A transaction data flow circulation method, device, equipment and medium

Abstract

This invention discloses a method, apparatus, device, and medium for data circulation. The method includes: acquiring data to be traded; determining the circulation mode of the data to be traded based on the trading scenario; the circulation mode includes a competitive mode driven by profit and based on price signals to achieve data circulation; a symbiotic mode driven by the market and based on the synergy of policy regulation and market mechanisms to achieve data circulation; and a symbiotic mode driven by value and based on scenario-driven and artificial intelligence-enabled data circulation; processing the data to be traded through data engineering and / or multimedia engineering to obtain first transaction data; processing the first transaction data through knowledge engineering based on the application scenario requirements and circulation mode of the data to be traded to obtain second transaction data; and consuming the second transaction data according to the circulation mode of the data to be traded. This solution can improve the intelligence, flexibility, stability, and scenario applicability of data transactions.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, equipment and medium for the circulation of transaction data. Background Technology

[0002] The iterative evolution of digital technologies is profoundly reshaping the global landscape of production factors, propelling human society from the "information age" to the "data age." Applications centered on AI (Artificial Intelligence) models are driving data to become a new type of production factor. However, with data ownership not yet fully clearly defined, and driven by the practical needs of data sovereignty, trade secret protection, and personal privacy safeguards, government departments and businesses have generally constructed data barriers. This results in poor data connectivity across departments, regions, and industries, hindering the large-scale flow of data elements.

[0003] The data element market is characterized by the diversity of participants, the dynamism of scenarios, and the derivation of value. However, traditional data transaction model analysis frameworks often focus on single transaction forms such as platform transactions and on-exchange and off-exchange models, which are difficult to cover the collaborative interaction of heterogeneous participants such as government regulatory agencies, technology intermediary service providers, and third-party compliance agencies. Therefore, they are not sufficient to explain the interaction of data element markets and the logic of data circulation value realization in the context of AI. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and medium for the circulation of transaction data. It adopts a combination of data engineering, multimedia engineering, and knowledge engineering to address different circulation modes of the data to be traded, and uses artificial intelligence technology to achieve differentiated circulation of the data to be traded, thereby improving the intelligence, flexibility, stability, and applicability of data transactions.

[0005] According to one aspect of the present invention, a method for circulating transaction data is provided, the method comprising:

[0006] Acquire data to be traded, and determine the circulation mode of the data to be traded based on the trading scenario of the data; wherein, the circulation mode includes a competition mode, a coexistence mode and a symbiotic mode. The competition mode is driven by profit and realizes data circulation based on price signals. The coexistence mode is driven by the market and realizes data circulation based on the synergy of policy regulation and market mechanisms. The symbiotic mode is driven by value and realizes data circulation based on scenario-driven and artificial intelligence empowerment.

[0007] The data to be traded is processed through data engineering and / or multimedia engineering to obtain the first transaction data;

[0008] Based on the application scenario requirements and circulation model of the data to be traded, the first transaction data is processed through knowledge engineering to obtain the second transaction data; wherein, the knowledge engineering is built on big data and artificial intelligence, and has knowledge processing and access control functions;

[0009] The second transaction data is consumed according to the circulation pattern of the data to be traded.

[0010] According to another aspect of the present invention, a transaction data circulation device is provided, the device comprising:

[0011] The circulation mode determination module is used to acquire data to be traded and determine the circulation mode of the data to be traded based on the trading scenario of the data to be traded; wherein, the circulation mode includes a competition mode, a symbiotic mode and a coexistence mode. The competition mode is driven by profit and realizes data circulation based on price signals. The symbiotic mode is driven by the market and realizes data circulation based on the synergy of policy regulation and market mechanisms. The coexistence mode is driven by value and realizes data circulation based on scenario-driven and artificial intelligence empowerment.

[0012] The first data processing module is used to process the data to be traded through data engineering and / or multimedia engineering to obtain the first transaction data;

[0013] The second data processing module is used to process the first transaction data through knowledge engineering based on the application scenario requirements and circulation mode of the data to be traded to obtain the second transaction data; wherein, the knowledge engineering is built based on big data and artificial intelligence, and has knowledge processing and access control functions.

[0014] The data consumption module is used to consume the second transaction data according to the circulation pattern of the data to be traded.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the transaction data circulation method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the transaction data circulation method according to any embodiment of the present invention.

[0018] The technical solution of this invention involves acquiring data to be traded and determining the circulation mode of the data based on its trading scenario. These circulation modes include a competitive mode, a symbiotic mode, and a coexisting mode. The competitive mode is driven by profit and uses price signals to achieve data circulation. The symbiotic mode is driven by the market and uses policy regulation and market mechanisms in synergy to achieve data circulation. The coexisting mode is driven by value and uses scenario-driven and AI-enabled data circulation. First trading data is obtained by processing the data to be traded through data engineering and / or multimedia engineering. Second trading data is obtained by processing the first trading data through knowledge engineering based on the application scenario requirements and circulation mode of the data to be traded. Knowledge engineering is built based on big data and AI and has knowledge processing and access control functions. The second trading data is consumed according to its circulation mode. This technical solution, for different circulation modes of data to be traded, adopts a combination of data engineering, multimedia engineering, and knowledge engineering, and leverages AI technology to achieve differentiated circulation of the data to be traded, thereby improving the intelligence, flexibility, stability, and scenario applicability of data transactions.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a transaction data circulation method provided by an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of a data flow mode provided according to an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the operation mechanism of a data circulation ecosystem provided by an embodiment of the present invention;

[0024] Figure 4 This is a flowchart of another transaction data circulation method provided by an embodiment of the present invention;

[0025] Figure 5 This is a schematic diagram of the structure of a transaction data circulation device according to an embodiment of the present invention;

[0026] Figure 6 This is a schematic diagram of the structure of an electronic device that implements a transaction data circulation method according to an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1 This is a flowchart of a transaction data circulation method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where transaction data under different circulation modes is circulated in a differentiated manner. The method can be executed by a transaction data circulation device, which can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0031] S110: Obtain the data to be traded and determine the circulation mode of the data to be traded based on the trading scenario of the data to be traded.

