DIKWP semantic block chain and cross-agent semantic collaboration system

Abstract

The invention provides a set of cross-agent collaborative system which deeply fuses a DIKWP five-layer semantic structure (data, information, knowledge, wisdom and intention) and a block chain. The system performs semantic annotation on each transaction on a chain by using an ontology / knowledge graph to form a traceable multilayer semantic account book; semantic registration and query of a heterogeneous agent are supported through a standardized interface; an intelligent contract with semantic recognition and reasoning capabilities is introduced to automatically trigger cooperation, intention matching and right settlement; and low-level data is continuously improved into high-level knowledge and insight by using a synchronization mechanism of on-chain / off-chain collaborative reasoning and event driving. Through encryption, access control and governance mechanisms, the platform realizes semantic interoperation, credible sharing and intention alignment, the block chain is expanded from data evidence storage to a distributed knowledge base, the method is suitable for multi-party cooperation scenes such as medical consultation, supply chains, smart cities and scientific research, and performance and expansibility (such as side chains, subscription and under-chain reasoning) are considered at the same time.

Description

Technical Field

[0001] This invention relates to the fields of blockchain and artificial intelligence, particularly semantic technology and multi-agent collaboration. Specifically, this invention relates to a blockchain system based on a five-layer semantic structure of data-information-knowledge-wisdom-intent based on the DIKWP model, as well as systems and methods for cross-agent semantic collaborative work. Background Technology

[0002] With the development of artificial intelligence and blockchain technologies, there is a growing demand for leveraging semantic information to improve data understanding and collaboration efficiency. The traditional DIKW (Digital Intelligence, Data, Knowledge, Wisdom) model comprises four layers: Data, Information, Knowledge, and Wisdom, but it doesn't encompass the purpose or intent behind decisions. To address this, researchers proposed the DIKWP model, adding a fifth layer, "Purpose," to the DIKW framework to emphasize the intention-driven nature of agent decisions. The DIKWP model more comprehensively describes the evolution from raw data to actionable wisdom, using "intention" as the core element throughout each layer, allowing the value and interpretation of data / information to adjust according to changing objectives.

[0003] On the other hand, blockchain, as a decentralized distributed ledger technology, possesses unique advantages in data storage and sharing due to its immutability and traceability. However, traditional blockchains only process data at the syntactic level (such as transaction records or file hashes) and do not understand the semantic meaning of the data. In other words, the data recorded on the chain lacks interpretability for machines and cannot directly participate in advanced reasoning and intelligent processing. This limits the application of blockchain in complex knowledge management and intelligent collaboration scenarios. To address this deficiency, the concept of "semantic blockchain" has emerged. Its core idea is to introduce semantic web and knowledge graph technologies into the blockchain system, enabling the data in the ledger to possess self-describing, machine-understandable semantics. By combining ontology and knowledge graphs, semantic blockchain attempts to transform the blockchain from a pure data recorder into a "distributed knowledge base." For example, some research teams have proposed a semantic blockchain framework that integrates the DIKWP model with blockchain, structuring on-chain content into five semantic levels: data, information, knowledge, wisdom, and intent. This approach allows the blockchain to not only record raw data but also associate its semantic interpretation and intent, thereby constructing a machine-understandable and trustworthy semantic content ledger.

[0004] In multi-agent systems, different agents (which can be AI agents or human participants) need to share data and knowledge to collaboratively complete complex tasks. However, existing collaboration methods are often constrained by data silos and semantic inconsistencies: each agent often uses its own database or knowledge base, lacking a unified semantic standard, making information difficult to interoperate and fully utilize. Simultaneously, the lack of a trusted sharing mechanism poses risks of data tampering and intellectual property disputes. Although collaboration can be achieved using centralized servers or pre-agreed protocols, the lack of a distributed trust mechanism provided by blockchain makes it difficult to ensure secure and reliable data sharing across organizations or autonomous agents. Furthermore, traditional methods rarely explicitly consider agent intent alignment: that is, how to ensure that the actions and contributions of multiple agents are aligned towards a common goal when collaborating. Current technologies have not yet provided an effective solution that organically integrates semantic-level information expression, agent intent alignment, and the trusted sharing capabilities of blockchain to support efficient, reliable, and interpretable collaborative work among multiple agents. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a DIKWP semantic blockchain and cross-agent semantic collaboration system. This system aims to combine semantic web knowledge representation with a trusted blockchain ledger to support semantic collaboration and intent alignment among multiple agents. Specifically, this invention solves the following technical problems:

[0006] On-chain semantic structure registration: How to record data and its semantic relationships in a machine-understandable way on the blockchain, forming a multi-level structure from data to intent.

