Automatic data protocol system based on semantic standardization and application method thereof

Abstract

The invention discloses an automatic data protocol system based on semantic standardization and an application method thereof. The automatic data protocol system comprises a content acquisition engine which receives source data and preprocesses the source data; the file analysis and classification engine carries out standardization processing on the preprocessed source data to obtain a modularized semantic unit; the semantic engine converts the standardized content of the modular semantic unit into structured multi-layer semantic data; the semantic network engine constructs a complexdot semantic relationship network based on the multilayer semantic data; the path generation engine is used for constructing a transformpath semantic track on the basis of a complexdot semantic relationship network; the file generation engine packages the multi-layer semantic data to generate a. Oqi file; the verification engine verifies the semantic structure and integrity of the. Oqi file; and the AI interface gateway issues the verified. Oqi file to an AI native platform. The. Oqi file has structured and queriable semantic data, and it is ensured that different AI systems can conduct access, indexing and semantic comparison on the. Oqi file.

Description

Technical Field

[0001] The present application relates to the fields of intelligent data protocol technology and semantic standardization technology, and in particular to an automated data protocol system based on semantic standardization and its application method, for realizing semantic structuring, verification, and traceability of heterogeneous digital content and semantic interoperability between AIs. Background Art

[0002] With the prevalence of artificial intelligence (AI) systems, the demand for managing, structuring, and integrating complex digital content from heterogeneous sources has increased significantly. However, existing technical solutions, such as RDF, OWL, Schema.org, and traditional semantic databases and ontology frameworks, have inherent limitations. These technologies exhibit static structures that require manual configuration and cannot support automatic, autonomous, traceable, and verifiable deep semantic interactions between AIs.

[0003] Even lightweight semantic extension formats such as JSON-LD, which have been widely used in recent years, have certain advantages in Web semantic annotation, but are still limited to static data description and semantic mapping based on external parsing. They lack built-in semantic verification mechanisms, reasoning chain transparency, semantic difference tracking, and native interoperability between AIs, making it difficult to meet the semantic closed-loop requirements of the AI ​​ecosystem.

[0004] In addition, existing AI systems generally operate in a "black box" mode, and are unable to transparently disclose the reasoning process, logical path, and data sources used. This leads to serious defects in the verifiability, reliability, auditability, and semantic traceability of the content generated or processed by AI.

[0005] Therefore, there is an urgent need for a universal data protocol that can provide multi-level semantic expression, native AI readability, integrated verification capabilities, reasoning chain management, and complete semantic interoperability between AIs. Summary of the Invention

[0006] The purpose of this application is to provide an automated data protocol system based on semantic standardization and its application method to solve the above-mentioned technical problems existing in the prior art.

[0007] In a first aspect, the present application provides an automated data protocol system based on semantic standardization, the automated data protocol system comprising:

[0008] Content acquisition engine, which receives source data from different data sources, pre-processes the source data and stores it in a temporary buffer;

[0009] The file analysis and classification engine standardizes the pre-processed source data to obtain modular semantic units;

[0010] The semantic engine uses AI models and domain-specific ontologies to define the semantic logic of modular semantic units and convert the standardized content of modular semantic units into structured multi-layer semantic data;

[0011] The file generation engine encapsulates multi-layer semantic data and generates an .oqi file consisting of three parts: Header, Body, and Tail. The .oqi file is a native AI format file;

[0012] The semantic network engine builds complex semantic units across domains, hierarchies, and systems based on multi-layer semantic data, generating a complexdot semantic relationship network.

[0013] The path generation engine performs path deduction and path reconstruction on complex semantic units and their relationships based on the complexdot semantic relationship network, forming a unique and traceable transformer_path semantic trajectory;

[0014] The file generation engine encapsulates multi-layer semantic data, the complexdot semantic relationship network, and the transformer_path semantic track to generate a .oqi file consisting of three parts: Header, Body, and Tail. The .oqi file is a native AI format file. Among them, the multi-layer semantic data, the complexdot semantic relationship network, and the transformer_path semantic track are independent and complementary semantic layers that together form the core semantic framework of the .oqi file.

[0015] Verification engine, used to verify the semantic structure and integrity of .oqi files, determine the encrypted digital signature and traceable timestamp based on the verification semantic timestamp layer, and assign verification levels;

[0016] The AI ​​interface gateway is used to publish verified .oqi files to the AI ​​native platform, enabling external AI systems to access, index, and perform semantic comparisons of .oqi files.

[0017] In the .oqi file semantic framework of this application, there are three complementary and independent semantic levels, namely:

[0018] 1. Multilayer Semantic Data: This layer represents basic semantic information, including entities, attributes, semantic relationships, source references, language information, and other standard semantic fields. It belongs to the core semantic content layer of the .oqi file.

[0019] 2. ComplexDot Semantic Relationship Network: Operating on top of multi-layer semantic data, it is responsible for building a semantic relationship network across domains, hierarchies, and systems, forming a verifiable and traceable complex semantic topology, supporting complex semantic verification, contextual analysis, and cross-system semantic fusion.

[0020] 3. transformer_path semantic trajectory: Based on the complexdot semantic relationship network, it records the logical links and decision paths formed by the AI ​​system in the process of generating, deducing, and reasoning complex semantics, ensuring the traceability, transparency, and auditability of the AI ​​reasoning process.

[0021] The above three semantic levels work independently but cooperate with each other to build a complete semantic system of .oqi files, ensuring the innovation and uniqueness of the present invention in terms of semantic representation, semantic verification, semantic traceability, and cross-system interoperability.

[0022] Furthermore, the Header part of the .oqi file contains UUID, hash code, UTC timestamp, semantic version, pivot language, signing entity identifier and generation mode; the Body part of the .oqi file contains structured multi-layer semantic data, complexdot semantic relationship network generated by the semantic network engine and transformer_path semantic track generated by the path generation engine; the Tail part of the .oqi file contains a verified encrypted digital signature, a traceable timestamp, an API access public token for external verification of semantic integrity, a validation_level field for authenticating the reliability of the .oqi file, and a license_info field for determining the legal classification.

[0023] Furthermore, preprocessing includes classifying and identifying the content of the source data, determining the input path and file type of the source data, and generating a UUID. The input path is used to confirm the generation mode, and the verification level is determined based on the generation mode; the generation modes include auto, semi, and manual, and the verification levels include autogen, supervised, and certified.

