CN112258255A - Lodging industry taxpayer risk model analysis method and device - Google Patents
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Abstract
The invention provides a lodging industry taxpayer risk model analysis method and a device, which are used for periodically collecting room information issued by lodging industry taxpayers on the Internet and calculating the lowest price of a room; regularly collecting room check-in information of the lodging taxpayers registered by the public security system, and calculating check-in days; matching lodging industry taxpayer information of the internet, lodging industry taxpayer information of the public security system and lodging industry taxpayer tax registration information of the tax system; and calculating the actual income of the lodging industry taxpayer, comparing the actual income with the declared income, completing the establishment of a lodging industry risk taxpayer prediction model, and predicting the risk degree of the taxpayer. The invention calculates and calculates the actual income of the lodging taxpayers through a model algorithm based on the internet information, the public security system information and the tax system information, compares the actual income with the declaration income of the tax system taxpayers, and timely and accurately discovers the risk taxpayers with under-reporting, under-reporting and hidden sales income through data analysis.
Description
Technical Field
The invention belongs to the field of tax risk management, and particularly relates to a lodging taxpayer risk model analysis method and device.
Background
In the field of tax risk management, the analysis and management of tax risks of lodging taxpayers have many problems, the business volume of the lodging taxpayers is often larger, necessary data supervision is lacked, and no targeted lodging tax information analysis method exists, so that the situations of under-reporting, under-reporting and hidden sales income cannot be accurately found, and the taxpayers with tax risks cannot be accurately positioned.
Disclosure of Invention
The invention provides a method and a device for analyzing a risk model of a taxpayer in the lodging industry, which can timely and accurately find out the risk taxpayers with under-reporting, under-reporting and concealed sales income by using data analysis.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a lodging industry taxpayer risk model analysis method comprises the following steps:
s1, regularly collecting room information issued by the lodging taxpayer on the Internet, and calculating the lowest price of the room;
s2, regularly collecting the room check-in information of the lodging taxpayers registered by the public security system, and calculating the check-in days;
s3, matching the lodging industry taxpayer information of the Internet, the lodging industry taxpayer information of the public security system and the lodging industry taxpayer tax registration information of the tax system;
s4, calculating the actual income of the lodging industry taxpayer, comparing the actual income with the declared income, completing the establishment of the lodging industry risk taxpayer prediction model, and predicting the risk degree of the taxpayer.
Further, the specific collecting method in step S1 includes:
s101, using a crawler technology to regularly acquire room information including information of hotels, room types, room prices and the like published on the Internet;
s102, correspondingly setting the acquisition frequency according to the internet updating frequency of lodging taxpayers in the travel slack season and the busy season;
and S103, sampling according to the lowest price of the room.
Furthermore, in step S101, each time information of one lodging taxpayer is collected, the sleeping is performed, and a random number within 50 seconds is generated according to a random number algorithm as a sleeping duration; the step is used for simulating manpower, and the pressure of an internet website is not increased.
Further, the specific method of step S2 includes:
s201, collecting check-in information registered by a public security system comprises the following steps: the lodging industry taxpayer code, name, number of room to live, time to leave room;
s202, calculating the number of check-in days based on the check-in information, wherein the calculation rule is as follows:
(1) the 12 am of the check-in day before 4 am is calculated as 1 day;
(2) the time of the last 4 am before the last 12 am is calculated as 1 day;
(3) for a plurality of people who live in the same room in the same time period and leave the room in the same time period, calculating according to 1 room;
(4) for a plurality of people who live in at different times but leave the room at the same time: calculating according to the longest time;
(5) for a plurality of people who live in at different times and leave rooms at different times: and calculating the check-in starting time according to the earliest check-in person, calculating the check-out time according to the latest check-out person, and calculating the total check-in days from the check-in time and the check-out time.
Further, the specific method of step S3 includes:
s301, comparing whether the names are completely consistent or not, and automatically matching the names which are completely consistent;
s302, calculating by using a similarity algorithm when the names are inconsistent, and automatically matching when the similarity reaches a threshold value;
and S303, providing a matching interface for manual matching when the similarity is lower than the threshold value.
