Method, device and server for analyzing data flow

Abstract

The application provides a data flow analysis method and device and a server, and relates to the technical field of data processing, and comprises the following steps: obtaining a plurality of to-be-analyzed data sets of to-be-analyzed business systems; performing similarity analysis on to-be-analyzed data contained in each to-be-analyzed data set to obtain an analysis result; wherein the analysis result comprises a sharing relationship between each business system; and determining a data flow result between each business system according to the analysis result. The application can efficiently and accurately analyze the data flow of heterogeneous business systems, thereby assisting in managing the data life cycle based on the data flow.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus and server for analyzing data flow. Background Technology

[0002] For traditional enterprises or some public business platforms, their business systems are relatively independent. The technical architecture and system service providers used by each business system may differ, making it impossible to conduct unified data analysis and thus making it difficult to manage the data lifecycle of the business system effectively. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a data flow analysis method, apparatus and server that can efficiently and accurately analyze the data flow of heterogeneous business systems, thereby assisting in the management of the data lifecycle based on the data flow.

[0004] In a first aspect, embodiments of the present invention provide a method for analyzing data flow, comprising: acquiring a set of data to be analyzed from multiple business systems; performing similarity analysis on the data to be analyzed contained in each set of data to be analyzed to obtain analysis results; wherein the analysis results include sharing relationships between the business systems; and determining data flow results between the business systems based on the analysis results.

[0005] In one implementation, the step of acquiring the data set to be analyzed from multiple business systems includes: establishing communication connections with the multiple business systems; collecting metadata from each business system according to a specified data standard; performing data inventory processing on the metadata to determine the data attributes corresponding to the metadata; sending a preset data template to each business system; receiving shared data and data attributes corresponding to the shared data from each business system based on the preset data template; and for each business system, constructing the data set to be analyzed for that business system based on the metadata, the data attributes corresponding to the metadata, the shared data, and the data attributes corresponding to the shared data; wherein the data to be analyzed includes the metadata and the shared data.

[0006] In one embodiment, the step of performing similarity analysis on the data to be analyzed contained in each of the data sets to be analyzed to obtain the analysis results includes: for each of the business systems, calculating the data similarity between each of the data to be analyzed in the data set to be analyzed of that business system and each of the data to be analyzed in the data set to be analyzed of other business systems; and determining the target data to be analyzed from the data set to be analyzed of that business system and the target business system that shares the target data to be analyzed with that business system based on a preset first similarity threshold and the data similarity.

[0007] In one embodiment, the step of performing similarity analysis on the data to be analyzed contained in each of the datasets to be analyzed to obtain the analysis results further includes: determining the data dictionary corresponding to each of the business systems based on each of the datasets to be analyzed; calculating the dictionary similarity between the data dictionaries corresponding to each of the business systems; and determining the sharing relationship between each of the business systems based on a preset second similarity threshold and the dictionary similarity.

[0008] In one implementation, the step of determining the data flow results between the various business systems based on the analysis results includes: determining the target data to be analyzed shared by the various business systems based on the analysis results; and determining the data flow results between the various business systems based on the data attributes of the target data to be analyzed.

[0009] In one embodiment, the method further includes: generating a data directory based on the data attributes; wherein the data directory is used to describe the data attributes of the data to be analyzed and the business system in which the data to be analyzed resides.

[0010] In one embodiment, the method further includes: displaying the data directory through a directory display interface; and in response to a tag setting operation for the data directory, obtaining attribute tags and adding the attribute tags to the data directory.

[0011] Secondly, embodiments of the present invention also provide a data flow analysis device, comprising: a data acquisition module for acquiring a set of data to be analyzed from multiple business systems; a similarity analysis module for performing similarity analysis on the data to be analyzed contained in each set of data to be analyzed to obtain analysis results; wherein the analysis results include sharing relationships between the business systems; and a flow determination module for determining the data flow results between the business systems based on the analysis results.

[0012] Thirdly, embodiments of the present invention also provide a server, including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method described in any of the first aspects.

[0013] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the method described in any of the first aspects.

[0014] This invention provides a data flow analysis method, apparatus, and server. First, it acquires a set of data from multiple business systems to be analyzed. Then, it performs similarity analysis on the data within each set to obtain analysis results (including sharing relationships between the business systems). Finally, it determines the data flow results between the business systems based on the analysis results. This method acquires a set of heterogeneous data from multiple business systems, determines the sharing relationships between them through similarity analysis, and determines the data flow results based on these relationships. The data flow results provide crucial technology for data lifecycle management, thus enabling better management of the data lifecycle based on the data flow results.