[0024] Furthermore, standard processing includes identifying the format and structure of the source data, using AI tools to parse the source data to obtain semantically related content paragraphs, the semantic network engine constructs complex semantic units based on cross-domain, cross-level, and cross-system semantic relationships of semantically related content paragraphs, and the path generation engine performs path deduction and path reconstruction on complex semantic units and their relationships, and jointly constructs the parser_trace, media_context, semantic_hash, complexdot, and transformer_path fields used in the .oqi file to achieve the conversion from raw data to structured semantic data.

[0025] Furthermore, the Body part of the .oqi file includes entity type, attribute information, application field, semantic classification, semantic relationship, source reference, containing language and associated entities, complexdot semantic relationship network generated based on the semantic network engine, and transformer_path semantic trajectory generated based on the path generation engine.

[0026] Furthermore, the verification engine is configured with a "locked" mode to lock the validation_level field and license_info field in the Tail part; wherein, the verification engine automatically enables the "locked" mode when the .oqi file is published to the AI ​​native platform, and the external AI system can verify the API access public token, encrypted digital signature and validation_level field, complexdot semantic relationship network and transformer_path semantic trajectory through the AI ​​interface gateway.

[0027] Furthermore, the AI ​​interface gateway supports standard network protocols, generates publicly accessible semantic endpoints, and is configured to allow LLM or external AI systems to perform autonomous access control through the semantic endpoints, using API access public tokens to access, index, and semantically compare .oqi files.

[0028] Furthermore, the automated data protocol system also includes an automatic synchronization monitoring engine for monitoring the .oqi file, and when the source data of the .oqi file changes, updating the semantic structure of the .oqi file and generating a new version of the .oqi file;

[0029] Among them, the automatic synchronization monitoring engine periodically compares the source data of the new version of the .oqi file with the source data of the previous version of the .oqi file. When the source data of the .oqi file changes, the automatic synchronization monitoring engine generates a semantic difference file to record the differences between the new version of the .oqi file and the previous version of the .oqi file, including changes in attributes, relationships, time fields, semantic values, complexdot semantic relationship networks, and transformer_path semantic trajectories.

[0030] Furthermore, the automated data protocol system further includes a user interface layer engine configured to allow a user to generate and modify the .oqi file manually or semi-automatically through the user interface layer engine;

[0031] The verification levels of the .oqi file are supervised and certified, and the .oqi file is only authorized to modify semantic fields, which include multi-layer semantic data, complexdot semantic relationship networks, and transformer_path semantic tracks.

[0032] A second aspect of the present application provides an application method of an automated data protocol system, which is applied to the aforementioned automated data protocol system based on semantic standardization, and includes:

[0033] The content acquisition engine receives source data from different data sources, pre-processes the source data and stores it in a temporary buffer;

[0034] Based on the file analysis and classification engine, the pre-processed source data is standardized to obtain modular semantic units;

[0035] Based on the semantic engine, AI models and specific domain ontologies are used to define the semantic logic of modular semantic units, and the standardized content of modular semantic units is converted into structured multi-layer semantic data;

[0036] Based on the semantic network engine, multi-layer semantic data is constructed to obtain complex semantic units across domains, levels, and systems, and generate a complexdot semantic relationship network to achieve horizontal expansion and in-depth association of semantic data.

[0037] Based on the path generation engine, the complex semantic units in the complexdot semantic relationship network are deduced and reconstructed to form a unique and traceable transformer_path semantic track;

[0038] The file generation engine encapsulates multi-layer semantic data, the complexdot semantic relationship network, and the transformer_path semantic track to generate a .oqi file consisting of three parts: Header, Body, and Tail. The .oqi file is a native AI format file. Among them, the multi-layer semantic data, the complexdot semantic relationship network, and the transformer_path semantic track are independent and complementary semantic layers, which together form the core semantic framework of the .oqi file.

[0039] Verify the semantic structure and integrity of the .oqi file based on the verification engine, determine the encrypted digital signature and traceable timestamp based on the verification semantic timestamp layer, and assign a verification level;

[0040] The verified .oqi file is published to the AI ​​native platform based on the AI ​​interface gateway, enabling external AI systems to access, index, and perform semantic comparison of the .oqi file.

[0041] Different from the existing technology, the automated data protocol system of the present application obtains source data from different data sources through a content acquisition engine, thereby obtaining source data in multiple formats from multiple sources; converts unstructured source data into structured multi-layer semantic data readable by AI through a file analysis and classification engine and a semantic engine, greatly improving the efficiency and accuracy of semantic processing and reducing manual intervention; constructs cross-domain, cross-level and cross-system complex semantic units for multi-layer semantic data through a semantic network engine, generates a complexdot semantic relationship network, and realizes the horizontal expansion and in-depth association of semantic data; performs path deduction and path reconstruction on complex semantic units and their relationships through a path generation engine, forming a unique and traceable transformer_path semantic track, and realizing the transparency and verifiability of the reasoning process; completes the encapsulation of multi-layer semantic data, complexdot semantic relationship network and transformer_path semantic track through a file generation engine and generates an .oqi file, specifically using AI models and specific domain ontologies to perform logical analysis on semantic units. oqi files with a unified format and structure ensure the standardization and consistency of digital content in semantic representation, laying the foundation for semantic interoperability and long-term data compatibility. This is conducive to sharing and exchanging semantic information between different systems, platforms, and applications, and provides strong support for collaboration and reasoning between AIs. The verification engine determines the encrypted digital signature and traceable timestamp based on the verification semantic timestamp layer, providing reliable time authentication and data integrity protection for the oqi files, and assigning verification levels. This makes the generated oqi files highly credible and secure, meeting various application scenarios with high requirements for data quality and credibility, such as law and finance. The AI ​​interface gateway publishes the verified oqi files to the AI ​​native platform, allowing external AI systems to easily access, index, and semantically compare the oqi files, breaking information silos and promoting the circulation and sharing of semantic data between different AI systems.

