Cloud chain enterprise data integration method and device and computer equipment

Abstract

The application relates to the technical field of data processing, and discloses a cloud chain enterprise data fusion method and device and computer equipment, which comprises the following steps: acquiring enterprise original data of a plurality of enterprises participating in data fusion in a cloud chain, analyzing data elements of the enterprise original data; based on the data elements, analyzing data fusion relationships of the enterprise original data, and constructing a data fusion relationship graph of the enterprise original data; identifying high-potential fusion data in the enterprise original data, and constructing a three-dimensional number chain of the high-potential fusion data; constructing a fusion relationship table of the enterprise original data and the plurality of enterprises participating in data fusion, and performing data fusion on the enterprise original data and the plurality of enterprises participating in data fusion based on the fusion relationship table. The efficiency of cloud chain enterprise data fusion is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a cloud-based enterprise data integration method, apparatus, and computer equipment. Background Technology

[0002] Cloud computing technology is being used more and more widely in enterprise data management today, involving many important areas such as supply chain collaboration, financial risk control, and business decision-making. Enterprises accumulate a large amount of core data during their operations, including customer information, transaction records, and operational parameters. The reliable flow and secure protection of this data have become key requirements for driving business innovation and ecosystem collaboration.

[0003] Currently, existing technologies for data integration among cloud and blockchain enterprises mostly employ unified data mapping rules. This approach focuses primarily on the fundamental question of "whether data can be transferred across enterprises," without addressing the specific design requirements for key and critical aspects of data integration. This results in ambiguous definitions of data usage rights, a lack of fixed carriers to regulate the scope of data use during data circulation, and an inability to quantify the actual contribution and value of different data in the integration process, leading to low efficiency in data integration. Summary of the Invention

[0004] This invention provides a cloud blockchain enterprise data integration method, apparatus, and computer equipment to improve the efficiency of cloud blockchain enterprise data integration.

[0005] Firstly, a method for data integration among cloud-based enterprises is provided, including: Obtain the original enterprise data of multiple enterprises participating in data integration in the cloud chain, analyze the reusability, heterogeneity and non-exclusivity of the original enterprise data, and obtain data elements; Based on the data elements, analyze the data integration relationships of the enterprise's original data, and construct a data integration relationship map of the enterprise's original data according to the data integration relationships; Using the data integration relationship graph, high-potential integration data in the enterprise's original data is identified, and a three-dimensional data chain of the high-potential integration data is constructed; Based on the three-dimensional data chain, a data integration relationship table is constructed between the original enterprise data and multiple enterprises participating in the data integration. Based on the data integration relationship table, the original enterprise data and multiple enterprises participating in the data integration are integrated.

[0006] Secondly, a cloud-based enterprise data integration device is provided, characterized in that the device comprises: The data element analysis module is used to acquire the original enterprise data of multiple enterprises participating in data integration in the cloud chain, analyze the reusability, heterogeneity and non-exclusivity of the original enterprise data, and obtain data elements. The data relationship graph construction module is used to analyze the data integration relationship of the enterprise's original data based on the data elements, and construct the data integration relationship graph of the enterprise's original data according to the data integration relationship; A three-dimensional data chain construction module is used to identify high-potential integrated data in the enterprise's original data using the data integration relationship graph, and to construct a three-dimensional data chain of the high-potential integrated data. The data integration module is used to construct an integration relationship table between the original data of the enterprise and multiple enterprises participating in the data integration based on the three-dimensional data chain, and to integrate the original data of the enterprise with the multiple enterprises participating in the data integration based on the integration relationship table.