[0015] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a data flow analysis method provided in an embodiment of the present invention;

[0019] Figure 2An architecture diagram of a data flow analysis platform provided in an embodiment of the present invention;

[0020] Figure 3 A schematic diagram of the structure of a data flow analysis device provided in an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of the structure of a server provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. 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.

[0023] In the digital age, all business operations are data-driven. For traditional enterprises and related departments, understanding the data lifecycle is crucial. The internet industry, built on big data platforms, can easily achieve unified data management, targeted data lineage analysis, and control over data origins and destinations. However, for traditional enterprises or public business platforms, under stable operational conditions, timely system modifications are difficult. Each business system is relatively independent, typically operating as a monolithic service, scattered across different functional departments. Furthermore, each system must be built independently, with varying system service providers and technical architectures, making it impossible to construct a unified data analysis system and thus hindering effective management of the data lifecycle.

[0024] Based on this, the present invention provides a data flow analysis method, device and server, which can efficiently and accurately analyze the data flow of heterogeneous business systems, thereby assisting in the management of the data lifecycle based on the data flow.

[0025] To facilitate understanding of this embodiment, a data flow analysis method disclosed in this embodiment of the invention will first be described in detail. See [link to relevant documentation]. Figure 1 The diagram shows a flowchart of a data flow analysis method, which mainly includes the following steps S102 to S106:

[0026] Step S102: Obtain the data set to be analyzed from multiple business systems. This data set may include metadata and shared data from the business systems, as well as data attributes for both metadata and shared data. Metadata may include business data, database data, document data, etc. Shared data may include database table data, database shared management data, table shared relationship data, interface shared relationship data, etc. Data attributes can be used to characterize the data's scale, classification, and tags. In one implementation, a specified data standard can be pre-set, and metadata from each business system can be collected according to this standard. Alternatively, shared data from each business system can be obtained based on a pre-defined data template.

[0027] Step S104: Perform similarity analysis on the data to be analyzed contained in each dataset to obtain analysis results. The analysis results include the sharing relationships between different business systems. A sharing relationship can be understood as two or more business systems using the same data. For example, business system A and business system B both call interface 1, or business system A and business system B both call database table 2, etc. In one implementation, considering that the sharing relationships between some data in the datasets to be analyzed of different business systems may be uncertain, a similarity analysis is performed on the data to be analyzed contained in each dataset. When the similarity of the data to be analyzed in the datasets of two or more business systems is higher than a preset similarity threshold, it can be determined that these business systems have a sharing relationship. For example, when the similarity between data to be analyzed a in the dataset of business system A and data to be analyzed b in the dataset of business system B is higher than the preset similarity threshold, it can be determined that business system A and business system B used the same data, that is, there is a sharing relationship between business system A and business system B.

[0028] Step S106: Determine the data flow results between various business systems based on the analysis results. The data flow direction refers to the information on the sharing and open transfer of data. In one embodiment, the analysis results may further include data to be analyzed shared between business systems with a sharing relationship, and the data attributes of this data to be analyzed are marked with the data source, thereby allowing the determination of the data flow results between various business systems with a sharing relationship based on the data source.

[0029] The data flow analysis method provided in this embodiment of the invention obtains a set of data to be analyzed from multiple heterogeneous business systems, determines the sharing relationship between the various business systems through similarity analysis, and determines the data flow result between the various business systems based on the sharing relationship. The data flow result provides key technology for the management of the data lifecycle, thereby enabling better management of the data lifecycle based on the data flow result.

[0030] In one embodiment, the above-mentioned data flow analysis method can be applied to a data flow analysis platform, which includes a data synchronization system and a data integration system. The data synchronization system is used to access heterogeneous data sources (i.e., the above-mentioned business system) and perform data inventory processing on the acquired metadata to obtain the data attributes of the metadata. The data integration system is used to acquire the above-mentioned metadata and its data attributes, and is also used to acquire shared data from various heterogeneous data sources. In addition, the data integration system can also provide similarity analysis functions and data flow determination functions, etc.