[0042] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] Figure 1This is a structural diagram of an embodiment of an automated data protocol system based on semantic standardization of the present application;

[0045] Figure 2 This is a flow chart of an embodiment of an application method of the automated data protocol system of the present application;

[0046] Figure Number:

[0047] 1-Automated data protocol system based on semantic standardization; 10-Content acquisition engine; 20-File analysis and classification engine; 30-Semantic engine; 35-Semantic network engine; 36-Path generation engine; 40-File generation engine; 50-Verification engine; 60-AI interface gateway; 65-Semantic portal platform; 70-Automatic synchronization monitoring engine; 80-User interface layer engine. DETAILED DESCRIPTION

[0048] To help those skilled in the art better understand the technical solutions of this application, the semantically standardized automated data protocol system and its application method provided by this application are further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the described embodiments are only some of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0049] The terms "first," "second," and the like in this application are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0050] Due to the inherent limitations of existing semantic protocols (such as RDF, OWL, Schema.org, Semantic Scholar and other ontology systems), for example, they all have static structures that require manual configuration and cannot support automatic, autonomous, traceable and verifiable deep semantic interactions between AIs. Therefore, this application defines an AI semantic interaction standard that goes beyond existing protocols, creating a new way of transparent, trustworthy and traceable semantic collaboration between multiple AIs or between humans and AIs.

[0051] Specifically, this application proposes an automated data protocol system based on semantic standardization. This system can automatically generate .oqi files, which contain the multi-layer semantic structure of heterogeneous data and can be directly read by AI without parsing or preprocessing. This application does not rely on blockchain or distributed ledger systems. All verification, traceability, and verification levels are completed independently within the .oqi file, ensuring verifiable, traceable, and auditable semantic interactions without increasing system complexity and cost.

[0052] The .oqi file of this application is not only a data carrier, but also a computational semantic entity with built-in semantic logic, verification mechanism, and reasoning path, and has the ability to directly trigger, reason, compare and verify between different AI systems. Among them, the .oqi file of this application introduces the complexdot and transformer_path fields to record and crystallize the logical and semantic links between AIs, realize the transparency and verifiability of the reasoning process, and at the same time realize the traceability of the entire link, multi-level verification and computational-level trusted authentication of semantic content. This application builds an AI native ecosystem, and the generated .oqi file is an AI native semantic structured file, which enables different AIs to perform semantic access, indexing, comparison and verification without additional interpretation or conversion.

[0053] See also Figure 1 , Figure 1 This is a structural diagram of an embodiment of the automatic data protocol system based on semantic standardization of the present application. Figure 1 As shown, the semantic standardization-based automated data protocol system 1 of the present application includes a content acquisition engine 10, a file analysis and classification engine 20, a semantic engine 30, a semantic network engine 35, a path generation engine 36, a file generation engine 40, a verification engine 50 and an AI interface gateway 60.

[0054] Specifically, the content acquisition engine 10 of the present application receives source data from various data sources, pre-processes the source data, and stores it in a temporary buffer. The content acquisition engine 10 receives source data from data sources such as public databases, enterprise systems, local files, or Web APIs, performs preliminary classification, integrity verification, and preliminary standardization, generates a uniquely identified UUID, and determines the generation mode and preliminary verification level.

[0055] The different data sources may include automatic extraction from public online directories, direct uploads by users or enterprises through official portals, and unstructured source data, including various digital entities such as text, images, technical archives, videos, audio, software, and corporate specifications. For example, the content acquisition engine 10 may be used to parse content from B2B (Business-to-Business) websites, technology marketplaces, or industry portals using OCR (Optical Character Recognition), natural language processing, or computer vision technology, and generate .oqi files representing products or technical components even in the absence of official APIs or standardized data structures.

[0056] The .oqi protocol of this application, as a universal semantic protocol, provides a standardized representation for digital content, enabling digital content from different sources and in different formats to achieve semantic interoperability and sharing on a global scale, breaking the information island problem caused by format and structure differences in traditional technologies. In addition, it supports multilingual semantic representation and can achieve accurate content understanding and communication in different language environments. Secondly, it covers various media forms such as text, images, and videos, as well as various types of content such as industrial products, corporate information, and technical drawings, realizing cross-media and cross-domain semantic intercommunication.

[0057] Preprocessing involves classifying and identifying the source data, determining the source data's input path and file type, and generating a UUID. The input path is used to identify the generation mode. The UUID is a globally unique identifier assigned when the .oqi file is created, making each content permanently distinguishable.

[0058] Optionally, the generation mode includes auto, semi, and manual, where auto indicates that the data is generated completely by AI, semi indicates that the data is generated under human supervision, and manual indicates that the data is generated completely by humans. The generation mode is used to distinguish between automatic, semi-automatic, and manual generation modes.

[0059] The verification level is determined based on the generation mode. The verification levels include autogen, supervised, and certified. autogen means automatic generation, supervised means supervised verification, and certifiedl means official certification verification.

[0060] The content acquisition engine 10 of this application is only used to perform pre-processing of the source data. The generated data is not included in the final structure of the .oqi file, thereby ensuring a clear separation between the content acquisition and semantic representation stages. At the same time, the content acquisition engine 10 provides support as a necessary external input module. Before entering the core semantic process, it generates key fields based on the source data, such as generation_mode (generation mode), license_class (verification level) and created_at (the confirmed date of generation). These fields are then written to the HEADER of the .oqi file.

[0061] The file analysis and classification engine 20 of the present application is responsible for identifying and analyzing the structure and content of the pre-processed source data, decomposing it into modular semantic units to ensure compatibility with the .oqi protocol. Specifically, the file analysis and classification engine 20 of the present application standardizes the pre-processed source data to obtain modular semantic units that can ensure AI readability and compliance with the .oqi protocol.

[0062] Standard processing involves identifying the format and structure of source data and using AI tools to parse the source data to obtain semantically relevant content segments. This is used to construct the parser_trace, media_context, semantic_hash, complexdot, and transformer_path fields used in the .oqi file, transforming raw data into structured semantic data. Optionally, AI tools include OCR, natural language processing, and computer vision technologies, with different AI tools used to parse different source data.

[0063] The file analysis and classification engine 20 is configured to analyze multimedia digital content, including video, image, and audio files, automatically extract their semantic representations in .oqi format, and set the verification level to autogen, so that external AI systems can perform semantic queries without accessing the original binary files.

[0064] Extracting semantics from video content includes automatic transcription of audio tracks, identifying visual elements through image tagging technology, and analyzing titles and related metadata to generate structured attributes and semantic relationships in .oqi files.

[0065] Specifically, the process of automatic extraction from public online catalogs is recorded in the parser_trace field, and the generation of .oqi files is set to the verification level of autogen and includes semantic relations generated according to the controlled semantic dictionary, such as similar_to, compatible_with, and origin.

[0066] Among them, the complexdot field allows the collection of structured complex thoughts verified by external AI systems, so that the integrated semantic results can be used for comparison, verification, scenario analysis and decision making.

