Enterprise data platform for multi-source heterogeneous data

Abstract

The invention relates to the technical field of enterprise data management, and discloses an enterprise data medium station for multi-source heterogeneous data, which comprises an acquisition module, an analysis module, a reconstruction module and a management module. According to the application, efficient processing and management of enterprise multi-source heterogeneous data are realized by utilizing cooperative work of the acquisition module, the analysis module, the reconstruction module and the management module; the acquisition module comprehensively acquires data and generates metadata, the analysis module formulates a reconstruction strategy based on the metadata, the reconstruction module dynamically reconstructs the data and generates description, and the management module properly stores the data; on the basis of close cooperation of all the modules, a data island is effectively broken, the problems of incomplete data acquisition, inflexible reconstruction, nonstandard description, incomplete management and the like in the traditional technology are solved, the availability and decision-making value of enterprise data are improved, powerful support is provided for operation of enterprises in a complex data environment, and the system is suitable for popularization and application. And enterprises can better adapt to business change and development requirements.

Description

Technical Field

[0001] The present application relates to the field of enterprise data management technology, and specifically to an enterprise data middle platform for multi-source heterogeneous data. Background Art

[0002] In today's digital age, massive amounts of multi-source heterogeneous data are generated during enterprise operations. These data come from a wide range of sources, including various business systems within the enterprise, external partner organizations, and IoT devices. There are significant differences in their data structures, data formats, and semantics.

[0003] Traditional data processing technology exposes many problems when facing such a complex data environment, which are specifically manifested in: On the one hand, the data collection process lacks systematicity and efficiency, making it difficult to comprehensively and accurately obtain data from various data sources, and it is also impossible to effectively generate high-quality metadata to clearly describe the characteristics and sources of these data; On the other hand, in terms of data reconstruction, existing methods often lack flexibility and intelligence. Most of them are based on fixed rules or simple statistical analysis, and cannot adjust the reconstruction strategy in time according to the dynamic changes of enterprise business logic. As a result, the reconstructed data cannot meet the increasingly complex and changing business needs of enterprises. Moreover, the description of reconstructed data is not standardized and unified enough, which makes it extremely difficult to manage and understand the data; In addition, when managing these data, there is a lack of effective version management mechanisms, making it difficult to trace the data's change history and update information, which is not conducive to the company's long-term use of data and decision-making analysis.

[0004] Chinese patent number CN202110769462.X discloses a data middle-end system, construction method and device. Although the invention realizes processes such as data collection and classification, it lacks a mechanism for deep reconstruction and precise insertion of new data when faced with multi-source heterogeneous data, and the subject targeting and natural language application of the reconstructed data description are not perfect enough.

[0005] Furthermore, Chinese Patent No. CN202111017291.1 discloses an archival data archiving method, a computer device, and a computer-readable storage medium based on a data middle platform, but the invention mainly focuses on the archival data archiving link, and only detects specific attributes of the archival data in data processing, which cannot meet the dynamic analysis of multi-source heterogeneous data.

[0006] To sum up, a new technical solution for enterprise data with multi-source heterogeneous data is urgently needed to solve these problems, so as to achieve efficient data collection, intelligent reconstruction, standardized description and effective management, thereby improving the data utilization efficiency and decision-making ability of the enterprise. Summary of the invention

[0007] The purpose of this application is to provide an enterprise data middle platform for multi-source heterogeneous data to solve the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above-mentioned purpose, the present application discloses the following technical solutions: an enterprise data middle platform for multi-source heterogeneous data, comprising a collection module, an analysis module, a reconstruction module and a management module which are sequentially communicatively connected, wherein the collection module is also communicatively connected to the reconstruction module; The acquisition module is used to collect multi-source heterogeneous data generated when the enterprise conducts business, and generate metadata of the multi-source heterogeneous data, and transmit the metadata to the analysis module; wherein the metadata is used to describe the multi-source heterogeneous data; The analysis module is used to analyze the metadata and generate a reconstruction strategy for the metadata, and transmit the reconstruction strategy to the analysis module; wherein the reconstruction strategy is used to retrieve the corresponding multi-source heterogeneous data based on the analysis result of the metadata for structural reorganization and description; The reconstruction module is used to execute the reconstruction strategy, generate reconstruction data and corresponding description, and transmit the reconstruction data and the description to the user and the management module respectively; The management module is used to manage and store the reconstruction data and the description.