[0007] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the steps of the cloud-chain enterprise data integration method as described in any one of claims 1 to 8. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic diagram of an application environment for the cloud-chain enterprise data integration method in one embodiment of the present invention; Figure 2 This is a flowchart illustrating a cloud-chain enterprise data integration method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a cloud-chain enterprise data integration device in one embodiment of the present invention; Figure 4 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] The enterprise data dynamic annotation method provided in this embodiment of the invention can be applied to, for example... Figure 1 In this application environment, the client communicates with the server via a network. The server can deploy a data processing unit in the enterprise database, which includes a collection unit, a labeling unit, and a collaboration unit. This data processing unit analyzes the reusability, heterogeneity, and non-exclusivity of the enterprise's original data to obtain data elements. Based on these data elements, it analyzes the data integration relationships of the enterprise's original data and constructs a data integration relationship graph based on these relationships. Using this graph, it identifies high-potential integration data in the enterprise's original data and constructs a three-dimensional data chain for this high-potential integration data. Based on this three-dimensional data chain, it constructs an integration relationship table between the enterprise's original data and multiple participating enterprises in the data integration process. Based on this table, it performs data integration between the enterprise's original data and the multiple participating enterprises. The client can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will be described in detail below through specific embodiments.

[0012] Please see Figure 2 As shown, Figure 2 A flowchart illustrating the cloud-based enterprise data integration method provided in this embodiment of the invention includes the following steps: S1. Obtain the original enterprise data of multiple enterprises participating in data integration in the cloud chain, analyze the reusability, heterogeneity and non-exclusivity of the original enterprise data, and obtain data elements.

[0013] This invention provides a core foundational data source for cloud chain enterprise data integration by acquiring the original enterprise data of multiple enterprises participating in data integration within the cloud chain.

[0014] In detail, the raw data of participating companies can be collected by calling the standardized API interfaces opened by each participating company through the unified access platform of the cloud chain.

[0015] Furthermore, by analyzing the reusability, heterogeneity, and non-exclusivity of the original enterprise data, this embodiment of the invention demonstrates that data elements can transform the original data into standardized, tradable data elements, providing a value assessment basis for cloud blockchain data integration.

[0016] The reusability refers to the fact that data is not consumed after participating in the production process, can be used multiple times, and may cause the amount of data to increase further during use. The heterogeneity refers to the unique characteristics of data elements that distinguish them from the homogeneity of traditional production factors, indicating that the production value of two data bits may have significant differences, even for datasets of the same size. The non-exclusivity refers to the fact that it is technically impossible to exclude non-payers from using data services, and data can be infinitely copied for simultaneous use by multiple entities.

[0017] As an embodiment of the present invention, the reusability, heterogeneity, and non-exclusivity of the enterprise's original data are analyzed to obtain data elements, including: Data parsing is performed on the enterprise's raw data to identify thematic analysis data, business data, and basic data within the raw data. Based on the topic analysis data, the reuse potential and reuse constraints of the enterprise's original data in multiple business scenarios are analyzed to obtain the reusability of the enterprise's original data. Based on the business data, the quality differences and structural heterogeneity of the enterprise's original data under different application environments are evaluated to obtain the heterogeneity of the enterprise's original data. Based on the aforementioned basic data, the exclusivity of permissions and the exclusivity of technology when the enterprise's original data is used by multiple parties are analyzed to obtain the non-exclusivity of the enterprise's original data. The reusability, heterogeneity, and non-exclusivity are used as data elements of the enterprise's original data.

[0018] Thematic analysis data refers to indicator-type, statistical, and insight-type data oriented towards enterprise management and decision-making analysis themes. Business data refers to process-type and transaction-type data directly generated by enterprises in their daily operations and core business processes, such as "social security payment records," "job application," and "training registration records" for individual users. Basic data refers to benchmark-type and subject-type data that are uniformly managed, shared, and reused within the enterprise information system, such as "personnel," "assets," and "general equipment."