[0067] The transformer_path field allows for complete and traceable recording of the reasoning, deduction, association, or logical inference path performed by the AI ​​model when generating semantic content, enabling auditing and retroactive verification of the source and logical steps followed.

[0068] The combination of complexdot and transformer_path is used to significantly reduce AI hallucinations, improve decision transparency, and provide users with a reliable, verifiable, and comparable semantic basis for critical decision-making processes.

[0069] The semantic engine 30 of the present application uses AI models and specific domain ontologies to perform semantic logical definitions on modular semantic units, and converts the standardized content of modular semantic units into structured multi-layer semantic data, including attributes, relationships, context, reference sources and other controlled semantic information.

[0070] Specifically, multi-layer semantic data includes entity type (entity_type), attribute information (attributes), application field (application_fields), semantic classification (semRank), semantic relations (relations), source reference (origin), containing language, and associated entities. The semantic representation is designed to be independent of the original language and standardized through controlled pivot language transliteration and unique attribute, concept, and relationship encoding, executed according to a validated vocabulary. Based on this, each generated .oqi file can be accessed by artificial intelligence models through natural language commands or queries. Without the need for specific training or language parsing, it can be processed using the multi-layer semantic structure of the pivot language, without the limitations of the original language.

[0071] Specifically, entity types may be products, companies, files, etc., attribute information may be size, material, voltage, etc., semantic relationships may be derived, similar, contained, etc., and source references may be authors, URLs, IPs, etc.

[0072] Optionally, multi-layer semantic data may also include function tags (function_tags), such as AI_searchable, requires_sync, etc., or media context anchors (media_context), such as semantic references of images, videos, and audio, or semantic dependency fields (depends_on), or time validity and update timelines, such as valid_from, valid_to, and update_timeline.

[0073] The semantic network engine 35 of the present application is used to construct and organize complex semantic networks, create cross-domain, cross-system, and cross-level semantic connections, and generate complex semantic clusters for subsequent analysis, prediction, and verification operations.

[0074] Specifically, the semantic network engine 35 constructs the multi-layered semantic data generated by the semantic engine 30 across domains, systems, and hierarchies to generate complex semantic units and a complexdot semantic relationship network. The complexdot semantic relationship network is a static, explicit, and verifiable structure that ensures that all semantic relationships are explicitly and controlled, avoiding any automatic generation, implicit reasoning, or unsupervised assumptions. Furthermore, the complexdot semantic relationship network provides users, organizations, or systems with an auditable and transparent multidimensional semantic relationship map.

[0075] This application constructs a complexdot semantic relationship network through the semantic network engine 35 to achieve horizontal, verifiable, and traceable representation of complex semantic relationships, breaking through the limitations of traditional rigid ontology structures and providing support for transparent management of complex semantic relationships.

[0076] The path generation engine 36 of the present application performs path deduction and path reconstruction on complex semantic units and their relationships based on the complexdot semantic relationship network, forming a unique and traceable transformer_path semantic trajectory.

[0077] Specifically, the path generation engine 36 performs path deduction on the entities and relationships generated by the ComplexDot semantic relationship network, generating a unique and traceable transformer_path semantic track. This transformer_path semantic track allows for the comprehensive reconstruction of the semantic connection paths between complex semantic units, ensuring the transparency and auditability of the AI ​​reasoning process. Furthermore, the transformer_path semantic track fully records every step from the source data to the generation of the ComplexDot semantic relationship network, ensuring a clear and traceable logical path.

[0078] This application uses the path generation engine 36 to generate a unique and traceable transformer_path semantic trajectory, which records and verifies the semantic logic path of each step in the entire process of generating the .oqi file, completely solves the "black box" problem of the existing AI system, and improves the interpretability of AI data processing.

[0079] The file generation engine 40 of this application encapsulates multi-layer semantic data, complexdot semantic relationship network and transformer_path semantic trajectory, and generates an .oqi file consisting of three parts: Header, Body and Tail. The .oqi file is a native AI format file, that is, the format of the .oqi file is the AI ​​native semantic structured format defined in this application.

[0080] The header part of the .oqi file contains the UUID, hash code, UTC timestamp, semantic version, pivot language, signing entity identifier, and generation mode.

[0081] The Body part of the .oqi file contains structured multi-layer semantic data, complexdot semantic relationship network and transformer_path semantic track, including multi-layer representation of semantic attributes associated with digital content, classification based on controlled dictionaries, explicit and implicit semantic relationships between entities and subsystems, descriptors of pivot languages ​​and traceable source data such as origin, author and acquisition method, as well as complexdot semantic relationship network and transformer_path semantic track.

[0082] Optionally, key components of the .oqi protocol of the present application include a continuously updated controlled semantic dictionary for consistent and multi-level encoding of attributes, synonyms, taxonomies, entities, and semantic relationships within the .oqi structure, ensuring conceptual unity and interoperability between systems. The controlled semantic dictionary acts directly on the BODY block of the .oqi file.

[0083] The controlled semantic dictionary is accessible via a RESTful API, supports querying and retrieving terms and semantic relationships in standardized formats such as JSON-LD or RDF / XML, facilitates integration with external AI systems, and continuously updates dictionary content through automatic crawling, parsing analysis, or user-verified contributions. Furthermore, the indexing and retrieval mechanism based on semantic standards enables users to find the information they need more quickly and accurately from massive amounts of digital content, improving the efficiency and accuracy of information retrieval.

[0084] Furthermore, the Body part of the .oqi file also contains semantic subset modules that can be dynamically enabled according to the content type. These modules include one or more of the following fields: spec_map, function_map, media_context, depends_on, travel_module, and other optional modules. By dynamically enabling the corresponding semantic subset modules, it is ensured that the semantically standardized automated data protocol system 1 of this application has scalability, semantic adaptability and computational interoperability in different application fields.

[0085] The Tail section of the .oqi file contains a verified cryptographic digital signature, a traceable timestamp, an API access public token for external verification of semantic integrity, a validation_level field for certifying the authenticity of the .oqi file, and a license_info field for determining legal classification.

[0086] This application identifies multi-layer semantic data, complexdot semantic relationship network and transformer_path semantic trajectory as three independent and complementary semantic levels. Among them, multi-layer semantic data undertakes the basic semantic information expression, including entities, attributes, semantic relationships, source references, language information and other standard semantic fields, and belongs to the core semantic content layer of the .oqi file; the complexdot semantic relationship network runs on top of the multi-layer semantic data, and is responsible for the construction of semantic relationship networks across domains, levels and systems, forming a verifiable and traceable complex semantic topological structure, supporting complex semantic verification, situational analysis and cross-system semantic fusion; the transformer_path semantic trajectory runs based on the complexdot semantic relationship network, recording the logical links and decision paths formed by the AI ​​system in the process of generating, deducing and reasoning complex semantics, ensuring the traceability, transparency and auditability of the AI ​​reasoning process.

