CN118037462A - Tax management system and method based on data lake technology - Google Patents
Tax management system and method based on data lake technology Download PDFInfo
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- CN118037462A CN118037462A CN202410091452.9A CN202410091452A CN118037462A CN 118037462 A CN118037462 A CN 118037462A CN 202410091452 A CN202410091452 A CN 202410091452A CN 118037462 A CN118037462 A CN 118037462A
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Abstract
The invention discloses a tax management system and method based on a data lake technology, and relates to the technical field of tax management. In order to solve the problem that the existing tax management system has data dispersion, inconsistent management and the like, the provided system comprises: the data center module is used for receiving tax-related data from different sources, integrating and cleaning the tax-related data and storing the tax-related data in a data lake; the big data analysis module is used for analyzing tax-related data of the data lake by using a big data analysis tool to acquire appointed information; a machine learning module for predicting potential risk of the specified information in the data lake using the machine learning model; the intelligent tax collection management module is used for monitoring tax-related data of the data lake in real time and notifying related departments to check, warn or punish the illegal tax payers based on the predicted potential risks; and the personalized tax payment service module is used for providing customized tax payment service according to the history record and personal preference of the tax payer. The invention can improve the efficiency and accuracy of tax management.
Description
Technical Field
The invention relates to the technical field of tax management, in particular to a tax management system and method based on a data lake technology.
Background
In the general scheme of tax engineering issued by the national tax administration, the goal of the fourth period of tax is to realize "digitally driven tax" and realize the whole process management and service of tax data. Therefore, the tax four-stage system needs to integrate, clean and analyze tax data by utilizing modern information technology means, and achieves the functions of intelligent tax collection management, personalized tax collection service and the like.
Under the background, the tax management system and the tax management method based on the data lake technology are designed, and the problems of data dispersion, inconsistent management and the like of the existing system are solved by applying the data lake technology to the tax field.
Disclosure of Invention
Aiming at the needs and the shortcomings of the prior art development, the invention provides a tax management system and a tax management method based on a data lake technology, which realize the integration, deep mining and analysis of multi-source tax-related data and improve the efficiency and accuracy of tax management.
In a first aspect, the present invention provides a tax management system based on a data lake technology, which solves the technical problems described above by adopting the following technical scheme:
a tax management system based on data lake technology, comprising:
the data center module is used for receiving tax-related data from different sources, integrating and cleaning the tax-related data and storing the tax-related data in a data lake;
the big data analysis module is used for analyzing tax-related data of the data lake by using a big data analysis tool to acquire appointed information;
a machine learning module for predicting potential risk of the specified information in the data lake using the machine learning model;
The intelligent tax collection management module is used for monitoring tax-related data of the data lake in real time and notifying related departments to check, warn or punish the illegal tax payers based on the potential risks predicted by the machine learning module;
and the personalized tax payment service module is used for providing customized tax payment service according to the history record and personal preference of the tax payer.
Optionally, the big data analysis tool uses statistical methods, cluster analysis methods and association rule mining methods to analyze the data of the data lake.
Optionally, the machine learning model involved utilizes machine learning algorithms of supervised learning, unsupervised learning, and reinforcement learning to identify potential risks of tax-related data in the data lake.
Further optionally, the machine learning module involved includes a training sub-module, a prediction sub-module, and a feedback sub-module;
Manually marking potential risks based on historical tax-related data in the data lake, and constructing a training set and a testing set;
The training submodule trains a machine learning model by utilizing a training set;
The prediction submodule uses tax-related data in the test set as input, tests a machine learning model which is trained, and outputs predicted potential risks;
The feedback sub-module acquires the marked result of the tax-related data corresponding to the potential risk predicted by the machine learning model from the test set, compares the marked result based on the tax-related data with the predicted potential risk, and if the marked result is inconsistent with the marked result based on the tax-related data, feeds back and stores the tax-related data corresponding to the predicted potential risk and the marked result into the training set, and the training sub-module trains the machine learning model again by using the training set so as to complete updating of the machine learning model.
Optionally, the related intelligent tax collection management module comprises a monitoring sub-module, a risk assessment sub-module, a countermeasure selection sub-module and a subsequent monitoring sub-module;
The monitoring submodule is used for monitoring tax-related data of the data lake in real time;
The risk assessment submodule is used for carrying out risk assessment on real-time tax-related data of the tax payer by utilizing a pre-built tax-paying knowledge base and a machine learning model;
the coping measure selecting sub-module is used for selecting corresponding coping measures in a pre-constructed coping strategy library according to the risk assessment result and notifying related departments to check, warn or punish the illegal tax payers;
after adopting the countermeasure, the follow-up monitoring submodule is used for continuously monitoring follow-up tax-related data change conditions of the tax payer.
