Multi-aperture budget table processing method, system, equipment and medium

Abstract

The invention provides a multi-aperture budget table processing method, system and device, and a medium, and relates to the technical field of data processing, a budget aperture range is determined, and construction meets different business scene requirements; defining a budget data source framework, and determining a data table range; constructing a semantic object of the budget data source, and creating a semantic object table of the budget data source; constructing a document type table; according to the constructed semantic object and document type, defining a mapping relationship between different calibers and a budget data source; establishing a corresponding rule between the data of the data source and the budget table; according to business logic and financial specifications, presetting a budget rule, and verifying the processed data source; and storing the verified data to a preset budget table. And a corresponding rule and a mapping mode between the data of the data source and the budget table are established, and the adaptability of the system is enhanced. And a budget rule is preset to verify the processed data source, and data accuracy and integrity are verified. And the quality of the budget data is ensured.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and in particular, to a multi-caliber budget table processing method, system, device, and medium. Background Art

[0002] In the operation and management of enterprises, budget management, as a management tool, plays a key role in aspects such as reasonable resource allocation, business activity planning, cost control, and performance evaluation. In order to more accurately reflect the actual operation of enterprises and meet the needs of different management levels and business departments, multi-caliber budget management has been widely applied. Multi-caliber budget management requires classifying and analyzing budget data from multiple dimensions, such as dividing by business type, organizational structure, time period, etc., in order to provide more detailed and targeted budget information.

[0003] However, when dealing with multi-caliber budget management, simply dividing the budget according to a fixed time period or organizational department is difficult to meet the diverse business scenario needs of enterprises. In the management of budget data sources, there are scattered data, and the data formats and field definitions of different data sources are different. In the budget preparation process, there is a lack of a unified data source framework, resulting in problems such as data duplication and incorrect reference in data collection and integration. In related technologies, the correspondence between data source data and budget tables is usually fixed and pre-set by system developers. When the enterprise's business changes, such as adding new business segments or adjusting the budget table structure, the correspondence cannot be flexibly adjusted, resulting in the budget preparation being unable to adapt to business changes in a timely manner. Related technologies do not verify budget data, making it easy for incorrect and unreasonable data to enter the budget table, affecting the accuracy of the data. Summary of the Invention

[0004] This application provides a multi-caliber budget table processing method, which establishes the correspondence rules and mapping methods between data source data and budget tables, enhancing the adaptability of the system. Ensuring the correct use and conversion of data during the budget preparation process, improving the efficiency and accuracy of budget preparation. Presetting budget rules to verify the processed data source, verifying from the aspects of data accuracy and integrity, ensuring the quality of budget data.

[0005] The method includes: Determine the budget caliber range, and construct a budget caliber that meets the needs of different business scenarios and classifies and defines budget data; Define the budget data source framework and determine the range of data tables used to generate budget data; Construct a budget data source semantic object, create a semantic object table for the budget data source, and set business data tables and related field information in the semantic object table; Build a document type table, and according to the budget requirements of different calibers, set the corresponding budget data sources for each caliber; Define the mapping relationship between different calibers and budget data sources according to the constructed semantic objects and document types; Establish the corresponding rules between the data source data and the budget table based on the constructed budget data source; Preset budget rules according to business logic and financial norms, and verify the processed data source; Save the verified data to the preset budget table.

[0006] Furthermore, it should be noted that the corresponding rules between the data source data and the budget table in the steps also include: Select the budget data source document type and the budget table for which the budget data is to be generated, and set the corresponding relationship between all budget dimensions on the budget table and the document type information in the data source.

[0007] Furthermore, it should be noted that if a certain budget dimension is a fixed value unique to the budget table, then a constant is used; If a certain budget dimension is the same as a certain information value in the document type, then match based on the corresponding rules; If a certain budget dimension does not match the single value corresponding to a certain information value in the document type, then define a mapping relationship for single value mapping.

[0008] Furthermore, it should be noted that the data tables in the step of defining the budget data source framework include but are not limited to the tables in relational databases, the data sets in non-relational databases, and the real-time data streams from external interfaces, and build the preliminary mapping relationship between different calibers and the corresponding budget data sources.

[0009] Furthermore, it should be noted that the step of constructing the semantic object of the budget data source and creating the semantic object table of the budget data source also includes: Extract the field names and data types, and capture the glossary and data dictionary in the business documents as supplementary knowledge sources; use the pre-trained domain-enhanced BERT model to jointly encode the field names, annotations, and associated table names to generate semantic embedding vectors, construct a domain knowledge graph, and map the semantic embedding to the standard term nodes through a graph matching algorithm; Construct a semantic relationship graph, where the nodes are fields / tables and the edges are relationship types; based on the semantic relationship graph and combined with attention weights, obtain the relationship strength between fields, convert the attention edges into candidate business rules, and verify the effectiveness of the rules through historical data to obtain the business logic relationship between fields.