[0009] Preferably, the acquisition module includes a plurality of data acquisition submodules, each of the data acquisition submodules corresponds to a data source type, and each of the data acquisition submodules independently acquires data and generates the corresponding metadata.

[0010] Preferably, the generation process of the reconstruction strategy includes: Analyze the metadata based on a preset first classification relationship to obtain a first classification mode; wherein the first classification relationship is a relationship between type characteristics of multi-source heterogeneous data obtained based on historical analysis records; Analyzing each attribute and field in the metadata based on a preset first association relationship to obtain a first association pattern; wherein the first association relationship is an association of statistical characteristics between multi-source heterogeneous data obtained based on historical analysis records; The first classification pattern and the first association pattern are defined as a reconstruction strategy and transmitted to the reconstruction module.

[0011] Preferably, the process of generating the reconstructed data includes: Execute the reconstruction strategy to generate initial reconstruction data, determine whether there is new business logic, if yes, execute the preset dynamic reconstruction strategy, otherwise define the initial reconstruction data as reconstruction data and output it; The dynamic reconstruction strategy is specifically as follows: Analyze the new business logic and obtain a dynamic logic chain, which is used to characterize the generation logic of multi-source heterogeneous data when the enterprise develops new business; Based on the dynamic logic chain, newly added multi-source heterogeneous data is obtained; The dynamic logic chain is used to insert newly added multi-source heterogeneous data into the initial reconstructed data to generate reconstructed data; wherein, the inserting of the newly added multi-source heterogeneous data is to insert the newly added multi-source heterogeneous data that meets a preset insertion threshold into the initial reconstructed data based on a calculated insertion score, and the insertion score is a quantitative value obtained based on the reconstruction strategy and the dynamic reconstruction strategy, and is used to characterize the degree of matching between the newly added multi-source heterogeneous data and the initial reconstructed data.

[0012] Preferably, the generation process of the description comprises: Utilizing a preset subject database and natural language technology, the reconstruction data and the corresponding reconstruction process are recorded using a standardized description language and format to obtain a corresponding description; wherein the subject database stores standardized description languages ​​and formats corresponding to different subjects, and the subjects include at least enterprise service subjects, industrial service subjects, and comprehensive security subjects.

[0013] Preferably, the management module includes a data storage unit and an index unit, wherein the data storage unit is used to store the reconstructed data and the description, and the index unit is used to retrieve and locate the reconstructed data and the description based on keywords.

[0014] Preferably, the data acquisition submodule is also used to preprocess multi-source heterogeneous data.

[0015] Preferably, after generating the reconstruction strategy, the analysis module verifies the reconstruction strategy using a preset reconstruction knowledge base, wherein the reconstruction knowledge base stores expert knowledge for verifying the reconstruction strategy.

[0016] Preferably, the expert knowledge used to verify the reconstruction strategy is specifically: Construct a reconstructed knowledge graph based on the enterprise business process and data architecture. The nodes in the reconstructed knowledge graph are used to represent different business links, data entities and data processing operations, and the edges are used to represent the reconstructed relationships between different business links, data entities and data processing operations. Based on historical data reconstruction records, key paths and decision node information are obtained, and combined with the experience and judgment of domain experts, weights and priorities are assigned to the elements in the reconstructed knowledge graph; Collect common data standards and specifications, data quality assessment indicator system, and data security compliance requirements in the industry, and integrate them into the corresponding nodes and edges of the reconstructed knowledge graph; Using natural language processing technology to extract implicit knowledge and empirical rules from business documents, technical manuals and historical data processing records within the enterprise, and integrating them into the structure and attributes of the reconstructed knowledge graph; Continuously monitor the dynamic changes of the enterprise's business and the technological development trends, and update the reconstructed knowledge graph; When verifying the reconstruction strategy, the data classification pattern, association pattern and processing steps in the reconstruction strategy are matched and compared with the corresponding knowledge in the reconstructed knowledge graph, and based on the preset consistency rules and similarity thresholds, the reconstruction strategy that meets the consistency rules and the similarity thresholds is output.