[0087] The semantic framework of the .oqi file of this application is composed of multi-layer semantic data responsible for the basic structured semantic expression, the complexdot semantic relationship network responsible for the construction and maintenance of cross-domain, cross-level, and cross-system semantic relationships, and the transformer_path semantic track recording AI reasoning logic, semantic deduction and decision-making links. The three work together to construct a complete semantic framework of the .oqi file, ensuring the comprehensiveness of the semantic content, the transparency of the relationship and the traceability of the reasoning process.

[0088] The .oqi file of this application does not require traditional AI models for data analysis or training. The AI ​​system can directly read and perform reasoning, verification and comparison based on the multi-layer semantic data embedded in the file, completely avoiding the need for large-scale corpus training.

[0089] Specifically, the .oqi file structure of this application is designed specifically for AI models and can be natively read and processed by external AI systems without the need for additional parsing or conversion processes. This greatly improves the efficiency of external AI systems in understanding and processing digital content, and reduces the complexity and cost of AI application development. At the same time, through the complexdot and transformer_path fields, the generation and reuse of crystallized computational thinking between different external AI systems is achieved. Different AI systems can collaborate, verify, and correct complex thinking, thereby reducing repeated calculations and training, and accelerating the learning and evolution of AI systems.

[0090] Because the .oqi protocol of this application embeds structured, verified, and reasoning chain information, the AI ​​system can efficiently perform semantic reasoning and comparison even under low computing power, greatly improving AI computing efficiency and making it suitable for edge computing, low-power devices, and resource-constrained scenarios.

[0091] On the other hand, since the unstructured source data has been semantically structured, continuous training of large unstructured data sets is avoided, complex calculations and the load on computing systems such as GPUs and CPUs are reduced, computing and energy resources are significantly saved, and the high energy consumption growth model of existing AI systems based on large-scale training is changed. At the same time, pre-structured semantic content is provided to external AI systems, enabling external AI systems to quickly and accurately extract semantic relationships and source data without a lot of reasoning and guesswork, thereby speeding up the response speed of external AI systems and improving reasoning efficiency. The .oqi protocol of this application eliminates the need for repeated training of massive unstructured data in traditional AI systems, allowing AI systems to significantly reduce computing energy consumption and support a green and low-carbon AI development path.

[0092] Furthermore, the file generation engine 40 writes the verification level in the Tail portion of the .oqi file based on the source reference and the generation mode to ensure that the credibility classification is traceable.

[0093] The verification engine 50 of the present application is used to verify the semantic structure and integrity of the .oqi file, determine the encrypted digital signature and traceable timestamp based on the Verification Semantic Timestamp Layer (VSTL), and assign a verification level.

[0094] Each published version of the .oqi file is semantically compared with the previous version and registered by applying the Validation Semantic Timestamp Layer (VSTL), ensuring semantic continuity, traceability, and historicalization. Therefore, the verification engine 50 can traceably and immutably authenticate the date, content, and semantic structure of each generated .oqi file, ensuring the reliability and authenticity of the information contained therein.

[0095] Furthermore, the verification engine 50 is configured with a "locked" mode for locking the validation_level field and the license_info field in the Tail part, wherein the verification engine 50 is prohibited from modifying key fields such as date, digital signature, version history, UUID and semantic hash in the "locked" mode, thereby ensuring the legal and computational immutability of the .oqi file.

[0096] The verification engine 50 automatically enables "locked" mode when publishing the .oqi file to the AI ​​native platform. External AI systems can verify the API access token (access_token), encrypted digital signature (signature), and validation_level fields through the AI ​​interface gateway, ensuring its immutability and transparency at the legal and computational levels. Optionally, the .oqi file can be managed by the external AI system through a dedicated API.

[0097] The public visibility of the .oqi file is determined by configuration parameters in the access_token field or a specific digital key, enabling selective release of content for specific industries, geographic regions, or types of intelligent systems.

[0098] Furthermore, the verification engine 50 may optionally assign an update_priority score to each .oqi file, which may be used to guide semantic regeneration priorities in the AI ​​native platform based on criteria such as generation date, verification level, access pattern, and recent AI usage frequency.

[0099] Each .oqi file contains rich source data information, such as unique identifiers, timestamps, semantic hashes, version control, and traceability information. This ensures the authenticity and integrity of the content, accurately traces the source, version evolution, and semantic identity of digital content, and prevents information tampering and forgery. The .oqi protocol is also compatible with major existing copyright standards and legal compliance requirements, and can be integrated with various AI models, semantic platforms, and intelligent applications, demonstrating both backward compatibility and foresight.

[0100] The AI ​​interface gateway 60 of the present application is used to publish the verified .oqi file to the AI ​​native platform or an accessible semantic network, so that the external AI system can directly access, index and semantically compare the .oqi file.

[0101] Among them, the AI ​​interface gateway supports standard network protocols, generates publicly accessible semantic endpoints, and is configured to allow LLM (generative language model) or external AI systems or semantic crawlers to perform autonomous access control through semantic endpoints. It uses API access public tokens to access, index and semantically compare .oqi files without the need for structural conversion or pre-authorization requests, which facilitates interoperability and integration with various external AI systems.

[0102] Since the AI ​​interface gateway 60 can publish .oqi files directly to an AI native platform or an accessible semantic network, enterprises that lack corporate websites or public interfaces can also use the automated data protocol system 1 to generate corresponding .oqi files for technical corporate digital content and publish them to AI native platforms or accessible semantic networks accessible to other enterprises, thereby realizing the sharing of heterogeneous data.

[0103] Furthermore, the AI ​​interface gateway 60 is also used to automatically integrate .oqi files into semantic graphs and registries to support global comparison and retrieval.

[0104] The AI ​​Interface Gateway 60 supports distributed verification of the same .oqi file between different AI systems, cross-checking the semantic consistency and credibility of the content through multiple AI systems, reducing the risk of computational hallucinations and improving the quality and security of generated content.