[0031] For ease of understanding, embodiments of the present invention provide, as follows: Figure 2 The diagram shows an architecture of a data flow analysis platform. The data flow analysis system is connected to business systems A, B, and N. The data flow analysis system includes functions such as data inventory, customized template (i.e., the aforementioned data template) data collection, data dictionary similarity analysis, and shared data similarity analysis. The data flow analysis system can also provide functions such as data catalog display and analysis result summary.

[0032] In the above Figure 2 Based on this, embodiments of the present invention provide an implementation method for obtaining a set of data to be analyzed from multiple business systems, as described in steps 1 to 3 below:

[0033] Step 1: Establish communication connections with multiple business systems, collect metadata from each business system according to specified data standards, and perform data inventory processing on the metadata to determine the corresponding data attributes. In one implementation, multiple business systems can be accessed and analyzed through a data synchronization system to align data standards. The data synchronization system will collect metadata such as business data, database data, and document data from each business system according to these standards. Specifically, data collection can be automated using self-developed or open-source data acquisition tools. The data synchronization system will also perform data inventory on the metadata. Data inventory involves a complete data review of the business attributes, platform attributes, business data, and business metadata of the business systems. Optionally, the main content of the data inventory can include classifying and hierarchically managing the metadata, and sorting out the data's scale, classification, and tags according to the business system's plan.

[0034] Step 2 involves sending a pre-defined data template to each business system and receiving shared data and corresponding data attributes from each business system based on the pre-defined data template. In one implementation, for shared data between business systems, a pre-defined data template can be used. This template is then sent to each business system, which uploads the shared data and corresponding data attributes according to the template. The data integration system parses the data templates returned by the business systems to obtain shared data such as platform, system, subsystem, database, database sharing management, table sharing relationships, and interface sharing relationships.

[0035] Step 3: For each business system, construct the data set to be analyzed for that business system based on its metadata, the data attributes corresponding to the metadata, the shared data, and the data attributes corresponding to the shared data. The data to be analyzed includes metadata and shared data.

[0036] Regarding step S104 above, this embodiment of the invention provides an implementation method for performing similarity analysis on the data to be analyzed contained in each dataset to obtain the analysis results, as shown in the following methods one to two:

[0037] Method 1: (1) For each business system, calculate the data similarity between each piece of data to be analyzed in the data set to be analyzed of that business system and each piece of data to be analyzed in the data set to be analyzed of other business systems. Optionally, the data similarity between two pieces of data to be analyzed can be calculated based on data such as tables, fields, and business attributes. For example, the field repetition rate between data a to be analyzed in the data set to be analyzed of business system A and data b to be analyzed in the data set to be analyzed of business system B can be used as the data similarity. (2) Based on the preset first similarity threshold and data similarity, determine the target data to be analyzed from the data set to be analyzed of that business system, as well as the target business system that shares the target data to be analyzed with that business system. For example, assuming the first similarity threshold is 90%, when the field repetition rate between data a and data b to be analyzed is greater than or equal to 90%, both data a and data b to be analyzed can be determined as target data to be analyzed, and it can be determined that business system A and business system B share the data a to be analyzed, thereby determining the business relationship between business system A and business system B.

[0038] Method 2: (1) Determine the data dictionary corresponding to each business system based on each set of data to be analyzed; (2) Calculate the dictionary similarity between the data dictionaries corresponding to each business system; (3) Determine the sharing relationship between each business system based on the preset second similarity threshold and dictionary similarity. Among them, the data dictionary refers to the definition and description of data items, data structure, data flow, data storage, processing logic, etc. In one implementation, the data dictionary similarity between systems can be analyzed by collecting data from the enumeration table and using certain rule algorithms. Specifically, the enumeration fields that the business platform depends on are obtained by data sorting, and the relationship between business systems can be analyzed from them.

[0039] Regarding the aforementioned step S106, in one implementation, the target data to be analyzed shared among various business systems can be determined based on the analysis results. Then, the data flow results between the business systems can be determined based on the data attributes of the target data to be analyzed. For example, business system A shares data to be analyzed, a, which needs to be uploaded to a third-party database. This data to be shared, a, will carry a data source, indicating that the data to be shared, a, comes from business system A. Business system B can read the data to be shared, a, from the third-party database. Therefore, when it is determined that business system A and business system B have a sharing relationship, the data flow results between business system A and business system B can be determined based on the data source of the shared data.