[0017] Preferably, the management module records changes and update information of different versions based on version management of the reconstructed data and the description.

[0018] Beneficial effects: The enterprise data middle platform for multi-source heterogeneous data of the present application realizes efficient processing and management of multi-source heterogeneous data of the enterprise by utilizing the collaborative work of the acquisition module, analysis module, reconstruction module and management module; the acquisition module comprehensively acquires data and generates metadata, the analysis module formulates a reconstruction strategy based on this, the reconstruction module dynamically reorganizes data and generates descriptions, and the management module properly stores them; based on the close cooperation of various modules, it effectively breaks down data silos, solves the problems of traditional technologies in incomplete data collection, inflexible reconstruction, non-standard descriptions and imperfect management, improves the availability and decision-making value of enterprise data, provides strong support for the operation of enterprises in complex data environments, and enables enterprises to better adapt to business changes and development needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A structural block diagram of an enterprise data middle platform for multi-source heterogeneous data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0022] In this article, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprising..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0023] This embodiment discloses Figure 1 An enterprise data middle platform for multi-source heterogeneous data shown includes a collection module, an analysis module, a reconstruction module, and a management module that are sequentially connected in communication, wherein the collection module is also connected in communication with the reconstruction module; The collection module is used to collect multi-source heterogeneous data generated when the enterprise conducts business, and generate metadata of the multi-source heterogeneous data, and transmit the metadata to the analysis module; wherein the metadata is used to describe the multi-source heterogeneous data; The analysis module is used to analyze the metadata and generate a reconstruction strategy for the metadata, and transmit the reconstruction strategy to the analysis module; wherein the reconstruction strategy is used to retrieve the corresponding multi-source heterogeneous data based on the analysis results of the metadata for structural reorganization and description; The reconstruction module is used to execute the reconstruction strategy, generate reconstruction data and corresponding description, and transmit the reconstruction data and description to the user and management modules respectively; The management module is used to manage and store reconstruction data and descriptions.

[0024] Based on the above, this embodiment utilizes the collaborative work of the acquisition module, analysis module, reconstruction module and management module to achieve efficient processing and management of multi-source heterogeneous data of the enterprise; the acquisition module comprehensively acquires data and generates metadata, the analysis module formulates a reconstruction strategy based on this, the reconstruction module dynamically reorganizes the data and generates a description, and the management module properly stores it; based on the close cooperation of each module, it effectively breaks down data silos, solves the problems of traditional technologies in incomplete data collection, inflexible reconstruction, non-standard description and imperfect management, improves the availability and decision-making value of enterprise data, provides strong support for the operation of enterprises in complex data environments, and enables enterprises to better adapt to business changes and development needs.

[0025] Specifically, the collection module includes a plurality of data collection submodules, each of which corresponds to a data source type, and each of which independently collects data and generates corresponding metadata.

[0026] Based on the above, this embodiment uses multiple data collection submodules of the collection module to achieve targeted collection and metadata generation of data from different data sources. Each submodule operates independently to ensure the comprehensiveness and accuracy of data collection. Compared with the traditional single collection method, it greatly improves the collection efficiency and quality, thereby effectively solving the problem of difficulty in collecting multi-source heterogeneous data, laying a solid foundation for subsequent analysis, reconstruction and other links, enabling enterprises to integrate various data resources more quickly and accurately, improve overall data processing efficiency, and enhance the competitiveness of enterprise data management.