[0105] Furthermore, the semantic standardization-based automated data protocol system 1 of the present application also includes a semantic portal platform 65, wherein the semantic portal platform 65 is specifically configured to provide a platform entrance for users to access, navigate, and analyze the .oqi files, complexdot semantic relationship networks, and transformer_path semantic trajectories.

[0106] Specifically, the semantic portal platform 65 provides an interactive platform for human users to access, explore, and verify the complexdot semantic relationship network and the transformer_path semantic trajectories generated by it. This platform supports AI and human collaboration in the creation, review, and verification of complex semantic content, allowing users to inspect each complexdot semantic relationship network node and trace its path through the transformer_path semantic trajectories generated by the path generation engine 36.

[0107] Furthermore, the semantically standardized automated data protocol system 1 of the present application also includes an automatic synchronization monitoring engine 70, which is used to monitor the .oqi file. When the source data of the .oqi file changes, the semantic structure of the .oqi file is updated to generate a new version of the .oqi file.

[0108] Among them, the automatic synchronization monitoring engine 70 periodically compares the source data of the new version of the .oqi file with the source data of the previous version of the .oqi file. When comparing the source data of the .oqi file, the automatic synchronization monitoring engine generates a semantic difference file (semantic_diff) to record the differences between the new version of the .oqi file and the previous version of the .oqi file, including changes in attributes, relationships, time fields and semantic values, to ensure the traceability and transparency of the evolution process, while maintaining historical consistency and difference tracking with the previous version of the .oqi file.

[0109] During the iterative update process of the .oqi file, the automatic synchronization monitoring engine 70 will retain the complete historical sequence of all semantic versions of each entity represented in the .oqi format, track the semantic differences between versions in a structured manner, and support automatic comparison of iterations between different time points.

[0110] Among them, the automatic synchronization monitoring engine 70 uses the semantic_hash field to uniquely and automatically determine whether there is a significant semantic change in the content, and in this case triggers the generation of a new instance of the .oqi file; uses the update_priority field dynamically allocated according to the modification frequency, content semantic importance and dissemination range to optimize the computing resource management of the automatic regeneration of the .oqi file.

[0111] Specifically, the automatic synchronization monitoring engine 70 is configured to automatically compare .oqi files based on multi-layer semantic analysis of represented attributes, functional descriptors and encoded relationships to detect similarities, overlaps or variations between digital entities from different sources.

[0112] The automatic synchronization monitoring engine 70 is integrated with a semantic comparison function and is configured to automatically identify duplicate, derivative or evolved content, track semantic connections between different versions of the same digital object, and identify potential conflicts or duplicates.

[0113] Among them, the automatic synchronization monitoring engine 70 is configured to perform semantic comparison between non-text digital content including images, videos, audio files or three-dimensional models. The semantic comparison is based on .oqi semantic representation rather than original binary content, making automatic comparative analysis between heterogeneous media possible.

[0114] The results of semantic comparisons between .oqi files are presented in a structured form that is understandable to both human users and artificial intelligence agents, indicating the level of similarity, the entities involved, the exact date of the analysis, and the authentication signature of the comparison results.

[0115] The comparison between semantic entities is achieved by calculating a similarity coefficient based on a weighted measure applied to specific fields in the .oqi file. Specific fields can be, for example, semRank, application_fields, compatibility, linkedEntities, and material. The weights of different fields can be defined by the user or adaptively generated based on AI system parameters.

[0116] The automatic synchronization monitoring engine 70 supports instant semantic comparison of digital content from different sources, such as similar designs, patent comparison, media content comparison, etc. It can quickly and accurately find the similarities, differences and associations between content, providing strong support for intellectual property protection, product design optimization, market competition analysis, etc.

[0117] Furthermore, the semantically standardized automated data protocol system 1 of the present application also includes a user interface layer engine 80, which is configured so that users can manually or semi-automatically generate, modify, or verify .oqi files through the user interface layer engine 80. The verification levels of .oqi files are supervised and certified, and .oqi files are only authorized to modify semantic fields, while dates, digital signatures, historical records, and previous versions of .oqi files are protected, unchangeable, and publicly referenceable. Among them, the user interface layer engine 80 supports collaborative co-creation between AI and users. Users can selectively regenerate the semantics of certified .oqi files through the user interface layer engine 80 and record them in the update_timeline and oqi_lineage fields. All modification behaviors and the semantic timeline and semantic lineage of the .oqi file are recorded through the update_timeline and oqi_lineage fields.

[0118] The .oqi file can be generated automatically through AI semantic analysis, semi-automatically with human oversight, or manually by the user through the user interface layer engine 80 and after semantic verification and approval. In the semi-automatic mode, the .oqi file is initially created in an automatic mode, then verified and reviewed by a human operator, and the user can then modify the entity, relationship, and semantic labels through the user interface layer engine 80.

[0119] The user interface layer engine 80 provides an interactive graphical interface that allows users to approve, modify or complete the semantic content suggested by the system before final release when manually or semi-automatically generating .oqi files, thereby ensuring that the .oqi files released in supervised or certified mode are under user control.

[0120] The .oqi file generation process follows a deterministic sequence of eight functional engines, from content acquisition to final user interaction. Each module corresponds to a specific function, and this order cannot be changed, ensuring the semantic validity, time authentication, and AI-native interoperability of the file. Among them, the content acquisition engine 10 only serves as an external entry point for data ingestion. The file analysis and classification engine 20, semantic engine 30, file generation engine 40, verification engine 50, AI interface gateway 60, automatic synchronization monitoring engine 70, and user interface layer engine 80 in the .oqi protocol construct the .oqi file according to the three major macro structures in the order of HEADER, BODY, and TAIL.

[0121] The execution order of multiple functional engines ensures that the creation, verification and release processes of .oqi files have deterministic order and traceability, allowing semantic interoperability and knowledge sharing between different AI systems, effectively breaking through the limitations of existing AI black boxes.

[0122] This application also provides an application method for an automated data protocol system, wherein the application method can be executed by the semantically standardized automated data protocol system described in the above embodiment. In some possible implementations, the application method can also be implemented by a processor invoking computer-readable instructions stored in a memory.

[0123] See also Figure 2 , Figure 2 This is a flow chart of an embodiment of the method for applying the automated data protocol system of the present application. Specifically, the method for applying the automated data protocol system of the embodiment of the present disclosure may include the following steps:

[0124] Step S11: The content acquisition engine receives source data from different data sources, pre-processes the source data, and stores the pre-processed source data in a temporary buffer.

[0125] The content acquisition engine 10 receives source data from different data sources, pre-processes the source data and stores it in a temporary buffer. The specific operation process has been described in the above embodiment and will not be repeated here.