[0007] Agent Intent Alignment and Collaboration: How to enable multiple heterogeneous agents to share semantic information, align their collaborative intents, and achieve collaborative work in the absence of a central trust mechanism.

[0008] Semantic smart contract execution: How to use smart contracts to encode semantic rules and collaboration protocols to achieve automated semantic triggering and collaborative decision-making.

[0009] Semantic synchronization and reasoning: How to ensure that semantic information is kept synchronized and updated among multiple agents, and to provide real-time decision support based on shared knowledge for automatic reasoning.

[0010] This invention aims to construct a multi-agent trusted collaborative environment by introducing a blockchain platform with a five-layer semantic structure (DIKWP), combined with knowledge graphs and smart contract mechanisms. This environment enables the sharing of data and knowledge to be both "semantic" and "trustworthy," thereby significantly improving the efficiency and intelligence level of cross-agent collaboration.

[0011] To achieve the above objectives, this invention provides a DIKWP semantic blockchain and a cross-agent semantic collaboration system. This system combines the semantic hierarchy of the DIKWP model with the distributed ledger mechanism of a blockchain, and includes the following components:

[0012] The DIKWP Semantic Blockchain Platform employs an improved blockchain architecture, maintaining multiple interconnected chains of semantic content on-chain. Each on-chain transaction or block record is divided into five semantic layers: data, information, knowledge, wisdom, and intent. Each layer contains corresponding semantic elements. For example, the data layer stores raw data or identifiers, the information layer stores metadata and contextual information, the knowledge layer stores rules or relationships extracted from the information, the wisdom layer represents knowledge-based decision suggestions or insights, and the intent layer records the purpose or constraint strategies behind the transaction. Each layer's content is semantically annotated using a unified ontology concept, giving the on-chain data a clear meaning. Semantic metadata and ontology (such as knowledge graph fragments) are also stored on-chain to explain the meaning and type of each layer's content. Through this structured storage, the blockchain becomes a multi-layered semantic ledger, capable of tracing the evolutionary chain from data to knowledge and even intent.

[0013] Multi-Agent Interface Module: The system supports the access of multiple heterogeneous agents, including AI agents, IoT devices, or user applications. Each agent interacts with the blockchain through a standardized semantic interface, which includes a semantic registration submodule and a semantic query submodule. The semantic registration module converts the data / knowledge provided by the agent into DIKWP format and submits it to the blockchain; the semantic query module retrieves relevant semantic information from the blockchain according to the agent's needs. Before submitting data, agents can perform ontology annotation and semantic packaging, such as assigning semantic tags and context to raw sensor data, thereby forming transaction content that meets the requirements of on-chain storage. This invention also supports agents attaching intent descriptions when submitting transactions, which declare the intended use or collaborative goal of the data or knowledge, facilitating other agents to understand their motivations and achieve intent-aligned collaboration.

[0014] Semantic Smart Contract Module: Extended smart contracts are deployed on the blockchain, introducing semantic rule interpretation and reasoning capabilities. Traditional smart contracts typically execute based on pre-programmed conditions, while the semantic smart contracts of this invention can identify semantic content recorded on the chain, such as triggering logic by referencing ontology concepts and semantic relationships. Contracts can define cross-agent collaboration protocols and incentive mechanisms, for example: semantic subscription contracts automatically notify agents subscribed to a specific topic when new knowledge of a particular type is published; intent coordination contracts match agents with similar goals and organize collaborative tasks; intellectual property contracts automatically allocate rights or rewards to contributors according to predetermined rules when knowledge is used or reasoned to be adopted. Semantic smart contracts can utilize on-chain ontology for complex judgments, such as determining triggering conditions not only based on numerical comparisons but also on semantic relationships and logical reasoning. This gives contracts higher intelligence and adaptability, such as the ability to understand whether a field represents "temperature" or "account balance" and take different actions accordingly. Information from the intent layer can also be mapped to governance contract rules to drive behavior at each layer, ensuring that the system operates as intended.