[0019] In detail, when analyzing enterprise raw data, one can first use semantic analysis techniques from natural language processing to extract core concepts and related information from the data. Then, using data clustering algorithms such as K-means, the data can be divided into thematic analysis data, business data, and basic data based on its function and purpose. When analyzing reusability, a scenario adaptability algorithm can be used to compare the data with the requirements of multiple business scenarios. Combined with historical reuse records, the reuse potential can be analyzed. At the same time, a rule engine can be used to identify reuse constraints such as data format and timeliness requirements to determine the reusability of the enterprise raw data. Data quality testing tools can be used to assess the quality differences of data in different application environments from the dimensions of integrity and consistency. Structural similarity algorithms can be used to analyze the heterogeneity of data field composition and relationships to obtain the non-homogeneity of the enterprise raw data. By checking whether the data usage permissions are restricted to a single entity through the access control system, the exclusivity of permissions can be determined. Then, interface compatibility testing tools can be used to verify whether the data technical specifications support multi-entity parallel use to determine the technical exclusivity, thereby obtaining the non-exclusivity of the enterprise raw data.

[0020] S2. Based on the data elements, analyze the data integration relationship of the enterprise's original data, and construct a data integration relationship map of the enterprise's original data according to the data integration relationship.

[0021] This invention, through the analysis of data integration relationships based on the data elements, clarifies the logical connections between original enterprise data, thereby resolving the issues of ambiguous data relationships and lack of evidence in data integration.

[0022] The data integration relationship refers to the logical association rules and collaborative paths formed by the cross-organizational flow of enterprise raw data, specifically manifested as the structured associations of data elements such as flow direction, dependency relationship and usage rights between enterprises.

[0023] As an embodiment of the present invention, based on the data elements, analyzing the data integration relationship of the enterprise's original data includes: Query the reuse constraints, quality difference indicators, and permission exclusivity parameters of the data elements; Based on the aforementioned reuse constraints, determine the boundaries of the sharing permissions for original enterprise data among multiple enterprises; Based on the aforementioned quality difference indicators, quality matching rules for the enterprise's original data are constructed. Based on the aforementioned exclusive permission parameters, the hierarchical relationship of the enterprise's original data is set; Based on the shared permission boundaries and quality matching rules, construct the horizontal association rules for the enterprise's original data; Based on the hierarchical relationship, construct the vertical inheritance rules for the enterprise's original data; Based on the horizontal association rules and the vertical inheritance rules, the data integration relationships of the enterprise's original data are determined.

[0024] The reuse constraints refer to the conditions that limit the reuse of enterprise raw data across multiple business scenarios or enterprises, including restrictions on the scope of data use scenarios and timeliness constraints. The quality difference index refers to a quantitative indicator that measures the quality performance differences of enterprise raw data in different application environments, including completeness and consistency. The permission exclusivity parameter refers to a parameter describing whether the enterprise raw data is restricted from being used simultaneously by multiple entities at the permission level, including the upper limit of the number of entities allowed to access it and the exclusive identifier of the access permission. The shared permission boundary refers to the scope and restrictions on the sharing of enterprise raw data among multiple enterprises, determined based on the reuse constraints, to clarify which enterprises have the right to access the data and can share it. In which scenarios is the data used, and what permission rules must be followed during its use? The quality matching rule refers to the rule built based on the quality difference index to determine whether the quality of the enterprise's original data is suitable for the needs of different enterprises. The horizontal association rule refers to the association logic between similar or related business data of different enterprises after integrating the shared permission boundary and the quality matching rule. The vertical inheritance rule refers to the attribute and permission inheritance logic between data at different levels built based on the data hierarchy relationship. It is used to clarify the constraint transmission method of the upper-level data (such as core business data) to the lower-level data (such as derived analysis data) (such as the inheritance of permission scope and quality standards), as well as the dependency relationship of the lower-level data on the upper-level data.