[0032] The technical solution of this invention, from the perspective of information ecology theory, proposes that the circulation of data elements has phased characteristics, corresponding to different data circulation models. Information ecology theory is based on a four-element structure of "people-information-information technology-information environment," corresponding to the data element market's "participating entities-data ontology-technical foundation-policy environment." By analyzing the dynamic balance between data circulation and security governance, information ecology theory provides a full-chain analytical path for understanding the interactive relationships among multiple entities in data element circulation, the data evolution process, the role of technological support, and the adaptability to the external environment. Specifically, it includes the following:

[0033] First, this paper explores the roles and interaction logic of multiple stakeholders from a human perspective. From the perspective of participating entities, the data ecosystem encompasses diverse and heterogeneous roles, including data suppliers (such as enterprises and government agencies), data demanders (such as AI companies and research institutions), data service providers (such as data vendors and technology intermediaries), and data regulators (such as government regulatory departments). The interests and behavioral logics of these different stakeholders constitute the core driving force of the data circulation ecosystem. The willingness to authorize data directly affects the scale and quality of data supply. Enterprises possess both the attributes of data supply and demand; on the supply side, they pursue maximizing the returns from data assetization, while on the demand side, they seek high-value data resources around application scenarios, forming an inherent tension between supply and demand. Government agencies not only assume the function of public data providers, needing to balance the relationship between data openness and national security and public interest, but also act as regulators, leading the design of institutional rules and correcting market failures through policy intervention. Third-party service providers establish mutual trust through professional technology and compliance capabilities, reducing the interaction costs between stakeholders; their level of service professionalism directly affects the operational efficiency of the circulation ecosystem.

[0034] Secondly, this study examines the morphological evolution and value generation of data elements from an information perspective. From a morphological perspective, data elements exhibit a hierarchical evolutionary path: "raw data - dataset - data product - knowledge outcome." Different forms of data elements, as data ontology, serve as the fundamental carriers of circulating value. Data products, through deep processing and knowledge extraction using AI technology, are ultimately transformed into knowledge outcomes with decision-making value. The realization of the circulating value of data elements depends on the dynamic balance between information flow and security. Information flow is reflected in the efficiency of data transmission and sharing among different subjects and scenarios, while information security focuses on privacy protection, rights protection, and risk prevention during data circulation, directly impacting the willingness of subjects to participate. The value-derived nature of data elements determines that their circulation is not a one-time value transfer, but rather a value-added process achieved through continuous interaction. This provides a theoretical basis for the transformation of the data circulation paradigm from "transactional" to "interactive." Specifically, data transaction refers to a transaction between data suppliers and data demanders, using specific forms of data as the subject and currency or other equivalents as consideration; data interaction refers to the process of collaboration between various subjects using data, supporting business synergy and expansion through scenario and data value transformation.

[0035] Third, from the perspective of information technology, we focus on the functional upgrading and collaborative empowerment of the support system. The circulation of data elements presents the development characteristics of "tool empowerment - system support - ecosystem integration", and achieves reliable data transmission through functions such as data availability without visibility and full-process traceability; at present, we are building an integrated support system of "data-computing power-algorithm" with AI technology and distributed networks as the core, and promoting the deep integration of data elements and scenarios.

[0036] Fourth, we study the adaptation and evolution of institutional rules and market ecosystems from the perspective of the information environment. At the institutional level, effective policy coordination is the fundamental guarantee for the development and utilization of data elements, providing an institutional basis for guiding the open circulation of data elements. At the market level, we construct a standardized market operation system around data transaction standards, pricing mechanisms, and quality assessment methods. At the level of social trust, relying on institutional guarantees and technological support, we build a dual-anchoring mechanism of "government credibility and technological trust" to enhance the public's trust in data circulation. We can provide constraints and support guarantees for data circulation activities through technological foundations, policies, and market rules.

[0037] The data circulation ecosystem, driven by the non-linear synergy of technology, policy, and market forces, propels the evolution of data element circulation. Technological drivers optimize data processing efficiency and security through tools like privacy computing and blockchain, reducing interaction costs. Policy guidance regulates the environment and clarifies rights and responsibilities through regulatory systems such as rights confirmation and classification / grading. Market forces leverage network effects and scenario demands to stimulate value co-creation. These three forces work together within the four-element structure of "people-information-information technology-information environment," forming a complete transmission path that supports the three-stage evolution of data circulation: "competition-coexistence-symbiosis."

[0038] like Figure 2 As shown, data circulation models include competitive, symbiotic, and co-existing models. The competitive model, driven by profit and based on price signals, is suitable for single-market data exchange scenarios in existing markets. The symbiotic model, driven by the market and based on the synergy of policy regulation and market mechanisms, is suitable for scenarios such as the entry of public data in some incremental scenarios. The co-existing model, driven by value and based on scenario-driven and AI-enabled data circulation, is suitable for multi-source data with multiple roles in value co-creation scenarios. Figure 2 In this context, MCP (Model Context Protocol) is an open protocol designed to enable seamless integration between large language models (LLMs) and external data sources, tools, and services; A2A (Agent-to-Agent Protocol) is an open protocol designed to enable secure and standardized collaboration between different AI agents, breaking down framework and vendor barriers, and enabling heterogeneous agents to communicate and collaborate efficiently.

[0039] The data element circulation ecosystem follows an evolutionary pattern from simple to complex and from disorder to order, with the feedback loop of technology, policy, and market forces forming an endogenous driving force. Based on the coupling strength of these three forces, the ecosystem exhibits the stage characteristics of "competitive monopoly - government-enterprise coexistence - distributed symbiosis" (see Table 1), driving the gradual evolution of data circulation from the traditional "point-to-point" transaction model to an interactive model of ecosystem co-construction and sharing.

