Method, device and medium for analyzing and processing influence of upstream and downstream financial data correlation

Abstract

The application discloses an analysis processing method for upstream and downstream financial data correlation influence, a computer device and a storage medium. The method comprises the following steps: performing automatic script analysis on processing scripts corresponding to all job tables of a target data warehouse system to obtain final processing logic of the processing scripts; extracting influence analysis processes of table levels, field levels and data levels from the final processing logic, and manufacturing the influence analysis processes into influence analysis modular tools; and performing analysis on the upstream and downstream financial data correlation influence of a job table to be analyzed by using the influence analysis modular tools to obtain an analysis result of the upstream and downstream financial data correlation influence of the job table to be analyzed. In the above manner, the application increases the automatic analysis processing function of the upstream and downstream financial data correlation influence based on scripts, and improves the analysis processing efficiency of the financial data correlation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data warehouse, in particular to an analysis and processing method for upstream and downstream financial data correlation influence, a computer device and a storage medium. BACKGROUND

[0002] With the rise and rapid development of the Internet, a large amount of information and data is generated every moment, thereby forming a data warehouse, and enterprises begin to manage data in a scientific way, so as to accurately analyze and accurately judge the information of enterprise operation from different perspectives, and the information system constructed by using these technologies becomes a data warehouse system.

[0003] In a big data warehouse system, the frequency of change of upstream data is high, which will directly and indirectly affect downstream data. In the current technology, the influence analysis of data in the data warehouse system mainly includes an influence analysis system based on manual arrangement mapping and an influence analysis system for identifying table level influence of analysis jobs. The former adopts manual mapping, which is time-consuming and laborious, easy to make mistakes and inconvenient to maintain in the later period. The latter can only be limited to table level influence analysis, and inaccurate judgment will lead to the introduction of a large amount of secondary manual judgment, and the evaluation influence efficiency is low. SUMMARY

[0004] The technical problem solved by the present application is to provide an analysis and processing method for upstream and downstream financial data correlation influence, a computer device and a storage medium, which can increase the automatic analysis and processing function of the correlation influence of upstream and downstream financial data based on scripts, and improve the analysis and processing efficiency of the correlation of upstream and downstream financial data.

[0005] To solve the above technical problem, the first technical solution adopted by the present application is to provide an analysis and processing method for upstream and downstream financial data correlation influence, which comprises: performing script automatic analysis on all job tables corresponding to processing scripts of a target data warehouse system to obtain the final processing logic of the processing scripts; refining the influence analysis process of table level, field level and data level from the final processing logic, and manufacturing the influence analysis process into an influence analysis modular tool; and using the influence analysis modular tool to analyze the correlation influence of upstream and downstream financial data of the job tables to be analyzed to obtain the analysis result of the correlation influence of upstream and downstream financial data of the job tables to be analyzed.

[0006] To solve the above technical problem, the second technical solution adopted by the present application is to provide a computer device, which comprises a processor, a memory and a communication circuit. The communication circuit is used for communication connection. The memory stores a computer program. The processor is used for executing the computer program to realize the method provided by the first technical solution of the present application.

[0007] To solve the above technical problems, the third technical solution adopted in this application is: to provide a computer-readable storage medium, which stores a computer program, and the computer program can be executed by a processor to implement the method provided by the first technical solution of this application.

[0008] The beneficial effects of the present application are as follows: different from the existing technology, by automatically parsing the processing scripts corresponding to all the job tables of the target data warehouse system, the processing scripts include the processing logic for the tables, data, and fields in the target data warehouse system, and automatically parsing the scripts, the final processing logic of the processing scripts can be obtained, and the table-level, field-level, and data-level impact analysis processes are extracted from the final processing logic, and the impact analysis process is made into an impact analysis modular tool. Through the impact analysis process, the correlation dependency relationship between the tables, fields, and data in the target data warehouse can be obtained, and the impact analysis modular tool can analyze the job table to be analyzed. After analyzing the impact of the upstream and downstream financial data correlation of the job table to be analyzed using the impact analysis modular tool, the analysis results of the upstream and downstream financial data correlation impact of the job to be analyzed can be obtained. Using the impact analysis modular tool to analyze the impact of upstream data changes on downstream data can avoid the impact that is easily missed by human evaluation, and improve the processing efficiency of upstream and downstream financial data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is a flowchart of the first embodiment of the method for analyzing and processing the impact of upstream and downstream financial data correlation in this application;