[0025] In detail, a permission rule engine can be used to parse reuse constraints, transforming data usage scenarios and format adaptation requirements into executable permission rules. This defines the scope and limitations of data sharing among multiple enterprises, clarifying the boundaries of sharing permissions. Matching rules are established through quality difference indicators (such as accuracy deviation and timeliness level), for example, using similarity algorithms to calculate the compatibility between data units. Through permission exclusivity parameters (such as role permission levels and technical compatibility indicators), based on the degree to which data is exclusively possessed by a single entity or shared by multiple entities, the enterprise's original data is divided into different levels, establishing clear hierarchical relationships, such as a core layer, a shared layer, and a public layer. Association analysis is then employed. The analysis algorithm integrates shared permission boundaries and quality matching rules to identify the association logic of original data from different enterprises in scenarios where permissions are compatible and quality is matched, and constructs horizontal association rules. Based on the established hierarchical relationship, the algorithm analyzes the dependency relationship and transmission logic between data at different levels, and determines the inheritance mode of data attributes and permissions between levels, such as inheritance, association and aggregation relationships, to construct vertical inheritance rules. The algorithm integrates horizontal association rules and vertical inheritance rules through a relationship fusion model (such as a meta-model) to comprehensively judge the association strength and transmission path of original data of enterprises across multiple enterprises, and finally determines the complete data integration relationship.

[0026] Furthermore, by constructing a data integration relationship map of the enterprise's original data based on the data integration relationship, the abstract enterprise original data integration relationship can be transformed into a visual map, intuitively showing the connection structure and interaction path between multiple enterprise data.

[0027] The data integration relationship graph refers to a visual network model built on integration relationships, which presents the flow path and interaction rules of data elements between enterprise nodes in the form of a graph structure.

[0028] As an embodiment of the present invention, a data integration relationship map of the enterprise's original data is constructed based on the data integration relationship, including: Based on the data integration relationship, identify the data entities and association rules of the enterprise's original data; Based on the data entities, construct graph nodes for the enterprise's original data; Based on the association rules, define the graph connection edges of the enterprise's original data; Assign weight attributes based on data features to each map node to obtain weighted nodes; Assign a strength value based on the fusion relationship to each connection edge in the graph; Based on the weight nodes and the intensity values, a data integration relationship graph of the enterprise's original data is constructed.

[0029] In detail, entity recognition technology can be used to extract data objects with independent business significance from the enterprise's original data as data entities. Simultaneously, rule extraction algorithms are used to parse the relationship logic between data entities (such as sharing, inheritance, etc.) from the integration relationships, resulting in association rules. Based on the identified data entities, graph database modeling technology is used to assign a unique identifier to each entity, transforming it into an independent node in the graph, and associating it with the entity's corresponding basic information (such as data source, business type), constructing graph nodes. According to the parsed association rules, relationship mapping tools are used to transform the relationship logic of data entities in the rules (such as horizontal association, vertical inheritance) into connection relationships between nodes in the graph. Define the meaning of each connection edge (e.g., "sharing" or "inheritance") to complete the definition of graph connection edges; for each graph node, use an attribute weighting algorithm to transform the characteristics of data elements (reusability, heterogeneity, non-exclusivity) into quantifiable weight values, and then allocate weight proportions according to the degree of influence of each element on data integration to obtain weighted nodes with weight attributes; assign strength values ​​to each connection edge according to the tightness of the integration relationship (e.g., transaction frequency, data dependency depth), and use a normalization algorithm to transform multi-dimensional indicators (e.g., interaction frequency, data flow) into standardized strength coefficients; integrate weighted nodes and strength edges, and use the storage and computing engine of the graph database (e.g., Apache TinkerPop) to construct a complete graph model.

[0030] S3. Using the data integration relationship graph, identify high-potential integration data in the enterprise's original data and construct a three-dimensional data chain of the high-potential integration data.

[0031] This invention, through the data integration relationship graph, identifies high-potential integration data within the enterprise's original data. This allows for the selection of data units with high circulation value, strong correlation, and reusability potential from massive amounts of original enterprise data. For example, in a supply chain graph, it is discovered that a core supplier's "real-time inventory data" node is simultaneously connected to the manufacturer's production plan, the logistics provider's scheduling system, and the financial institution's risk control model. Based on this, the inventory data is identified as high-potential integration data capable of driving multi-business collaboration.

[0032] The high-potential integrated data refers to data elements with significant circulation value and cross-enterprise collaboration potential, identified through data integration relationship graph analysis.