[0040] Table 1. Three-Force Driven Mechanism and Stage Characteristics of the Data Factor Market

[0041]

[0042] Specifically, the competition phase is driven by profit, with data supply and demand entities allocating resources through price signals and competitive strategies. In the early stages of market development, leading platform companies leverage their user base and technological advantages to create a positive feedback loop of "resources-revenue," while smaller entities, due to limited data scale, fall into a "value vacuum," resulting in a market structure characterized by "leader dominance and resource concentration." The circulation method in this stage is primarily transactional, essentially a "commodity buying and selling" of data elements. While this activates the initial market activity, the lack of deep collaboration and institutional safeguards makes it difficult to resolve deeper issues such as data ownership definition and privacy protection, leading to a dual dilemma of "demand-side cost distortion" and "supply-side value idleness."

[0043] The symbiotic stage is market-driven at its core, using policy regulation and market mechanisms to intervene and correct market failures caused by profit-seeking. The government promotes the orderly entry of public data and restructures the ecological niche relationships among stakeholders by breaking down administrative data barriers, building compliant transaction frameworks, and cultivating intermediary service systems. Stakeholders shift from discrete competition to a collaborative network of complementary functions: the government transforms from a passive regulator to a rule-maker and resource provider; leading enterprises focus on high-value-added aspects such as data processing and modeling; SMEs emphasize scenario-based application innovation; and technology intermediaries assume the function of trust building. In this stage, transactional and interactive methods coexist and interact, and the value of data upgrades from "single-point transactions" to "network collaboration," effectively balancing the tension between compliance costs and circulation efficiency.

[0044] The symbiotic stage is value-driven at its core, with the maturity of AI technology, the upgrading of scenario requirements, and the improvement of the trusted data space becoming key driving factors. Technologies such as privacy computing and smart contracts enable secure sharing of data that is "usable but not visible," and distributed networks break the dependence on central nodes, forming a multi-center, flat architecture. Each node is both a data provider and a value creator, and the relationship between the main parties shifts from a "zero-sum game" to a "win-win symbiosis." The governance model upgrades from "human supervision" to "technological autonomy," and value assessment develops from static pricing to dynamic valuation. The deep integration of data elements and intelligent technologies promotes value co-creation from pilot projects to large-scale applications, realizing the value effect of "cross-domain derivation - network multiplication."

[0045] In this embodiment, transaction data can first be obtained from different channels (such as government, enterprise, and individual channels) based on project management and operation and maintenance monitoring. See [link to relevant documentation]. Figure 3In this context, "data to be traded" refers to data elements that need to be traded and circulated, which can be understood as data provided by data suppliers that is available for trading and circulation. For example, data to be traded can include structured data and / or unstructured data, such as relational data, knowledge, components, cloud network data, and video data. Transaction scenarios can be used to characterize the data trading environment, such as single-market data integration scenarios in an existing market, public data entry scenarios with partial incremental growth, and multi-source data with multiple roles in value co-creation scenarios.

[0046] In this embodiment, after acquiring the data to be traded, the circulation mode of the data can be determined based on the trading scenario of the data. Optionally, determining the circulation mode of the data to be traded based on the trading scenario includes: if the trading scenario of the data to be traded is a single market data docking scenario, then the circulation mode of the data to be traded is determined to be a competitive mode; if the trading scenario of the data to be traded is a public data entry scenario, then the circulation mode of the data to be traded is determined to be a symbiotic mode; if the trading scenario of the data to be traded is a multi-source data, multi-role scenario, then the circulation mode of the data to be traded is determined to be a symbiotic mode.

[0047] S120, the transaction data to be processed is obtained by data engineering and / or multimedia engineering.

[0048] In this embodiment, after obtaining the data to be traded, a dual-track engineering approach (i.e., data engineering and multimedia engineering) can be used to perform basic processing such as cleaning and labeling on the data to be traded to obtain the first transaction data, thereby realizing the resource utilization of the data to be traded (i.e., transforming the raw data into standardized data resources). See [link to relevant documentation]. Figure 3 Data engineering and multimedia engineering can be used for the initial processing of structured and unstructured data, respectively. As the foundational architecture for traditional data circulation, they treat data as a static resource that can be standardized and processed, relying on a layered architecture of "acquisition-storage-processing-application" to achieve process-oriented operations by decomposing the whole. For example, data engineering can include data integration, data governance, data processing, and data services, forming a complete standard system for integration / governance / processing / service; multimedia engineering can include image understanding, multimodal parsing (i.e., multi-dimensional image recognition), dynamic scheduling, and event alerting, which can activate "silent" data. The first transaction data can refer to the transaction data obtained after the initial processing of the transaction data.

[0049] In this embodiment, optionally, the first transaction data is obtained by processing the transaction data to be processed through data engineering and / or multimedia engineering, including: processing the structured data in the transaction data to be processed through data engineering to obtain structured transaction data; wherein, data engineering includes data integration, data governance, data processing, and data services; processing the unstructured data in the transaction data to be processed through multimedia engineering to obtain unstructured transaction data; wherein, multimedia engineering includes image understanding and multimodal parsing; and determining the first transaction data based on the structured transaction data and / or unstructured transaction data.

[0050] Specifically, if the data to be traded contains only structured data, data engineering can be used to process the data to obtain structured transaction data, which is then designated as the first transaction data. If the data to be traded contains only unstructured data, data engineering can be used to process the data to obtain unstructured transaction data, which is then designated as the first transaction data. If the data to be traded contains both structured and unstructured data, data engineering can be used to process the structured data to obtain structured transaction data, and simultaneously, multimedia engineering can be used to process the unstructured data to obtain unstructured transaction data. Both structured and unstructured transaction data are then combined and designated as the first transaction data.

[0051] S130, based on the application scenario requirements and circulation mode of the data to be traded, the first transaction data is processed through knowledge engineering to obtain the second transaction data.