[0027] Specifically, the generation process of the reconstruction strategy includes: The metadata is analyzed based on a preset first classification relationship to obtain a first classification mode; wherein the first classification relationship is a relationship between type characteristics of multi-source heterogeneous data obtained based on historical analysis records; Analyze each attribute and field in the metadata based on a preset first association relationship to obtain a first association pattern; wherein the first association relationship is an association of statistical characteristics between multi-source heterogeneous data obtained based on historical analysis records; The first classification pattern and the first association pattern are defined as a reconstruction strategy and transmitted to a reconstruction module.

[0028] Based on the above, this embodiment uses the first classification relationship and the first association relationship analysis metadata based on historical analysis records to generate a reconstruction strategy, thereby achieving the scientificity and effectiveness of the reconstruction strategy. Through in-depth mining of the characteristics and statistical characteristics of multi-source heterogeneous data types, a classification and association model that fits the actual data situation is constructed, overcoming the defect of the traditional reconstruction strategy of lacking in-depth analysis, improving the rationality of data reorganization, ensuring that the reconstructed data can better meet the business needs of the enterprise, and helping the enterprise to obtain more valuable information from complex data.

[0029] Specifically, the process of generating reconstructed data includes: Execute the reconstruction strategy to generate initial reconstruction data and determine whether there is new business logic. If yes, execute the preset dynamic reconstruction strategy; otherwise, define the initial reconstruction data as reconstruction data and output it; Dynamic reconstruction strategy, specifically: Analyze the new business logic and obtain a dynamic logic chain, which is used to characterize the generation logic of multi-source heterogeneous data when the enterprise develops new business; Based on the dynamic logic chain, newly added multi-source heterogeneous data is obtained; By utilizing a dynamic logic chain, newly added multi-source heterogeneous data are inserted into the initial reconstructed data to generate reconstructed data; wherein, inserting the newly added multi-source heterogeneous data is to insert the newly added multi-source heterogeneous data that meets a preset insertion threshold into the initial reconstructed data based on a calculated insertion score, and the insertion score is a quantitative value obtained based on a reconstruction strategy and a dynamic reconstruction strategy, and is used to characterize the degree of matching between the newly added multi-source heterogeneous data and the initial reconstructed data.

[0030] As a preferred implementation of this embodiment, this embodiment uses an insertion score calculation formula to calculate the insertion value, and the insertion value calculation formula is specifically: in, is the initial reconstruction data obtained based on the reconstruction strategy With the newly added multi-source heterogeneous data The matching score of is the initial reconstruction data obtained based on the dynamic reconstruction strategy With the newly added multi-source heterogeneous data The matching score of and is the weight coefficient, and , The calculated relative Insertion score; In this embodiment, the calculation method of the matching score can be but not limited to the matching degree obtained based on any existing similarity analysis method; Furthermore, when determining the insertion position, the newly added multi-source heterogeneous data is calculated With the initial reconstruction data Insertion score , choose to insert the position with the highest score As a new multi-source heterogeneous data The insertion position of , and when there are multiple positions with the same highest insertion score, the insertion position is determined after further weighting based on preset priority rules, for example, such as data type priority, data timestamp order, etc.

[0031] Based on the above, this embodiment uses the analysis and processing of new business logic in the dynamic reconstruction strategy to achieve rapid response of reconstructed data to business changes. By identifying the dynamic logic chain, obtaining new data and inserting it reasonably according to the insertion score, the reconstructed data can adapt to the new business development of the enterprise in a timely manner. Compared with traditional static reconstruction, it effectively solves the problem of data update lag, ensures that the enterprise can always make decisions based on the latest and most appropriate data during the business development process, and enhances the enterprise's adaptability and innovation vitality.