[0010] Furthermore, it should be noted that the step of establishing the corresponding rules between the data source data and the budget table based on the constructed budget data source also includes: Extract the dimensional fields of the budget table and record the format requirements for each dimension; Sort the dimensions according to business requirements and give priority to processing high-priority fields during data source mapping; Filter out the fields in the data source that match the budget dimensions and establish the mapping relationship between the fields and the dimensions; Define the filtering conditions according to the mapping relationship and the budget dimension requirements, and execute the filtering process; Perform format conversion, calculation, and saving on the filtered data source fields.

[0011] Furthermore, it should be noted that the steps of presetting budget rules according to business logic and financial specifications and validating the processed data source data also include: Validate the format of the data fields, and use the hash algorithm to deduplicate the combination of key fields to identify and process duplicate records; Or / and, calculate the quantile status of the current budget data and the historical same period based on historical data. If the Z-score quantile > the preset value, mark it as an outlier; Or / and, compare the dimension set defined in the budget table to verify whether the data source provides complete fields, and use set operations to check whether there are any missing dimensions; define field markings based on a preset algorithm to detect missing items and generate repair information.

[0012] This application also provides a multi-caliber budget table processing system, which includes: Determine the budget caliber range, and construct a budget caliber that meets the requirements of different business scenarios and classifies and defines budget data; Define the budget data source framework and determine the range of data tables used to generate budget data; Construct a budget data source semantic object, create a semantic object table for the budget data source, and set the business data table and related field information in the semantic object table; Construct a document type table, and set the corresponding budget data source for each caliber according to the budget requirements of different calibers; Define the mapping relationship between different calibers and the budget data source according to the constructed semantic object and document type; Based on the constructed budget data source, establish the corresponding rules between the data source data and the budget table; Preset budget rules according to business logic and financial specifications for validating the processed data source data; Save the verified data to the preset budget table.

[0013] According to another embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the multi-caliber budget table processing method are implemented.

[0014] According to still another embodiment of the present application, a storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the multi-caliber budget table processing method are implemented.

[0015] As can be seen from the above technical solutions, the present application has the following advantages: The multi-caliber budget table processing method provided by the present application first determines the budget caliber range and constructs budget calibers that meet the requirements of different business scenarios. This makes the budget preparation closely fit the business reality, enhances the support of the budget for the business, and improves the classification comparability of budget data. Define the budget data source framework, construct budget data source semantic objects and document type tables, and comprehensively and standardly manage the data sources. Through the semantic object table, the business data table, field information, filtering conditions, etc. are clarified, and through the document type table, the association between the budget caliber and the data source is realized. This effectively solves the problem of chaotic data source management, ensures the accuracy of the budget data source, improves the efficiency of data collection and integration, and enhances the quality of budget data. Define the mapping relationship between the caliber and the data source. Establish the corresponding rules between the data source and the budget table to ensure the accuracy of data conversion and reduce the data processing error rate. Preset budget rules and verify the data. According to the business logic and financial norms, budget rules, verify the processed data source. Verify from the aspects of data accuracy, rationality, and integrity, effectively solve the problem of the lack of effective data verification in the existing system, and ensure the quality of budget data. Improve the reliability of budget data and save the verified budget data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the present application, the drawings required for description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a flowchart of the multi-caliber budget table processing method; Figure 2 It is a flowchart of an embodiment of the multi-caliber budget table processing method; Figure 3 It is an example diagram of budget definition; Figure 4 It is an example diagram of the corresponding definition between the budget table and the data source; Figure 5 It is a schematic diagram of an electronic device. Detailed implementation manners

[0018] The multi-caliber budget table processing method provided by this application supports preparing a budget table from other modules without switching the budget module, meeting the budget preparation requirements of business personnel in different modules, reducing the preparation difficulty, and improving the convenience of users in preparing the budget table.

[0019] The steps of the multi-caliber budget table processing method will be described in detail below. For illustration rather than limitation, specific details such as a specific system structure and technology are proposed to thoroughly understand the embodiments of this application. However, those skilled in the art should clearly understand that this application can also be implemented in other embodiments without these specific details.

[0020] Statements such as "in one embodiment" or "in some embodiments" described in this application mean that the specific features, structures, or characteristics described in the embodiment are included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" that appear in different places in this application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0021] To make the application purpose, features, and advantages of this application more obvious and understandable, the technical solutions protected by this application will be clearly and completely described below by using specific embodiments and the accompanying drawings. Obviously, the embodiments described below are only a part of the embodiments of this application, rather than all embodiments. Based on the embodiments in this patent, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this patent.