[0052] In this embodiment, after obtaining the first transaction data, the second transaction data can be obtained by processing the first transaction data through knowledge engineering based on the application scenario requirements and circulation model of the data to be traded, thereby realizing the assetization and productization of the data to be traded. See [link to relevant documentation]. Figure 3 The application scenario requirements reflect the actual needs of the data's domain during the data transaction process. For example, application scenarios could involve agriculture, healthcare, financial risk control, precision marketing, smart cities (such as digital twin cities), and intelligent manufacturing. Knowledge engineering is built upon big data and artificial intelligence, and possesses knowledge processing and access control functions. Knowledge processing drives data knowledge transformation, while access control ensures asset controllability. Knowledge processing can include knowledge extraction, knowledge refinement, knowledge reasoning, knowledge planning, and knowledge evaluation. Access control functions are used to securely manage data ownership. Secondary transaction data refers to transaction data obtained after secondary processing of the primary transaction data, i.e., transaction data obtained after processing the primary transaction data.

[0053] In this embodiment, optionally, the knowledge engineering construction process includes: constructing a multi-agent cognitive collaboration model based on metadata management using a knowledge graph as a carrier to realize knowledge extraction, knowledge refinement and access control; establishing a dynamic cognitive reasoning mechanism using a machine learning-based knowledge reasoning engine to realize knowledge reasoning and knowledge planning; and constructing a comprehensive evaluation system covering economic, social and knowledge value to realize knowledge evaluation.

[0054] Specifically, knowledge engineering, by introducing artificial intelligence cognitive modeling technology, has achieved a shift from a "physical layer operation" to a "cognitive layer interaction" model. First, it constructs a theoretical model of multi-agent cognitive collaboration, using knowledge graphs as a carrier to transform data into "entity-attribute-relationship" triple cognitive units. This enables heterogeneous entities such as governments and enterprises to achieve knowledge interaction within a unified semantic space, forming a collaborative evolutionary model of "public knowledge base - private knowledge value-added." Second, it establishes a dynamic evolutionary cognitive reasoning mechanism. A machine learning-based knowledge reasoning engine can adaptively deepen data interaction through reinforcement learning. When the data interaction frequency exceeds a critical threshold, it automatically triggers rule updates, forming a closed-loop feedback of "interaction-learning-optimization." Furthermore, it integrates theories of multi-dimensional value measurement, constructing a comprehensive evaluation system covering economic, social, and knowledge values, transforming the value release of data circulation into a computable cognitive process.

[0055] Furthermore, we will construct a data circulation infrastructure guided by the integration of data and intelligence. The construction of this infrastructure provides a path to address the challenges of data circulation, such as lack of trust, uneven computing power, and high compliance costs. The deep integration of AI technology with this infrastructure provides crucial support for achieving "secure circulation and efficient value-added services." Using a trusted data space as the core carrier of this data circulation infrastructure, we will leverage AI technology to empower functional upgrades and model innovation, thereby building a "data-intelligent collaborative, trusted, and efficient" infrastructure system.

[0056] Specifically, at the compliance adaptation level, an AI-driven compliance engine is embedded, transforming regulations such as the Data Security Law and the Personal Information Protection Law into executable technical modules. Enterprises can directly call standardized compliance components to complete data anonymization and privacy protection operations upon integration, eliminating the need for self-developed adaptation systems and reducing the institutional compliance costs for SMEs. At the computing power supply level, an AI-scheduled distributed computing power pool is built, forming an integrated "data-computing power-algorithm" supply model. Computing power resources are dynamically allocated based on scenario characteristics such as data processing scale and real-time requirements. SMEs in the data industry can complete large-scale data modeling and knowledge extraction through the data space without building their own high-performance computing centers, leveraging economies of scale to reduce unit computing power costs and promoting the transformation of computing power resources from being dominated by leading players to being universally shared. At the trust building level, a dual-anchoring mechanism of "sovereign trust + technological trust" is established. The government, as the operating entity, provides credibility endorsement, ensuring the neutrality and authority of the data space; blockchain technology enables on-chain evidence storage throughout the entire data interaction process, AI algorithms monitor abnormal access behavior in real time, and smart contracts automatically execute trust agreements, forming a dual trust guarantee of "technical anti-counterfeiting and authoritative certification." By leveraging the synergy of blockchain and AI, data ownership can be traced throughout its entire lifecycle. From initial data collection to the creation of derived value, each stage records the contributor's contribution through timestamps and smart tags, ensuring the fair distribution of subsequent value-added revenue and solving the problem of value loss caused by one-time buyouts. This data-driven revenue distribution mechanism not only protects the legitimate rights and interests of data providers but also incentivizes all stakeholders to participate in the circulation process.

[0057] In this embodiment, optionally, the first transaction data is processed using knowledge engineering based on the application scenario requirements and circulation mode of the data to be traded to obtain the second transaction data. This includes: if the circulation mode of the data to be traded is a competitive mode, then the first transaction data is automatically cleaned using artificial intelligence to obtain candidate transaction data; if the circulation mode of the data to be traded is a symbiotic mode, then a transaction knowledge graph is constructed using artificial intelligence based on the first transaction data and policy regulations to obtain candidate transaction data; if the circulation mode of the data to be traded is a symbiotic mode, then an integrated network of cognition-decision-service is constructed using artificial intelligence, and the first transaction data is processed based on the integrated network and multilateral protocols to obtain candidate transaction data; and the candidate transaction data is encapsulated and combined according to the application scenario requirements of the data to be traded to obtain the second transaction data.

[0058] It should be noted that, with the deepening of the digital economy and the integration of data and intelligence technologies, the flow of data elements has evolved from transactions to interactions. In the competitive stage, the transactional approach focuses on the transfer of ownership or usage rights of the data itself, with value realization limited to the immediate benefits of a single transaction, and the network effects and derivative value of data are difficult to fully realize. In the symbiotic stage, interactive methods begin to emerge, with entities engaging in limited collaboration through data sharing and joint processing, expanding value realization to a dual dimension of "transaction revenue + collaborative value enhancement." In the symbiotic stage, interactive methods deepen, with multiple entities continuously collaborating around scenario needs, achieving value co-creation through knowledge extraction, model training, and scenario integration. Data elements transform from "transaction objects" to "value co-creation carriers," exhibiting exponential growth in value realization.