[0010] Figure 2 This is a flowchart of a modular tool for producing an impact analysis process of the first embodiment of the analysis and processing method for the impact of upstream and downstream financial data correlation of the present application;

[0011] Figure 3 This is a flowchart of a modular tool for using an impact analysis process in accordance with the first embodiment of the method for analyzing and processing the impact of upstream and downstream financial data correlations of the present application;

[0012] Figure 4 is a schematic block diagram of the structure of an embodiment of a computer device of the present application;

[0013] Figure 5 It is a schematic block diagram of the structure of an embodiment of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0015] If the technical solution of this application involves personal information, the product using the technical solution of this application has clearly informed the individual of the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using the technical solution of this application has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, a clear and prominent sign is set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that they agree to the collection of their personal information; or on the personal information processing device, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload their personal information; among which, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

[0016] like Figures 1 to 3 As shown, the method for analyzing and processing the impact of upstream and downstream financial data correlations described in this application may include: S100: Automatically parsing the processing scripts corresponding to all job tables in the target data warehouse system to obtain the final processing logic of the processing scripts. S200: Extracting the table-level, field-level, and data-level impact analysis processes from the final processing logic, and constructing the impact analysis processes into a modular impact analysis tool. S300: Using the modular impact analysis tool, analyzing the impact of upstream and downstream financial data correlations on the job table to be analyzed, and obtaining analysis results of the upstream and downstream financial data correlation impacts of the job to be analyzed.

[0017] By automatically parsing the processing scripts corresponding to all job tables in the target data warehouse system, which include processing logic for the tables, data, and fields in the target data warehouse system, the final processing logic of the processing scripts can be obtained. From the final processing logic, the table-level, field-level, and data-level impact analysis processes are extracted. The impact analysis processes are then transformed into a modular impact analysis tool. Through the impact analysis process, the dependency relationships between tables, fields, and data in the target data warehouse can be obtained. The modular impact analysis tool can be used to analyze the job tables to be analyzed. After analyzing the impact of upstream and downstream financial data on the job tables to be analyzed using the modular impact analysis tool, the analysis results of the upstream and downstream financial data impact of the job to be analyzed can be obtained. Using the modular impact analysis tool to analyze the impact of upstream data changes on downstream data can avoid the impacts that are easily missed by manual evaluation and improve the efficiency of analyzing and processing upstream and downstream financial data relationships.

[0018] The following is a detailed description of the first embodiment of the method for analyzing and processing the impact of upstream and downstream financial data correlation in this application.

[0019] S100: Automatically parse the processing scripts corresponding to all the job tables of the target data warehouse system to obtain the final processing logic of the processing scripts.

[0020] A processing script refers to a script that operates on the tables, fields, and data in the target data warehouse system. By automatically parsing the processing scripts corresponding to all job tables in the target data warehouse system, the final processing logic of the processing script can be obtained, that is, the processing process corresponding to all tables, fields, and data in the target data warehouse system can be obtained, thereby obtaining the impact and correlation relationship between all tables, fields, and data in the target data warehouse system. For example, by automatically parsing the processing script, it can be obtained that field_1 in table_c is obtained by performing some processing operations on field_2 in table_b. Continuing to parse the script, it is found that field_2 in table_b is obtained by performing some processing operations on field_3 in table_a. Continuing to recursively parse the script, it is not found that field_3 in table_a is related to other tables, fields, or data in the data warehouse. Therefore, it can be obtained that the final processing logic of field_1 in table_c is related to field_2 in table_b and field_3 in table_a. Therefore, when the impact of upstream data changes field_1 in table_c, field_2 in table_b and field_3 in table_a will also be affected.

[0021] Optionally, since the temporary table includes processing for each field of the final write table in the target data warehouse, the server may parse all temporary tables corresponding to each processing script to obtain the primary processing logic for each field, then identify all fields of the final write table in the target data warehouse system, and obtain the processing logic for each field of the final write table based on the temporary dependencies within the primary processing logic corresponding to each field of the final write table, thereby obtaining the final processing logic for all fields of the final write table in the target data warehouse system. For details, see the following steps included in S100:

[0022] S110: Parse each processing script of all job tables to obtain all temporary tables of each processing script.

[0023] The server parses the processing scripts corresponding to all job tables to obtain all temporary tables corresponding to each processing script. The temporary tables represent the processing logic of tables, fields, and data in the target data warehouse system to obtain the primary processing logic.