In a second aspect, the invention provides a tax management method based on a data lake technology, which solves the technical problems and adopts the following technical scheme:
A tax administration method based on data lake technology comprises the following steps:
Pre-building and training a machine learning model;
tax related data from different sources is received, integrated, cleaned and stored in a data lake;
analyzing tax-related data of the data lake by using a big data analysis tool to obtain appointed information;
Predicting potential risks of the specified information in the data lake by using the trained machine learning model;
and (3) monitoring tax-related data of the data lake in real time, notifying related departments of checking, warning or punishing the illegal tax payers based on the potential risk predicted by the machine learning model, and simultaneously providing customized tax-paying services according to the history records and personal preferences of the tax payers.
Optionally, the big data analysis tool uses statistical methods, cluster analysis methods and association rule mining methods to analyze the data of the data lake.
Optionally, the machine learning model involved utilizes machine learning algorithms of supervised learning, unsupervised learning, and reinforcement learning to identify potential risks of tax-related data in the data lake.
Optionally, the specific process of building and training the machine learning model is as follows:
manually marking potential risks based on historical tax-related data in the data lake, and constructing a training set and a verification set;
training a machine learning model with a training set;
Testing a trained machine learning model by using tax-related data in the test set as input, and outputting predicted potential risks by the machine learning model;
And acquiring a marked result of the tax-related data corresponding to the potential risk predicted by the machine learning model from the test set, comparing the marked result based on the tax-related data with the predicted potential risk, and if the marked result and the marked result are inconsistent, feeding back and saving the tax-related data corresponding to the predicted potential risk to the training set, and training the machine learning model again by using the training set to finish updating of the machine learning model.
Optionally, constructing a tax payment knowledge base and a coping strategy base based on risk assessment in advance;
In the process of monitoring tax-related data of a data lake in real time, performing risk assessment on real-time tax-related data of tax payers by utilizing a pre-built tax-paying knowledge base and a machine learning model, selecting corresponding coping measures in a pre-built coping strategy base according to a risk assessment result, and notifying related departments to check, warn or punish illegal tax payers;
After the countermeasure is adopted, the follow-up tax-related data change condition of the tax payer is continuously monitored.
Compared with the prior art, the tax management system and method based on the data lake technology have the beneficial effects that:
(1) The invention adopts the data lake technology to integrate tax-related data from a plurality of channels together to form a unified tax-related data center so as to efficiently manage the data; meanwhile, by combining a big data analysis technology and a machine learning technology, deep mining and analysis can be performed on tax-related data, potential risk points can be found timely, the efficiency and accuracy of tax management are improved, and the problems of data dispersion, inconsistent management and the like of the conventional tax management system can be solved;
(2) The invention also has the functions of intelligent tax collection management, personalized tax payment service and the like, and can greatly improve the service quality of tax payers.
Drawings
FIG. 1 is a connection block diagram of a first embodiment of the present invention;
fig. 2 is a flow chart of a method according to a second embodiment of the present invention.
Detailed Description
In order to make the technical scheme, the technical problems to be solved and the technical effects of the invention more clear, the technical scheme of the invention is clearly and completely described below by combining specific embodiments.
Embodiment one:
referring to fig. 1, this embodiment proposes a tax management system based on data lake technology, which includes:
the data center module is used for receiving tax-related data from different sources, integrating and cleaning the tax-related data and storing the tax-related data in a data lake;
the big data analysis module is used for analyzing tax-related data of the data lake by using a big data analysis tool to acquire appointed information;
a machine learning module for predicting potential risk of the specified information in the data lake using the machine learning model;
The intelligent tax collection management module is used for monitoring tax-related data of the data lake in real time and notifying related departments to check, warn or punish the illegal tax payers based on the potential risks predicted by the machine learning module;
and the personalized tax payment service module is used for providing customized tax payment service according to the history record and personal preference of the tax payer.
In this embodiment, the big data analysis tool analyzes the data of the data lake using a statistical method, a cluster analysis method, and an association rule mining method. The machine learning model involved utilizes machine learning algorithms of supervised learning, unsupervised learning, and reinforcement learning to identify potential risks of tax-related data in the data lake.
In this embodiment, the machine learning module includes a training sub-module, a prediction sub-module, and a feedback sub-module;
Manually marking potential risks based on historical tax-related data in the data lake, and constructing a training set and a testing set;
The training submodule trains a machine learning model by utilizing a training set;
The prediction submodule uses tax-related data in the test set as input, tests a machine learning model which is trained, and outputs predicted potential risks;
The feedback sub-module acquires the marked result of the tax-related data corresponding to the potential risk predicted by the machine learning model from the test set, compares the marked result based on the tax-related data with the predicted potential risk, and if the marked result is inconsistent with the marked result based on the tax-related data, feeds back and stores the tax-related data corresponding to the predicted potential risk and the marked result into the training set, and the training sub-module trains the machine learning model again by using the training set so as to complete updating of the machine learning model.