The invention also provides a device for analyzing the risk model of the taxpayer in the lodging industry, which comprises:
the Internet information acquisition module is used for periodically acquiring room information issued by the lodging taxpayers on the Internet and calculating the lowest price of the room;
the public security system information acquisition module is used for periodically acquiring the room check-in information of the lodging taxpayers registered by the public security system and calculating the check-in days;
the matching module is used for matching the lodging industry taxpayer information of the internet, the lodging industry taxpayer information of the public security system and the lodging industry taxpayer tax registration information of the tax system;
and the calculation module is used for calculating the actual income of the lodging industry taxpayer, comparing the actual income with the declared income, completing the establishment of a lodging industry risk taxpayer prediction model and predicting the risk degree of the taxpayer.
Further, the internet information acquisition module comprises:
the system comprises a crawler acquisition unit, a database acquisition unit and a database acquisition unit, wherein the crawler acquisition unit is used for periodically acquiring room information including information of hotels, house types, room prices and the like published on the Internet by using a crawler technology;
the frequency setting unit is used for correspondingly setting the acquisition frequency according to the update frequency of the lodging taxpayers in the travel slack season and the busy season on the Internet;
and the lowest sampling unit is used for controlling sampling according to the lowest price of the room.
Furthermore, the crawler collection unit is provided with a dormancy subunit and a random time length subunit, the dormancy subunit controls the dormancy of the information of each acquired lodging taxpayer, and the random time length subunit generates a random number within 50 seconds as the dormancy time length according to a random number algorithm; the step is used for simulating manpower, and the pressure of an internet website is not increased.
Further, the information acquisition module of the public security system comprises:
the information acquisition unit is used for acquiring the check-in information registered by the public security system and comprises: the lodging industry taxpayer code, name, number of room to live, time to leave room;
and a day number calculating unit for calculating the number of days of check-in based on the check-in information, wherein the calculation rule is as follows:
(1) the 12 am of the check-in day before 4 am is calculated as 1 day;
(2) the time of the last 4 am before the last 12 am is calculated as 1 day;
(3) for a plurality of people who live in the same room in the same time period and leave the room in the same time period, calculating according to 1 room;
(4) for a plurality of people who live in at different times but leave the room at the same time: calculating according to the longest time;
(5) for a plurality of people who live in at different times and leave rooms at different times: and calculating the check-in starting time according to the earliest check-in person, calculating the check-out time according to the latest check-out person, and calculating the total check-in days from the check-in time and the check-out time.
Further, the matching module comprises:
the comparison unit is used for comparing whether the names are completely consistent or not and automatically matching the names which are completely consistent;
the similarity calculation unit is used for calculating the inconsistent names by using a similarity calculation method, and the similarity reaches the automatic matching of a threshold value;
and a manual matching unit, wherein the similarity is lower than a threshold value, and a matching interface is provided for manual matching.
Compared with the prior art, the invention has the following beneficial effects:
the invention calculates and calculates the actual income of the lodging taxpayers through a model algorithm based on the internet information, the public security system information and the tax system information, compares the actual income with the declaration income of the tax system taxpayers, and timely and accurately discovers the risk taxpayers with under-reporting, under-reporting and hidden sales income through data analysis.
Drawings
FIG. 1 is a flow chart of risk prediction model building according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
The embodiment of the invention relates to a tax payer in the lodging industry, which comprises hotels, lodging residents and the like, wherein the hotels are taken as an example, and the invention is described in detail by combining the attached drawings.
Fig. 1 is a flow chart of risk prediction model establishment according to an embodiment of the present invention, in which:
the method comprises the following steps: regularly collecting room information of hotels on the Internet, and calculating the lowest price of the rooms;
by using the crawler technology, hotel information published on the Internet, including information of hotels, house types, room prices and the like, can be collected at least once a month according to the publishing frequency of the hotels. The collection frequency can be properly increased in the busy season of travel. In order to ensure the accuracy of the model result, sampling is carried out according to the lowest price of a hotel room.
In the hotel information collection process, the crawler technology sleeps once every time hotel information is collected, and the sleeping time is used for sleeping by generating a random number within 50 seconds according to a random number algorithm so as to simulate manpower without increasing the pressure of an internet website.
