Data processing method and device, equipment, medium and program product
By constructing a lineage knowledge base and a reporting rule base, the system automatically identifies problematic nodes in the reporting of regulatory data in the financial industry, solving the problem of low efficiency and accuracy in data quality attribution analysis, and achieving efficient root cause analysis and report generation.
Patent Information
- Application Number
- CN202511646153.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-11
- Publication Date
- 2026-02-06
AI Technical Summary
In the reporting of regulatory data in the financial industry, the efficiency and accuracy of data quality attribution analysis are low, the coverage of manual processing is low and the timeliness is poor, which cannot meet the high requirements of regulatory authorities.
By building a lineage knowledge base and a reporting rule base, the system automatically determines the production path of the target reporting element, identifies nodes that violate the rules, and matches them with a historical case database to determine the problem type and root cause, generating a report.
It improves the efficiency and accuracy of data attribution analysis, avoids the limitations of human cognition, and meets the timeliness requirements of regulatory authorities.
Smart Images
Figure CN121481464A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of big data technology, specifically to a data processing method, apparatus, device, medium, and program product. Background Technology
[0002] Regarding the reporting of regulatory data in the financial industry, relevant units are placing increasingly higher demands on the quality of reported data. Not only has the number of regulatory rule verification items surged, but it also requires support for penetrating and traceable supervision. For data with quality issues, attribution analysis and reporting of causes are also necessary. The timeliness requirements for data reporting and reporting are also gradually increasing. Currently, attribution analysis of banking data quality is still mainly based on experience and manual processing. Manual attribution analysis has low coverage, biased attributions, and poor timeliness. Therefore, how to improve the efficiency and accuracy of attribution analysis of problematic data has become a pressing technical problem that needs to be solved by those skilled in the art. Summary of the Invention
[0003] In view of the above problems, this application provides a data processing method, apparatus, device, medium and program product with high efficiency and accuracy.
[0004] According to a first aspect of this application, a data processing method is provided, comprising: acquiring a target reporting element; determining the production path of the target reporting element based on a pre-built lineage knowledge base; identifying nodes on the production path that violate reporting rules as problem nodes based on a pre-built reporting rule base; determining the problem type and root cause of the problem node based on the reporting rules violated by the problem node; and generating a report containing the problem node, the reporting rules violated by the problem node, the problem type, and the root cause.
[0005] According to an embodiment of this application, the data processing method further includes: determining all associated nodes of the problem node in the lineage knowledge base; and using all associated nodes as the influence range of the target reporting element.
[0006] According to an embodiment of this application, the step of pre-constructing a bloodline knowledge base includes: acquiring reporting data, the reporting data including m reporting elements, where m is an integer greater than or equal to 1; determining the path generated by each of the m reporting elements based on the production logs of the reporting data; and storing the paths generated by the m reporting elements in the bloodline knowledge base.
[0007] According to an embodiment of this application, the step of determining the problem type and root cause of a problem node based on the reporting rules violated by the problem node includes: matching the problem node and the reporting rules violated by the node with problem nodes and violation reporting rules in a historical case library to obtain the problem type and root cause corresponding to the problem node and the reporting rules violated by the node, wherein the historical case library includes a mapping relationship between problem nodes, violation reporting rules, problem types and root causes.
[0008] According to an embodiment of this application, the report is stored in a historical case library, and the storage format of the report includes at least one of text, tables, and images.
[0009] According to an embodiment of this application, the historical case database supports a query function.
[0010] A second aspect of this application provides a data processing apparatus, comprising: an acquisition module for acquiring a target reporting element; a first determination module for determining the production path of the target reporting element based on a pre-built lineage knowledge base; a second determination module for determining nodes on the production path that violate reporting rules, as problem nodes, based on a pre-built reporting rule base; a third determination module for determining the problem type and root cause of the problem node based on the reporting rules violated by the problem node; and a generation module for generating a report from the problem node, the reporting rules violated by the problem node, the problem type, and the root cause.