[0015] Semantic Reasoning and Synchronization Mechanism: This system integrates a semantic reasoning engine, supporting automated reasoning and collaborative updates of on-chain knowledge. The reasoning engine can be implemented on-chain or off-chain: for example, a lightweight reasoning logic or rule engine can be deployed on-chain to perform real-time reasoning on newly added knowledge to generate high-level insights; complex reasoning can be handled by off-chain intelligent agents, with the results fed back to the chain. The system ensures synchronized interaction across layers through an event-triggered mechanism: when lower-layer data is updated, corresponding information extraction or knowledge update processes are automatically triggered (e.g., when the data layer adds raw data, the information layer generates a summary; when the information layer adds facts, the knowledge layer's reasoning rules generate new knowledge). This layered linkage is achieved through smart contracts or oracles, ensuring consistent evolution across all layers of DIKWP. Because on-chain data has explicit semantics, smart contracts or intelligent agents can directly reason about on-chain knowledge, enabling automated on-chain decision support. For example, a contract can detect a specific pattern in the knowledge layer (a conclusion reached through semantic reasoning) and automatically execute a collaborative task. Through a semantic synchronization mechanism, all connected intelligent agents can obtain the latest knowledge state in real time, ensuring cognitive consistency during collaboration.

[0016] Knowledge Graph and Trusted Sharing: This invention utilizes a knowledge graph (KG) as the organizational form of semantic information. In implementation, standard knowledge representations (such as RDF triples and OWL ontologies) can be embedded into blockchain storage, or semantic definitions can be obtained by on-chain transactions referencing off-chain knowledge graph databases. The blockchain provides immutable proof of existence and access control for entries in the knowledge graph. Each semantic transaction, when recorded on the chain, generates a corresponding knowledge entry and its source proof, enabling different agents to reach a consensus on the authenticity of shared knowledge. The system ensures the security of knowledge sharing through encryption and access control: sensitive data can be stored in encrypted form, with only authorized agents holding the decryption key through smart contracts; the ontology URI or hash recorded on the chain ensures that the semantic definitions obtained by the caller have not been tampered with. During knowledge sharing and collaboration, the inherent consensus mechanism of the blockchain ensures that all participants consistently recognize the order and content of knowledge updates, eliminating single points of failure and the possibility of cheating. Data / knowledge contributed by agents can be rewarded through token incentives, realizing digital property rights management of knowledge assets. For example, when data provided by an agent proves to be crucial for completing a shared task, the smart contract can automatically reward it with tokens according to a pre-agreed agreement. This mechanism encourages trusted knowledge sharing and collaboration, promoting a virtuous cycle in the multi-agent knowledge ecosystem.

[0017] In summary, the system of this invention achieves a deep integration of the DIKWP five-layer semantic structure with blockchain technology: it inherits the rich semantic expression capabilities of knowledge graphs while utilizing blockchain to ensure data immutability and traceability of rights. In particular, the introduction of a "Purpose" layer permeates all layers and guides system behavior, enabling multiple agents to understand each other's goals when sharing data, thereby achieving intent alignment in cross-agent collaboration. Through semantic smart contracts and reasoning mechanisms, the system directly embeds collaboration rules into the on-chain knowledge network, making it an autonomous "trustworthy semantic collaboration platform" that supports more intelligent, efficient, and reliable multi-agent collaborative work.

[0018] The DIKWP semantic blockchain and cross-agent semantic collaboration system provided by this invention have the following beneficial effects:

[0019] Semantic interoperability and interpretability: By introducing the DIKWP semantic structure and ontology knowledge onto the blockchain, this system enables data from different sources to be understood and associated within a unified semantic framework, achieving cross-domain and cross-agent data interoperability. The blockchain records not only the data itself but also its meaning interpretation and usage descriptions, making the AI ​​decision-making process more transparent and interpretable. Collaborating parties can trace how data is processed into knowledge and the basis for final decisions, forming a complete knowledge evolution chain.