[0126] Step S12: Based on the file analysis and classification engine, the pre-processed source data is standardized to obtain modular semantic units.

[0127] The file analysis and classification engine 20 performs standardization on the pre-processed source data to obtain modular semantic units. The specific operation process has been described in the above embodiment and will not be repeated here.

[0128] Step S13: Based on the semantic engine, the AI ​​model and the specific domain ontology are used to perform semantic logical definition on the modular semantic unit, and the standardized content of the modular semantic unit is converted into structured multi-layer semantic data.

[0129] Among them, the semantic engine 30 uses AI models and specific domain ontology to perform semantic logical definition of modular semantic units, and converts the standardized content of modular semantic units into structured multi-layer semantic data. The specific operation process has been explained in the above embodiment and will not be repeated here.

[0130] Step S14: constructing multi-layer semantic data based on the semantic network engine to obtain cross-domain, cross-level, and cross-system complex semantic units, and generating a complexdot semantic relationship network.

[0131] Among them, the semantic network engine 35 constructs the multi-layer semantic data generated by the semantic engine 30, obtains complex semantic units across domains, levels, and systems, and generates a complexdot semantic relationship network. The specific operation process has been described in the above embodiment and will not be repeated here.

[0132] Step S15: Based on the path generation engine, path deduction and path reconstruction are performed on the complex semantic units in the complexdot semantic relationship network to form a unique and traceable transformer_path semantic track.

[0133] Among them, the path generation engine 36 performs path deduction and path reconstruction on the complex semantic units within the complexdot semantic relationship network to form a unique and traceable transformer_path semantic trajectory. The specific operation process has been described in the above embodiment and will not be repeated here.

[0134] Step S16: Encapsulate the multi-layer semantic data based on the file generation engine to generate an .oqi file consisting of three parts: Header, Body and Tail.

[0135] The file generation engine 40 encapsulates the multi-layer semantic data and generates an .oqi file consisting of three parts: Header, Body and Tail. The specific operation process has been described in the above embodiment and will not be repeated here.

[0136] Step S17: Verify the semantic structure and integrity of the .oqi file based on the verification engine, determine the encrypted digital signature and traceable timestamp based on the verification semantic timestamp layer, and assign a verification level.

[0137] Among them, the verification engine 50 verifies the semantic structure and integrity of the .oqi file, determines the encrypted digital signature and traceable timestamp based on the verification semantic timestamp layer, and assigns the verification level corresponding to the .oqi file. The specific operation process has been described in the above embodiment and will not be repeated here.

[0138] Step S18: Based on the AI ​​interface gateway, the verified .oqi file is published to the AI ​​native platform to enable external AI systems to access, index, and perform semantic comparison of the .oqi file.

[0139] Among them, the AI ​​interface gateway 60 publishes the verified .oqi file to the AI ​​native platform. The specific operation process has been described in the above embodiment and will not be repeated here.

[0140] Furthermore, in other embodiments, after executing step S16, steps S19 and S20 may be executed, wherein the .oqi file is periodically monitored in step S19, and the .oqi file is modified and corrected in step S20. Specifically, the application method of the automated data protocol system of the embodiment of the present disclosure may further include the following steps:

[0141] Step S19: Periodically monitor the .oqi file based on the automatic synchronization monitoring engine.

[0142] Among them, the automatic synchronization monitoring engine 70 periodically monitors the .oqi file. When the source data of the .oqi file changes, the semantic structure of the .oqi file is updated to generate a new version of the .oqi file. The specific operation process has been described in the above embodiment and will not be repeated here.

[0143] Step S20: Generate or modify the .oqi file based on the user interface layer engine.

[0144] The user manually or semi-automatically generates and modifies the .oqi file through the user interface layer engine 80. The specific operation process has been described in the above embodiment and will not be repeated here.

[0145] The automated data protocol system 1 based on semantic standardization proposed in this application is applicable to a wide range of industries and fields, including but not limited to: legal field: document traceability, contract verification, and legal content certification; industrial and manufacturing fields: product catalog management, technical specification sheet interoperability, supply chain verification and tracking; financial field: automated auditing, accounting document and report verification, and contract traceability; medical field: medical record semantic management, medical protocols, and AI-assisted medical research; scientific and academic fields: open semantic repositories, scientific data sharing, cross-domain meta-research and ontology management; government and public management fields: regulatory semantic tracking, administrative AI interoperability, and transparent document supervision.

[0146] At the same time, the automated data protocol system 1 based on semantic standardization proposed in this application can be widely used in multiple practical scenarios, including but not limited to: industrial product catalog scenarios, used to convert technical information of products such as mechanical, electronic, and optical components into certifiable semantic representations, thereby improving semantic interoperability across systems and platforms; internal enterprise technical data management scenarios, converting complex technical specification documents into .oqi format files that can be directly read by AI, to achieve standardized management and sharing of data assets; document scenarios such as regulations, technical manuals, and instructions, with the help of the system of the present invention, for semantic structuring, verification, and publication, to improve the standardization, traceability, and AI compatibility of documents; online information platform scenarios, converting existing platform content into .oqi format, supporting direct access, indexing, and semantic comparison by external AI systems, and expanding data openness capabilities.

[0147] All of the above application scenarios are based on the complexdot semantic network, transformer_path semantic trajectory, and standardized .oqi file structure, and are within the scope of protection of this invention while maintaining the deterministic process and functional sequence described in this application. Any semantic standardization, semantic verification, and AI native interface process proposed in this application, and following the described functional modules and logical paths, are considered to be implementation methods of this application.

[0148] In addition, the semantic standardization-based automated data protocol system 1 of the present application is also particularly suitable for entities such as small and medium-sized enterprises, individual merchants, and creators that lack websites or standardized online platforms. Through automatically generated .oqi semantic files, these enterprises or individuals can directly connect products, technologies, services, content and other information in AI native semantic format to the global AI semantic network without building websites or developing APIs, and achieve access, indexing, verification and semantic comparison by global AI systems, greatly reducing the threshold for information release, breaking through traditional Internet structure and language barriers, and enhancing the visibility, credibility and participation of small and micro enterprises in the global semantic network, promoting the fair flow of information and equal sharing of knowledge.