[0040] Additionally, please continue to see Figure 2 , Figure 2 The data flow analysis system can also provide a data catalog display function. In one implementation, a data catalog can be generated based on data attributes. The data catalog describes the data attributes of the data to be analyzed and the business system in which the data resides. The data catalog does not contain actual data, but rather describes data attributes and the business system in which they reside, facilitating user retrieval of required data based on the catalog. Furthermore, the data catalog can be displayed through a catalog display interface, and responding to tag setting operations for the data catalog, it can retrieve attribute tags and add them to the data catalog. For example, data belonging to the same business can be given the same attribute tags, thereby facilitating full lifecycle management of the data catalog by the user.

[0041] In summary, the data flow analysis method provided by the embodiments of the present invention defines a complete data flow analysis process, realizes standard data inventory, data analysis, data result display, and data flow output steps and technical solutions, and provides a templated collection scheme to support customized collection. In addition, the data flow analysis method provided by the embodiments of the present invention can analyze the flow between data based on similarity algorithms, and also provides a standard data directory management system that can manage the data directory throughout its entire lifecycle.

[0042] Regarding the data flow analysis method provided in the foregoing embodiments, this invention provides a data flow analysis device, see [link to related document]. Figure 3 The diagram shows the structure of a data flow analysis device, which may include the following parts:

[0043] The data acquisition module 302 is used to acquire the data set to be analyzed from multiple business systems.

[0044] The similarity analysis module 304 is used to perform similarity analysis on the data to be analyzed contained in each dataset to obtain analysis results; wherein, the analysis results include the sharing relationships between various business systems;

[0045] The flow direction determination module 306 is used to determine the data flow direction between various business systems based on the analysis results.

[0046] The data flow analysis device provided in this embodiment of the invention acquires a set of data to be analyzed from multiple heterogeneous business systems, determines the sharing relationship between the various business systems through similarity analysis, and determines the data flow result between the various business systems based on the sharing relationship. The data flow result provides key technology for the management of data lifecycle, thereby enabling better management of data lifecycle based on the data flow result.

[0047] In one embodiment, the data acquisition module 302 is further configured to: establish communication connections with multiple business systems, collect metadata from each business system according to specified data standards, perform data inventory processing on the metadata to determine the data attributes corresponding to the metadata; and send a preset data template to each business system, receive shared data and data attributes corresponding to the shared data from each business system based on the preset data template; for each business system, construct a set of data to be analyzed for that business system based on the metadata, data attributes corresponding to the metadata, shared data, and data attributes corresponding to the shared data contained in that business system; wherein the data to be analyzed includes metadata and shared data.

[0048] In one embodiment, the similarity analysis module 304 is further configured to: for each business system, calculate the data similarity between each piece of data to be analyzed in the data set to be analyzed of the business system and each piece of data to be analyzed in the data set to be analyzed of other business systems; and determine the target data to be analyzed from the data set to be analyzed of the business system and the target business system that shares the target data to be analyzed with the business system based on a preset first similarity threshold and data similarity.

[0049] In one implementation, the similarity analysis module 304 is further configured to: determine the data dictionary corresponding to each business system based on each set of data to be analyzed; calculate the dictionary similarity between the data dictionaries corresponding to each business system; and determine the sharing relationship between each business system based on a preset second similarity threshold and the dictionary similarity.

[0050] In one implementation, the flow direction determination module 306 is further configured to: determine the target data to be analyzed shared among the various business systems based on the analysis results; and determine the data flow direction results among the various business systems based on the data attributes of the target data to be analyzed.

[0051] In one embodiment, the above-mentioned apparatus further includes a target generation module, configured to: generate a data directory based on data attributes; wherein the data directory is used to describe the data attributes of the data to be analyzed and the business system in which the data to be analyzed resides.

[0052] In one embodiment, the above-described apparatus further includes a labeling module, configured to: display a data directory through a directory display interface; and, in response to a label setting operation for the data directory, obtain attribute labels and add the attribute labels to the data directory.

[0053] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0054] This invention provides a server, specifically, the server includes a processor and a memory; the memory stores a computer program, which, when run by the processor, executes the method described in any of the above embodiments.

[0055] Figure 4 This is a schematic diagram of the structure of a server provided in an embodiment of the present invention. The server 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43. The processor 40, the communication interface 43 and the memory 41 are connected through the bus 42. The processor 40 is used to execute executable modules, such as computer programs, stored in the memory 41.

