CN115829769A - Data auditing method, terminal and computer storage medium - Google Patents
Data auditing method, terminal and computer storage medium Download PDFInfo
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- CN115829769A CN115829769A CN202211561431.6A CN202211561431A CN115829769A CN 115829769 A CN115829769 A CN 115829769A CN 202211561431 A CN202211561431 A CN 202211561431A CN 115829769 A CN115829769 A CN 115829769A
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Abstract
The application relates to a data auditing method, a terminal and a computer storage medium. The data auditing method comprises the following steps: determining a data auditing model and first model data of the data auditing model according to the first service data, wherein the first service data comprises ERP historical service data and third-party historical service data; determining second model data of the data auditing model according to the second service data and the data auditing model, wherein the second service data comprises current service data of the ERP and current service data of a third party; and auditing the second model data according to the first model data, and executing corresponding operation according to the auditing result of the second model data. According to the data auditing method and device, the data auditing model and the first model data are determined according to the first business data, and the second model data determined based on the second business data and the data auditing model are audited according to the first model data, so that the traceability of data sources is ensured, and the data auditing efficiency and accuracy are improved.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to a data auditing method, a terminal and a computer storage medium.
Background
In the production and operation activities of enterprises, data auditing is one of the most basic and important work, and an accurate and efficient data auditing method is a powerful guarantee for the normal operation of the production and operation activities of the enterprises; taking financial data auditing as an example, most of the current financial data auditing modes of enterprises are that manual summary auditing is carried out on financial data submitted by each department to generate a financial statement; and each department extracts the required data from the ERP system, and incorporates the data into the manual data maintained by each department to generate the financial data of each department.
The current financial data auditing mode relates to a large amount of ERP production forms and manual data, the data volume is large, the data sources are complicated, the financial data submitted by each department are manually summarized and audited, and the data sources cannot be traced; the data of various calibers such as delivery, production and the like are involved, the chain of data auditing tasks is long, very long auditing and checking time is needed from the data acquisition to the financial statement generation, and the data auditing efficiency is low; in addition, it is difficult to confirm the rationality of adjustment of financial data submitted by each department, and it is difficult to ensure the accuracy of data audit.
Disclosure of Invention
In view of the above technical problems, the present application provides a data auditing method, a terminal and a computer storage medium, so as to ensure traceability of data sources and improve data auditing efficiency and accuracy.
The application provides a data auditing method, which comprises the following steps: determining a data auditing model and first model data of the data auditing model according to first service data, wherein the first service data comprises ERP historical service data and third-party historical service data; determining second model data of the data auditing model according to second service data and the data auditing model, wherein the second service data comprises current ERP service data and current third-party service data; and auditing the second model data according to the first model data, and executing corresponding operation according to the auditing result of the second model data.
In one embodiment, determining a data auditing model and first model data of the data auditing model according to first business data includes: performing first data processing on the first business data, and storing the first business data subjected to the first data processing into a corresponding theme of a data warehouse; and performing second data processing on the first business data stored in each topic of the data warehouse, and determining a data auditing model and auditing dimensions of the data auditing model.
In one embodiment, determining a data audit model and first model data of the data audit model according to first business data includes: and performing third data processing on the first business data stored in each topic of the data warehouse according to the auditing dimension of the data auditing model, and determining the first model data of the data auditing model.
In an embodiment, determining second model data of the data audit model according to second business data and the data audit model includes: performing the first data processing on the second business data, and storing the second business data subjected to the first data processing into a corresponding theme of the data warehouse; and performing the third data processing on the second business data stored in each topic of the data warehouse according to the auditing dimension of the data auditing model, and determining the second model data of the data auditing model.
In one embodiment, auditing the second model data from the first model data includes: classifying cost data in the first model data and the second model data by adopting a classification algorithm, and determining abnormal cost data in the second model data; wherein the cost data comprises standard cost data and actual cost data.
In an embodiment, the auditing the second model data according to the first model data further includes: establishing a revenue prediction model based on a regression algorithm; training the revenue prediction model according to a first revenue calculation in the first model data and first revenue-related data required to determine the first revenue calculation; determining an income prediction value according to second income related data required for determining a second income calculation value in the second model data and the income prediction model after training; determining the second revenue calculation value as abnormal revenue data when the revenue prediction value is more than a preset value different from the second revenue calculation value.
In one embodiment, before the auditing the second model data according to the first model data, the method includes: preprocessing the first model data and the second model data; the preprocessing comprises at least one of data feature classification, data feature index establishment, median supplement on general data and feature pruning on non-general data.
