Data kinship analysis method, device, electronic device and storage medium

Abstract

The present invention relates to the field of data analysis, and discloses a data lineage analysis method, including: obtaining business data corresponding to a user input data query request from a business system, parsing the field lineage relationship between fields in the business data, saving the business data to a message queue and then loading it to a local disk, triggering an asynchronous thread on the local disk to run the business data through the asynchronous thread, parsing the field inheritance relationship between fields in the business data during the asynchronous thread running process; constructing a field association file of the business data according to the field lineage relationship and the field inheritance relationship, marking the identification attributes between the fields in the field association file; storing the data in the business data in a graph database according to the field association file and the identification attributes, so as to obtain the data lineage relationship between the data in the business data. In addition, the present invention also relates to blockchain technology, and the field association file can store blockchains. The present invention can improve the efficiency of data lineage analysis.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a data blood relationship analysis method, device, electronic device and computer-readable storage medium. Background Art

[0002] Data governance has entered the era of intelligence. Accurate tracking and tracing of data can effectively improve the location of data problems in big data systems while improving data quality. The traditional method of analyzing data lineage relationships is usually for developers to manually analyze SQL based on the task scripts in the development platform to find out the data lineage relationships. However, this method requires developers to analyze the task scripts in the development platform one by one, which requires a lot of developer time and energy, thus affecting the efficiency of data lineage relationship analysis. Summary of the invention

[0003] The present invention provides a data lineage relationship analysis method, device, electronic device and computer-readable storage medium, the main purpose of which is to improve the efficiency of data lineage relationship analysis.

[0004] To achieve the above purpose, the present invention provides a data kinship analysis method, comprising:

[0005] Receive a data query request input by a user, obtain business data from a business system according to the data query request, use a computing engine to parse the field lineage relationship between fields in the business data, and save the business data to a preset message queue;

[0006] Loading the business data in the message queue into a local disk, and triggering an asynchronous thread on the local disk to run the business data through the asynchronous thread, and parsing the field inheritance relationship between the fields of the business data during the running of the asynchronous thread;

[0007] According to the field blood relationship and the field inheritance relationship, construct a field association file of the business data, and mark identification attributes between fields in the field association file;

[0008] According to the field association file and the identification attribute, the data in the business data is stored in a graph database to obtain the data lineage relationship between the data in the business data.

[0009] Optionally, acquiring business data from a business system according to the data query request includes:

[0010] Identifying a query object and a query field of the data query request;

[0011] Converting the data query request into a query statement according to the query object and the query field;

[0012] According to the query statement, the backend database of the business system is queried for business data.

[0013] Optionally, the using a computing engine to analyze the field blood relationship between the fields in the business data includes:

[0014] Using the hook function in the computing engine to capture the corresponding relationship between the input field and the output field of the business data during the query process;

[0015] According to the corresponding relationship, a decision tree of the input field and the output field is constructed using a decision algorithm in the computing engine;

[0016] Calculate the node path of any two fields in the decision tree using a depth traversal algorithm;

[0017] According to the node path, the association relationship of the corresponding fields is determined, and the association relationship is used as the field blood relationship between the fields in the business data.

[0018] Optionally, loading the business data in the message queue into a local disk includes:

[0019] Serializing the business data in the message queue to obtain serialized data;

[0020] The serialized data is transferred to the local disk in a first-in-first-out order.

[0021] Optionally, the parsing of the field inheritance relationship between fields of the business data during the running of the asynchronous thread includes:

[0022] Deserialize the business data to obtain deserialized data, and identify the calling relationship between the fields in the deserialized data;

[0023] According to the calling relationship, identifying the parent class field and the child class field of the deserialized data during the running of the asynchronous thread;

[0024] A field inheritance relationship between fields in the business data is generated according to the parent class field and the child class field.