[0032] Specifically, the generation process of the description includes: Utilizing a preset subject database and existing natural language technology, a standardized description language and format are used to record the reconstruction data and the corresponding reconstruction process to obtain a corresponding description; wherein the subject database stores standardized description languages ​​and formats corresponding to different subjects, and the subjects include at least enterprise service subjects, industrial service subjects and comprehensive security subjects.

[0033] Based on the above, this embodiment uses a preset subject database and a method of generating descriptions using natural language technology to achieve standardization and comprehensibility of reconstructed data descriptions. Standard language formats are used to record data and reconstruction processes for different topics, which is convenient for users and management module operations. Compared with the previous chaotic descriptions, the convenience of data management is greatly improved, the efficient circulation and sharing of internal enterprise data is promoted, the efficiency of data utilization is improved, and the communication cost is reduced.

[0034] The specific management module includes a data storage unit and an index unit. The data storage unit is used to store the reconstructed data and descriptions, and the index unit is used to retrieve and locate the reconstructed data and descriptions based on keywords.

[0035] Based on the above, this embodiment uses the data storage unit and index unit of the management module to achieve efficient storage and retrieval of reconstructed data and descriptions. The data storage unit ensures safe data storage, and the index unit quickly locates data based on keywords, solving the problems of disordered storage and difficult retrieval in traditional data management. It enables enterprises to quickly obtain required data, improve data query efficiency, accelerate business processes, and enhance the agility of enterprise operations.

[0036] Specifically, the data acquisition submodule is also used to preprocess multi-source heterogeneous data.

[0037] Based on the above, this embodiment uses the data acquisition submodule to preprocess multi-source heterogeneous data to improve data quality. Through preprocessing operations such as removing noise and standardizing formats, the quality of the original data is improved, providing high-quality input for subsequent links. Compared with the data processing process without preprocessing, it effectively reduces errors and deviations caused by data problems, improves the reliability and stability of the entire data center, and ensures that corporate decisions are based on reliable data.

[0038] Specifically, after generating the reconstruction strategy, the analysis module verifies the reconstruction strategy using a preset reconstruction knowledge base, and the reconstruction knowledge base stores expert knowledge used to verify the reconstruction strategy.

[0039] Based on the above, this embodiment uses a preset reconstruction knowledge base to verify the reconstruction strategy to enhance the reliability of the reconstruction strategy. By integrating various knowledge with the reconstruction knowledge graph and comparing and verifying with the reconstruction strategy, the blindness of traditional strategy verification is avoided. The problem that the reconstruction strategy may be unreasonable is solved, ensuring that the reconstruction process is in line with the actual situation of the enterprise and industry standards, improving the success rate and effectiveness of data reconstruction, and promoting the standardization process of enterprise data management.

[0040] Specifically, the expert knowledge used to verify the reconstruction strategy is: Construct a reconstructed knowledge graph based on the enterprise business process and data architecture. The nodes in the reconstructed knowledge graph are used to represent different business links, data entities and data processing operations, and the edges are used to represent the reconstructed relationships between different business links, data entities and data processing operations. Based on historical data reconstruction records, key paths and decision node information are obtained, and combined with the experience and judgment of domain experts, weights and priorities are assigned to the elements in the reconstructed knowledge graph; Collect common data standards and specifications, data quality assessment indicator systems, and data security compliance requirements in the industry, and integrate them into the corresponding nodes and edges of the reconstructed knowledge graph; Use natural language processing technology to extract implicit knowledge and empirical rules from internal business documents, technical manuals, and historical data processing records, and integrate them into the structure and attributes of the reconstructed knowledge graph; Continuously monitor the dynamic changes of enterprise business and technological development trends, and update and reconstruct the knowledge graph; When verifying the reconstruction strategy, the data classification pattern, association pattern and processing steps in the reconstruction strategy are matched and compared with the corresponding knowledge in the reconstructed knowledge graph. Based on the preset consistency rules and similarity thresholds, a reconstruction strategy that meets the consistency rules and the similarity threshold is output.