[0024] S111: Parse all temporary tables to obtain primary processing logic.

[0025] After the server parses the script and obtains a temporary table, it parses the temporary table and obtains the primary processing logic corresponding to the temporary table, that is, the association and dependency relationship between the tables, fields, and data corresponding to the temporary table. For example, by parsing temporary table A, the dependency relationship between field field_a in table table_a and field field_b in table table_b is obtained. After the server parses all temporary tables corresponding to each processing script, it can obtain the primary processing logic corresponding to all temporary tables in the target data warehouse. For example, by parsing temporary table A, the dependency relationship between field field_a in table table_a and field field_b in table table_b is obtained. Then, the primary processing logic of field field_a in table table_a can be associated with field field_b in table table_b. For example, by parsing temporary table A, the association relationship between table table_d and table table_e is obtained. Then, the primary processing logic of table table_d can be associated with table table_e.

[0026] Optionally, after parsing the processing script to obtain all temporary tables corresponding to the processing script, the server can identify the temporary table name, field name, field processing logic, and conditional dependency corresponding to the temporary table to parse the temporary table. For details, see the following steps included in S111:

[0027] S1111: Parse all temporary tables to obtain temporary table names, field names, field processing logic, where conditions, where condition dependent tables and fields, join conditions, join condition dependent tables and fields, "group by" and "order by" logic and dependent tables and fields.

[0028] Field processing logic is the process of extracting the processing scope of a field. For example, the statement "case when ACCT_TYPE = '101' then JYBS else 0 end as JB_KH_BY" is the processing scope of the field JB_KH_BY. This means that the field JB_KH_BY is associated with the fields ACCT_TYPE and JYBS.

[0029] Creating a temporary table involves creating the temporary table name, field names, field processing logic, where conditions, tables and fields dependent on the where conditions, join conditions, tables and fields dependent on the join conditions, "group by" and "order by" logic and dependent tables and fields, etc. By parsing the temporary table's corresponding statement, the temporary table's fields, field processing logic, tables and fields dependent on the temporary table due to where conditions, tables and fields dependent on join conditions, and tables and fields dependent on "group by" and "order by" conditions are obtained. For example, if the creation statement of temporary table #table_a contains a where condition that associates it with field_1 of table_b, then the tables and fields dependent on the where condition of temporary table #table_a include table_b and field_1 of table_b.

[0030] S112: Identify all fields of the final write table in the target data warehouse system, perform recursive processing based on the temporary dependencies within the primary processing logic corresponding to each field of the final write table, obtain the processing logic of each field of the final write table, and combine the processing logic of all fields to obtain the final processing logic.

[0031] For example, if the target data warehouse system contains table_a, which is the final write table, all fields in table_a, such as field_1, field_2, and field_3, are identified. For field_1, recursive processing is performed from the temporary dependencies obtained by parsing all temporary tables to obtain the processing logic for field_1. The primary processing logic then determines that field_1 has a dependency relationship with field_2 in table_b. Recursive processing is then performed to determine whether field_2 in table_b has a dependency relationship with table_c. The system then searches to see if field_2 in table_b and table_c are related to other tables, fields, or data in the data warehouse system. If so, recursive processing is continued to find all temporary dependencies. If not, the processing logic for field_1 is associated with field_2 in table_b and table_c, and the basis for the association is recorded (e.g., where conditions, join conditions, "group by" and "order by" conditions).

[0032] The server obtains the processing logic of each field that is finally written into the table, and aggregates the processing logic of all fields to obtain the final processing logic. The final processing logic represents all associated dependencies between all tables, fields, or data in the target data warehouse system.

[0033] S200: Extract the impact analysis process at the table level, field level, and data level from the final processing logic, and make the impact analysis process into a modular impact analysis tool.

[0034] After the server obtains the final processing logic, it extracts the impact analysis processes at the table, field, and data levels and creates a modular impact analysis tool. After the tables, fields, and data to be analyzed are input into the modular impact analysis tool, the tool's output outputs all changes to the tables, fields, and data caused by the changes to the tables, fields, and data to be analyzed.