[0025] Optionally, constructing the field association file of the business data according to the field blood relationship and the field inheritance relationship includes:

[0026] The fields in the business data that have both the field blood relationship and the field inheritance relationship are used as cluster center points;

[0027] The remaining fields in the business data are connected to the cluster center point using a conceptual relationship model to form a field association file.

[0028] Optionally, the marking of identification attributes between fields in the field association file includes:

[0029] Query the field ID and field number between the fields in the field association file;

[0030] The field ID and the field number are concatenated to obtain identification attributes between fields in the field association file.

[0031] In order to solve the above problems, the present invention further provides a data blood relationship analysis device, the device comprising:

[0032] A blood relationship analysis module is used to receive a data query request input by a user, obtain business data from a business system according to the data query request, analyze the field blood relationship between fields in the business data using a computing engine, and save the business data to a preset message queue;

[0033] An inheritance relationship parsing module, used for loading the business data in the message queue into a local disk, triggering an asynchronous thread on the local disk to run the business data through the asynchronous thread, and parsing the field inheritance relationship between the fields of the business data during the running of the asynchronous thread;

[0034] A field association file construction module, used to construct a field association file of the business data according to the field blood relationship and the field inheritance relationship, and mark identification attributes between fields in the field association file;

[0035] A blood relationship acquisition module is used to store the data in the business data into a graph database according to the field association file and the identification attribute to obtain the data blood relationship between the data in the business data.

[0036] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0037] at least one processor; and,

[0038] a memory communicatively connected to the at least one processor; wherein,

[0039] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to implement the data blood relationship analysis method described above.

[0040] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned data blood relationship analysis method.

[0041] It can be seen that the embodiment of the invention first queries the business data of the data query request input by the user from the business system, and uses the computing engine to parse the field lineage relationship between the fields in the business data, so as to understand the source of the fields in the business data, thereby ensuring the premise of tracing the lineage relationship between subsequent business data, and saves the business data to a preset message queue, which can realize asynchronous processing of the business data, improve the data processing speed of large amounts of data, and meet the data processing needs of different users; secondly, the embodiment of the present invention loads the business data in the message queue to the local disk, and triggers an asynchronous thread on the local disk to run the business data through the asynchronous thread, parse the business data in the The field inheritance relationship between fields during the running of asynchronous threads can obtain the calling relationship of the fields in the business data, thereby ensuring the premise of identifying the data lineage relationship between each data in the business data; further, the embodiment of the present invention constructs a field association file for the business data based on the field lineage relationship and the field inheritance relationship, and marks the identification attributes between the fields in the field association file. In combination with the field association file and the identification attributes, the data in the business data is stored in a graph database to obtain the data lineage relationship between the data in the business data, which can avoid excessive manual analysis of the data lineage relationship between the data, thereby improving the efficiency of data lineage relationship analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of a flow chart of a data kinship analysis method provided by an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of modules of a data blood relationship analysis device provided by an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of the internal structure of an electronic device for implementing a data blood relationship analysis method provided by an embodiment of the present invention;

[0045] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0047] The embodiment of the present application provides a method for analyzing data lineage relationships. The execution subject of the data lineage relationship analysis method includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the data lineage relationship analysis method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0048] Reference Figure 1 FIG. 1 is a flow chart of a method for analyzing data lineage relationships according to an embodiment of the present invention. In an embodiment of the present invention, the method for analyzing data lineage relationships includes:

[0049] S1. Receive a data query request input by a user, obtain business data from a business system according to the data query request, use a computing engine to parse the field blood relationship between fields in the business data, and save the business data to a preset message queue.

[0050] In an embodiment of the present invention, the data query request is generated based on different user needs. For example, user A's need is to query data about auto insurance claims in the past year, and user B's need is to query data about life insurance claims in the past year. The business system can be understood as a data platform for data interaction with users in actual business scenarios, which includes an interactive page platform, a data management platform, and a data storage platform.

[0051] As an embodiment of the present invention, obtaining business data from a business system according to the data query request includes: identifying a query object and a query field of the data query request, converting the data query request into a query statement according to the query object and the query field, and querying the business data from a background database of the business system according to the query statement.