[0020] Trusted Sharing and Data Sovereignty: Leveraging the immutability and decentralized trust mechanism of blockchain, the authenticity and tamper-proof nature of data and knowledge shared during multi-agent collaboration are ensured. Each agent's published semantic content is timestamped and digitally signed, allowing other participants to independently verify its integrity and origin. Fine-grained access control and usage billing (such as licensing and revenue tracking) are implemented through smart contracts, empowering data / knowledge providers with sovereign management capabilities over their digital assets. This effectively protects intellectual property rights, eliminates trust barriers in data sharing, and promotes secure cooperation between organizations.

[0021] Intent-aligned collaborative intelligence: This invention uniquely incorporates "Purpose" into the collaborative framework. By explicitly recording the goals and constraints of each collaborating party on-chain, the system can automatically match and coordinate the intentions of agents. Compared to traditional collaborative systems that only focus on the task itself, adding an intent layer can avoid policy conflicts and improve collaborative efficiency. For example, when the intentions of multiple agents show a common goal, the system can establish an alliance or assign tasks accordingly, guiding all parties to act towards a unified goal; conversely, when intention conflicts are detected, early warnings can be issued or resolved through contract arbitration. This intent alignment mechanism improves the collaborative consistency and decision-making rationality of multi-agent systems.

[0022] Semantic Smart Contracts and Automated Reasoning: The smart contracts in this system possess semantic awareness capabilities, enabling them to automatically trigger collaborative processes or decision-making actions based on on-chain knowledge and ontology rules. For example, in a supply chain scenario, when the blockchain detects that warehouse inventory data (data layer) triggers the "insufficient inventory" semantic rule (knowledge layer) and the transaction's intent indicates an urgent replenishment need (intent layer), the contract can automatically notify the supplier agent to ship the goods and execute the pre-set order transaction. Because the contract can understand high-level semantics, its responses are more intelligent and flexible, not limited to simple threshold judgments. With the support of a semantic reasoning engine, the system can also automatically derive new conclusions from existing knowledge, and the contract can then take action accordingly, achieving preliminary on-chain autonomous reasoning functionality. This reduces human intervention, making the collaborative process more efficient and autonomous.

[0023] Improving System Performance and Scalability: Through a layered semantic structure and modular design, this system ensures rich functionality while also prioritizing performance scalability. The separation of low-level raw data and high-level inference results ensures that basic transaction performance is unaffected by complex inference; heavy inference computations can be performed off-chain by a proxy and then back on-chain, thus reducing the load on the main chain. Each agent only subscribes to and processes semantic content relevant to itself, avoiding interference from irrelevant data and improving collaborative communication efficiency. The system also supports horizontal scaling by adding sidechains or parachains to carry different types of semantic content. Furthermore, different application domains can customize dedicated ontology and semantic contracts to form customizable collaborative subsystems without modifying the main chain architecture. Therefore, this invention possesses excellent versatility and scalability, capable of adapting to the collaborative needs of multi-agent systems of various scales.

[0024] In summary, this invention, through a semantically hierarchical blockchain design, realizes an innovative platform that enables trust, semantic interoperability, and intent collaboration among multiple agents. It has broad application prospects and significant advantages in fields requiring cross-agent collaboration, such as intelligent manufacturing, smart cities, medical collaboration, and supply chain management. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the overall architecture of the system of the present invention.

[0026] Figure 2 This is a flowchart of the module interaction for multi-agent semantic collaboration.

[0027] Figure 3 This is a schematic diagram of the integrated structure of blockchain and semantic graph. Detailed Implementation

[0028] The system architecture and workflow of the present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the following embodiments are for illustrative purposes only and are not intended to limit the present invention. Various modifications or substitutions can be made by those skilled in the art without departing from the spirit of the present invention.