In an embodiment, according to the result of the auditing of the second model data, corresponding operations are performed, which include: when abnormal data exist in the second model data, sending abnormal data information to a target object associated with the abnormal data so that the target object can recheck and adjust the abnormal data; when the abnormal data does not exist in the second model data, generating a financial statement according to the second model data; wherein the anomaly data comprises anomaly cost data and anomaly revenue data.
The application also provides a terminal, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps of the data auditing method when executing the computer program.
The application also provides a computer storage medium, which stores a computer program, and the computer program realizes the steps of the data auditing method when being executed by a processor.
According to the data auditing method, the terminal and the computer storage medium, the data auditing model and the first model data are determined according to the first service data comprising the ERP historical service data and the third-party historical service data, and the second model data determined based on the second service data comprising the ERP current service data and the third-party current service data and the data auditing model are audited according to the first model data, so that the traceability of data sources is ensured, and the data auditing efficiency and accuracy are improved.
Drawings
Fig. 1 is a schematic flow chart of a data auditing method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a terminal according to a second embodiment of the present application.
Detailed Description
The technical solution of the present application is further described in detail with reference to the drawings and specific embodiments of the specification. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, "and/or" includes any and all combinations of one or more of the associated listed items.
Fig. 1 is a schematic flowchart of a data auditing method according to an embodiment of the present application. As shown in fig. 1, the data auditing method of the present application may include the following steps:
step S101: determining a data auditing model and first model data of the data auditing model according to the first service data, wherein the first service data comprises ERP historical service data and third-party historical service data;
optionally, the historical business data is business data with data generation time in a first time range (e.g., last 5 years, last 3 months, etc.); the ERP historical service data comprises historical order data, historical financial voucher data, material in-out warehouse historical information, historical inventory data and the like of an ERP system; the third-party historical service data comprises historical service data manually maintained by each department; optionally, a data interface is arranged in the ERP system, and third-party historical service data is imported into the ERP system, so that the ERP historical service data and the third-party historical service data are directly obtained from the ERP system.
In one embodiment, determining the data auditing model and the first model data of the data auditing model according to the first service data includes:
performing first data processing on the first business data, and storing the first business data subjected to the first data processing into a corresponding theme of a data warehouse;
performing second data processing on the first business data stored in each topic of the data warehouse, and determining a data auditing model and auditing dimensions of the data auditing model;
and performing third data processing on the first business data stored in each topic of the data warehouse according to the auditing dimensionality of the data auditing model, and determining the first model data of the data auditing model.
Optionally, the topics of the data warehouse include revenue, cost, expense, cash flow, inventory, other consolidated adjustment data, and the like.
Optionally, performing a first data process on the first service data, including: the method comprises the steps that time-series business data such as real-time delivery data are synchronized through an F l i nk/Kafka synchronous belt, and processing, classification, conversion and extraction processing are carried out on the synchronized business data; and synchronizing other service data through the N if i/Sqoop offline, and cleaning, analyzing, screening and de-duplicating the synchronized service data.
Optionally, performing second data processing on the first service data stored in each topic of the data warehouse, and determining the data audit model and the audit dimension of the data audit model, including:
and sorting and combining the first service data stored in each topic of the data warehouse, and determining a data auditing model and an auditing dimension of the data auditing model.
Optionally, performing third data processing on the first service data stored in each topic of the data warehouse according to the auditing dimension of the data auditing model, and determining the first model data of the data auditing model, including:
and extracting or calculating the first business data stored in each topic of the data warehouse according to the auditing dimensionality of the data auditing model, and determining the first model data of the data auditing model.
Optionally, the data auditing model is shown in table 1, and the data in columns 1, 3, and 5 in table 1 are auditing dimensions; according to the auditing dimensionalities of the 1 st, 3 rd and 5 th columns in the table 1, extracting or calculating first service data stored in each topic of the data warehouse to obtain a plurality of tables 1 filled with the data of the 2 nd, 4 th and 6 th columns, and summarizing the data of the plurality of tables 1 to obtain first model data of a data auditing model.
TABLE 1 data audit model
Step S102: determining second model data of the data auditing model according to the second service data and the data auditing model, wherein the second service data comprises current service data of the ERP and current service data of a third party;
optionally, the current service data is service data whose data generation time is in a second time range (e.g., the current day, the current month, etc.); the current business data of the ERP system comprises current order data, current financial voucher data, current material warehouse-in and warehouse-out information, current inventory data and the like of the ERP system; the third party current business data comprises current business data manually maintained by each department; similarly, a data interface is arranged in the ERP system, and the third-party current service data is imported into the ERP system, so that the ERP current service data and the third-party current service data are directly obtained from the ERP system.