In this embodiment, the intelligent tax administration module includes a monitoring sub-module, a risk assessment sub-module, a countermeasure selection sub-module, and a subsequent monitoring sub-module;
The monitoring submodule is used for monitoring tax-related data of the data lake in real time;
The risk assessment submodule is used for carrying out risk assessment on real-time tax-related data of the tax payer by utilizing a pre-built tax-paying knowledge base and a machine learning model;
the coping measure selecting sub-module is used for selecting corresponding coping measures in a pre-constructed coping strategy library according to the risk assessment result and notifying related departments to check, warn or punish the illegal tax payers;
after adopting the countermeasure, the follow-up monitoring submodule is used for continuously monitoring follow-up tax-related data change conditions of the tax payer.
Embodiment two:
Referring to fig. 2, this embodiment proposes a tax management method based on data lake technology, which includes the following steps:
s1, pre-constructing and training a machine learning model, wherein the specific process is as follows:
manually marking potential risks based on historical tax-related data in the data lake, and constructing a training set and a verification set;
training a machine learning model with a training set;
Testing a trained machine learning model by using tax-related data in the test set as input, and outputting predicted potential risks by the machine learning model;
And acquiring a marked result of the tax-related data corresponding to the potential risk predicted by the machine learning model from the test set, comparing the marked result based on the tax-related data with the predicted potential risk, and if the marked result and the marked result are inconsistent, feeding back and saving the tax-related data corresponding to the predicted potential risk to the training set, and training the machine learning model again by using the training set to finish updating of the machine learning model.
S2, receiving tax related data from different sources, integrating and cleaning the tax related data, and storing the tax related data in a data lake.
S3, analyzing tax-related data of the data lake by using a big data analysis tool to obtain specified information.
The big data analysis tool uses a statistical method, a cluster analysis method and an association rule mining method to analyze tax-related data of the data lake.
S4, predicting the potential risk of the specified information in the data lake by using the trained machine learning model.
The machine learning model identifies potential risks of tax-related data in the data lake using machine learning algorithms of supervised learning, unsupervised learning, and reinforcement learning.
S5, constructing a tax knowledge base and a coping strategy base based on risk assessment in advance;
The tax-related data of the data lake are monitored in real time, the real-time tax-related data of the tax payer is subjected to risk assessment by utilizing a pre-constructed tax-paying knowledge base and a machine learning model, corresponding coping measures are selected in a pre-constructed coping strategy base according to a risk assessment result, and related departments are notified to check, warn or punish the illegal tax payer; after adopting the countermeasure, continuing to monitor the follow-up tax-related data change condition of the tax payer;
S6, providing customized tax payment service according to the history record and personal preference of the tax payer.
The supplementary ones are:
Based on the first embodiment and the second embodiment, the method can be applied to tax risk assessment, intelligent tax collection management and tax payment service optimization. Specifically, tax authorities collect and integrate various tax-related data by using a data lake technology, and then deeply mine tax-paying behaviors of enterprises by using a big data analysis means to evaluate whether tax risks exist; the data lake technology can help tax authorities to realize intelligent tax collection management, potential risk points are found by analyzing massive tax-related data in real time, measures are taken pertinently, and tax collection management efficiency is improved; the data lake technology can collect and analyze tax payer behavior data and know the requirements of tax payers, so that more accurate and personalized tax payment service is provided.
Based on the first embodiment and the second embodiment, the tax authority can be helped to monitor the whole chain from production to sales by the tax payer by utilizing the data lake technology in the future so as to ensure compliance of tax; the tax authority can be helped to know tax income conditions in real time based on intelligent analysis of big data, and future tax development trend is predicted, so that scientific basis is provided for policy formulation; the tax authority can be helped to simplify the tax flow by utilizing the data lake technology, and the service experience of tax payers, such as self-service tax returns, online consultation and the like, can be improved.
In summary, the tax management system and the tax management method based on the data lake technology are adopted, and tax related data from a plurality of channels are integrated together to form a unified tax related data center by adopting the data lake technology so as to efficiently manage the data; meanwhile, by combining a big data analysis technology and a machine learning technology, deep mining and analysis can be performed on tax-related data, potential risk points can be found timely, the efficiency and accuracy of tax management are improved, and the problems of data dispersion, inconsistent management and the like of the conventional tax management system can be solved.
The foregoing has outlined rather broadly the principles and embodiments of the present invention in order that the detailed description of the invention may be better understood. Based on the above-mentioned embodiments of the present invention, any improvements and modifications made by those skilled in the art without departing from the principles of the present invention should fall within the scope of the present invention.