[0029] Overall architecture: See Figure 1 The DIKWP semantic blockchain and cross-agent semantic collaboration system of the present invention includes multiple agents (such as...) Figure 1The diagram shows agents A, B, C, etc., and a DIKWP semantic blockchain platform. The blockchain platform includes components such as an on-chain semantic data ledger, a semantic smart contract module, and an ontology / knowledge graph library. Multiple agents connect to the blockchain platform via a network, enabling them to submit data and knowledge to the blockchain or retrieve required knowledge from it. Each agent can be an AI agent, robot, sensor device, or human-computer interaction terminal running a specific task. Agents interact with the platform through a semantic interface (API). Submitted data is first semantically labeled and structured by client components, forming a transaction containing the five layers of DIKWP elements, and then sent to the blockchain network.

[0030] After receiving a semantic transaction submitted by an agent, the blockchain network verifies and records it through a consensus mechanism. Each transaction contains fields such as data layer, information layer, knowledge layer, wisdom layer, and intent layer, as well as related semantic metadata. During storage, these hierarchical structures can be represented in formats such as JSON-LD or RDF for easy on-chain parsing. Ontology references within the transaction point to an on-chain knowledge graph or a pre-agreed ontology identifier, ensuring consistent semantic interpretation across all participants. When the blockchain packages a transaction into a new block, all nodes in the network store this record with its semantic structure, achieving distributed storage of semantic data.

[0031] Semantic Contracts and Inference Execution: Once a new semantic transaction is recorded on the blockchain, the semantic smart contract module monitors the transaction content. See also Figure 2 First, agent A submits a piece of data containing semantic information to the blockchain (step 1). This transaction may trigger a pre-deployed semantic contract (step 2): the contract examines the content of the transaction, such as data type, contained ontology concepts, and intent fields. If specific semantic conditions are met (e.g., a certain type of event occurs, or multiple knowledge fragments can lead to new conclusions), the contract automatically executes the corresponding logic. The execution actions can include: writing new knowledge to the blockchain, issuing notifications, calling external interfaces, etc. Subsequently, the blockchain notifies relevant subscribers of the newly generated knowledge or events through a publish-subscribe mechanism. Figure 2 As a subscriber, Agent B, upon detecting a semantic update in the blockchain broadcast (step 3), retrieves new knowledge content or reasoning results through a query interface (step 4) and applies them to its own task. Throughout the process, all interactions are semantically based: Agent B can understand the meaning implied by the received information (because of the unified ontology definition), thus taking the correct action.

[0032] It is worth mentioning that this invention utilizes knowledge graphs and inference engines to enable blockchain to not only statically store data but also dynamically generate new knowledge. For example, when several agents submit different information about the same event, the on-chain knowledge inference rules can synthesize this information to derive a higher level of knowledge (the wisdom layer). This inference process can be executed on-chain by smart contracts or completed off-chain by trusted nodes, with the results then uploaded to the blockchain. Figure 2 In the illustrated process, when agent A submits data that satisfies the inference premises, the semantic contract triggers the inference module to run, generating new knowledge entries and storing them in the blockchain. Subsequently, other agents become aware of this conclusion through on-chain notifications. This demonstrates the semantic synchronization and inference capabilities of this system: the contribution of any single agent can be processed by the blockchain and elevated into shared knowledge wealth, synchronized with all relevant parties.

[0033] Blockchain and Semantic Graph Integration: See Figure 3 This invention tightly integrates the blockchain layer with the semantic graph layer to achieve the unification of data storage and knowledge representation. Figure 3 The left side illustrates a blockchain transaction logically containing five layers of semantic content (DIKWP structure), while the right side illustrates the concept nodes of the knowledge graph / ontology layer and the semantic relationships between them. Fields at each layer of an on-chain transaction are linked to corresponding concepts in the knowledge graph through reference pointers or identifiers. For example, the raw data fields of the data layer can be appended with an ontology concept identifier, indicating the type of data (e.g., sensor readings, transaction amounts); the context description fields of the information layer can be linked to entities in the knowledge graph (e.g., geographic location entities, device entities); rules or relationships in the knowledge layer can reference relationship types in the ontology (e.g., "belongs to," "cause"), or be stored directly on the chain as triples; decision suggestions in the wisdom layer may be linked to policy knowledge (e.g., the best course of action to deal with a situation), with the knowledge graph providing the semantic definition of the solution; the intent layer often corresponds to the classification of goals or intents in the ontology (e.g., concepts like "urgent task," "long-term optimization," etc.). Through these connections, blockchain records and knowledge graph semantics achieve a two-way link: the blockchain provides a trusted data source and version management for the knowledge graph, while the knowledge graph provides semantic interpretation and reasoning support for blockchain data.