[0011] A third aspect of this application provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.
[0012] A fourth aspect of this application also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.
[0013] The fifth aspect of this application also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.
[0014] According to some embodiments of this application, by obtaining the target reporting element and using a pre-built lineage knowledge base, the production path of the target reporting element can be determined; based on a pre-built reporting rule base, nodes that violate the reporting rules on the production path can be identified, thus obtaining problem nodes; based on the reporting rules violated by the problem node, the problem type and root cause of the problem node can be determined; and a report can be generated based on the problem node, the reporting rules violated by the problem node, the problem type, and the root cause. This application, through automated and intelligent data processing methods, can obtain the mapping relationship between problem nodes, the reporting rules violated by the problem node, the problem type, and the root cause, and generate a report. This can accelerate the efficiency and accuracy of root cause analysis, avoid the bias caused by the limitations of human cognition, and ensure the timeliness required by regulatory authorities. Attached Figure Description
[0015] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:
[0016] Figure 1 The illustrations depict application scenarios of data processing methods, apparatuses, devices, media, and program products according to embodiments of this application.
[0017] Figure 2 A flowchart illustrating a data processing method according to an embodiment of this application is shown schematically.
[0018] Figure 3 A flowchart illustrating the pre-construction of a kinship knowledge base according to an embodiment of this application is shown schematically;
[0019] Figure 4 This schematic diagram illustrates a structural block diagram of a data processing apparatus according to an embodiment of the present application;
[0020] Figure 5 A block diagram schematically illustrates an electronic device suitable for implementing a data processing method according to an embodiment of this application. Detailed Implementation
[0021] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.
[0022] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.
[0023] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.
[0024] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).
[0025] Regarding the reporting of regulatory data in the financial industry, relevant units are placing increasingly higher demands on the quality of reported data. Not only has the number of regulatory rule verification items surged, but it also requires support for penetrating and traceable supervision. For data with quality issues, attribution analysis and reporting of causes are also necessary. The timeliness requirements for data reporting and reporting are also gradually increasing. Currently, attribution analysis of banking data quality is still mainly based on experience and manual processing. Manual attribution analysis has low coverage, biased attributions, and poor timeliness. Therefore, how to improve the efficiency and accuracy of attribution analysis of problematic data has become a pressing technical problem that needs to be solved by those skilled in the art.
[0026] Embodiments of this application provide a data processing method, apparatus, electronic device, computer-readable storage medium, and computer program product. The data processing method includes: acquiring a target reporting element; determining the production path of the target reporting element based on a pre-built lineage knowledge base; identifying nodes on the production path that violate reporting rules as problem nodes based on a pre-built reporting rule base; determining the problem type and root cause of the problem node based on the reporting rules violated by the problem node; and generating a report containing the problem node, the reporting rules violated by the problem node, the problem type, and the root cause.
[0027] It should be noted that the data processing methods, apparatus, electronic devices, computer-readable storage media, and computer program products of this application can be used in the field of big data technology, or in any field other than big data technology, such as the financial field. The field of this application is not limited here.
[0028] Figure 1The illustrations depict application scenarios of data processing methods, apparatuses, devices, media, and program products according to embodiments of this application.
[0029] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.
[0030] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).
[0031] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.
[0032] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.
[0033] It should be noted that the data processing method provided in the embodiments of this application can generally be executed by server 105. Correspondingly, the data processing device provided in the embodiments of this application can generally be located in server 105. The data processing method provided in the embodiments of this application can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the data processing device provided in the embodiments of this application can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.
[0034] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.
[0035] The following will be based on Figure 1 The described scene, through Figure 2 and Figure 3 The data processing method according to the embodiments of this application will be described in detail.
[0036] Figure 2 A flowchart illustrating a data processing method according to an embodiment of this application is shown.
[0037] like Figure 2 As shown, the data processing method of this embodiment includes operations S210 to S250.