[0033] As an embodiment of the present invention, the data integration relationship graph is used to identify high-potential integration data in the enterprise's original data, including: Calculate the centrality index of each node in the data integration relationship graph, so as to determine the importance of each node in the data integration relationship graph based on the centrality index; Evaluate the weight of the associated edges of each node in the data integration relationship graph, so as to determine the connection strength between data in the data integration relationship graph based on the weight of the associated edges; Based on the importance and the connection strength, the key data flow paths of the enterprise's original data in the data integration relationship graph are determined; Based on the key data flow path, high-potential integration data are selected from the enterprise's original data.

[0034] The connection strength refers to the quantitative representation of the closeness of the relationship and interaction capability between two data nodes in the graph. It integrates multiple factors such as edge weight attributes, historical interaction frequency and relationship stability to measure the strength of the actual connection between data entities. The key data flow path refers to the core path in the data integration relationship graph that is composed of high-importance nodes and high-connection-strength edges and can support efficient data flow and cross-enterprise collaboration.

[0035] In detail, graph analysis algorithms (such as PageRank or degree centrality algorithms) can be used to calculate the centrality index of each node in the data integration relationship graph. The importance of a node is assessed by quantifying the breadth and depth of its connections in the graph topology. Weight analysis algorithms are used to quantify the strength of each connection edge, and the actual weight of the edge is determined by combining the integration relationship type represented by the edge (such as horizontal association or vertical inheritance). The higher the weight, the stronger the connection between the corresponding data. Path analysis algorithms are used to traverse the data flow paths in the graph, prioritizing paths containing high-importance nodes and high connection strength. By comparing the comprehensive scores of different paths (integrating node importance and edge strength), key data flow paths are determined. Path matching technology is used to locate all nodes contained in the key data flow paths, and the original enterprise data corresponding to these nodes is filtered out to obtain high-potential integration data.

[0036] Furthermore, by constructing a three-dimensional data chain of the high-potential integrated data, this embodiment of the invention can build a three-dimensional standardized circulation framework for high-potential data, transforming abstract data value into standardized digital commodities that are quantifiable, tradable, and regulated.

[0037] The three-dimensional data chain refers to a structured descriptive framework that simultaneously establishes digital definitions of ownership, content, and value for a data asset.

[0038] As an embodiment of the present invention, constructing the three-dimensional data chain of the high-potential converged data includes: Perform digital identity binding and ownership confirmation operations on high-potential integrated data to obtain data with confirmed ownership. A standardized interface for the confirmed rights data is constructed, and the confirmed rights data is productized and encapsulated using the standardized interface to obtain the transaction target data; Calculate the data value of the high-potential convergence data; Based on the data information of the confirmed rights data, the transaction target information of the transaction target data, and the data value, a three-dimensional data chain of the high-potential financing data is constructed.

[0039] The aforementioned "confirmed ownership data" refers to high-potential integrated data derived from data that contains a unique digital identity and clear ownership information after digital identity binding and ownership confirmation operations. The aforementioned "transaction target data" refers to structured data that can be directly used for cross-enterprise transactions.

[0040] In detail, blockchain digital identity authentication technology can be used to generate unique hash identifiers (i.e., digital identities) for data. Smart contracts can then be used to retrieve exclusive permission records and original ownership certificates from data elements for cross-verification, confirming the data's ownership subject and authorization chain, thus forming ownership-confirming data containing digital identity and ownership information. Based on industry-standard data interface specifications (such as the OPC UA protocol for industrial data), interface development tools define data access formats, transmission protocols, and permission verification rules. Data productization and encapsulation tools then package the ownership-confirming data with usage scenarios, permission scope, and other constraints based on this interface, forming transaction target data that can be directly used for transactions. Based on the data information of the ownership-confirming data (including digital identity and ownership subject), the transaction target information of the transaction target data (including interface specifications and usage constraints), and the data value, blockchain distributed ledger technology is used to associate and store the three types of information in timestamp order. These are then linked together using hash values ​​to form an immutable chain structure, thereby constructing a three-dimensional data chain covering ownership confirmation, transaction, and value dimensions.