[0035] Optionally, after obtaining the association dependencies between all tables, fields, and data in the target data warehouse system, table-level, field-level, and data-level impact analysis processes can be extracted and made into an impact analysis process modular tool to improve the analysis and processing efficiency of data associations. For details, see the following steps included in S200:

[0036] S210: Extract the impact analysis processes at the table level, field level, and data level from the final processing logic, combine the impact analysis processes at the table level, field level, and data level to obtain several combined processes, and make the several combined processes into an impact analysis modular tool. The impact analysis modular tool includes a table-level impact analysis modular tool, a field-level impact analysis modular tool, and a data-level impact analysis modular tool.

[0037] The table-level impact analysis modular tool, the field-level impact analysis modular tool, and the data-level impact analysis modular tool can be used separately. For example, only the table-level impact analysis modular tool can be used to analyze changes in tables in the target warehouse system caused by changes in upstream data. The table-level impact analysis modular tool, the field-level impact analysis modular tool, and the data-level impact analysis modular tool can also be used together. For example, the change content can be input into the input ends of the table-level impact analysis modular tool and the field-level impact analysis modular tool at the same time to analyze the tables, fields, and other contents associated with the changed content.

[0038] S300: Analyze the impact of upstream and downstream financial data association on the job table to be analyzed using the impact analysis modular tool to obtain the analysis result of the impact of upstream and downstream financial data association of the job to be analyzed.

[0039] The job table to be analyzed may be a table to be analyzed, a field to be analyzed, data to be analyzed, etc.

[0040] The server parses the processing scripts corresponding to all job tables in the target data warehouse, obtains the final processing logic for all fields that are ultimately written to the table in the data warehouse, and constructs the final processing logic into an impact analysis modular tool. When the server receives an instruction to analyze the impact of changes in upstream data on downstream data, the server uses the impact analysis modular tool to analyze the impact of the correlation between upstream and downstream financial data on the job table to be analyzed. The impact analysis modular tool will extract the impact of changes in upstream data on downstream data from the final processing logic.

[0041] Optionally, after extracting the table-level, field-level, and data-level impact analysis processes from the final processing logic and creating a modular impact analysis tool, the modular table-level impact analysis tool can be used to perform impact analysis on the table-level impact caused by upstream data changes. For details, see the following steps included in S300:

[0042] S310: Input the changed table name, whether it is an exact match, and the deepest recursive level into the table-level impact analysis modular tool, and output the impact analysis results of the changed table name, affected table name, and level.

[0043] Whether an exact match is required refers to whether the change table name entered at the input end of the table-level impact analysis modular tool requires an exact match. When an exact match is selected, the table-level impact analysis modular tool only performs an impact analysis on the input change table and outputs other tables associated with that change table as impact tables. When an exact match is not required, the table-level impact analysis modular tool performs a fuzzy match on the input change table name. For example, if "vendor" is entered in the change table name column at the input end of the table-level impact analysis modular tool and N is entered in the "Exact Match" column, the table-level impact analysis modular tool will perform an impact analysis on all tables in the target data warehouse system whose table names contain "vendor."

[0044] The deepest level of recursion is to avoid wasting time on unnecessary searches. The input requires the impact analysis modular tool to recurse to the deepest level of data to avoid the impact analysis modular tool always recursively searching for tables associated with the input table. For example, if the data input in the deepest level of recursion column is 10, then the impact analysis modular tool will recurse to 10 levels on the change table. If the data input in the deepest level of recursion column is 1, then the impact analysis modular tool only needs to obtain the tables directly associated with the change table. The output end of the impact analysis modular tool will output the level corresponding to the impact table name. For example, table table_1 is the impact table obtained by the second recursive impact analysis of table table_a. Then the output end will output a piece of data "Change table name: table table_a, Impact table name: table table_1, Level: 2".

[0045] Optionally, the column for exact match can use Y or N, 1 or 0 to indicate whether an exact match is required. Entering Y or 1 indicates an exact match, and entering N or 0 indicates a fuzzy match.

[0046] The server can use the table-level impact analysis modular tool to analyze the impact of changes to one or more tables in the target data warehouse system caused by upstream data on all tables in the target data warehouse system. When the server receives an instruction to analyze the impact of changes in upstream data on downstream data, it inputs the acquired changed table name, whether it is an exact match, the deepest recursive level, and other content into the input of the impact analysis modular tool. The impact analysis modular tool analyzes the impact of data associations based on the input content and outputs the impact analysis results of the changed table name, affected table name, and level at the output. For example, if an entire table in the target data warehouse system is offline due to changes in upstream data, the table name of the table is input into the input of the table-level impact analysis modular tool. After performing an impact analysis on the table, the table-level impact analysis modular tool obtains all tables associated with the table and the affected levels, and outputs all tables associated with the table as the affected table names and the affected levels to the output of the table-level impact analysis modular tool.