[0149] The above are merely embodiments of the present application and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. An automated data protocol system based on semantic standardization, characterized in that: include: A content acquisition engine receives source data from different data sources, pre-processes the source data and stores it in a temporary buffer; A file analysis and classification engine that standardizes the pre-processed source data to obtain modular semantic units; A semantic engine, which uses AI models and domain-specific ontologies to perform semantic logic definitions on the modular semantic units and convert the standardized content of the modular semantic units into structured multi-layer semantic data; A semantic network engine, which constructs cross-domain, cross-level, and cross-system complex semantic units based on the multi-layer semantic data and generates a complexdot semantic relationship network; A path generation engine that performs path deduction and path reconstruction on the complex semantic units and their relationships based on the complexdot semantic relationship network to form a unique and traceable transformer_path semantic track; A file generation engine encapsulates the multi-layer semantic data, the complexdot semantic relationship network, and the transformer_path semantic track to generate a .oqi file consisting of three parts: Header, Body, and Tail. The .oqi file is a native AI format file. The multi-layer semantic data, the complexdot semantic relationship network, and the transformer_path semantic track are independent and complementary semantic levels that together constitute the core semantic framework of the .oqi file. A verification engine for verifying the semantic structure and integrity of the .oqi file, determining the encrypted digital signature and traceable timestamp based on the verification semantic timestamp layer, and assigning a verification level; The AI ​​interface gateway is used to publish the verified .oqi file to the AI ​​native platform, enabling external AI systems to access, index and semantically compare the .oqi file.

2. The automated data protocol system according to claim 1, wherein: The header part of the .oqi file contains UUID, hash code, UTC timestamp, semantic version, pivot language, signing entity identifier, and generation mode; The Body part of the .oqi file contains structured multi-layer semantic data, the complexdot semantic relationship network generated by the semantic network engine, and the transformer_path semantic track generated by the path generation engine; The Tail part of the .oqi file contains a verified encrypted digital signature, a traceable timestamp, an API access public token for external verification of semantic integrity, a validation_level field for authenticating the authenticity of the .oqi file, and a license_info field for determining legal classification.

3. The automated data protocol system according to claim 1, wherein: The preprocessing includes classifying and identifying the content of the source data, determining the input path and file type of the source data, and generating a UUID. The input path is used to confirm the generation mode, and the verification level is determined based on the generation mode; the generation modes include auto, semi, and manual, and the verification levels include autogen, supervised, and certified.

4. The automated data protocol system according to claim 1, wherein: The standard processing includes identifying the format and structure of the source data, using AI tools to parse the source data to obtain semantically related content paragraphs, the semantic network engine constructing complex semantic units based on cross-domain, cross-level, and cross-system semantic relationships of the semantically related content paragraphs, and the path generation engine performing path deduction and path reconstruction on the complex semantic units and their relationships, and jointly constructing the parser_trace, media_context, semantic_hash, complexdot, and transformer_path fields used in the .oqi file.

5. The automated data protocol system according to claim 1, wherein: The Body part of the .oqi file includes entity type, attribute information, application field, semantic classification, semantic relationship, source reference, including language and associated entities, complexdot semantic relationship network generated based on the semantic network engine, and transformer_path semantic trajectory generated based on the path generation engine.

6. The automated data protocol system according to claim 2, wherein: The verification engine is configured with a "locked" mode to lock the validation_level field and the license_info field in the Tail part; wherein, the verification engine automatically enables the "locked" mode when the .oqi file is published to the AI ​​native platform, and the external AI system can verify the API access public token, encrypted digital signature, validation_level field, the complexdot semantic relationship network, and the transformer_path semantic trajectory through the AI ​​interface gateway.

7. The automated data protocol system according to claim 2, wherein: The AI ​​interface gateway supports standard network protocols, generates publicly accessible semantic endpoints, and is configured to allow LLM or external AI systems to perform autonomous access control through the semantic endpoints, and use API access public tokens to access, index and semantically compare the .oqi files.

8. The automated data protocol system according to claim 1, wherein: The automated data protocol system further includes an automatic synchronization monitoring engine for monitoring the .oqi file. When the source data of the .oqi file changes, the semantic structure of the .oqi file, the complexdot semantic relationship network, and the transformer_path semantic track are updated to generate a new version of the .oqi file. Among them, the automatic synchronization monitoring engine periodically compares the source data of the new version of the .oqi file with the source data of the previous version of the .oqi file. When the source data of the .oqi file changes, the automatic synchronization monitoring engine generates a semantic difference file to record the differences between the new version of the .oqi file and the previous version of the .oqi file, including changes in attributes, relationships, time fields, semantic values, the complexdot semantic relationship network, and the transformer_path semantic trajectory.

9. The automated data protocol system according to claim 3, wherein: The automated data protocol system further includes a user interface layer engine configured so that a user can generate and modify the .oqi file manually or semi-automatically through the user interface layer engine; Among them, the verification level of the .oqi file is supervised and certified, and the .oqi file is only authorized to modify semantic fields, which include the multi-layer semantic data, the complexdot semantic relationship network and the transformer_path semantic trajectory.

10. An application method of an automated data protocol system, characterized in that: The application method is applied to the semantically standardized automated data protocol system according to any one of claims 1 to 9, and the application method includes: The content acquisition engine receives source data from different data sources, pre-processes the source data and stores the source data in a temporary buffer; Standardizing the pre-processed source data based on a file analysis and classification engine to obtain modular semantic units; Based on the semantic engine, the AI ​​model and the specific domain ontology are used to perform semantic logical definition on the modular semantic unit, and the standardized content of the modular semantic unit is converted into structured multi-layer semantic data; Constructing the multi-layer semantic data based on a semantic network engine to obtain complex semantic units across domains, levels, and systems, and generating a complexdot semantic relationship network; Based on the path generation engine, the complex semantic units in the complexdot semantic relationship network are path deduced and reconstructed to form a unique and traceable transformer_path semantic track; The multi-layer semantic data, the complexdot semantic relationship network, and the transformer_path semantic track are encapsulated based on a file generation engine to generate a .oqi file consisting of three parts: Header, Body, and Tail. The .oqi file is a native AI format file. The multi-layer semantic data, the complexdot semantic relationship network, and the transformer_path semantic track are independent and complementary semantic levels that together constitute the core semantic framework of the .oqi file. Verify the semantic structure and integrity of the .oqi file based on the verification engine, determine the encrypted digital signature and traceable timestamp based on the verification semantic timestamp layer, and assign a verification level; The verified .oqi file is published to the AI ​​native platform based on the AI ​​interface gateway, enabling external AI systems to access, index and semantically compare the .oqi file.

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