[0041] Preferably, calculating the data value of the high-potential financial data includes: Analyze the original data value, active data value, and potential data value of the high-potential integration data; The data quality of the high-potential integrated data is evaluated based on the original data value, the active data value, and the potential data value. Based on the data quality, the data value of the high-potential integration data is calculated.

[0042] Detailed data characteristics that identify high-potential integrated data include data size, source credibility, structural completeness, update frequency, access popularity, and interaction activity. The original value of the data can be evaluated based on data size, source credibility, and structural completeness. The active value of the data can be obtained through dynamic monitoring and evaluation based on update frequency, access popularity, and interaction activity. The potential value of the data can be evaluated by predicting the possibility of expanded application of high-potential integrated data in future business scenarios using machine learning algorithms. It should be further explained that all values ​​need to be evaluated in conjunction with the target application scenarios and parameters in the actual application scenarios.

[0043] Furthermore, as an embodiment of the present invention, the formula for calculating data value is as follows:

[0044] in, Indicates the value of data. This indicates a data cycle terminal with high potential for data integration. This indicates the beginning of the data cycle for high-potential integrated data. This indicates that high-potential integration data is available at a specific point in time. The cost function, A function representing the frequency of use of high-potential integration data. The quality score function represents the data with high potential for integration. This represents the revenue prediction function for high-potential financial data. A function representing the scarcity of high-potential integrated data. express Adjustment coefficient, express Adjustment coefficient, This represents the discount rate for high-potential financial data.

[0045] It should be further explained that the core idea behind the data value calculation formula is to treat data as a dynamic asset that can continuously generate value over time, rather than a static resource. It simulates the discounted cash flow method used in the financial sector to assess long-term investment returns, comprehensively considering multiple factors throughout the data's lifecycle, including acquisition costs, usage activity, quality reliability, future revenue potential, and market scarcity. The data value calculated using this formula is more comprehensive, encompassing the data's inherent cost, reflecting its actual performance in daily use, and considering the impact of future value and scarcity. Moreover, the discounting process avoids overestimating future value, resulting in a more realistic assessment. This provides a reliable reference for both data transaction pricing and circulation decisions.

[0046] S4. Based on the three-dimensional data chain, construct a data integration relationship table between the original enterprise data and multiple enterprises participating in the data integration, and integrate the original enterprise data with the multiple enterprises participating in the data integration based on the data integration relationship table.

[0047] This invention, through the construction of a data exchange relationship table based on the three-dimensional data chain, can accurately map standardized data assets in the three-dimensional data chain to target enterprises, forming a matching relationship table that can guide the execution of specific data exchange business.

[0048] As an embodiment of the present invention, based on the three-dimensional data chain, a data integration relationship table is constructed between the original enterprise data and multiple enterprises participating in the data integration, including: Based on the three-dimensional data chain, the core fields of the enterprise's original data are determined. The core fields include the enterprise's original data identifier, the information on the ownership of the enterprise's data, the transaction target, and the value calculation results. Based on the core fields, construct a table showing the relationship between the original enterprise data and the data integration of multiple participating enterprises.

[0049] In detail, metadata extraction tools can be used to parse key information from the three-dimensional data chain. Data pattern recognition technology can be used to automatically extract information on the rights holders in data ownership confirmation, the data product specifications defined in the transaction target, and the pricing parameters generated in the value assessment process, forming a standardized set of core fields. Based on the determined core fields, a basic framework for the integration relationship table is built using a table modeling tool. Each core field is mapped to a column in the table (such as "data identifier column", "participating enterprise column", "ownership confirmation column", etc.). Then, according to the "correspondence between the original enterprise data and the participating enterprises", matching information is retrieved from the three-dimensional data chain and filled into the corresponding cells in the table to obtain the integration relationship table.

[0050] Furthermore, by integrating the original enterprise data with multiple participating enterprises based on the integration relationship table, this embodiment of the invention can compliantly and efficiently share the original enterprise data with the participating enterprises, thereby realizing the actual flow and collaborative utilization of data elements.