[0047] Optionally, after refining the table level, field level, and data level impact analysis processes from the final processing logic and making the impact analysis modular tool, the field level impact caused by the upstream data changes can be analyzed using the field level impact analysis modular tool. For details, refer to the following steps included in S300:

[0048] S320: input the changed table name, whether the table name is accurately matched, the changed field name, whether the field name is accurately matched, the changed field position, and the deepest recursion level into the field level impact analysis modular tool, and output the changed table name, the changed field, the impact table name, the impact field, and the level impact analysis result.

[0049] Whether the changed table name and the changed field name need to be accurately matched refers to whether the changed table name and the changed field name input into the input end of the field level impact analysis modular tool need to be accurately matched. When accurate matching is selected, the field level impact analysis modular tool only analyzes the impact of the changed field in the input changed table, obtains the tables and fields associated with the changed field in the input changed table, and outputs the table name of the obtained associated table and the field name of the field as the impact table name and the impact field name. When no accurate matching is selected, the field level impact analysis modular tool performs fuzzy matching on the input changed table name and changed field name. For example, the changed table name input into the changed table name column of the input end of the field level impact analysis modular tool is vendor, the table name accurate matching column is input as Y, the changed field name input into the changed field name column is address, the field name accurate matching column is input as N, and the deepest recursion level column is input as 10. Then, the field level impact analysis modular tool analyzes the impact of all fields with the field name containing address in the table vendor in the target data warehouse system. For example, the changed table name input into the changed table name column of the input end of the field level impact analysis modular tool is vendor, the table name accurate matching column is input as N, the changed field name input into the changed field name column is address, the field name accurate matching column is input as N, and the deepest recursion level column is input as 10. Then, the field level impact analysis modular tool analyzes the impact of all fields with the field name containing address in all tables with the table name containing vendor in the target data warehouse system.

[0050] Optionally, the table name accurate matching column and the field name accurate matching column can be represented by Y or N, 1 or 0, etc. to indicate whether accurate matching is needed. Inputting Y or 1 represents accurate matching, and inputting N or 0 represents fuzzy matching.

[0051] The input for the field to be changed can include options such as All, Logical Processing, Where Conditions, Join Conditions, "Group By," and "Order By." If you select Logical Processing, the affected tables and fields after the logical processing change is retrieved from the final processing logic. If you select Where Conditions, the where conditions associated with the field, along with the tables and fields dependent on the where conditions, are retrieved from the final processing logic. If you select Join Conditions, the join conditions associated with the field, along with the tables and fields dependent on the join conditions, are retrieved from the final processing logic. If you select "Group By" and "Order By," the group by and order by logic and dependent tables and fields associated with the field are retrieved from the final processing logic. If you select All, all tables and fields associated with the field are retrieved from the final processing logic. The input for the field to be changed can include one or a combination of All, Logical Processing, Where Conditions, Join Conditions, "Group By," and "Order By." For example, if you enter the where condition and join condition in the position column of the change field, the where condition, the where condition-dependent table and field, and the join condition, the join condition, and the join condition-dependent table and field associated with the field will be obtained from the final processing logic.

[0052] The server can use the field-level impact analysis modular tool to analyze the impact of upstream data changes on all tables or fields within the target data warehouse system caused by a specific field or fields in a specific table or tables within the target data warehouse system. When the server receives an instruction to analyze the impact of upstream data changes on downstream data, it inputs the acquired information, such as the changed table name, whether the table name matches exactly, the changed field name, whether the field name matches exactly, the location of the changed field, and the deepest recursive level, into the impact analysis modular tool's input. The impact analysis modular tool then analyzes the impact of data associations based on the input information and outputs the impact analysis results, including the changed table name, changed field, affected table name, affected field, and level. For example, if upstream data changes cause a change in field_1 in table_a in the target data warehouse system, the impact analysis modular tool will be used to analyze the changes to all tables and fields in the target data warehouse system caused by the change in field_1 in table_a. In the input bar of the Field-Level Impact Analysis Modular Tool, enter table_a, Y, field_1, Y, and the where condition in the order of changing the table name, whether the table name matches exactly, changing the field name, whether the field name matches exactly, and changing the field's position. The Field-Level Impact Analysis Modular Tool analyzes the where condition corresponding to the field in the table. That is, it obtains the where condition, the where condition-dependent table, and the field from the final processing logic associated with the field. It then obtains the names of the associated tables and fields as the affected table and field names, and outputs them to the output terminal of the Field-Level Impact Analysis Modular Tool.