[0056] The memory 41 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 43 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0057] Bus 42 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0058] The memory 41 is used to store programs. After receiving an execution instruction, the processor 40 executes the program. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0059] Processor 40 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 40 or by instructions in software form. Processor 40 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 41. The processor 40 reads the information in memory 41 and, in conjunction with its hardware, completes the steps of the above method.

[0060] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0061] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, 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 covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of analyzing data flow direction, characterized by, The method comprises: acquiring a plurality of to-be-analyzed data sets of a plurality of business systems to be analyzed; wherein the to-be-analyzed data sets comprise metadata and shared data of each of the business systems, the metadata comprises business data, database data and document data, and the shared data comprises library table data, library shared management data, table shared relationship data and interface shared relationship data; performing similarity analysis on to-be-analyzed data contained in each of the to-be-analyzed data sets to obtain an analysis result; wherein the analysis result comprises a shared relationship between two or more business systems, and the shared relationship indicates that the two or more business systems use the same data; determining target to-be-analyzed data shared between the business systems according to the analysis result, and determining a data flow direction result between the business systems according to data attributes of the target to-be-analyzed data; wherein the data attributes of the target to-be-analyzed data are marked with data sources.

2. The method of claim 1, wherein, The step of acquiring the to-be-analyzed data sets of the plurality of business systems to be analyzed comprises: establishing a communication connection with the plurality of business systems, collecting metadata of each of the business systems according to a specified data standard, and performing data inventory processing on the metadata to determine data attributes corresponding to the metadata; sending a preset data template to each of the business systems, and receiving shared data and data attributes corresponding to the shared data that are fed back by each of the business systems based on the preset data template; for each of the business systems, constructing a to-be-analyzed data set of the business system based on the metadata contained in the business system, the data attributes corresponding to the metadata, the shared data, and the data attributes corresponding to the shared data.

3. The method of claim 1, wherein, The step of performing similarity analysis on to-be-analyzed data contained in each of the to-be-analyzed data sets to obtain an analysis result comprises: for each of the business systems, calculating a data similarity between each of the to-be-analyzed data in the to-be-analyzed data set of the business system and each of the to-be-analyzed data in the to-be-analyzed data set of another business system; determining target to-be-analyzed data from the to-be-analyzed data set of the business system and target business systems that share the target to-be-analyzed data with the business system according to a preset first similarity threshold and the data similarity.

4. The method of claim 1, wherein, The step of performing similarity analysis on to-be-analyzed data contained in each of the to-be-analyzed data sets to obtain an analysis result further comprises: determining data dictionaries corresponding to the business systems according to each of the to-be-analyzed data sets; calculating a dictionary similarity between the data dictionaries corresponding to the business systems; determining a shared relationship between the business systems according to a preset second similarity threshold and the dictionary similarity.

5. The method of claim 1, wherein, The method further comprises: generating a data directory according to the data attributes; wherein the data directory is used to describe data attributes of the to-be-analyzed data and business systems in which the to-be-analyzed data are located.

6. The method of claim 5, wherein, The method further comprises: displaying the data directory through a directory display interface; in response to a label setting operation on the data directory, acquiring an attribute label and adding the attribute label to the data directory.

7. An analysis device for data flow direction, characterized in that, The method comprises: The data acquisition module is configured to acquire a plurality of sets of to-be-analyzed data of a plurality of business systems to be analyzed, wherein each set of to-be-analyzed data includes metadata and shared data of each business system, the metadata includes business data, database data and document data, and the shared data includes library table data, library shared management data, table shared relationship data and interface shared relationship data. The similarity analysis module is configured to perform similarity analysis on to-be-analyzed data included in each set of to-be-analyzed data to obtain an analysis result, wherein the analysis result includes a shared relationship between two or more business systems, and the shared relationship indicates that the two or more business systems use the same data. The flow direction determination module is configured to determine target to-be-analyzed data shared between the business systems according to the analysis result, and determine a data flow direction result between the business systems according to a data attribute of the target to-be-analyzed data, wherein the data attribute of the target to-be-analyzed data is marked with a data source.

8. A server, characterized by The computer readable storage medium stores computer executable instructions, and the computer executable instructions, when invoked and executed by the processor, cause the processor to implement the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable instructions, and the computer executable instructions, when invoked and executed by the processor, cause the processor to implement the method of any one of claims 1 to 6.

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