[0022] As Figure 1 This is a flowchart of the multi-caliber budget table processing method provided for this embodiment. The multi-caliber budget table processing method includes the following steps: S101: Determine the budget caliber range and construct a budget caliber that meets the requirements of different business scenarios and classifies and defines budget data.

[0023] In some embodiments of this application, for various business scenarios and financial analysis requirements, different budget caliber boundaries are defined to construct a budget caliber.

[0024] The budget caliber involved in this embodiment can be divided based on multiple dimensions such as business type, department, and time period, laying a foundation for accurately configuring budget data sources later and ensuring that budget preparation can comprehensively cover various business requirements.

[0025] It should be noted that defining the boundaries of different budget calibers means delimiting the data scope covered by each budget caliber, the applicable business scenarios, as well as the relevant calculation logics and rule boundaries. In this way, it can be ensured that during the budget preparation and analysis processes, various types of data can be accurately attributed to the corresponding calibers, avoiding confusion and misuse.

[0026] For example, when differentiating the sales budget caliber, it is clearly stipulated that the sales amount of the sales channels is calculated according to the actual amount at the time of the transaction, and only includes the order amount of the confirmed receipt of goods. In this way, the boundary of the sales budget caliber is defined in terms of the data scope and calculation logic. The business scenario boundary stipulates which specific business activities and scenarios each budget caliber applies to. For example, the new product R & D budget caliber is only applicable to product projects that are in the R & D stage and have not been launched into the market, and the improvement and optimization costs of mature products are classified into the product maintenance budget caliber. Constructing the budget caliber is to integrate different budget calibers according to certain logical relationships and hierarchical structures to form an organic whole. Constructing the budget caliber can cover various business scenarios and financial analysis needs of users. Standardize and standardize such as formulating a unified data format, definition, calculation method, and report template for each budget caliber.

[0027] Specific implementation process steps: S1011: Obtain the user's past budget data and financial reports. S1012: Determine the classification dimensions of the budget caliber based on the budget data, financial reports, and relevant information. For example, determine to divide the budget caliber by business segments, time periods (monthly, quarterly, annually), and organizational structures (headquarters, branches, subsidiaries). Under each classification dimension, list the possible budget calibers.

[0028] S1012: For each preliminarily formulated budget caliber, delimit the data scope it contains.

[0029] This embodiment also formulates the calculation rules for the data within each budget caliber. In the cost budget, it is clearly defined that the calculation method of the raw material cost is the purchase unit price multiplied by the purchase quantity, and it is stipulated to be allocated to the raw material cost at a certain ratio. S1013: Establish clear hierarchical relationships and associations among the various budget calibers. Unify the data format of the budget caliber, such as retaining two decimal places for amount data and the date format being unified as "YYYY - MM - DD"; formulate a unified budget definition to ensure that different departments have the same understanding of the same budget caliber. S1014: Test the constructed budget caliber using historical data and simulated business scenarios. Select some representative business data, classify, calculate, and summarize them according to the rules of the budget caliber, and check whether it can reflect the user's business situation.

[0030] This embodiment can make the budget preparation more in line with the actual business operations of users and meet the needs of different business scenarios. S102: Define the budget data source framework and determine the scope of data tables used to generate budget data.

[0031] In some embodiments of the present application, for budget requirements of different calibers, determine the scope of data tables used to generate budget data, and clearly specify the specific data tables participating in generating budget data.

[0032] The data tables in this embodiment cover various types of data storage structures, including but not limited to tables in relational databases, data sets in non-relational databases, and real-time data streams from external interfaces, and construct a preliminary mapping relationship between different calibers and corresponding budget data sources.

[0033] As an example of the present application, defining the budget data source framework can scan the user's ERP data assets or CRM data assets and list all data tables and views that may be used for budget preparation.

[0034] Classify according to structured data types or unstructured data types, combined with update frequency and storage location.

[0035] According to the budget caliber requirements, eliminate redundant or irrelevant data sources. For example, the sales budget may depend on the "sales order table", "inventory table", and "customer table". The source of budget data is clarified, ensuring the accuracy and reliability of the data and avoiding data omission or incorrect reference.

[0036] S103: Construct a semantic object for the budget data source, create a semantic object table for the budget data source, and set business data tables and related field information in the semantic object table.

[0037] In this embodiment, create a semantic object table for the budget data source and set business data tables and related field information in the semantic object table.

[0038] It should be noted that this embodiment can extract field names and data types. Here, SQL statements can be used to obtain the field information of a certain data table in the database, including field names and data types. Record the extracted field names and data types in the semantic object sub-table.