[0059] The functions of knowledge engineering exhibit a phased deepening characteristic. In the competition phase, data resourceization is at its core. Data engineering focuses on the efficiency of supply and demand matching, and AI technology is mainly used for data cleaning and automated processing, not yet deeply penetrating the cognitive level. In the symbiotic phase, policy-driven initiatives promote the assetization of data knowledge. Knowledge engineering, relying on technologies such as knowledge graphs and metadata management, transforms policy regulations into compliance constraint nodes in the transaction knowledge graph, internalizing external regulations into cognitive logic, thereby establishing ownership relationships and usage rules for data resources. In the symbiotic phase, AI technology empowerment becomes dominant. Knowledge engineering constructs an integrated "cognition-decision-service" network through multimodal large-scale models, achieving distributed cognitive collaboration based on multilateral protocols such as MCP, promoting "local interaction leading to global emergence." The development from data engineering to knowledge engineering essentially represents a deepening transformation of data circulation from "transaction" to "interaction," providing a reference for understanding the evolutionary patterns of data transaction circulation.

[0060] Against this backdrop, if the data to be traded is in a competitive mode, artificial intelligence can be used to automatically clean the first transaction data to obtain candidate transaction data; if the data to be traded is in a symbiotic mode, artificial intelligence can be used to construct a transaction knowledge graph based on the first transaction data and policy regulations to obtain candidate transaction data; if the data to be traded is in a symbiotic mode, artificial intelligence can be used to construct an integrated network of cognition, decision-making, and services, and the first transaction data can be processed based on the integrated network and multilateral agreements to obtain candidate transaction data, thereby achieving intelligent data processing under different circulation modes.

[0061] After obtaining candidate transaction data, it can be packaged and combined according to the application scenario requirements of the data to be traded to obtain second transaction data, thereby realizing data productization and forming data products and services that can be directly applied. It should be noted that different application scenarios have significantly different requirements for data elements, and this difference directly affects the way and extent to which data value is realized. In basic application scenarios such as financial risk control and precision marketing, data requirements are mainly manifested in the requirements for data accuracy and real-time performance, driving the formation of standardized data product trading models. In deeply integrated scenarios such as smart cities and intelligent manufacturing, data requirements exhibit characteristics of multi-source fusion and collaborative computing, giving rise to interactive circulation models based on technologies such as privacy computing and federated learning. See also Figure 3 Taking the agricultural scenario as an example, operations such as document review, report writing, intelligent data retrieval and querying, intent insight, image retrieval and video search can be performed based on agricultural indicators to ultimately obtain data products that meet the needs of the agricultural scenario as secondary transaction data.

[0062] S140, consume the second transaction data according to the circulation pattern of the data to be traded.

[0063] In this embodiment, different methods can be used to consume the second transaction data according to the circulation pattern of the data to be traded. For example, see [link to example]. Figure 2 and Figure 3 If the data to be traded is in a competitive mode, it can be served by data service providers to data demanders (such as financial institutions, reimbursement agencies, and enterprises) for data consumption. If the data to be traded is in a symbiotic mode, it can be listed as a product on data trading venues (such as data trading venues in Shenzhen, Shanghai, and Guizhou) and served to data demanders for data consumption. If the data to be traded is in a symbiotic mode, it can be listed as a product on data interaction venues (such as data interaction venues in Shenzhen, Shanghai, and Guizhou) and served to data demanders for data consumption based on multilateral agreements.

[0064] The release of data value is the result of the combined effect of data orientation and scenario-driven factors. In the competition stage, value release is mainly reflected in the realization of transaction value, that is, obtaining direct economic benefits through the market trading of data products. Value release is primarily characterized by fragmented, point-like value realization, mainly constrained by factors such as low data quality and insufficient scenario adaptability, lacking a systematic value cycle mechanism. In the symbiotic stage, with the improvement of policies and regulations and the upgrading of infrastructure, value release begins to expand into multiple dimensions. Firstly, economic value is enhanced through more efficient market allocation; secondly, social value is realized through the open sharing of public data. Through the synergistic effect of policy guidance and market mechanisms, a relatively stable value creation model is formed. In the symbiotic stage, AI technology can empower value release to enter a deeper form, with new value forms such as knowledge value and innovation value constantly emerging, forming a virtuous cycle of value creation. For example, in the scenario of a digital twin city, the deep coupling of data and scenarios not only realizes the direct value of improved operational efficiency but also spawns derivative values ​​such as new business models and service forms, forming a value multiplier effect.

[0065] The technical solution of this invention involves acquiring data to be traded and determining the circulation mode of the data based on its trading scenario. These circulation modes include a competitive mode, a symbiotic mode, and a coexisting mode. The competitive mode is driven by profit and uses price signals to achieve data circulation. The symbiotic mode is driven by the market and uses policy regulation and market mechanisms in synergy to achieve data circulation. The coexisting mode is driven by value and uses scenario-driven and AI-enabled data circulation. First trading data is obtained by processing the data to be traded through data engineering and / or multimedia engineering. Second trading data is obtained by processing the first trading data through knowledge engineering based on the application scenario requirements and circulation mode of the data to be traded. Knowledge engineering is built based on big data and AI and has knowledge processing and access control functions. The second trading data is consumed according to its circulation mode. This technical solution, for different circulation modes of data to be traded, adopts a combination of data engineering, multimedia engineering, and knowledge engineering, and leverages AI technology to achieve differentiated circulation of the data to be traded, thereby improving the intelligence, flexibility, stability, and scenario applicability of data transactions.

[0066] In this embodiment, optionally, the method further includes: determining the target regulatory approach based on the data attributes of the data to be traded; wherein the data attributes include basic public data and derived data; and conducting security supervision on the circulation process of the data to be traded based on the target regulatory approach.

[0067] It should be noted that the phased evolution of the data element circulation ecosystem dictates that the governance system needs to possess both stability and flexibility, upholding the bottom line of security while leaving sufficient space for innovation. In this embodiment, based on the four-element structure of "human-information-information technology-information environment" in information ecology theory, and combined with the three-stage evolutionary pattern of "competition-coexistence-symbiosis," a hierarchical and categorized security supervision system is constructed.