[0038] In operation S210, the target reporting element is obtained.
[0039] In some examples, the financial industry is required to submit data to comply with regulatory requirements. The submitted data may include m submission elements, and the target submission element may be a submission element that has been returned due to a problem and ordered to conduct attribution analysis.
[0040] In operation S220, the production path of the target reporting element is determined based on the pre-built lineage knowledge base.
[0041] In some examples, the lineage knowledge base may include the generation process of each reporting element. For example, the reporting data includes a reporting table containing fields 1, 2, and 3, etc., which can be understood as reporting elements. For ease of understanding, let's assume that the generation process of field 1 is: obtaining source table 1 and source table 2, merging source table 1 and source table 2 to obtain processing table 1, and subtracting the first and second fields in processing table 1 to obtain field 1. Therefore, the production path of field 1 can be obtained as: obtaining source table 1 and source table 2 - merging source table 1 and source table 2 to obtain processing table 1 - subtracting the first and second fields in processing table 1. This production path has three nodes: obtaining source table 1 and source table 2, merging source table 1 and source table 2 to obtain processing table 1, and subtracting the first and second fields in processing table 1. The above assumptions are merely illustrative; the specific content of the production path for reporting elements, including target reporting elements, should conform to the actual situation. The above assumptions should not be construed as limiting this application.
[0042] As some possible ways to achieve this, such as Figure 3 As shown, the steps for pre-constructing a bloodline knowledge base include operations S310 to S330.
[0043] In operation S310, the reported data is obtained. The reported data includes m reported elements, where m is an integer greater than or equal to 1.
[0044] In operation S320, based on the production log of the reported data, determine the path generated by each of the m reported elements.
[0045] In operation S330, the paths generated by m reporting elements are stored in the lineage knowledge base.
[0046] Operations S310 to S330 facilitate the construction of a bloodline knowledge base, thus laying the foundation for the implementation of operation S220.
[0047] When operating S230, nodes that violate the reporting rules on the production path are identified as problem nodes based on a pre-built reporting rule base.
[0048] In some examples, the reporting rule base can store reporting rules, which can be rules issued by regulatory authorities or internal rules developed by reporting agencies to improve the reporting pass rate, etc. Continuing with the example of the production path for field 1: obtaining source table 1 and source table 2 - merging source table 1 and source table 2 to obtain processing table 1 - subtracting the first and second fields in processing table 1. Assuming field 1 is the target reporting element, the steps of obtaining source table 1 and source table 2, merging source table 1 and source table 2 to obtain processing table 1, and subtracting the first and second fields in processing table 1 can be compared with the reporting rules that the generated nodes must follow in the reporting rule base. This allows us to identify nodes that violate the reporting rules, thus revealing the problematic nodes.
[0049] In operation S240, the problem type and root cause of the problem node are determined based on the reporting rules violated by the problem node.
[0050] As one possible approach, steps include determining the problem type and root cause of a problem node based on the reporting rules violated by the problem node, including matching operations.
[0051] Matching operation: This involves matching the problem node and the reporting rules it violates with problem nodes and violated reporting rules in the historical case database. This yields the problem type and root cause corresponding to the problem node and the violated reporting rules. The historical case database includes the mapping relationships between problem nodes, violated reporting rules, problem types, and root causes. Therefore, the matching operation facilitates the process of determining the problem type and root cause of a problem node based on the reporting rules it violates.
[0052] When operating S250, a report is generated that includes the problem node, the reporting rules violated by the problem node, the problem type of the problem node, and the root cause.