[0039] Capture the glossary and data dictionary in business documents as supplementary knowledge sources; Exemplarily speaking, through text mining technology, parse these documents and extract information related to data source fields.

[0040] The trained BERT model is used to jointly encode field names, annotations, and associated table names to generate semantic embedding vectors, construct a domain knowledge graph, and map the semantic embeddings to standard term nodes through a graph matching algorithm. Here, the text is input into the BERT model, which encodes the input text through a neural network structure and finally outputs a semantic embedding vector. This vector contains the position and feature information of the field in the semantic space and can reflect the semantic relationship between the field and other related concepts.

[0041] In this embodiment, a domain knowledge graph within the enterprise can be pre-constructed, which contains various business terms, financial accounts, entities, and the relationships between them. Using the graph matching algorithm, the generated semantic embedding vectors are matched with the nodes in the knowledge graph. The algorithm tries to find the knowledge graph node with the closest semantics to the vector by calculating the similarity between vectors or other matching metrics. For example, the cosine similarity is used to calculate the similarity between the semantic embedding vector and the vectors of each node in the knowledge graph, and the node with the highest similarity is used as the matching result, thereby mapping the data source field to the standard term node. In this way, a semantic relationship graph is constructed, where the nodes are fields / tables and the edges are relationship types.

[0042] Based on the semantic relationship graph, this embodiment uses a graph attention network to calculate the attention weights between nodes to reflect the strength of the relationship between fields. For each node, by calculating the attention weights and based on the node, the feature vector of the node, and the information of the edge between them, the attention weights between them are obtained. The higher the weight, the closer the relationship. The field relationships corresponding to the edges with higher attention weights are converted into candidate business rules.

[0043] This embodiment can also use historical data to perform anti-verification on the generated candidate business rules, extract the data of relevant fields from the historical data, and perform calculations and verifications according to the rules. If the rule holds in most cases in the historical data, then the rule is considered valid; if there is a large amount of data that does not conform to the rule, then the rule needs to be adjusted or re-analyzed.

[0044] For the above semantic relationship graph, this embodiment also uses the method of verifying the data source, combines whether the same semantic fields are type-compatible, checks whether the screening conditions lead to data holes, constructs an anomaly detection model based on isolation forest to identify data distributions that do not conform to semantic constraints, and issues anomaly prompt information for the detected anomalies.

[0045] It should be noted that the business data table in this embodiment can adopt a data table view based on query logic. For each data table, filtering conditions for the data in the data table are set to screen out the data used for budget preparation. In terms of field information, key attributes such as field number, name, data type, and length are recorded to clearly define the data structure and screening rules of the budget data source, providing a key basis for subsequent data processing.

[0046] S104: Construct a document type table, and for each caliber, set the corresponding budget data source according to the budget requirements of different calibers.

[0047] In some embodiments of the present application, a document type table is established. For the budget requirements of different calibers, multiple budget data sources are defined to correspond to them. In the document type table, the uniquely identifiable document type, name, associated semantic object of the budget data source, module identifier representing different calibers, and semantic object fields corresponding to the key information of the budget data are clearly recorded.

[0048] By setting the corresponding budget data source for each caliber, multi-dimensional association settings can be realized, enabling the accurate positioning and management of the budget data source under different calibers, and facilitating the system to quickly obtain and process the corresponding data according to different business scenarios and budget requirements.

[0049] S105: Define the mapping relationship between different calibers and the budget data source according to the constructed semantic object and document type.

[0050] In this embodiment, according to the semantic object and document type constructed in step S103 and step S104, the mapping relationship between different calibers and the budget data source is further refined. Ensure that each budget caliber can be accurately associated with the corresponding data source, its semantic object, and document type, guaranteeing the accuracy of data acquisition.

[0051] S106: Establish the corresponding rules between the data source data and the budget table based on the constructed budget data source.

[0052] In some embodiments of the present application, according to the previously constructed budget data source, the corresponding rules between the data source data and the dimensions of the budget table are established.

[0053] This embodiment involves operations such as screening, converting, and summarizing the data source data to make it match the dimension requirements of the budget table.

[0054] Specifically, extract the dimension fields of the budget table and record the format requirements for each dimension; sort the dimensions according to business requirements and prioritize the processing of high-priority fields during data source mapping; filter out the fields in the data source that match the budget dimensions and establish the mapping relationship between the fields and the dimensions; define the filtering conditions according to the mapping relationship and the budget dimension requirements and execute the filtering process; perform format conversion, calculation, and saving on the filtered data source fields.