In one embodiment, determining second model data of the data audit model according to the second service data and the data audit model includes:
performing first data processing on the second business data, and storing the second business data subjected to the first data processing into a corresponding theme of a data warehouse;
and performing third data processing on the second business data stored in each topic of the data warehouse according to the auditing dimensionality of the data auditing model, and determining second model data of the data auditing model.
Step S103: and auditing the second model data according to the first model data, and executing corresponding operation according to the auditing result of the second model data.
In one embodiment, the auditing the second model data from the first model data includes:
classifying cost data in the first model data and the second model data by adopting a classification algorithm, and determining abnormal cost data in the second model data;
the cost data comprises standard cost data and actual cost data.
The first model data are normal data which are obtained through manual auditing and adjusting based on historical service data, and the cost data have continuity numerical characteristics, so that the purpose of auditing the cost data in the second model data according to the cost data in the first model data can be achieved by adopting a classification algorithm.
Optionally, the classification algorithm is a support vector machine (gaussian kernel method); illustratively, the standard cost data of the first model data and the second model data are mixed, a support vector machine (gaussian kernel method) is adopted to classify the mixed standard cost data into 0 and 1, the standard cost data classified into 0 is normal standard cost data, and the standard cost data classified into 1 is abnormal standard cost data.
In one embodiment, the auditing the second model data according to the first model data further comprises:
establishing a revenue prediction model based on a regression algorithm;
training a revenue prediction model according to a first revenue calculation in the first model data and first revenue-related data required to determine the first revenue calculation;
determining an income prediction value according to second income related data required for determining a second income calculation value in the second model data and by combining a trained income prediction model;
determining the second revenue calculation value as abnormal revenue data when the revenue prediction value deviates from the second revenue calculation value by more than a preset value.
Optionally, the income calculation value is an order amount or tax free income in the model data of the data auditing model; the income related data required for determining the income calculation value comprises product information such as product type, product model, host number, material and the like, and quotation, presentation information, receipt return information and the like corresponding to the product information;
optionally, the regression algorithm is a Gradient Boosting Decision Tree (GBDT) algorithm; building a revenue prediction model through the weighted accumulation of a plurality of GBDTs; optionally, third party traffic data in the GBDT data source is given a higher weight.
Optionally, training the revenue prediction model using first revenue-related data required to determine the first revenue calculation in the first model data as an input value and the first revenue calculation in the first model data as an expected output value; after the income prediction model is trained, inputting second income related data required for determining a second income calculation value in second model data into the income prediction model to obtain an income prediction value; and comparing the income predicted value with a second income calculation value in the second model data, and if the deviation of the income predicted value and the second income calculation value is greater than a preset value (such as 15%), determining the second income calculation value in the second model data as abnormal income data.
Optionally, before performing an audit on the second model data according to the first model data, the method includes:
preprocessing the first model data and the second model data;
the preprocessing comprises at least one of data feature classification, data feature index establishment, median supplement on general data and feature pruning on non-general data.
Exemplarily, a data feature (XX ton X section telescopic arm crawler type) may become a high-dimensional data feature (XX ton X section telescopic arm crawler type) in various departments, systems and various manually-entered data, a local sensitive hash algorithm is adopted to perform dimensionality reduction on the high-dimensional data feature, and an approximate feature of the feature is efficiently traversed based on the dimensionality reduced data feature to unify the data feature and complete data feature classification.
In one embodiment, according to the result of the audit of the second model data, corresponding operations are performed, including:
when abnormal data exist in the second model data, sending abnormal data information to a target object associated with the abnormal data so that the target object can review and adjust the abnormal data;
when the second model data does not have abnormal data, generating a financial statement according to the second model data;
the abnormal data comprises abnormal cost data and abnormal income data.
Optionally, when abnormal data exists in the second model data, the abnormal data, data source information such as ERP current business data corresponding to the abnormal data, third-party current business data and the like, and abnormal data information such as abnormal data generation time and the like are pushed to a person in charge of the abnormal data in real time through a data pushing interface (such as an AP I interface); the data push interface is managed based on the Netty technology.
Optionally, the financial statement comprises data such as an operation target, a profit and loss target, a cash flow index and an inventory index, and can be developed step by step to trace back a data source.
According to the data auditing method provided by the embodiment of the application, the data auditing model and the first model data are determined according to the first service data comprising the ERP historical service data and the third-party historical service data, and the second model data determined based on the second service data comprising the ERP current service data and the third-party current service data and the data auditing model are audited according to the first model data, so that the traceability of data sources is ensured, and the data auditing efficiency and accuracy are improved.