[0053] According to the data processing method of this application embodiment, by acquiring the target reporting element and determining the production path of the target reporting element based on a pre-built lineage knowledge base, the nodes that violate the reporting rules on the production path can be identified based on a pre-built reporting rule base, thus obtaining problem nodes. Based on the reporting rules violated by the problem node, the problem type and root cause of the problem node can be determined. A report can be generated based on the problem node, the reporting rules violated by the problem node, the problem type, and the root cause. This application, through an automated and intelligent data processing method, can obtain the mapping relationship between problem nodes, the reporting rules violated by the problem node, the problem type, and the root cause, and generate a report. This can accelerate the efficiency and accuracy of root cause analysis, avoid the bias caused by the limitations of human cognition, and ensure the timeliness required by regulatory authorities.
[0054] According to some embodiments of this application, the data processing method may further include a first determination operation and a second determination operation.
[0055] The first step is to determine all related nodes of the problem node in the lineage knowledge base.
[0056] The second step is to determine the scope of influence of all associated nodes as the target reporting element.
[0057] The first and second determination operations can facilitate the determination of the scope of influence of the target reporting element, thereby determining the scope of rectification and improving rectification efficiency.
[0058] According to some embodiments of this application, reports are stored in a historical case library, and the storage format of the reports includes at least one of text, tables, and images. This allows for diversified report storage, facilitating retrieval and review.
[0059] According to some embodiments of this application, the historical case library supports a query function.
[0060] In some examples, the query operation supports parsing data in the historical case library through methods such as semantic segmentation, keyword extraction, OCR recognition, and row and column structure analysis. The query function facilitates staff access to the historical case library, maximizing its data support capabilities.
[0061] Based on the above data processing method, this application also provides a data processing apparatus. The following will be combined with... Figure 4 The device is described in detail.
[0062] Figure 4 A schematic block diagram of a data processing apparatus according to an embodiment of this application is shown.
[0063] like Figure 4As shown, the data processing device 10 includes an acquisition module 1, a first determination module 2, a second determination module 3, a third determination module 4, and a generation module 5.
[0064] Module 1 is used to obtain the target reporting element.
[0065] The first determining module 2 is used to determine the production path of the target reporting element based on the pre-built lineage knowledge base.
[0066] The second determination module 3 is used to determine, based on a pre-built reporting rule base, nodes on the production path that violate the reporting rules, and designate them as problem nodes.
[0067] The third determination module 4 is used to determine the problem type and root cause of the problem node based on the reporting rules violated by the problem node.
[0068] The generation module 5 is used to generate a report containing the problem node, the reporting rules violated by the problem node, the problem type of the problem node, and the root cause.
[0069] According to some embodiments of this application, the data processing apparatus may further include a fifth determining module and a sixth determining module.
[0070] The fifth determination module is used to determine all associated nodes of the problem node in the bloodline knowledge base.
[0071] The sixth determining module is used to determine the influence range of the target reporting element as all the associated nodes.
[0072] According to some embodiments of this application, the data processing apparatus may further include a construction module for pre-building a kinship knowledge base. The construction module may include an acquisition unit, a determination unit, and a storage unit.
[0073] The acquisition unit is used to acquire the reported data, which includes m reported elements, where m is an integer greater than or equal to 1.
[0074] The determining unit is used to determine the path generated by each of the m reporting elements based on the production log of the reported data.
[0075] The storage unit is used to store the paths generated by the m reporting elements in the lineage knowledge base.
[0076] According to some embodiments of this application, the third determining module may include a matching unit.
[0077] The matching unit is used to match the problem node and the reporting rule violated by the node with the problem nodes and the reporting rule violations in the historical case library to obtain the problem type and root cause corresponding to the problem node and the reporting rule violated by the node. The historical case library includes the mapping relationship between problem nodes, reporting rule violations, problem types and root causes.
[0078] According to the data processing apparatus 10 of this application embodiment, by acquiring the target reporting element and determining the production path of the target reporting element based on a pre-built lineage knowledge base, and by identifying nodes on the production path that violate the reporting rules based on a pre-built reporting rule base, problem nodes can be obtained. Based on the reporting rules violated by the problem node, the problem type and root cause of the problem node can be determined. A report can be generated based on the problem node, the reporting rules violated by the problem node, the problem type, and the root cause. This application, through an automated and intelligent data processing method, can obtain the mapping relationship between problem nodes, the reporting rules violated by the problem node, the problem type, and the root cause, and generate a report. This can accelerate the efficiency and accuracy of root cause analysis, avoid the bias caused by the limitations of human cognition, and ensure the timeliness required by regulatory authorities.