[0068] Specifically, for fundamental public data involving national security, public interests, and sensitive personal information, a comprehensive, end-to-end regulatory approach will be adopted, establishing a closed-loop mechanism of "authorization and approval - usage registration - full-process audit." A unified, government-led authorized operation platform will serve as the core distribution channel, ensuring that data sovereignty and security bottom lines are not breached. For derivative data with strong scenario-specific characteristics and uneven value density, an inclusive and prudent sandbox regulatory approach will be adopted. Testing scopes will be defined within specific industry data spaces, allowing market entities to explore new integrated applications. By setting risk trigger thresholds and innovation tolerance ratios, compliance requirements and innovative vitality will be balanced.

[0069] This plan, through its structure, utilizes a tiered and categorized security supervision system to provide a basic rule framework for data circulation, clearly defining the circulation boundaries and operational norms for different types of data, which helps to achieve a precise match between governance effectiveness and ecological evolution.

[0070] Example 2

[0071] Figure 4 This is a flowchart of a transaction data circulation method provided in Embodiment 2 of the present invention. This embodiment is based on the above embodiment and optimized. Specifically, the optimization is as follows: the method further includes: determining multi-dimensional evaluation indicators for the data to be traded during the circulation process; wherein, the multi-dimensional indicators include structural dimensions, behavioral dimensions, and risk dimensions; if the multi-dimensional evaluation indicators exceed the reference threshold range in the circulation mode, an early warning mechanism is triggered.

[0072] like Figure 4 As shown, the method in this embodiment specifically includes the following steps:

[0073] S210: Obtain the data to be traded and determine the circulation mode of the data to be traded based on the trading scenario of the data to be traded.

[0074] Among them, the circulation models include the competition model, the symbiotic model, and the coexistence model. The competition model is driven by profit and realizes data circulation based on price signals. The symbiotic model is driven by the market and realizes data circulation based on the synergy of policy regulation and market mechanisms. The coexistence model is driven by value and realizes data circulation based on scenario-driven and artificial intelligence empowerment.

[0075] S220, the transaction data to be processed is obtained by data engineering and / or multimedia engineering.

[0076] S230, based on the application scenario requirements and circulation mode of the data to be traded, the first transaction data is processed through knowledge engineering to obtain the second transaction data.

[0077] Among them, knowledge engineering is built on big data and artificial intelligence, and has knowledge processing and access control functions.

[0078] S240, consumes the second transaction data according to the circulation pattern of the data to be traded.

[0079] The specific implementation of S210-S240 can be found in the detailed description in Embodiment 1 above, and will not be repeated here.

[0080] S250 defines multi-dimensional evaluation indicators for data to be traded during the circulation process; these multi-dimensional indicators include structural, behavioral, and risk dimensions.

[0081] For example, from a structural dimension, indicators such as the integrity of the ecological structure, the density of network connections, and the concentration of subjects are selected to construct the organizational form of the ecosystem; from a behavioral dimension, parameters such as the frequency of interaction, the depth of collaboration, and the efficiency of value circulation are set to reflect the quality of subject interaction; from a risk dimension, variables such as the incidence of compliance disputes, the density of security incidents, and the growth rate of rights disputes are included to capture potential risks and hidden dangers.

[0082] S260. If the multi-dimensional evaluation indicators exceed the reference threshold range under the circulation mode, an early warning mechanism will be triggered.

[0083] In this embodiment, historical evolution data and scenario simulation results can be combined to set differentiated reference threshold ranges for different stages. During the competition stage, the focus is on monitoring market concentration and innovation investment intensity to prevent leading companies from forming monopolistic barriers. During the symbiotic stage, the focus is on the efficiency of government-enterprise collaboration and the rate of public data sharing, providing early warnings of overlapping ecosystem niches and "free-riding" behavior. During the symbiotic stage, the focus is on the stability of the distributed trust network and the fairness of value distribution, preventing technological monopolies and protocol vulnerabilities. For each stage, if the multi-dimensional evaluation indicators exceed the reference threshold range of the corresponding circulation model, an early warning mechanism is triggered so that relevant departments can handle the situation promptly. The content and method of the early warning can be flexibly set according to actual needs; this embodiment does not impose specific limitations on this. For example, when early warning indicators show a continuous decline in the integrity of the ecosystem structure in a certain field, regulatory departments can optimize governance strategies by expanding the scope of derivative data sandbox testing and increasing support for small and medium-sized entities.

[0084] Furthermore, we need to build a phased, coordinated, symbiotic, and co-creative data industry ecosystem. The cultivation of this ecosystem faces significant structural contradictions. Large enterprises leverage their data scale advantages to achieve resource aggregation effects, while SMEs, limited by their resource endowments, face a development dilemma of "high participation costs and low returns" in cross-industry data integration. To address the ecological contradictions and development needs at different stages, we need to formulate differentiated ecosystem cultivation strategies to achieve a positive evolution of the ecosystem from imbalance to balance, and from simple to complex.

[0085] The competitive market's "leader-dominated, resource-concentrated" characteristics during the competitive phase have led to development difficulties for small and medium-sized data businesses due to insufficient data scale and weak technical capabilities, while the solidified ecosystem structure has inhibited innovation. First, a basic guarantee mechanism of ecosystem compensation fund should be established to subsidize the initial investments of SMEs in data cleaning and standardization, lowering market entry barriers. Second, policy guidance should be strengthened to ensure balanced resource allocation, providing low-cost data resources to SMEs through targeted opening of public data and the construction of data resource docking platforms. Third, a fair competitive market environment should be built, and anti-monopoly and anti-unfair competition rules should be improved to curb leading companies from squeezing the survival space of SMEs through data monopolies and algorithmic discrimination. The ecosystem structure should be gradually optimized to promote the market's transformation from leader-dominated to diversified competition, laying the foundation for ecosystem evolution.