[0055] In this embodiment, the time data format in the data source can be uniformly converted according to the format requirements of the time dimension of the budget table; the numerical data source data can be calculated and summarized according to specific business rules. For the differences in the budget table dimensions under different calibers, the corresponding configured strategies allow users to select data source fields through the visual configuration interface and map them to the corresponding dimensions of the budget table, and the system automatically records and stores these corresponding relationships to provide an accurate mapping basis for budget data generation.

[0056] S107: Preset budget rules according to business logic and financial specifications to verify the processed data source.

[0057] In this embodiment, comprehensive budget rules are preset according to business logic and financial specifications to verify the data source data processed above. This embodiment performs accuracy verification based on the budget rules to ensure that no errors occur in the data source data during the conversion and calculation processes. It is also possible to evaluate the generated budget data with the help of business logic and historical data to determine whether it is within a reasonable range.

[0058] The verification method of this embodiment can also check whether the budget data contains all the dimensions and data items required by the budget table. Through rigorous verification rules, the quality and reliability of the budget data are guaranteed.

[0059] S108: Save the verified data to the preset budget table.

[0060] In this embodiment, the verified data is saved to the specified budget table. During the saving process, the system automatically records information such as the data source, generation time, and relevant parameters involved in the calculation for subsequent data traceability and auditing. At the same time, a data update mechanism is established so that when the data source changes or the budget caliber and corresponding rules are adjusted, the budget table data can be regenerated and updated in a timely manner to ensure that the budget data always remains consistent with the actual business situation.

[0061] It can be seen that in the method of this embodiment, the data after passing the budget rule verification is accurately filled into the preset budget table according to the structure and format requirements of the budget table through the system interface or data transfer tool. During the saving process, information such as the data source and generation time is recorded for subsequent traceability. Of course, after the budget preparation is completed, data storage is performed to form the official budget data available for users, providing a basis for budget execution, monitoring, and analysis.

[0062] In an embodiment of the present invention, based on step S106, as Figure 2 shown, a possible embodiment will be given below to non-restrictively elaborate on its specific implementation scheme.

[0063] Step S1061: Determine the data source range, construct a semantic object table, and create a semantic object main table in the database.

[0064] Each row in the table records the key information of a business data table or view. For each data source, a unique code is assigned, and the code can be generated using a specific coding rule, such as performing a hash operation by combining information such as the data source name and creation time. Record the name of the data source, the corresponding actual data table or view name, and the data filtering conditions for this data source.

[0065] For each data source in the semantic object main table, a corresponding sub-table is created. The sub-table is used to detail the information of each field in the data source. It includes the association with the internal code of the main table to trace the data source to which the field belongs; assign a serial number to each field to determine the order of the field in the data source; record the field name, the actual field identifier (field name in the data table), the data type (such as character type, numeric type, date type, etc.), and the length of the data type.

[0066] This embodiment also constructs a document type table, which is used to associate the budget caliber and the budget data source semantic object. Each row in the table represents a document type, and the document type has a unique identifier, which can be a sequential number or a unique number generated in combination with business rules. Record the name of the document type for intuitive identification.

[0067] This embodiment associates the document type with the module identifier of the budget caliber to clarify the budget caliber to which the document type belongs. At the same time, record the internal code of the budget data source semantic object corresponding to this document type to establish the connection between the document type and the data source.

[0068] Step S1062: Define the operation interface to display all the defined budget data source document types.

[0069] The user selects the corresponding document type according to the current budgeting task to be performed. All available budget table templates within the enterprise are displayed. The user selects the specific budget table from which to generate budget data. For the selected budget table in this embodiment, the system displays all its budget dimensions, such as time dimension, product dimension, region dimension, amount dimension, etc.

[0070] For the mapping relationship in this embodiment, when a certain budget dimension corresponds to a certain information value in the document type but the values on both sides are inconsistent, for example, the value of the customer level field in the data source is "A, B, C", while the value of the customer classification dimension in the budget table is "high, medium, low". The user opens the mapping relationship setting interface in the system and creates a mapping rule, that is, "A" corresponds to "high", "B" corresponds to "medium", and "C" corresponds to "low". The system saves this mapping rule for value conversion during subsequent data processing. Step S1063: According to the data source document type selected by the user in the budget correspondence definition step, the system obtains the corresponding data source table information from the document type table.

[0071] In this embodiment, data that meets the conditions can be queried and obtained from the database according to the data source table and filtering conditions. Of course, the system can call a pre-set budget rule library to verify the obtained data. The budget rule library contains various types of rules, such as data accuracy rules, rationality rules, integrity rules, etc. After being verified by the budget rules, the system saves the data that meets the requirements into the specified budget table according to the structure and format of the selected budget table. During the saving process, the system automatically records metadata information such as the data source, data generation time, start date and end date of data acquisition, etc.