[0052] Among them, the query object refers to the query target of the data query request, such as the above-mentioned "auto insurance", the query field is used to represent the query node of the data query request, and the query statement can be converted into the SQL programming language to form an SQL statement.

[0053] It should be understood that the business data is obtained by querying from the business system through SQL statements, and there will be many query fields in the SQL statements, and the query fields correspond to the corresponding target fields to form the corresponding business data. Therefore, the embodiment of the present invention uses a computing engine to parse the field lineage relationship between the fields in the business data to understand the source of the fields in the business data, thereby ensuring the premise of tracing the lineage relationship between subsequent business data. Among them, the field lineage relationship can be understood as a relationship similar to the lineage relationship in human society formed between fields during the process of field generation, processing, circulation and extinction, which can characterize the dependency information between the input field and the output field generated by the business data during the loading and running process.

[0054] As an embodiment of the present invention, the use of a computing engine to parse the field blood relationship between fields in the business data includes: using a hook function in the computing engine to capture the correspondence between input fields and output fields of the business data during a query process, and based on the correspondence, using a decision algorithm in the computing engine to construct a decision tree of the input fields and output fields, and using a deep traversal algorithm to calculate the node path of any two fields in the decision tree, and based on the node path, determining the association relationship between the corresponding fields, and using the association relationship as the field blood relationship between the fields in the business data.

[0055] Among them, the input field can be understood as the query field in the data query request, the output field can be understood as the field in the business data, the computing engine can be constructed through the Spark computing framework, the hook function can be a hook function, the decision algorithm can be an AST algorithm, the deep traversal algorithm can be a vertical traversal algorithm, the node path refers to the connection path between any two fields, and the association relationship refers to the field relationship determined according to the node path, which includes a parallel relationship, an inclusion relationship, etc.

[0056] Furthermore, an embodiment of the present invention maintains the business data in a preset message queue to realize asynchronous processing of the business data, improve the data processing speed of large amounts of data, and meet the data processing needs of different users, wherein the preset message queue can be a kafka message queue, and the storage of the business data can be realized by calling the interface of the message queue.

[0057] S2. Load the business data in the message queue into the local disk, and trigger an asynchronous thread on the local disk to run the business data through the asynchronous thread, and parse the field inheritance relationship between the fields of the business data during the running of the asynchronous thread.

[0058] In an embodiment of the present invention, the local disk refers to a disk used to store data on a local client, and the asynchronous thread refers to the smallest unit used to run a business data task, which can be created using the Java language, such as creating a thread by inheriting the Thread class of Java.

[0059] In an optional embodiment of the present invention, the loading of the business data in the message queue into the local disk includes: serializing the business data in the message queue to obtain serialized data, and transmitting the serialized data to the local disk in a first-in-first-out order. The serialization refers to the process of converting the state information of the business data into a form that can be stored or transmitted, which can be implemented by Java language.

[0060] Furthermore, in an embodiment of the present invention, the asynchronous thread is triggered on the local disk to run the business data through the asynchronous thread to identify the field inheritance relationship between fields in the business data, thereby realizing the premise of identifying the data lineage relationship between data in the business data. The field inheritance relationship can be understood as a relationship between fields having the same attributes and behaviors.

[0061] Further, as an embodiment of the present invention, the parsing of the field inheritance relationship between fields of the business data during the running process includes: deserializing the business data to obtain deserialized data, identifying the calling relationship between the fields in the deserialized data, identifying the parent class fields and child class fields of the deserialized data during the running of the asynchronous thread based on the calling relationship, and generating the field inheritance relationship between the fields in the business data based on the parent class fields and child class fields.

[0062] The deserialization corresponds to the above serialization, which can be understood as the process of restoring the byte sequence in the data to a Java object, and the calling relationship can be realized by the upward transformation method in the Java language.