[0086] The symbiotic stage, driven by both policy and market forces, promotes efficient resource integration, with public data reshaping the ecosystem. However, issues remain, such as low efficiency in government-enterprise collaboration and insufficient integration of public and market data. To address these challenges, three measures are proposed: First, establish a government-enterprise data collaboration platform, leveraging technologies like privacy computing and knowledge graphs to achieve "usable but invisible" integration of public and market data, thus transforming the government from regulator to rule-maker and resource provider. Second, optimize the function of the ecosystem compensation fund, shifting from basic support to integration incentives, providing R&D subsidies and risk compensation to entities participating in government-enterprise data integration projects, and encouraging complementary collaborations between leading enterprises and SMEs. Third, cultivate a professional intermediary service system, supporting the development of third-party entities such as data vendors, compliance service providers, and technology providers, and improving supporting services such as data cleaning, rights assessment, and security auditing to reduce collaboration costs between entities.

[0087] The symbiotic stage builds a value co-creation network to achieve distributed collaborative governance and support ecosystem upgrades. However, it faces challenges such as technological ethical risks and unfair value distribution. Therefore, priority should be given to supporting high-end application scenarios such as cross-industry data fusion and AI large-scale model training. First, explore and deepen the innovative empowering role of the ecosystem compensation fund to promote the transformation of data industry participants from data trading to knowledge production. Second, promote the diversified application of the data trust system, establishing differentiated trust models for different types of data. Sensitive personal data should be subject to closed trusts with strict limitations on its use, while industry-derived data should be subject to open trusts with dynamic optimization. Professional trustees should be used to achieve a balance of rights and interests among data providers, users, and the public interest. Third, build a distributed governance mechanism, relying on multilateral agreements such as MCP and A2A to establish a diversified governance system with autonomous governance by stakeholders, automatic technological regulation, and third-party supervision and evaluation. Smart contracts should clarify the rights and responsibilities of each stakeholder, and AI algorithms should be used to monitor the ecosystem's operational status in real time to ensure fair value distribution and compliance with technological ethics. Through these measures, the ecosystem will evolve from collaborative value-added to symbiotic co-creation, achieving an exponential release of data element value.

[0088] The technical solution of this invention also determines multi-dimensional evaluation indicators for the data to be traded during the circulation process; wherein, the multi-dimensional indicators include structural, behavioral, and risk dimensions; and triggers an early warning mechanism when the multi-dimensional evaluation indicators exceed the reference threshold range under the circulation mode. It can use the critical phase transition early warning system to capture imbalance signals in ecological evolution by monitoring indicator fluctuations in real time, providing quantitative basis for regulatory adjustments, and helping to further achieve precise matching between governance effectiveness and ecological evolution.

[0089] Example 3

[0090] Figure 5 This is a schematic diagram of a transaction data circulation device provided in Embodiment 3 of the present invention. This device can execute the transaction data circulation method provided in any embodiment of the present invention, and possesses the corresponding functional modules and beneficial effects for executing the method. For example... Figure 5 As shown, the device includes:

[0091] The circulation mode determination module 310 is used to acquire data to be traded and determine the circulation mode of the data to be traded based on the trading scenario of the data to be traded; wherein, the circulation mode includes a competition mode, a coexistence mode and a symbiotic mode. The competition mode is driven by profit and realizes data circulation based on price signals. The coexistence mode is driven by the market and realizes data circulation based on the synergy of policy regulation and market mechanisms. The symbiotic mode is driven by value and realizes data circulation based on scenario-driven and artificial intelligence empowerment.

[0092] The first data processing module 320 is used to process the data to be traded through data engineering and / or multimedia engineering to obtain the first transaction data;

[0093] The second data processing module 330 is used to process the first transaction data through knowledge engineering based on the application scenario requirements and circulation mode of the data to be traded to obtain the second transaction data; wherein, the knowledge engineering is constructed based on big data and artificial intelligence, and has knowledge processing and access control functions.

[0094] The data consumption module 340 is used to consume the second transaction data according to the circulation mode of the data to be traded.

[0095] Optionally, the circulation mode determination module 310 is specifically used for:

[0096] If the transaction scenario of the data to be traded is a single market data docking scenario, then the circulation mode of the data to be traded is determined to be a competitive mode;

[0097] If the transaction scenario of the data to be traded is a public data entry scenario, then the circulation mode of the data to be traded is determined to be a co-occurrence mode;

[0098] If the transaction scenario of the data to be traded is a multi-source data, multi-role scenario, then the circulation mode of the data to be traded is determined to be a symbiotic mode.

[0099] Optionally, the first data processing module 320 is specifically used for:

[0100] Structured transaction data is obtained by processing the structured data in the data to be traded through data engineering; wherein, the data engineering includes data integration, data governance, data processing and data services;

[0101] Unstructured transaction data is obtained by processing the unstructured data in the data to be traded through multimedia engineering; wherein, the multimedia engineering includes image understanding and multimodal parsing;

[0102] The first transaction data is determined based on the structured transaction data and / or the unstructured transaction data.

[0103] Optionally, the construction process of the knowledge engineering includes:

[0104] Using knowledge graphs as a carrier and metadata management as a basis, a multi-agent cognitive collaboration model is constructed to realize knowledge extraction, knowledge refinement and access control.

[0105] A dynamic cognitive reasoning mechanism is established using a machine learning-based knowledge reasoning engine to achieve knowledge reasoning and knowledge planning;

[0106] Construct a comprehensive evaluation system that encompasses economic, social, and knowledge value to enable knowledge assessment.

[0107] Optionally, the second data processing module 330 is specifically used for:

[0108] If the circulation mode of the data to be traded is a competitive mode, then candidate transaction data is obtained by automatically cleaning the first transaction data using artificial intelligence;

[0109] If the circulation mode of the data to be traded is a co-occurrence mode, then candidate transaction data are obtained by constructing a transaction knowledge graph based on the first transaction data and the policy regulations using artificial intelligence;

[0110] If the circulation mode of the data to be traded is a symbiotic mode, then an integrated network of cognition, decision-making and service is constructed through artificial intelligence, and the first transaction data is processed based on the integrated network and multilateral protocols to obtain candidate transaction data;

[0111] The candidate transaction data is encapsulated and combined according to the application scenario requirements of the data to be traded to obtain the second transaction data.