[0041] Based on the above, this embodiment uses the construction and application of the reconstruction knowledge graph to achieve comprehensive and professional verification of the reconstruction strategy. By integrating multi-source knowledge such as business processes and expert experience and continuously updating it, it provides solid support for strategy verification. Compared with simple rule verification, it can more accurately judge the pros and cons of reconstruction strategies, optimize the reconstruction process, improve the professionalism and accuracy of enterprise data processing, and enhance the core competitiveness of enterprise data management.

[0042] Specifically, the management module records the changes and update information of different versions based on the version management of the reconstructed data and descriptions.

[0043] Based on the above, this embodiment uses the version management function of the management module to effectively track the reconstruction data and description changes. Recording different version information facilitates the backtracking and analysis of data evolution, solving the problem of version confusion and untraceability in traditional data management. It enables enterprises to clearly understand the data development process, better use historical data to assist decision-making, and improve the scientificity and sustainability of enterprise data management.

[0044] In summary, the enterprise data middle platform for multi-source heterogeneous data in this embodiment realizes efficient processing and management of enterprise multi-source heterogeneous data by utilizing the collaborative work of the acquisition module, analysis module, reconstruction module and management module; the acquisition module comprehensively acquires data and generates metadata, the analysis module formulates a reconstruction strategy based on this, the reconstruction module dynamically reorganizes data and generates descriptions, and the management module properly stores; based on the close cooperation of various modules, data silos are effectively broken, and the problems of traditional technologies in incomplete data collection, inflexible reconstruction, non-standard descriptions and imperfect management are solved, the availability and decision-making value of enterprise data are improved, and strong support is provided for the operation of enterprises in complex data environments, so that enterprises can better adapt to business changes and development needs.

[0045] In the embodiments provided in the present application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to implement the functions described herein, or a combination thereof. For software implementation, part or all of the processes of the embodiment can be completed by instructing the relevant hardware through a computer program. When implemented, the above program can be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein the communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that can be accessed by a computer. Computer-readable storage media can include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer.

[0046] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An enterprise data platform for multi-source heterogeneous data, characterized in that: It includes a collection module, an analysis module, a reconstruction module and a management module which are communicatively connected in sequence, wherein the collection module is also communicatively connected with the reconstruction module; The acquisition module is used to collect multi-source heterogeneous data generated when the enterprise conducts business, and generate metadata of the multi-source heterogeneous data, and transmit the metadata to the analysis module; wherein the metadata is used to describe the multi-source heterogeneous data; The analysis module is used to analyze the metadata and generate a reconstruction strategy for the metadata, and transmit the reconstruction strategy to the analysis module; wherein the reconstruction strategy is used to retrieve the corresponding multi-source heterogeneous data based on the analysis result of the metadata for structural reorganization and description; The reconstruction module is used to execute the reconstruction strategy, generate reconstruction data and corresponding description, and transmit the reconstruction data and the description to the user and the management module respectively; The management module is used to manage and store the reconstruction data and the description.

2. The enterprise data middle platform for multi-source heterogeneous data according to claim 1, characterized in that: The acquisition module includes a plurality of data acquisition submodules, each of which corresponds to a data source type, and each of which independently acquires data and generates corresponding metadata.

3. The enterprise data middle platform for multi-source heterogeneous data according to claim 1, characterized in that: The generation process of the reconstruction strategy includes: Analyze the metadata based on a preset first classification relationship to obtain a first classification mode; wherein the first classification relationship is a relationship between type characteristics of multi-source heterogeneous data obtained based on historical analysis records; Analyzing each attribute and field in the metadata based on a preset first association relationship to obtain a first association pattern; wherein the first association relationship is an association of statistical characteristics between multi-source heterogeneous data obtained based on historical analysis records; The first classification pattern and the first association pattern are defined as a reconstruction strategy and transmitted to the reconstruction module.