[0053] Optionally, after extracting the table-level, field-level, and data-level impact analysis processes from the final processing logic and creating a modular impact analysis tool, the modular data-level impact analysis tool can be used to perform impact analysis on the data-level impact caused by upstream data changes. For details, see the following steps included in S300:

[0054] S330: Input the data content and impact type into the data-level impact analysis modular tool, and output the impact analysis results of the data content, impact table, impact field, and level.

[0055] The input content of the impact type can be a function, select*, a specified string, field filtering data: =, in, like, >, <, constants, etc. The data content can be a number, a string, etc. The final processing logic of the field includes the basis of the field and other tables or fields or data, where the basis includes the basis of association with specific data. When the server receives the data content and the impact type, it searches the final processing logic for the tables and fields associated with the data content and whose association basis corresponds to the impact type based on the association basis and data content corresponding to the impact type, and outputs the obtained results to the output end of the impact analysis modular tool.

[0056] like Figure 4 As shown, the computer device 10 described in the computer device embodiment of the present application may specifically include a processor 110 and a memory 120. The memory 120 is coupled to the processor 110.

[0057] The processor 110 is used to control the operation of the computer device 10. The processor 110 may also be referred to as a CPU (Central Processing Unit). The processor 110 may be an integrated circuit chip having signal processing capabilities. The processor 110 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. A general-purpose processor may be a microprocessor, or the processor 110 may be any conventional processor.

[0058] The memory 120 is used to store computer programs and can be RAM, ROM, or other types of storage devices. Specifically, the memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory is used to store at least one program code.

[0059] The processor 110 is configured to execute the computer program stored in the memory 120 to implement the method for analyzing and processing the impact of upstream and downstream financial data associations as described in the embodiment of the method for analyzing and processing the impact of upstream and downstream financial data associations of the present application.

[0060] In some embodiments, the computer device 10 may further include a peripheral device interface 130 and at least one peripheral device. The processor 110, memory 120, and peripheral device interface 130 may be connected via a bus or signal lines. Each peripheral device may be connected to the peripheral device interface 130 via a bus, signal lines, or circuit boards. Specifically, the peripheral device may include at least one of a radio frequency circuit 140, a display screen 150, an audio circuit 160, and a power supply 170.

[0061] The peripheral device interface 130 can be used to connect at least one I / O (Input / Output)-related peripheral device to the processor 110 and the memory 120. In some embodiments, the processor 110, the memory 120, and the peripheral device interface 130 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 110, the memory 120, and the peripheral device interface 130 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0062] The RF circuit 140 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 140 communicates with communication networks and other communication devices via electromagnetic signals and serves as the communication circuitry for the computer device 10. The RF circuit 140 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the RF circuit 140 includes an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The RF circuit 140 can communicate with other terminals via at least one wireless communication protocol. Such wireless communication protocols include, but are not limited to, the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 140 may also include circuitry related to Near Field Communication (NFC), which is not limited in this application.

[0063] The display screen 150 is used to display a user interface (UI). This UI may include graphics, text, icons, videos, or any combination thereof. When the display screen 150 is a touch screen display, it is also capable of collecting touch signals on or above the surface of the display screen 150. These touch signals can be input as control signals to the processor 110 for processing. In this case, the display screen 150 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there can be a single display screen 150, located on the front panel of the computer device 10; in other embodiments, there can be at least two display screens 150, located on different surfaces of the computer device 10 or in a foldable design; in still other embodiments, the display screen 150 can be a flexible display screen, located on a curved or foldable surface of the computer device 10. Furthermore, the display screen 150 can be configured as a non-rectangular irregular shape, i.e., a special-shaped screen. The display screen 150 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0064] The audio circuit 160 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals to be input into the processor 110 for processing, or to be input into the radio frequency circuit 140 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there can be multiple microphones, which are respectively arranged in different parts of the computer device 10. The microphone can also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 110 or the radio frequency circuit 140 into sound waves. The speaker can be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 160 may also include a headphone jack.