[0063] Based on the identification of the field inheritance relationship, the calling relationship of the fields in the business data can be obtained, thereby ensuring the premise of identifying the blood relationship between each data in the business data.

[0064] S3. Construct a field association file of the business data according to the field blood relationship and the field inheritance relationship, and mark identification attributes between fields in the field association file.

[0065] It should be understood that the field lineage relationship can determine the association relationship between the input field and the output field in the business data, and the field inheritance relationship can determine the calling relationship between the fields in the business data. Therefore, the embodiment of the present invention constructs the conversion relationship of the fields in the business data based on the field lineage relationship and the field inheritance relationship to describe the data lineage relationship between the data in the business data.

[0066] As an embodiment of the present invention, the field association file of the business data is constructed based on the field lineage relationship and the field inheritance relationship, including: taking the fields in the business data that have both the field lineage relationship and the field inheritance relationship as cluster center points, and using a conceptual relationship model to connect the remaining fields in the business data with the cluster center points to form a field association file.

[0067] Exemplarily, the business data contains 1,000 fields, and there are 50 fields that contain both field lineage relationships and field inheritance relationships. Therefore, the 50 fields are used as cluster center points, and the remaining 950 fields are used as points to be connected. According to the relationship between each field in the 1,000 fields, the conceptual relationship model is used to connect the cluster center points and the points to be connected to form a field association file for the 1,000 fields.

[0068] Furthermore, in order to ensure the privacy and security of the field association file, the field association file may also be stored in a blockchain node.

[0069] Based on the field association file, the connection relationship between each data field in the business data can be vividly displayed, so that the data blood relationship between the data in the business data can be quickly determined to achieve data monitoring.

[0070] Furthermore, the embodiment of the present invention determines the identity between the fields in the field association file by marking the identification attributes between the fields in the field association file, thereby facilitating the rapid search of subsequent data relationships. As an embodiment of the present invention, the marking of the identification attributes between the fields in the field association file includes: querying the field ID and field number between the fields in the field association file, splicing the field ID and the field number to obtain the identification attributes between the fields in the field association file. Among them, the field ID refers to the unique information identifier of the field, and the field number refers to the task number of the field in the asynchronous thread in the above-mentioned business data running process, and the splicing can be achieved by splicing characters, such as "-".

[0071] S4. According to the field association file and the identification attribute, the data in the business data is stored in a graph database to obtain the data kinship relationship between the data in the business data.

[0072] In an embodiment of the present invention, the graph database is a non-relational database that uses graph theory to store relationship information between entities. The graph database may be a Neo4j database. Furthermore, based on the field association file and the identification attribute, the embodiment of the present invention stores the data in the business data into the graph database through a data import tool to obtain and display the blood relationship between the data in the business data, wherein the data import tool may be a neo4j-import tool. The data blood relationship may be understood as a relationship similar to the blood relationship in human society formed between data during the process of data generation, processing, flow and extinction, which may characterize the dependency information between input data and output data generated during the loading and running of business data.

[0073] Furthermore, after storing the data in the business data into the graph database, the present invention also includes: setting a read-write separation interface in the graph database to achieve read-write separation for subsequent data lineage relationship acquisition and improve rapid query of data lineage relationships.

[0074] It can be seen that the embodiment of the present invention first queries the business data of the data query request input by the user from the business system, and uses the computing engine to parse the field lineage relationship between the fields in the business data, so as to understand the source of the fields in the business data, thereby ensuring the premise of tracing the lineage relationship between subsequent business data, and saves the business data to a preset message queue, which can realize asynchronous processing of the business data, improve the data processing speed of large amounts of data, and meet the data processing needs of different users; secondly, the embodiment of the present invention loads the business data in the message queue to the local disk, and triggers an asynchronous thread on the local disk to run the business data through the asynchronous thread, and parse the business data in the preset message queue. The field inheritance relationship between fields during the running of the asynchronous thread can obtain the calling relationship of the fields in the business data, thereby ensuring the premise of identifying the data lineage relationship between each data in the business data; further, the embodiment of the present invention constructs a field association file of the business data according to the field lineage relationship and the field inheritance relationship, and marks the identification attributes between the fields in the field association file, and stores the data in the business data in a graph database in combination with the field association file to obtain the data lineage relationship between the data in the business data, which can avoid excessive manual analysis of the data lineage relationship between the data, thereby improving the efficiency of data lineage relationship analysis.

