innovatively proposes and adopts a matrix directory mechanism, using it to drive the decoupling of business views and bridging the gap between technical and business perspectives. Business personnel can intuitively grasp key semantic relationships through customizable matrix views (such as a "customer-order relationship sub-matrix") without needing to understand the underlying technical details. For example, through the view, business personnel can intuitively view key relationships, such as only displaying = and → relationships. Technical personnel can directly obtain machine-readable relationship data (JSON / Protobuf format) by calling standardized APIs (such as getRelation(SKi,SKj,DEp,DEq)). The API provides a matrix access interface, reducing the ETL mapping development cycle by 80%. Therefore, this invention enables seamless cross-role adaptation.

[0053] This invention achieves a leap in computational efficiency through a "sparse storage + matrix block indexing" architecture, resulting in storage space compression of two orders of magnitude. By using identifiers indicating no relationship between data elements, it skips data elements irrelevant to the data. Specifically, the z identifier can skip 99% of irrelevant elements, reducing the storage footprint of a 10k×10k data element relationship matrix from 50GB in the graph scheme to less than 100MB. Furthermore, by constructing a data relationship network that supports coordinate indexing, this invention reduces the 1200ms latency of multi-hop graph traversal to 5ms, improving query efficiency by 99.6% and achieving millisecond-level relationship query response.

[0054] This invention provides a data relationship network formation system based on data standards. The system includes a data standard management layer, a relationship analysis engine layer, and a matrix generation and directory service layer. The data standard management layer determines the data type and establishes respective data standards based on the data type. Each data standard includes data elements, a standardized set of relational symbols, and relation determination rules. The relationship analysis engine layer analyzes the logical relationships between data elements in different data standards, identifies unrelated data elements, and stores the coordinate-relationship values ​​of related data elements as a sparse storage engine. The matrix generation and directory service layer uses a data standard as the core to form a dedicated relationship matrix based on the logical relationships between each data element of that data standard and data elements of other data standards. It then generates a data relationship network based on the dedicated relationship matrices of multiple data standards. The data relationship network utilizes the sparse storage engine for path indexing.

[0055] The data standard management layer covers all types of relationships across various scenarios, and can accurately express equivalence relationships (such as customer ID = user number), derived relationships (such as order amount → unit price × quantity), and value range constraints (such as document type). The data standard management layer utilizes an explicit unrelated identifier mechanism, using the z symbol to clearly mark unrelated data elements, eliminating storage redundancy and semantic ambiguity inherent in traditional dense matrices. This includes business logic such as the complete set of certificates.

[0056] The relation analysis engine layer performs name similarity calculation, syntax tree parsing, and / or value range inclusion detection based on the standardized relation symbol set and relation determination rules. The standardized relation symbol set includes equivalent relation symbols, derived relation symbols, value range constraint symbols, similarity symbols, and no-relation symbols.

[0057] Preferably, the system further includes a data processing layer, which, when processing the data, uses an identifier indicating that there is no relationship between data elements to skip data elements that are irrelevant to the data.

[0058] Preferably, the data processing layer includes a data storage unit, a data compression unit, and a data calculation unit, which are used to store data, compress data, and perform calculations on data, respectively.

[0059] The present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the data relationship network generation method based on data standards described above.

[0060] Figure 4 illustrates a schematic diagram of the physical structure of an electronic device. As shown in Figure 4, the electronic device may include: a processor 410, a communication interface 420, a memory 830, and a communication bus 440. The processor 410, communication interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a data relationship network generation method based on data standards, which has been described in detail previously and will not be repeated here.

[0061] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] The present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the aforementioned data relationship network generation method based on data standards.

[0063] The data relationship network generation method and system based on data standards provided by this invention solves the technical contradictions between semantic expression, computational efficiency, and cross-role adaptation through a three-layer innovative design of semantic relationship symbolization, data matrix structure sparsity, and data relationship network cataloging. It realizes machine-parseable encoding of business logic and decouples business views driven by matrix catalogs, that is, it achieves direct access to business with "one standard, one view" through the catalog mechanism. For the first time, it uniformly achieves semantic accuracy, computational efficiency, and ease of use in the field of data standards.

[0064] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0065] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating a data relationship network based on data standards, characterized in that, The method includes: determining the data type to which the data belongs, and establishing respective data standards based on the data type, wherein the data standards include data elements, a standardized set of relation symbols, and relation determination rules; analyzing the logical relationships between data elements in different data standards, identifying unrelated data elements, and storing coordinate-relationship value pairs for related data elements as a sparse storage engine, wherein the coordinates precisely locate the relationships between data elements; forming a dedicated relation matrix based on the logical relationships between each data element of the data standard and data elements of other data standards, with one data standard as the core; generating a data relation network based on the dedicated relation matrices of multiple data standards, wherein the data relation network uses the sparse storage engine for path indexing.

2. The data relationship network generation method based on data standards according to claim 1, characterized in that, The standardized set of relation symbols covers all relation types in business scenarios and can be parsed by machines.

3. The data relationship network generation method based on data standards according to claim 2, characterized in that, The establishment of respective data standards based on the data types includes: using the standardized relation symbol set to achieve lossless conversion from business rules to machine-resolvable relations.

4. The data relationship network generation method based on data standards according to claim 1, characterized in that, The analysis of the logical relationships between data elements in different data standards includes: performing name similarity calculation, syntax tree parsing, and / or value range inclusion detection based on the standardized relation symbol set and relation determination rules. The standardized relation symbol set includes equivalence relation symbols, derived relation symbols, value range constraint symbols, similarity symbols, and no-relationship symbols.

5. The data relationship network generation method based on data standards according to claim 1, characterized in that, The method further includes: when processing the data, using an identifier that indicates no relationship between data elements to skip data elements that are irrelevant to the data.

6. The data relationship network generation method based on data standards according to claim 5, characterized in that, The processing of the data includes storing, compressing, and computing the data.

7. A data relationship network formation system based on data standards, characterized in that, The system comprises a data standard management layer, a relationship analysis engine layer, and a matrix generation and directory service layer. The data standard management layer determines the data type and establishes corresponding data standards based on the data type. These data standards include data elements, a standardized set of relational symbols, and relation determination rules. The relationship analysis engine layer analyzes the logical relationships between data elements in different data standards, identifies unrelated data elements, and stores coordinate-relationship value pairs for related data elements as a sparse storage engine. The coordinates precisely locate the relationships between data elements. The matrix generation and directory service layer, centered on a data standard, forms a dedicated relationship matrix based on the logical relationships between each data element of that data standard and data elements of other data standards. It then generates a data relationship network based on these dedicated relationship matrices from multiple data standards. This data relationship network utilizes the sparse storage engine for path indexing.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the data relationship network generation method based on data standards as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the data relationship network generation method based on data standards as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the data relationship network generation method based on data standards as described in any one of claims 1 to 6.

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