[0112] Optionally, the device further includes: a security monitoring module, used for:

[0113] The target regulatory approach is determined based on the data attributes of the data to be traded; wherein, the data attributes include basic public data and derivative data;

[0114] The circulation process of the data to be traded is subject to security supervision based on the aforementioned target supervision method.

[0115] Optionally, the device further includes: a warning module, used for:

[0116] Determine multi-dimensional evaluation indicators for the data to be traded during the circulation process; wherein, the multi-dimensional indicators include structural dimensions, behavioral dimensions, and risk dimensions;

[0117] If the multi-dimensional evaluation indicators exceed the reference threshold range under the circulation mode, an early warning mechanism will be triggered.

[0118] The transaction data circulation device provided in this embodiment of the invention can execute a transaction data circulation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0119] Example 4

[0120] Figure 6A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0121] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0122] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0123] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as transaction data circulation methods.

[0124] In some embodiments, the transaction data circulation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the transaction data circulation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the transaction data circulation method by any other suitable means (e.g., by means of firmware).

[0125] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0126] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0127] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0128] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0129] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0130] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0131] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0132] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for circulating transaction data, characterized in that, The method includes: Acquire data to be traded, and determine the circulation mode of the data to be traded based on the trading scenario of the data; wherein, the circulation mode includes a competition mode, a coexistence mode and a symbiotic mode. The competition mode is driven by profit and realizes data circulation based on price signals. The coexistence mode is driven by the market and realizes data circulation based on the synergy of policy regulation and market mechanisms. The symbiotic mode is driven by value and realizes data circulation based on scenario-driven and artificial intelligence empowerment. The data to be traded is processed through data engineering and / or multimedia engineering to obtain the first transaction data; Based on the application scenario requirements and circulation model of the data to be traded, the first transaction data is processed through knowledge engineering to obtain the second transaction data; wherein, the knowledge engineering is built on big data and artificial intelligence, and has knowledge processing and access control functions; The second transaction data is consumed according to the circulation pattern of the data to be traded.

2. The method according to claim 1, characterized in that, Determining the circulation mode of the data to be traded based on the transaction scenario includes: If the transaction scenario of the data to be traded is a single market data docking scenario, then the circulation mode of the data to be traded is determined to be a competitive mode; If the transaction scenario of the data to be traded is a public data entry scenario, then the circulation mode of the data to be traded is determined to be a co-occurrence mode; If the transaction scenario of the data to be traded is a multi-source data, multi-role scenario, then the circulation mode of the data to be traded is determined to be a symbiotic mode.

3. The method according to claim 1, characterized in that, The first transaction data is obtained by processing the data to be traded through data engineering and / or multimedia engineering, including: Structured transaction data is obtained by processing the structured data in the data to be traded through data engineering; wherein, the data engineering includes data integration, data governance, data processing and data services; Unstructured transaction data is obtained by processing the unstructured data in the data to be traded through multimedia engineering; wherein, the multimedia engineering includes image understanding and multimodal parsing; The first transaction data is determined based on the structured transaction data and / or the unstructured transaction data.

4. The method according to claim 1, characterized in that, The construction process of the knowledge engineering includes: Using knowledge graphs as a carrier and metadata management as a basis, a multi-agent cognitive collaboration model is constructed to realize knowledge extraction, knowledge refinement and access control. A dynamic cognitive reasoning mechanism is established using a machine learning-based knowledge reasoning engine to achieve knowledge reasoning and knowledge planning; Construct a comprehensive evaluation system that encompasses economic, social, and knowledge value to enable knowledge assessment.

5. The method according to claim 4, characterized in that, Based on the application scenario requirements and circulation model of the data to be traded, the first transaction data is processed through knowledge engineering to obtain the second transaction data, including: If the circulation mode of the data to be traded is a competitive mode, then candidate transaction data is obtained by automatically cleaning the first transaction data using artificial intelligence; If the circulation mode of the data to be traded is a co-occurrence mode, then candidate transaction data are obtained by constructing a transaction knowledge graph based on the first transaction data and the policy regulations using artificial intelligence; If the circulation mode of the data to be traded is a symbiotic mode, then an integrated network of cognition, decision-making and service is constructed through artificial intelligence, and the first transaction data is processed based on the integrated network and multilateral protocols to obtain candidate transaction data; The candidate transaction data is encapsulated and combined according to the application scenario requirements of the data to be traded to obtain the second transaction data.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: The target regulatory approach is determined based on the data attributes of the data to be traded; wherein, the data attributes include basic public data and derivative data; The circulation process of the data to be traded is subject to security supervision based on the aforementioned target supervision method.

7. The method according to any one of claims 1-5, characterized in that, The method further includes: Determine multi-dimensional evaluation indicators for the data to be traded during the circulation process; wherein, the multi-dimensional indicators include structural dimensions, behavioral dimensions, and risk dimensions; If the multi-dimensional evaluation indicators exceed the reference threshold range under the circulation mode, an early warning mechanism will be triggered.

8. A transaction data circulation device, characterized in that, The device includes: The circulation mode determination module is used to acquire data to be traded and determine the circulation mode of the data to be traded based on the trading scenario of the data to be traded; wherein, the circulation mode includes a competition mode, a symbiotic mode and a coexistence mode. The competition mode is driven by profit and realizes data circulation based on price signals. The symbiotic mode is driven by the market and realizes data circulation based on the synergy of policy regulation and market mechanisms. The coexistence mode is driven by value and realizes data circulation based on scenario-driven and artificial intelligence empowerment. The first data processing module is used to process the data to be traded through data engineering and / or multimedia engineering to obtain the first transaction data; The second data processing module is used to process the first transaction data through knowledge engineering based on the application scenario requirements and circulation mode of the data to be traded to obtain the second transaction data; wherein, the knowledge engineering is built based on big data and artificial intelligence, and has knowledge processing and access control functions. The data consumption module is used to consume the second transaction data according to the circulation pattern of the data to be traded.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the transaction data circulation method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the transaction data circulation method according to any one of claims 1-7.

Images