4. The enterprise data middle platform for multi-source heterogeneous data according to claim 1, characterized in that: The generation process of the reconstructed data includes: Execute the reconstruction strategy to generate initial reconstruction data, determine whether there is new business logic, if yes, execute the preset dynamic reconstruction strategy, otherwise define the initial reconstruction data as reconstruction data and output it; The dynamic reconstruction strategy is specifically as follows: Analyze the new business logic and obtain a dynamic logic chain, which is used to characterize the generation logic of multi-source heterogeneous data when the enterprise develops new business; Based on the dynamic logic chain, newly added multi-source heterogeneous data is obtained; The dynamic logic chain is used to insert newly added multi-source heterogeneous data into the initial reconstructed data to generate reconstructed data; wherein, the inserting of the newly added multi-source heterogeneous data is to insert the newly added multi-source heterogeneous data that meets a preset insertion threshold into the initial reconstructed data based on a calculated insertion score, and the insertion score is a quantitative value obtained based on the reconstruction strategy and the dynamic reconstruction strategy, and is used to characterize the degree of matching between the newly added multi-source heterogeneous data and the initial reconstructed data.

5. The enterprise data middle platform for multi-source heterogeneous data according to claim 1, characterized in that: The description generation process includes: Utilizing a preset subject database and natural language technology, the reconstruction data and the corresponding reconstruction process are recorded using a standardized description language and format to obtain a corresponding description; wherein the subject database stores standardized description languages ​​and formats corresponding to different subjects, and the subjects include at least enterprise service subjects, industrial service subjects, and comprehensive security subjects.

6. The enterprise data middle platform for multi-source heterogeneous data according to claim 1, characterized in that: The management module includes a data storage unit and an index unit. The data storage unit is used to store the reconstructed data and the description. The index unit is used to retrieve and locate the reconstructed data and the description based on keywords.

7. The enterprise data middle platform for multi-source heterogeneous data according to claim 2, characterized in that: The data acquisition submodule is also used to pre-process multi-source heterogeneous data.

8. The enterprise data middle platform for multi-source heterogeneous data according to claim 3, characterized in that: After generating the reconstruction strategy, the analysis module verifies the reconstruction strategy using a preset reconstruction knowledge base, wherein the reconstruction knowledge base stores expert knowledge for verifying the reconstruction strategy.

9. The enterprise data middle platform for multi-source heterogeneous data according to claim 8, characterized in that: The expert knowledge used to verify the reconstruction strategy is specifically: Construct a reconstructed knowledge graph based on the enterprise business process and data architecture. The nodes in the reconstructed knowledge graph are used to represent different business links, data entities and data processing operations, and the edges are used to represent the reconstructed relationships between different business links, data entities and data processing operations. Based on historical data reconstruction records, key paths and decision node information are obtained, and combined with the experience and judgment of domain experts, weights and priorities are assigned to the elements in the reconstructed knowledge graph; Collect common data standards and specifications, data quality assessment indicator system, and data security compliance requirements in the industry, and integrate them into the corresponding nodes and edges of the reconstructed knowledge graph; Using natural language processing technology to extract implicit knowledge and empirical rules from business documents, technical manuals and historical data processing records within the enterprise, and integrating them into the structure and attributes of the reconstructed knowledge graph; Continuously monitor the dynamic changes of the enterprise's business and the technological development trends, and update the reconstructed knowledge graph; When verifying the reconstruction strategy, the data classification pattern, association pattern and processing steps in the reconstruction strategy are matched and compared with the corresponding knowledge in the reconstructed knowledge graph, and based on the preset consistency rules and similarity thresholds, the reconstruction strategy that meets the consistency rules and the similarity thresholds is output.

10. The enterprise data middle platform for multi-source heterogeneous data according to claim 1, characterized in that: The management module records the changes and update information of different versions based on the version management of the reconstruction data and the description.

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