[0065] Power supply 170 is used to power various components in computer device 10. Power supply 170 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 170 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, while a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0066] For a detailed description of the functions and execution processes of each functional module or component in the computer device embodiment of the present application, please refer to the description in the above-mentioned embodiment of the method for analyzing and processing the correlation impact of upstream and downstream financial data of the present application, which will not be repeated here.

[0067] In the several embodiments provided in this application, it should be understood that the disclosed method for analyzing and processing the impact of correlations between computer devices and upstream and downstream financial data can be implemented in other ways. For example, the various embodiments of the computer device described above are merely illustrative. For example, the division of modules or units is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0068] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0069] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0070] See Figure 5 , if the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium 200. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions / computer programs to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, as well as electronic devices such as computers, mobile phones, laptops, tablet computers, cameras, etc. having the above-mentioned storage medium.

[0071] The description of the execution process of the program data in the computer-readable storage medium can be referred to the above-mentioned embodiment of the method for analyzing and processing the correlation impact of upstream and downstream financial data of the present application, and will not be repeated here.

[0072] The above description is merely an embodiment of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for analyzing and processing the impact of upstream and downstream financial data correlation, characterized in that: include: Automatically parsing the processing scripts corresponding to all job tables of the target data warehouse system to obtain the final processing logic of the processing scripts; Extracting table-level, field-level, and data-level impact analysis processes from the final processing logic, and making the impact analysis processes into a modular impact analysis tool; Utilizing the impact analysis modular tool to analyze the impact of upstream and downstream financial data association on the job table to be analyzed, and obtaining an analysis result of the impact of upstream and downstream financial data association on the job table to be analyzed; The extracting of table-level, field-level, and data-level impact analysis processes from the final processing logic and producing the impact analysis processes into an impact analysis modular tool comprises: extracting table-level, field-level, and data-level impact analysis processes from the final processing logic, combining the table-level, field-level, and data-level impact analysis processes to obtain a plurality of combined processes, and producing the plurality of combined processes into an impact analysis modular tool, wherein the impact analysis modular tool comprises a table-level impact analysis modular tool, a field-level impact analysis modular tool, and a data-level impact analysis modular tool; The automatic script parsing of the processing scripts corresponding to all the job tables of the target data warehouse system includes: parsing each processing script of all the job tables to obtain all temporary tables of each processing script; parsing all the temporary tables to obtain primary processing logic; identifying all fields of the final write table in the target data warehouse system, performing recursive processing based on temporary dependencies within the primary processing logic corresponding to each field of the final write table to obtain the processing logic of each field of the final write table, and combining the processing logic of all fields to obtain the final processing logic; Parsing all temporary tables to obtain primary processing logic includes: parsing all temporary tables to obtain temporary table names, field names, field processing logic, where conditions, where condition dependent tables and fields, join conditions, join condition dependent tables and fields, "groupby" and "orderby" logic and dependent tables and fields.

2. The method according to claim 1, characterized in that , The analysis of the impact of upstream and downstream financial data association on the job table to be analyzed using the impact analysis modular tool to obtain the analysis results of the impact of upstream and downstream financial data association on the job table to be analyzed includes: The changed table name, whether it is an exact match, and the deepest recursive level are input into the table-level impact analysis modular tool, and the impact analysis results of the changed table name, affected table name, and level are output.

3. The method according to claim 1, characterized in that , The analysis of the impact of upstream and downstream financial data association on the job table to be analyzed using the impact analysis modular tool to obtain the analysis results of the impact of upstream and downstream financial data association on the job table to be analyzed includes: The changed table name, whether the table name is an exact match, the changed field name, whether the field name is an exact match, the position of the changed field, and the deepest recursive level are input into the field-level impact analysis modular tool, and the impact analysis results of the changed table name, changed field, affected table name, affected field, and level are output.

4. The method according to claim 1, characterized in that , The analysis of the impact of upstream and downstream financial data association on the job table to be analyzed using the impact analysis modular tool to obtain the analysis results of the impact of upstream and downstream financial data association on the job table to be analyzed includes: The data content and impact type are input into the data-level impact analysis modular tool, and the impact analysis results of the data content, impact table, impact field and level are output.

5. A computer device, characterized in that: include: A processor, a memory and a communication circuit, wherein the processor is connected to the memory and the communication circuit respectively; The communication circuit is used for communication connection, the memory is used for storing a computer program, and the processor is used for executing the computer program to implement the method according to any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that A computer program is stored, and the computer program can be executed by a processor to implement the method according to any one of claims 1 to 4.

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