[0072] Furthermore, as a refinement and extension of the specific implementation manner of the above embodiment, in order to completely illustrate the specific implementation process of presetting budget rules and verifying the processed data source according to business logic and financial norms in the steps of this embodiment, the method further includes: Step S1071: By defining a multi-level data accuracy verification mechanism, ensure that there are no format errors or logical errors in the data source during the conversion and calculation process.

[0073] Here, standardize the verification of the format of data fields, including date format, currency format, and numerical range.

[0074] Use the hash algorithm to deduplicate the combination of key fields and identify and process duplicate records.

[0075] Parse the calculation formula in the budget table, verify the syntax correctness of the formula through symbolic execution technology, and check whether the denominator is zero or the result is negative.

[0076] Step S1072: Through dual verification of business logic and historical data, determine whether the generated budget data is within a reasonable range.

[0077] This embodiment calculates the Z-score quantile of the current budget data and the same period in the past three years based on historical data, and marks it as an outlier if the Z-score quantile > 3. Optionally, an LSTM neural network can be used to predict the reasonable value interval, and the deviation can be measured by the RMSE indicator to ensure the rationality of the budget data.

[0078] Step S1073: Compare the dimension set defined in the budget table, verify whether the data source provides complete fields, and use set operations to check whether there are gaps in the required dimensions.

[0079] Verify cross-table relationships and check data connectivity through foreign key constraints. Define required field markers based on JSON Schema, automatically detect missing items and generate repair information.

[0080] The method of this embodiment performs standardized verification of the data field format, avoids data processing errors caused by inconsistent formats, and improves the availability and accuracy of data. The key field combination is deduplicated using a hash algorithm, and duplicate records are effectively identified and processed to avoid interference of data redundancy on the budget results. In step S1073, the budget table definition dimension set is compared to ensure that the data source provides complete fields, and set operations are used to check the missing required dimensions, thereby ensuring the structural integrity of the budget data.

[0081] A specific implementation method is given below in combination with the above multi-caliber budget table processing method. In the specific implementation method, the process of implementing budget table compilation from the statistical report module includes the following contents and steps: S1001: Preset the budget data source semantic object, as shown in Table 1 and Table 2.

[0082] Table 1

[0083] Table 2

[0084] Combine the above method to calculate the statistics report BB01 data source semantic object master table preset SQL. The specific execution method is: insert into LSSIMA (SIMA_OBJID,SIMA_DISP,SIMA_TABN,SIMA_CONDI) values ('TOSRYSQS','Report Center','TOSRYSQS',null); -- The preset SQL for the sub-table of the semantic object of the statistical report BB01 data source, and the specific execution method is as follows: insert into LSSOBJ (SOBJ_OBJID,SOBJ_ORD,SOBJ_DISP,SOBJ_FIELD,SOBJ_LENGTH, SOBJ_LENGTH) values('TOSRYSQS','001','Organization ID','ORGID','C','36'); insert into LSSOBJ (SOBJ_OBJID,SOBJ_ORD,SOBJ_DISP,SOBJ_FIELD,SOBJ_LENGTH, SOBJ_LENGTH) values('TOSRYSQS','002','Data item ID','DATAITEMID','C','36'); insert into LSSOBJ (SOBJ_OBJID,SOBJ_ORD,SOBJ_DISP,SOBJ_FIELD,SOBJ_LENGTH, SOBJ_LENGTH) values('TOSRYSQS','003','Date','DATE','D','8'); insert into LSSOBJ (SOBJ_OBJID,SOBJ_ORD,SOBJ_DISP,SOBJ_FIELD,SOBJ_LENGTH, SOBJ_LENGTH) values('TOSRYSQS','004','Data value','VALUE','F','100'); insert into LSSOBJ (SOBJ_OBJID,SOBJ_ORD,SOBJ_DISP,SOBJ_FIELD,SOBJ_LENGTH, SOBJ_LENGTH) values('TOSRYSQS','005','Data item number','DATAITEMCODE','C','100'); insert into LSSOBJ (SOBJ_OBJID,SOBJ_ORD,SOBJ_DISP,SOBJ_FIELD,SOBJ_LENGTH, SOBJ_LENGTH) values('TOSRYSQS','006','Data ID','DATAID','C','100'); S1002: Preset the document type of the budget data source. As shown in Table 3.

[0085] Table 3

[0086] -- Document types of statistical report form BB01 ， The specific execution method is as follows: insert into TBYWLC (TBYWLC_DJLX, TBYWLC_MK, TBYWLC_NAME, TBYWLC_OBJID) values('145540dd-d163-4b73-8be7-45e48bd62c68', 'BA', 'Report 01', 'TOSRYSQS').

[0087] S1003: As Figure 3 and Figure 4 shown, select the budget table and data source for corresponding definition.