[0075] like Figure 2 The figure shows a functional module diagram of the data blood relationship analysis device of the present invention.

[0076] The data kinship analysis device 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the data kinship analysis device can include a kinship analysis module 101, an inheritance analysis module 102, a field association file construction module 103, and a kinship acquisition module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, which are stored in the memory of the electronic device.

[0077] In this embodiment, the functions of each module / unit are as follows:

[0078] The blood relationship analysis module 101 is used to receive a data query request input by a user, obtain business data from a business system according to the data query request, analyze the field blood relationship between fields in the business data using a computing engine, and save the business data to a preset message queue;

[0079] The inheritance relationship parsing module 102 is used to load the business data in the message queue into the local disk, and trigger an asynchronous thread on the local disk to run the business data through the asynchronous thread, and parse the field inheritance relationship between the fields of the business data during the running of the asynchronous thread;

[0080] The field association file construction module 103 is used to construct the field association file of the business data according to the field blood relationship and the field inheritance relationship, and mark the identification attributes between the fields in the field association file;

[0081] The blood relationship acquisition module 104 is used to store the data in the business data into a graph database according to the field association file and the identification attribute to obtain the data blood relationship between the data in the business data.

[0082] In detail, each module in the data kinship analysis device 100 in the embodiment of the present invention is used in the same manner as described above. Figure 1 The data blood relationship analysis method described in the present invention has the same technical means and can produce the same technical effects, so I will not go into details here.

[0083] like Figure 3 , which is a schematic diagram of the structure of an electronic device 1 for implementing the data blood relationship analysis method of the present invention.

[0084] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a data kinship analysis program.

[0085] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device 1, and uses various interfaces and lines to connect the various components of the entire electronic device 1, and executes or executes programs or modules stored in the memory 11 (for example, executing data kinship analysis programs, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0086] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 can also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the data kinship analysis program, etc., but also can be used to temporarily store data that has been output or is to be output.

[0087] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0088] The communication interface 13 is used for communication between the above-mentioned electronic device 1 and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices 1. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0089] Figure 3 Only the electronic device 1 having components is shown, and those skilled in the art can understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0090] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0091] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0092] The data blood relationship analysis program stored in the memory 11 of the electronic device 1 is a combination of multiple computer programs. When running in the processor 10, it can achieve:

[0093] Receive a data query request input by a user, obtain business data from a business system according to the data query request, use a computing engine to parse the field lineage relationship between fields in the business data, and save the business data to a preset message queue;

[0094] Loading the business data in the message queue into a local disk, and triggering an asynchronous thread on the local disk to run the business data through the asynchronous thread, and parsing the field inheritance relationship between the fields of the business data during the running of the asynchronous thread;

[0095] According to the field blood relationship and the field inheritance relationship, construct a field association file of the business data, and mark identification attributes between fields in the field association file;

[0096] According to the field association file and the identification attribute, the data in the business data is stored in a graph database to obtain the data lineage relationship between the data in the business data.

[0097] Specifically, the specific implementation method of the processor 10 for the above-mentioned computer program can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0098] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0099] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of the electronic device 1, the computer program can implement:

[0100] Receive a data query request input by a user, obtain business data from a business system according to the data query request, use a computing engine to parse the field lineage relationship between fields in the business data, and save the business data to a preset message queue;

[0101] Loading the business data in the message queue into a local disk, and triggering an asynchronous thread on the local disk to run the business data through the asynchronous thread, and parsing the field inheritance relationship between the fields of the business data during the running of the asynchronous thread;

[0102] According to the field blood relationship and the field inheritance relationship, construct a field association file of the business data, and mark identification attributes between fields in the field association file;

[0103] According to the field association file and the identification attribute, the data in the business data is stored in a graph database to obtain the data lineage relationship between the data in the business data.