[0034] During implementation, two integration methods can be adopted: First, key ontologies and metadata are stored on-chain, with all nodes maintaining a lightweight, shared subset of the ontology for basic semantic interpretation. Second, the complete knowledge graph is stored off-chain, with on-chain records only storing hashes or indexes pointing to the off-chain knowledge base. When in-depth semantic computation is required, detailed knowledge is provided by off-chain services. Regardless of the method, updates to the ontology and knowledge graph must be approved through the blockchain's governance mechanism to ensure semantic consistency and trustworthiness. In a specific embodiment, collaboration between different industries can load corresponding industry-standard ontologies (e.g., medical collaboration loads a medical ontology, supply chain collaboration loads a logistics ontology). Then, the data submitted by each agent will be semantically annotated according to this ontology, ensuring semantic consistency across organizations. As collaboration deepens, new concepts or relationships can also be added to the ontology through a predetermined process and broadcast to all nodes for updates, thereby dynamically evolving the knowledge graph and maintaining collaborative cognitive synchronization.

[0035] Intelligent Agent Collaboration Application Scenarios: The system of this invention can be widely applied to scenarios requiring multi-party collaboration and intensive data and knowledge. The following uses medical diagnostic collaboration as an example to illustrate the system's workflow: Multiple medical AIs and hospital databases act as intelligent agents connected to the blockchain, each holding different aspects of patient information and experience rules. First, all parties define a unified medical ontology (including concepts such as symptoms, test indicators, and diagnostic results) through this system. When a patient's complex case requires consultation, each hospital's intelligent agent packages the patient's examination data, imaging information, etc., into a DIKWP format according to the ontology definition and submits it to the chain, with the intent layer specifying the purpose of "collaborative diagnosis." After receiving the data, the blockchain triggers preliminary data aggregation and analysis via a semantic contract (executed by the information layer → knowledge layer conversion module, such as statistical analysis of abnormal indicators). Next, different AI model intelligent agents perform diagnostic reasoning based on the shared data on the chain in their respective areas of expertise and submit the results to the chain in the form of knowledge layer transactions. The semantic reasoning engine further integrates multi-party knowledge, generating possible diagnostic conclusions and recommended treatment plans (insights) at the wisdom layer, and recording the relevant evidence and confidence levels. Ultimately, at the intent layer, the system combines patient wishes and ethical constraints, and through smart contracts, selects the optimal solution to submit to the doctor. Throughout the process, the data and knowledge contributions of all participants are transparently recorded, and all conclusions are verifiable and tamper-proof. If the knowledge of a particular AI is crucial to the final diagnosis, that AI agent can automatically receive a corresponding reward. This invention enables secure sharing of data and expertise among different medical entities, improving the accuracy and efficiency of diagnosis. This is just one example; the invention is also applicable to numerous fields such as supply chain logistics collaboration (where companies share inventory and transportation information to optimize scheduling), multi-departmental collaboration in smart cities (real-time sharing of urban sensor data and decision-making strategies to respond to emergencies), and scientific research collaboration networks (cross-institutional sharing of research data and models for collaborative research).

[0036] In summary, this invention, through specific system architecture design and process mechanisms, realizes a novel semantic blockchain collaborative platform. Within this platform, multiple agents can maintain their autonomy while collaborating and evolving together through shared semantic knowledge on the blockchain. This invention ensures both the transparency and trustworthiness of the collaboration process and enhances the system's perception and adaptation capabilities to high-level semantic goals, thus holding significant importance for advancing next-generation distributed intelligent systems.