[0051] In detail, based on the correspondence between the original data of enterprises and the participating enterprises in the data integration relationship table, as well as the rules for confirmation of rights and transaction targets, access permissions for the corresponding data can be assigned to the participating enterprises and secure transmission can be achieved, thus completing data integration.

[0052] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0053] In one embodiment, a cloud-based enterprise data integration device is provided, which corresponds one-to-one with the cloud-based enterprise data integration method described in the above embodiments. For example... Figure 3 As shown, the data annotation device includes a data element analysis module 201, a data relationship graph construction module 202, a three-dimensional data chain construction module 203, and a data integration module 204. Detailed descriptions of each functional module are as follows: The data element analysis module 201 is used to acquire the original enterprise data of multiple enterprises participating in data integration in the cloud chain, analyze the reusability, heterogeneity and non-exclusivity of the original enterprise data, and obtain data elements. The data relationship graph construction module 202 is used to analyze the data integration relationship of the enterprise's original data based on the data elements, and construct a data integration relationship graph of the enterprise's original data based on the data integration relationship; The three-dimensional data chain construction module 203 is used to identify high-potential data in the enterprise's original data using the data integration relationship graph, and to construct a three-dimensional data chain of the high-potential data. The data integration module 204 is used to construct an integration relationship table between the original enterprise data and multiple enterprises participating in the data integration based on the three-dimensional data chain, and to integrate the original enterprise data with the multiple enterprises participating in the data integration based on the integration relationship table.

[0054] For specific limitations regarding the data annotation device, please refer to the limitations on the data annotation method above, which will not be repeated here. Each module in the aforementioned data annotation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0055] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps on the client side of a cloud-based enterprise data integration method.

[0056] It should be noted that the functions or steps that the computer equipment can perform can be referred to in the relevant descriptions of the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0057] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0058] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0059] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. In the above multiple embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A cloud-based enterprise data integration method, characterized in that, include: Obtain the original enterprise data of multiple enterprises participating in data integration in the cloud chain, analyze the reusability, heterogeneity and non-exclusivity of the original enterprise data, and obtain data elements; Based on the data elements, analyze the data integration relationships of the enterprise's original data, and construct a data integration relationship map of the enterprise's original data according to the data integration relationships; Using the data integration relationship graph, high-potential integration data in the enterprise's original data is identified, and a three-dimensional data chain of the high-potential integration data is constructed; Based on the three-dimensional data chain, a data integration relationship table is constructed between the original enterprise data and multiple enterprises participating in the data integration. Based on the data integration relationship table, the original enterprise data and multiple enterprises participating in the data integration are integrated.

2. The cloud-based enterprise data integration method as described in claim 1, characterized in that, Constructing the three-dimensional data chain of the high-potential converged data includes: Perform digital identity binding and ownership confirmation operations on high-potential integrated data to obtain data with confirmed ownership. A standardized interface for the confirmed rights data is constructed, and the confirmed rights data is productized and encapsulated using the standardized interface to obtain the transaction target data; Calculate the data value of the high-potential convergence data; Based on the data information of the confirmed rights data, the transaction target information of the transaction target data, and the data value, a three-dimensional data chain of the high-potential financing data is constructed.

3. The cloud-based enterprise data integration method as described in claim 2, characterized in that, The calculation of the data value of the high-potential financial data includes: Analyze the original data value, active data value, and potential data value of the high-potential integration data; The data quality of the high-potential integrated data is evaluated based on the original data value, the active data value, and the potential data value. Based on the data quality, the data value of the high-potential integration data is calculated.

4. The cloud-based enterprise data integration method as described in claim 1, characterized in that, Analyzing the reusability, heterogeneity, and non-exclusivity of the enterprise's original data yields data elements, including: Data parsing is performed on the enterprise's raw data to identify thematic analysis data, business data, and basic data within the raw data. Based on the topic analysis data, the reuse potential and reuse constraints of the enterprise's original data in multiple business scenarios are analyzed to obtain the reusability of the enterprise's original data. Based on the business data, the quality differences and structural heterogeneity of the enterprise's original data under different application environments are evaluated to obtain the heterogeneity of the enterprise's original data. Based on the aforementioned basic data, the exclusivity of permissions and the exclusivity of technology when the enterprise's original data is used by multiple parties are analyzed to obtain the non-exclusivity of the enterprise's original data. The reusability, heterogeneity, and non-exclusivity are used as data elements of the enterprise's original data.