[0104] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0105] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0107] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0108] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0109] The blockchain referred to in this invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0110] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0111] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The second and other words are used to indicate names, but not to indicate any particular order.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for analyzing data blood relationship, characterized in that: The method comprises: Receive a data query request input by a user, obtain business data from a business system according to the data query request, use a computing engine to parse the field lineage relationship between fields in the business data, and save the business data to a preset message queue; Loading the business data in the message queue into a local disk, and triggering an asynchronous thread on the local disk to run the business data through the asynchronous thread, and parsing the field inheritance relationship between the fields of the business data during the running of the asynchronous thread; According to the field blood relationship and the field inheritance relationship, construct a field association file of the business data, and mark identification attributes between fields in the field association file; According to the field association file and the identification attribute, the data in the business data is stored in a graph database to obtain the data kinship relationship between the data in the business data; The method of using the computing engine to analyze the field blood relationship between the fields in the business data includes: using the hook function in the computing engine to capture the corresponding relationship between the input field and the output field of the business data during the query process; based on the corresponding relationship, using the decision algorithm in the computing engine to build a decision tree of the input field and the output field; using the deep traversal algorithm to calculate the node path of any two fields in the decision tree; based on the node path, determining the association relationship between the corresponding fields, and using the association relationship as the field blood relationship between the fields in the business data; The parsing of the field inheritance relationship between the fields of the business data during the running of the asynchronous thread includes: deserializing the business data to obtain deserialized data, identifying the calling relationship between the fields in the deserialized data; identifying the parent class field and the child class field of the deserialized data during the running of the asynchronous thread according to the calling relationship; generating the field inheritance relationship between the fields in the business data according to the parent class field and the child class field; The field association file of the business data is constructed based on the field lineage relationship and the field inheritance relationship, including: taking the fields in the business data that have both the field lineage relationship and the field inheritance relationship as cluster center points; and using a conceptual relationship model to connect the remaining fields in the business data with the cluster center points to form a field association file.

2. The data kinship analysis method according to claim 1, characterized in that: The acquiring of business data from the business system according to the data query request includes: Identifying a query object and a query field of the data query request; Converting the data query request into a query statement according to the query object and the query field; According to the query statement, the backend database of the business system is queried for business data.

3. The data kinship analysis method according to claim 1, characterized in that: The step of loading the business data in the message queue to the local disk includes: Serializing the business data in the message queue to obtain serialized data; The serialized data is transferred to the local disk in a first-in-first-out order.

4. The data kinship analysis method according to claim 1, characterized in that: The identification attributes between the marking fields in the field association file include: Query the field ID and field number between the fields in the field association file; The field ID and the field number are concatenated to obtain identification attributes between fields in the field association file.

5. A data lineage relationship analysis device, used to implement the data lineage relationship analysis method as claimed in any one of claims 1 to 4, characterized in that: The device comprises: A blood relationship analysis module is used to receive a data query request input by a user, obtain business data from a business system according to the data query request, analyze the field blood relationship between fields in the business data using a computing engine, and save the business data to a preset message queue; An inheritance relationship parsing module, used for loading the business data in the message queue into a local disk, triggering an asynchronous thread on the local disk to run the business data through the asynchronous thread, and parsing the field inheritance relationship between the fields of the business data during the running of the asynchronous thread; A field association file construction module, used to construct a field association file of the business data according to the field blood relationship and the field inheritance relationship, and mark identification attributes between fields in the field association file; A blood relationship acquisition module is used to store the data in the business data into a graph database according to the field association file and the identification attribute to obtain the data blood relationship between the data in the business data.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the data blood relationship analysis method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the data blood relationship analysis method as described in any one of claims 1 to 4 is implemented.

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