Fig. 2 is a schematic structural diagram of a terminal provided in this application. The terminal of the application includes: a processor 110, a memory 111, and a computer program 112 stored in the memory 111 and operable on the processor 110. The steps in the above-described data auditing method embodiments are implemented when the computer program 112 is executed by the processor 110.
The terminal may include, but is not limited to, a processor 110, a memory 111. Those skilled in the art will appreciate that fig. 2 is only an example of a terminal and is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or different components, e.g., the terminal may also include input-output devices, network access devices, buses, etc.
The Processor 110 may be a Central Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an application Specific integrated circuit (AS ic), an off-the-shelf programmable Gate Array (FPGA) or other programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 111 may be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. The memory 111 may also be an external storage device of the terminal, such as a plug-in hard disk provided on the terminal, a Smart Media Card (SMC), a Secure Digital (SD) Card, a flash memory Card (F l ash Card), and the like. Further, the memory 111 may also include both an internal storage unit of the terminal and an external storage device. The memory 111 is used for storing computer programs and other programs and data required by the terminal. The memory 111 may also be used to temporarily store data that has been output or is to be output.
The application also provides a computer storage medium, on which a computer program is stored, and when being executed by a processor, the computer program implements the steps in the data auditing method embodiment.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
As used herein, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, including not only those elements listed, but also other elements not expressly listed.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A data auditing method is characterized by comprising the following steps:
determining a data auditing model and first model data of the data auditing model according to first service data, wherein the first service data comprises ERP historical service data and third-party historical service data;
determining second model data of the data auditing model according to second service data and the data auditing model, wherein the second service data comprises current ERP service data and current third-party service data;
and auditing the second model data according to the first model data, and executing corresponding operation according to the auditing result of the second model data.
2. The data auditing method of claim 1, where determining a data auditing model and first model data for the data auditing model from first business data comprises:
performing first data processing on the first business data, and storing the first business data subjected to the first data processing into a corresponding theme of a data warehouse;
and performing second data processing on the first business data stored in each topic of the data warehouse, and determining a data auditing model and auditing dimensions of the data auditing model.
3. A data review method as claimed in claim 2 wherein determining a data review model and first model data for the data review model from first business data comprises:
and performing third data processing on the first business data stored in each topic of the data warehouse according to the auditing dimensionality of the data auditing model, and determining first model data of the data auditing model.
4. A data auditing method according to claim 1 or 3, wherein determining second model data for the data auditing model from second business data and the data auditing model comprises:
performing the first data processing on the second business data, and storing the second business data subjected to the first data processing into a corresponding theme of the data warehouse;
and performing the third data processing on the second business data stored in each topic of the data warehouse according to the auditing dimension of the data auditing model, and determining the second model data of the data auditing model.
5. A data auditing method according to claim 1, in which auditing the second model data from the first model data comprises:
classifying cost data in the first model data and the second model data by adopting a classification algorithm, and determining abnormal cost data in the second model data;
wherein the cost data comprises standard cost data and actual cost data.
6. A data auditing method according to claim 1, wherein auditing the second model data in dependence upon the first model data, further comprises:
establishing a revenue prediction model based on a regression algorithm;
training the revenue prediction model according to a first revenue calculation in the first model data and first revenue-related data required to determine the first revenue calculation;
determining an income prediction value according to second income related data required for determining a second income calculation value in the second model data and by combining the trained income prediction model;
determining the second revenue calculation value as abnormal revenue data when the revenue prediction value is more than a preset value different from the second revenue calculation value.
7. A data auditing method according to claim 5 or 6, before auditing the second model data from the first model data, comprising:
preprocessing the first model data and the second model data;
the preprocessing comprises at least one of data feature classification, data feature index establishment, median supplement on general data and feature pruning on non-general data.
8. A data auditing method according to claim 5 or 6, characterised in that, in dependence on the result of the auditing of the second model data, corresponding operations are performed, including:
when abnormal data exist in the second model data, sending abnormal data information to a target object associated with the abnormal data so that the target object can check and adjust the abnormal data;
when the abnormal data does not exist in the second model data, generating a financial statement according to the second model data;
wherein the anomaly data comprises anomaly cost data and anomaly revenue data.
9. A terminal, characterized in that the terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the data auditing method according to any one of claims 1 to 8 when executing the computer program.
10. A computer storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of a data auditing method according to any one of claims 1 to 8.
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