Step two: regularly collecting hotel check-in information of a public security system, and calculating the number of hotel check-in days:
the hotel check-in information of the public security system is collected and comprises a hotel code, a hotel name, a check-in room number, check-in time and check-out time, check-in days are calculated based on the check-in information, the check-in days are accurate in requirement, and through actual correction and comparison, a check-in days calculation algorithm is calculated according to the following algorithm and is most suitable for actual conditions:
1. the survival time is as follows:
(1) the live was entered 4 am in the morning,
(2) check-in 4 am later;
2. the time of returning the house:
(1) before the 12 o' clock at noon,
(2) after 12 am;
3. and (3) judging:
(1) even if 1 day was counted at 12 am on the day before 4 am,
(2) 1 day is counted after 4 am and the 12 am of the coming day;
4. judging by multiple persons:
(1) precondition:
a plurality of people enter the same room, and the merging time is coincident, so that the plurality of people enter the room;
in the same time period, intercepting the hour for judgment;
the same room refers to the room number;
judging the room returning time when hours are intercepted;
(2) and (3) judging a rule:
for multiple persons, the persons enter the same room in the same time period and the exits in the same time period are calculated according to 1 room;
for check-in at different times but check-out at the same time: calculating according to the longest time;
for check-in at different times and check-out at different times: calculating the check-in starting time according to the earliest check-in personnel, calculating the check-out time according to the latest check-out personnel, and calculating the total check-in days from the check-in time and the check-out time;
step three: matching the hotel information of the internet, the hotel information of the public security system and the tax registration information of the tax system
The hotel names of the hotel information of the internet, the hotel information of the public security system and the tax registration information of the tax system may be inconsistent, and need to be automatically matched according to a similarity algorithm and manually confirmed:
1. firstly, the names are completely consistent and automatically matched;
2. automatic matching with similarity exceeding 90 by calculation using the similarity algorithm of oracle;
3. other information, developing corresponding functions (such as providing a matching interface and the like) to support the user to perform manual matching or other matching methods.
Step four: calculating the actual income of the hotel and comparing the actual income with the declared income to determine the concealed sales income taxpayer.
And taking a month or a quarter as a period, and calculating the actual income of the taxpayer according to a formula:
the actual income is the lowest price of the house in days;
the income difference is the actual income-declared sales income;
and (3) dividing risk grades according to the range of income difference values:
high risk: over 100 million
Intermediate risk: 50 to 100 ten thousand
There is a risk: within 50 ten thousand.
And (4) completing the establishment of a housing industry risk taxpayer prediction model through the processes of the first step to the fourth step, and predicting the risk degree of the taxpayer by hiding the amount of sales income.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A lodging industry taxpayer risk model analysis method is characterized by comprising the following steps:
s1, regularly collecting room information issued by the lodging taxpayer on the Internet, and calculating the lowest price of the room;
s2, regularly collecting the room check-in information of the lodging taxpayers registered by the public security system, and calculating the check-in days;
s3, matching the lodging industry taxpayer information of the Internet, the lodging industry taxpayer information of the public security system and the lodging industry taxpayer tax registration information of the tax system;
s4, calculating the actual income of the lodging industry taxpayer, comparing the actual income with the declared income, completing the establishment of the lodging industry risk taxpayer prediction model, and predicting the risk degree of the taxpayer.
2. The lodging industry taxpayer risk model analysis method according to claim 1, wherein the specific collection method of the step S1 is as follows:
s101, using a crawler technology to regularly acquire room information published on the Internet;
s102, correspondingly setting the acquisition frequency according to the internet updating frequency of lodging taxpayers in the travel slack season and the busy season;
and S103, sampling according to the lowest price of the room.
3. The lodging industry taxpayer risk model analysis method according to claim 2, wherein in step S101, each time information of one lodging industry taxpayer is collected, the sleeping is performed, and a random number within 50 seconds is generated according to a random number algorithm as a sleeping duration.
4. The lodging industry taxpayer risk model analysis method according to claim 1, wherein the specific method of the step S2 comprises:
s201, collecting check-in information registered by a public security system comprises the following steps: the lodging industry taxpayer code, name, number of room to live, time to leave room;
s202, calculating the number of check-in days based on the check-in information, wherein the calculation rule is as follows:
(1) the 12 am of the check-in day before 4 am is calculated as 1 day;
(2) the time of the last 4 am before the last 12 am is calculated as 1 day;
(3) for a plurality of people who live in the same room in the same time period and leave the room in the same time period, calculating according to 1 room;
(4) for a plurality of people who live in at different times but leave the room at the same time: calculating according to the longest time;
(5) for a plurality of people who live in at different times and leave rooms at different times: and calculating the check-in starting time according to the earliest check-in person, calculating the check-out time according to the latest check-out person, and calculating the total check-in days from the check-in time and the check-out time.