Claims

1. A DIKWP semantic blockchain and cross-agent semantic collaboration system, characterized in that, include: The Semantic Blockchain Ledger Module is used to store blockchain data with a multi-layered semantic structure. Each blockchain transaction is divided into a data layer, information layer, knowledge layer, wisdom layer and intent layer according to the DIKWP model, and is accompanied by semantically labeled metadata so that the content recorded on the chain has machine-understandable semantics. A multi-agent interface module supports interaction between multiple agent nodes and the semantic blockchain. It includes a semantic registration submodule and a semantic query submodule. The semantic registration submodule annotates the raw data and knowledge provided by the agents with semantics, encapsulates it into a DIKWP five-layer structure, and writes it to the blockchain. The semantic query submodule retrieves relevant semantic content from the blockchain based on the agents' requests and returns the results, enabling agents to publish and obtain semantic data on the chain. A semantic smart contract module, deployed on the blockchain platform, executes predefined semantic collaboration rules and logic. The smart contract can identify semantic markers and intent information in blockchain transactions and automatically trigger collaborative operations or inference calculations based on semantic relationships defined by the ontology, coordinating the interaction of multiple agents. The knowledge graph integration module is used to integrate a predetermined ontology and knowledge graph with the blockchain ledger. The module associates the semantic elements of on-chain transactions with the concepts and relationships in the knowledge graph, enabling the blockchain ledger to connect with external or on-chain stored knowledge graph data and providing a unified semantic reference framework. The semantic reasoning and synchronization module is used to monitor changes in semantic data on the blockchain and execute reasoning rules. It automatically upgrades low-level data updates to high-level knowledge or wisdom layer decisions, and synchronously broadcasts the new knowledge obtained through reasoning to all relevant intelligent agents through the blockchain consensus network, thereby ensuring the consistency and timely sharing of semantic information among multiple intelligent agents.

2. The system according to claim 1, characterized in that, When recording each transaction, the semantic blockchain ledger module uses an immutable on-chain notarization method to save the semantic content and source identity of the transaction, and assigns a unique identifier and timestamp to each layer of semantic elements to support cross-level traceability and rights tracking.

3. The system according to claim 1, characterized in that, The semantic smart contract module includes: an intent matching contract, used to compare intent layer descriptions in transactions submitted by different smart agents, and triggering a collaborative process or suggesting the formation of a task alliance when a predefined matching relationship is detected between the intent targets of two or more smart agents; and a knowledge reward contract, used to automatically calculate and distribute digital token rewards to the smart agent that made the original contribution according to preset rules when on-chain knowledge is accessed or referenced by other smart agents, so as to incentivize the sharing of trusted knowledge.

4. The system according to claim 1, characterized in that, The knowledge graph integration module uses semantic web standards such as RDF / OWL to represent knowledge. The ontology concept definitions and semantic relationships can be directly stored on the blockchain or referenced from the off-chain knowledge graph database in the form of hash indexes. When the ontology or knowledge graph needs to be updated, approval and version management are carried out through blockchain governance mechanisms (such as voting contracts) to ensure semantic consistency among all nodes.

5. The system according to claim 1, characterized in that, The semantic reasoning and synchronization module includes several inter-layer conversion rules or modules: used to realize semantic enhancement from the data layer to the information layer, rule extraction from the information layer to the knowledge layer, and decision reasoning from the knowledge layer to the wisdom layer. Each conversion module is implemented by a pre-configured algorithm model or smart contract. When the lower-level data meets specific conditions, the corresponding semantic conversion is automatically executed, and the result is recorded in the corresponding level in a new on-chain transaction form.

6. The system according to claim 1, characterized in that, Each agent node maintains a local DIKWP semantic cache or knowledge base to store semantic information obtained from the blockchain and supports local reasoning and fast querying. The local semantic cache is updated in real time with the blockchain ledger through a subscription mechanism, so that the agent can still have the latest shared knowledge when running offline for a short period of time, and can synchronize the data changes generated during the offline period to the chain once connected to the network.

7. The system according to claim 1, characterized in that, The system also includes a security and privacy module for access control and encryption protection of sensitive data involved in on-chain storage and agent interaction. The module uses technologies such as attribute encryption and zero-knowledge proof to ensure that only authorized agents can interpret specific semantic content and verify the authenticity of data without compromising privacy, thereby meeting data privacy and security requirements while achieving semantic collaboration.

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