5. The cloud-based enterprise data integration method as described in claim 1, characterized in that, Based on the aforementioned data elements, the data integration relationships of the enterprise's original data are analyzed, including: Query the reuse constraints, quality difference indicators, and permission exclusivity parameters of the data elements; Based on the aforementioned reuse constraints, determine the boundaries of the sharing permissions for original enterprise data among multiple enterprises; Based on the aforementioned quality difference indicators, quality matching rules for the enterprise's original data are constructed. Based on the aforementioned exclusive permission parameters, the hierarchical relationship of the enterprise's original data is set; Based on the shared permission boundaries and quality matching rules, construct the horizontal association rules for the enterprise's original data; Based on the hierarchical relationship, construct the vertical inheritance rules for the enterprise's original data; Based on the horizontal association rules and the vertical inheritance rules, the data integration relationships of the enterprise's original data are determined.

6. The cloud-based enterprise data integration method as described in any one of claims 1, characterized in that, Based on the aforementioned data integration relationships, a data integration relationship map of the enterprise's original data is constructed, including: Based on the data integration relationship, identify the data entities and association rules of the enterprise's original data; Based on the data entities, construct graph nodes for the enterprise's original data; Based on the association rules, define the graph connection edges of the enterprise's original data; Assign weight attributes based on data features to each map node to obtain weighted nodes; Assign a strength value based on the fusion relationship to each connection edge in the graph; Based on the weight nodes and the intensity values, a data integration relationship graph of the enterprise's original data is constructed.

7. The cloud-based enterprise data integration method as described in any one of claims 1, characterized in that, Using the data integration relationship graph, high-potential integration data in the enterprise's original data is identified, including: Calculate the centrality index of each node in the data integration relationship graph, so as to determine the importance of each node in the data integration relationship graph based on the centrality index; Evaluate the weight of the associated edges of each node in the data integration relationship graph, so as to determine the connection strength between data in the data integration relationship graph based on the weight of the associated edges; Based on the importance and the connection strength, the key data flow paths of the enterprise's original data in the data integration relationship graph are determined; Based on the key data flow path, high-potential integration data are selected from the enterprise's original data.

8. The cloud-based enterprise data integration method as described in any one of claims 1, characterized in that, Based on the aforementioned three-dimensional data chain, a data integration relationship table is constructed between the original enterprise data and multiple enterprises participating in the data integration process, including: Based on the three-dimensional data chain, the core fields of the enterprise's original data are determined. The core fields include the enterprise's original data identifier, the information on the ownership of the enterprise's data, the transaction target, and the value calculation results. Based on the core fields, construct a table showing the relationship between the original enterprise data and the data integration of multiple participating enterprises.

9. A cloud-based enterprise data integration device, characterized in that, The device includes: The data element analysis module is used to acquire the original enterprise data of multiple enterprises participating in data integration in the cloud chain, analyze the reusability, heterogeneity and non-exclusivity of the original enterprise data, and obtain data elements. The data relationship graph construction module is used to analyze the data integration relationships of the enterprise's original data based on the data elements, and construct a data integration relationship graph of the enterprise's original data based on the data integration relationships. A three-dimensional data chain construction module is used to identify high-potential integrated data in the enterprise's original data using the data integration relationship graph, and to construct a three-dimensional data chain of the high-potential integrated data. The data integration module is used to construct an integration relationship table between the original data of the enterprise and multiple enterprises participating in the data integration based on the three-dimensional data chain, and to integrate the original data of the enterprise with the multiple enterprises participating in the data integration based on the integration relationship table.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the cloud-chain enterprise data integration method as described in any one of claims 1 to 8.

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