[0079] According to embodiments of this application, any multiple modules among the acquisition module 1, the first determining module 2, the second determining module 3, the third determining module 4, and the generation module 5 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this application, at least one of the acquisition module 1, the first determining module 2, the second determining module 3, the third determining module 4, and the generation module 5 can be at least partially implemented as a hardware circuit, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented by any other reasonable means of integrating or packaging the circuit, or implemented in any one of the three methods of software, hardware, and firmware, or in a suitable combination of any of them. Alternatively, at least one of the acquisition module 1, the first determining module 2, the second determining module 3, the third determining module 4, and the generation module 5 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.
[0080] Figure 5 A block diagram schematically illustrates an electronic device suitable for the above-described method according to an embodiment of this application.
[0081] like Figure 5As shown, an electronic device 900 according to an embodiment of this application includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.
[0082] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in said one or more memories.
[0083] According to embodiments of this application, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the input / output (I / O) interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.
[0084] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.
[0085] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include ROM 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903 described above.
[0086] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this application.
[0087] When the computer program is executed by the processor 901, it performs the functions defined in the system / apparatus of this application embodiment. According to the embodiments of this application, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.
[0088] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 909, and / or installed from a removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.
[0089] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, it performs the functions defined in the system of this application embodiment. According to the embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.
[0090] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, languages such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0091] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0092] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.
Claims
1. A data processing method, characterized in that, include: Retrieve the target reporting element; Based on a pre-built lineage knowledge base, determine the production path of the target reporting element; Based on a pre-built reporting rule base, nodes on the production path that violate the reporting rules are identified as problem nodes; Based on the reporting rules violated by the problematic node, determine the problem type and root cause of the problematic node; A report will be generated containing the problematic node, the reporting rules violated by the problematic node, the problem type of the problematic node, and the root cause.
2. The data processing method according to claim 1, characterized in that, Also includes: Identify all associated nodes of the problem node in the bloodline knowledge base; All associated nodes are considered as the scope of influence of the target reporting element.
3. The data processing method according to claim 1, characterized in that, The steps for pre-building a kinship knowledge base include: Obtain the reporting data, which includes m reporting elements, where m is an integer greater than or equal to 1; Based on the production logs of the reported data, determine the path generated by each of the m reported elements; The paths generated by the m reporting elements are stored in the lineage knowledge base.
4. The data processing method according to claim 1, characterized in that, The steps for determining the problem type and root cause of the problem node based on the reporting rules violated by the problem node include: The problem node and the reporting rule violated by the node are matched with the problem nodes and the reporting rule violations in the historical case library to obtain the problem type and root cause corresponding to the problem node and the reporting rule violated by the node. The historical case library includes the mapping relationship between problem nodes, reporting rule violations, problem types and root causes.
5. The data processing method according to claim 1, characterized in that, The report is stored in a historical case library, and the report is stored in at least one of the following formats: text, table, and image.
6. The data processing method according to claim 5, characterized in that, The historical case database supports query functionality.
7. A data processing apparatus, characterized in that, include: The acquisition module is used to acquire the target reporting element; The first determining module is used to determine the production path of the target reporting element based on a pre-built lineage knowledge base; The second determining module is used to determine, based on a pre-built reporting rule base, nodes on the production path that violate the reporting rules, as problem nodes; The third determining module is used to determine the problem type and root cause of the problem node based on the reporting rules violated by the problem node; The generation module is used to generate a report from the problem node, the reporting rules violated by the problem node, the problem type of the problem node, and the root cause.
8. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 6.