5. The lodging industry taxpayer risk model analysis method according to claim 1, wherein the specific method of the step S3 comprises:
s301, comparing whether the names are completely consistent or not, and automatically matching the names which are completely consistent;
s302, calculating by using a similarity algorithm when the names are inconsistent, and automatically matching when the similarity reaches a threshold value;
and S303, providing a matching interface for manual matching when the similarity is lower than the threshold value.
6. An apparatus for analyzing a risk model of a taxpayer in lodging industry, comprising:
the Internet information acquisition module is used for periodically acquiring room information issued by the lodging taxpayers on the Internet and calculating the lowest price of the room;
the public security system information acquisition module is used for periodically acquiring the room check-in information of the lodging taxpayers registered by the public security system and calculating the check-in days;
the matching module is used for matching the lodging industry taxpayer information of the internet, the lodging industry taxpayer information of the public security system and the lodging industry taxpayer tax registration information of the tax system;
and the calculation module is used for calculating the actual income of the lodging industry taxpayer, comparing the actual income with the declared income, completing the establishment of a lodging industry risk taxpayer prediction model and predicting the risk degree of the taxpayer.
7. The lodging industry taxpayer risk model analysis device according to claim 6, wherein the internet information collection module comprises:
the crawler collecting unit is used for regularly collecting room information published on the Internet by using a crawler technology;
the frequency setting unit is used for correspondingly setting the acquisition frequency according to the update frequency of the lodging taxpayers in the travel slack season and the busy season on the Internet;
and the lowest sampling unit is used for controlling sampling according to the lowest price of the room.
8. The lodging industry taxpayer risk model analysis device according to claim 7, wherein the crawler collection unit is provided with a dormancy subunit and a random duration subunit, the dormancy subunit controls dormancy once every time information of one lodging industry taxpayer is collected, and the random duration subunit generates a random number within 50 seconds as dormancy duration according to a random number algorithm.
9. The lodging industry taxpayer risk model analysis device according to claim 6, wherein the public security system information collection module comprises:
the information acquisition unit is used for acquiring the check-in information registered by the public security system and comprises: the lodging industry taxpayer code, name, number of room to live, time to leave room;
and a day number calculating unit for calculating the number of days of check-in based on the check-in information, wherein the calculation rule is as follows:
(1) the 12 am of the check-in day before 4 am is calculated as 1 day;
(2) the time of the last 4 am before the last 12 am is calculated as 1 day;
(3) for a plurality of people who live in the same room in the same time period and leave the room in the same time period, calculating according to 1 room;
(4) for a plurality of people who live in at different times but leave the room at the same time: calculating according to the longest time;
(5) for a plurality of people who live in at different times and leave rooms at different times: and calculating the check-in starting time according to the earliest check-in person, calculating the check-out time according to the latest check-out person, and calculating the total check-in days from the check-in time and the check-out time.
10. The lodging industry taxpayer risk model analysis device of claim 6, wherein the matching module comprises:
the comparison unit is used for comparing whether the names are completely consistent or not and automatically matching the names which are completely consistent;
the similarity calculation unit is used for calculating the inconsistent names by using a similarity calculation method, and the similarity reaches the automatic matching of a threshold value;
and a manual matching unit, wherein the similarity is lower than a threshold value, and a matching interface is provided for manual matching.
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2020
- 2020-09-23 CN CN202011008805.2A patent/CN112258255A/en active Pending
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| US20010044734A1 (en) * | 2000-09-01 | 2001-11-22 | Audit Protection Insurance Services, Inc. | Method, system, and software for providing tax audit insurance |
| CN101520929A (en) * | 2009-02-24 | 2009-09-02 | 上海大学 | Method for managing tax fund based on data acquisition |
| CN101957952A (en) * | 2010-09-30 | 2011-01-26 | 无锡蝶尚酒店管理有限公司 | Self-service check-in management system and method for hotels |
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Application publication date: 20210122 |