[0088] The budget table in this embodiment is a multi-dimensional model, and the data on the budget table is stored and displayed by dimension. Each budget dimension on the budget table needs to be set correspondingly. When the budget data is generated, the data of the data source will be converted into the internal code value of the budget table dimension data dictionary according to the corresponding rules and stored.

[0089] S1004: Generate an interface based on the budget data.

[0090] The execution method is: public Task <string>ImputationData(string parameterList).

[0091] For the above parameters, Table: Data source table name TableCondi: Condition BillType: Document type ID ZZlist: Administrative organization ID StartData: Start date (8-digit date format, e.g., 20200101) EndDate: End date (8-digit date format, e.g., 20200131) Method return value: Success: Empty Failure: Failure message Code example: HashMap<String, Object> parameterList = new HashMap<>(); parameterList.put("Table", sSourceTable); / / Data source table parameterList.put("TableCondi", sSourceTableCondi); / / Filter conditions for the data source table parameterList.put("BillType", sBillType); / / Document type ID parameterList.put("ZZlist", sZZlist); / / Administrative group Weaving ID parameterList.put("StartData", sStartDate); / / Start date (8-digit date format, e.g., 20200101） parameterList.put("EndDate", sEndDate); / / End date (8-digit date format, e.g., 20200131） LinkedHashMap<String, Object> inputInfo = new LinkedHashMap<>(); inputInfo.put("parameterList", JSONSerializer. serialize (parameterList)); RpcClient rpcClient = SpringBeanUtils. getBean (RpcClient.class); String resultInfo = rpcClient.invoke(String.class, "Inspur.CB.BudgetController.IBudgetController.ImputationData", "cbc", inputInfo, null); if (resultInfo.equals("")) { resultInfo = "Success";} Combined with the above example, by determining the budget caliber range, a budget caliber that meets the requirements of different business scenarios is constructed. This makes the budget preparation closely fit the actual business situation, effectively solves the problem of the disconnection between the single budget caliber and business scenarios, improves the support of the budget for the business, and provides a more accurate data basis for enterprise decision-making. Define the budget data source framework, construct the semantic object of the budget data source and the document type table, ensure the accuracy of the budget data source, improve the efficiency of data collection and integration, and enhance the quality of budget data. Combine the standardization and automated verification of budget rules to ensure the accuracy of budget data.

[0092] The following is an embodiment of the multi-caliber budget table processing system provided by the present disclosure. This system belongs to the same inventive concept as the multi-caliber budget table processing method of the above embodiments. For the details not described in detail in the embodiment of the multi-caliber budget table processing system, reference can be made to the embodiment of the multi-caliber budget table processing method.

[0093] The system includes: a data construction and classification module, which is used to determine the budget caliber range and construct a budget caliber that meets the requirements of different business scenarios and classifies and defines budget data.

[0094] A data range definition module, which is used to define the budget data source framework and determine the data table range for generating budget data.

[0095] A semantic creation module, which is used to construct the semantic object of the budget data source, create the semantic object table of the budget data source, and set the business data table and related field information in the semantic object table.

[0096] A data source setting module, which is used to construct the document type table and set the corresponding budget data source for each caliber according to the budget requirements of different calibers.

[0097] A mapping relationship setting module, which is used to define the mapping relationship between different calibers and budget data sources according to the constructed semantic object and document type.

[0098] A rule establishment module, which is used to establish the corresponding rules between the data source data and the budget table according to the constructed budget data source.

[0099] A data verification module is used to preset a budget rule according to business logic and financial specifications for verifying the processed data source data.

[0100] A data storage module is used to store the verified data on a preset budget table.

[0101] As Figure 5 shown, the present application also provides an electronic device, including a display module 103, a memory 102, a processor 101, and a computer program stored on the memory and executable on the processor 101. When the processor 101 executes the program, the steps of the multi-caliber budget table processing method are implemented.

[0102] In the embodiments of the present invention, the electronic device includes, but is not limited to, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described herein and / or claimed.

[0103] In the embodiments of the present application, the processor 101 can be implemented by using at least one of an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to execute the functions described herein. In some cases, such an implementation can be implemented in the controller. For a software implementation, an implementation of a process or function can be implemented with a separate software module that allows execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any suitable programming language. The software code can be stored in the memory and executed by the controller.

[0104] The display module 103 is used to display information input by the user or provided to the user. The display module 103 can include a display panel, and the display panel can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc.

[0105] The memory 102 can be used to store software programs and various data. The memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0106] This application also provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the multi-aperture budget table processing method are implemented.