Claims (10)
1. A tax administration system based on data lake technology, comprising:
the data center module is used for receiving tax-related data from different sources, integrating and cleaning the tax-related data and storing the tax-related data in a data lake;
the big data analysis module is used for analyzing tax-related data of the data lake by using a big data analysis tool to acquire appointed information;
a machine learning module for predicting potential risk of the specified information in the data lake using the machine learning model;
The intelligent tax collection management module is used for monitoring tax-related data of the data lake in real time and notifying related departments to check, warn or punish the illegal tax payers based on the potential risks predicted by the machine learning module;
and the personalized tax payment service module is used for providing customized tax payment service according to the history record and personal preference of the tax payer.
2. The tax management system of claim 1, wherein the big data analysis tool analyzes the data of the data lake using a statistical method, a cluster analysis method, and an association rule mining method.
3. The tax management system of claim 1, wherein the machine learning model utilizes machine learning algorithms of supervised learning, unsupervised learning, and reinforcement learning to identify potential risks of tax-related data in the data lake.
4. The tax management system of claim 3, wherein the machine learning module comprises a training sub-module, a prediction sub-module, and a feedback sub-module;
Manually marking potential risks based on historical tax-related data in the data lake, and constructing a training set and a testing set;
The training submodule trains a machine learning model by utilizing a training set;
The prediction submodule uses tax-related data in the test set as input, tests a machine learning model which is trained, and outputs predicted potential risks;
The feedback sub-module acquires the marked result of the tax-related data corresponding to the potential risk predicted by the machine learning model from the test set, compares the marked result based on the tax-related data with the predicted potential risk, and if the marked result is inconsistent with the marked result based on the tax-related data, feeds back and stores the tax-related data corresponding to the predicted potential risk and the marked result into the training set, and the training sub-module trains the machine learning model again by using the training set so as to complete updating of the machine learning model.
5. The tax management system based on data lake technology of claim 1, wherein the intelligent tax administration module comprises a monitoring sub-module, a risk assessment sub-module, a countermeasure selection sub-module, and a subsequent monitoring sub-module;
The monitoring submodule is used for monitoring tax-related data of the data lake in real time;
The risk assessment submodule is used for carrying out risk assessment on real-time tax-related data of the tax payer by utilizing a pre-built tax-paying knowledge base and a machine learning model;
the coping measure selecting sub-module is used for selecting corresponding coping measures in a pre-constructed coping strategy library according to the risk assessment result and notifying related departments to check, warn or punish the illegal tax payers;
after adopting the countermeasure, the follow-up monitoring submodule is used for continuously monitoring follow-up tax-related data change conditions of the tax payer.
6. The tax management method based on the data lake technology is characterized by comprising the following steps of:
Pre-building and training a machine learning model;
tax related data from different sources is received, integrated, cleaned and stored in a data lake;
analyzing tax-related data of the data lake by using a big data analysis tool to obtain appointed information;
Predicting potential risks of the specified information in the data lake by using the trained machine learning model;
and (3) monitoring tax-related data of the data lake in real time, notifying related departments of checking, warning or punishing the illegal tax payers based on the potential risk predicted by the machine learning model, and simultaneously providing customized tax-paying services according to the history records and personal preferences of the tax payers.
7. The tax administration method as claimed in claim 6, wherein the big data analysis tool analyzes data of the data lake using a statistical method, a cluster analysis method and an association rule mining method.
8. The method of claim 6, wherein the machine learning model utilizes machine learning algorithms of supervised learning, unsupervised learning, and reinforcement learning to identify potential risks of tax-related data in the data lake.
9. The tax administration method based on the data lake technique of claim 8, wherein the specific process of constructing and training the machine learning model is as follows:
manually marking potential risks based on historical tax-related data in the data lake, and constructing a training set and a verification set;
training a machine learning model with a training set;
Testing a trained machine learning model by using tax-related data in the test set as input, and outputting predicted potential risks by the machine learning model;
And acquiring a marked result of the tax-related data corresponding to the potential risk predicted by the machine learning model from the test set, comparing the marked result based on the tax-related data with the predicted potential risk, and if the marked result and the marked result are inconsistent, feeding back and saving the tax-related data corresponding to the predicted potential risk to the training set, and training the machine learning model again by using the training set to finish updating of the machine learning model.
10. The tax administration method based on the data lake technique as claimed in claim 6, wherein a tax payment knowledge base and a coping strategy base based on risk assessment are constructed in advance;
In the process of monitoring tax-related data of a data lake in real time, performing risk assessment on real-time tax-related data of tax payers by utilizing a pre-built tax-paying knowledge base and a machine learning model, selecting corresponding coping measures in a pre-built coping strategy base according to a risk assessment result, and notifying related departments to check, warn or punish illegal tax payers;
After the countermeasure is adopted, the follow-up tax-related data change condition of the tax payer is continuously monitored.
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