[0107] The storage medium can be any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0108] In the storage medium, the readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, and the readable medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0109] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.< / string>

Claims

1. A multi-caliber budget table processing method, characterized in that, The method includes: Determine the budget caliber range, construct a budget caliber that meets the requirements of different business scenarios and classifies budget data; Define the budget data source framework and determine the range of data tables used to generate budget data; Construct a semantic object for the budget data source, create a semantic object table for the budget data source, and set business data tables and related field information in the semantic object table; Construct a document type table, and set the corresponding budget data source for each caliber according to the budget requirements of different calibers; Define the mapping relationship between different calibers and budget data sources based on the constructed semantic object of the budget data source and the document type table; Establish the corresponding rules between the data source data and the budget table based on the constructed budget data source; Preset budget rules according to business logic and financial specifications, and verify the processed data source; Save the verified data to the preset budget table.

2. The multi-caliber budget table processing method according to claim 1, wherein The corresponding rules between the data source data and the budget table in the steps further include: Select the budget data source document type and the budget table for which the budget data is to be generated, and set the corresponding relationship between all budget dimensions on the budget table and the document type information in the data source.

3. The multi-caliber budget table processing method according to claim 2, wherein If a certain budget dimension is a fixed value unique to the budget table, then use a constant; If a certain budget dimension is the same as a certain information value in the document type, then match based on the corresponding rules; If a certain budget dimension does not match the single value corresponding to a certain information value in the document type, then define a mapping relationship for single value mapping.

4. The multi-caliber budget table processing method according to claim 1, wherein The data tables in the step of defining the budget data source framework include, but are not limited to, tables in relational databases, data sets in non-relational databases, and real-time data streams from external interfaces, and construct a preliminary mapping relationship between different calibers and the corresponding budget data sources.

5. The multi-caliber budget table processing method according to claim 1, wherein The step of constructing a semantic object for the budget data source and creating a semantic object table for the budget data source further includes: Extract field names and data types, and capture the glossary and data dictionary in business documents as supplementary knowledge sources; use a pre-trained domain-enhanced BERT model to jointly encode field names, annotations, and associated table names to generate semantic embedding vectors, construct a domain knowledge graph, and map the semantic embedding to standard term nodes through a graph matching algorithm; Construct a semantic relationship graph, where the nodes in the semantic relationship graph are fields / tables and the edges are relationship types; based on the semantic relationship graph and combined with attention weights, obtain the relationship strength between fields, convert the attention edges into candidate business rules, and verify the effectiveness of the rules through historical data to obtain the business logic relationship between fields.

6. The multi-caliber budget table processing method according to claim 1, wherein The step of establishing the corresponding rules between the data source data and the budget table based on the constructed budget data source further includes: Extract the dimension fields of the budget table and record the format requirements for each dimension; Prioritize dimensions according to business requirements and process high-priority fields first during data source mapping; Screen out fields that match the budget dimension from the data source and establish the mapping relationship between the fields and the dimension; Define the screening conditions according to the mapping relationship and the requirements of the budget dimension, and execute the screening process; Perform format conversion, calculation, and saving on the screened data source fields.

7. The multi-caliber budget table processing method according to claim 1, characterized in that The step of presetting budget rules for verifying the processed data source data according to business logic and financial specifications further includes: Verify the format of the data fields, and use the hash algorithm to deduplicate the combination of key fields to identify and process duplicate records; Or / and, calculate the quantile status of the current budget data and the same period in history based on historical data. If the Z-score quantile > the preset value, mark it as an outlier; Or / and, compare the dimension set defined in the budget table, verify whether the data source provides complete fields, and use set operations to check whether there are vacancies in the dimensions; define field marks based on a preset algorithm, detect missing items, and generate repair information.

8. A multi-caliber budget table processing system, characterized in that The system is used to implement the multi-caliber budget table processing method described in any one of claims 1 to 7; the system includes: A data construction and classification module for determining the budget caliber range, constructing budget calibers that meet the requirements of different business scenarios, and classifying and defining budget data; A data range definition module for defining the budget data source framework and determining the data table range for generating budget data; A semantic creation module for constructing a semantic object of the budget data source, creating a semantic object table of the budget data source, and setting business data tables and related field information in the semantic object table; A data source setting module for constructing a document type table and setting the corresponding budget data source for each caliber according to the budget requirements of different calibers; A mapping relationship setting module for defining the mapping relationship between different calibers and the budget data source according to the constructed semantic object and document type; A rule establishment module for establishing the corresponding rules between the data source data and the budget table based on the constructed budget data source; A data verification module for presetting budget rules according to business logic and financial specifications for verifying the processed data source data; A data saving module for saving the verified data to a preset budget table.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the multi-caliber budget table processing method described in any one of claims 1 to 7.

10. A storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-caliber budget table processing method described in any one of claims 1 to 7.

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