CN115775093A - Method and device for constructing sensitive commodity library of sale and sale items - Google Patents
Method and device for constructing sensitive commodity library of sale and sale items Download PDFInfo
- Publication number
- CN115775093A CN115775093A CN202211448114.3A CN202211448114A CN115775093A CN 115775093 A CN115775093 A CN 115775093A CN 202211448114 A CN202211448114 A CN 202211448114A CN 115775093 A CN115775093 A CN 115775093A
- Authority
- CN
- China
- Prior art keywords
- commodity
- enterprise
- risk
- commodities
- sale
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 230000002159 abnormal effect Effects 0.000 claims abstract description 27
- 238000005065 mining Methods 0.000 claims abstract description 19
- 238000010276 construction Methods 0.000 claims description 5
- 238000009825 accumulation Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000008676 import Effects 0.000 abstract description 12
- 238000004458 analytical method Methods 0.000 description 4
- 230000006399 behavior Effects 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 206010000117 Abnormal behaviour Diseases 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method and a device for constructing a sensitive commodity library of import and export items, which comprises the following steps: acquiring commodity information of the sale and sale items of enterprises; determining the main sales commodities and main purchase commodities of the enterprise according to the purchase and sale item commodity information of the enterprise; obtaining a plurality of sale-in item commodity combinations by calculating the Cartesian product of the bought commodities and the bought commodities; acquiring an abnormal commodity combination of the input and sales items according to the commodity code, the confidence coefficient and the support degree of the commodity combination of the input and sales items; calculating the deviation degree of the enterprise based on the quantity of the abnormal commodity combinations for sale items; according to the deviation degree, if the enterprise is determined to be a high-risk enterprise, mining high-risk entry commodities, high-risk sale commodities and high-risk entry commodity combinations of the enterprise; and the high-risk entry commodities, the high-risk sale commodities and the high-risk entry commodities are combined to construct the entry and sale item sensitive commodity library, so that the problems of strong dependence on business personnel and low efficiency of risk pre-judgment are solved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for constructing a sensitive commodity library of import and export items.
Background
At present, most of methods for analyzing the difference of commodities of sale and input items and further identifying abnormal enterprises are only suitable for business enterprises. For production type enterprises, due to the existence of a processing and manufacturing process, the difference of commodities of sale and input items is large, and the difficulty of analyzing whether the commodities of sale and input items have inconsistent abnormal behaviors or not by comparing the commodities of sale and input items is very large. In addition, the association and commonality mining between the items of sale and entrance of the inauguration enterprises is insufficient, and the experience accumulated by business personnel in the tax revenue risk prevention and control with inconsistent sale and entrance abnormity analysis and the conclusion obtained by the abnormal sale and entrance analysis can not be stored as sharable knowledge and reused by other people, so that the tax risk prevention and control greatly depends on the business personnel and the efficiency is low.
Disclosure of Invention
In view of the above problems, the present invention provides a method for constructing a stock of sensitive goods for sale entry, comprising:
acquiring commodity information of the sale and sale items of enterprises;
determining the bought commodities and the bought commodities of the enterprise according to the purchase and sale item commodity information of the enterprise;
obtaining a plurality of commodity combinations of the sale-in items by calculating the Cartesian product of the bought commodities and the bought commodities; according to the commodity code, the confidence coefficient and the support degree of the commodity combination with the input and sale items, obtaining an abnormal commodity combination with the input and sale items;
calculating the deviation degree of the enterprise based on the quantity of the abnormal commodity combinations for sale items; according to the deviation degree, if the enterprise is determined to be a high-risk enterprise, mining high-risk entry commodities, high-risk sale commodities and high-risk entry commodity combinations of the enterprise; and constructing the sensitive commodity library of the input and sales items by combining the high-risk input commodity, the high-risk sales item commodity and the high-risk input and sales item commodity.
Further, acquiring the commodity information of the sale-in item of the enterprise comprises:
and extracting the commodity information of the sale and sale items of the enterprise from the value-added tax invoice and the cargo detail data of the enterprise.
Further, after the step of obtaining the information of the commodity of the enterprise for sale item, the method further comprises the following steps:
the item code for each item is determined by a text classification tool.
Further, determining the merchandize and the bought merchandise of the enterprise according to the purchase and sale merchandise information of the enterprise includes:
respectively summarizing the total expense amount and/or the total entrance amount of the enterprise;
calculating the proportion of the sum of the corresponding commodities to the total sales and/or the total entrance sum for each commodity code, and arranging the sum in a descending order;
respectively accumulating the proportions of total sales items and/or total entry amounts of commodities one by one according to the sequence, and stopping accumulation when the proportions are larger than a preset threshold value;
and determining the accumulated commodities as the main sales commodities and/or the main purchase commodities of the enterprise.
Further, according to the commodity code, the confidence and the support degree of the commodity combination of the sale-in item, obtaining an abnormal sale-in item commodity combination, comprising:
if the front n bits of the commodity code of the input commodity and the commodity code of the sales commodity in the input and sales commodity combination are the same, judging that the input and sales commodity combination is normal, otherwise, judging that the input and sales commodity combination is in a state to be determined;
and calculating the confidence coefficient and the support degree of the commodity combination of the input and sales items to be determined as the state, judging the commodity combination of the input and sales items with the support degree and the confidence coefficient both larger than a preset threshold value as normal, and judging the rest commodity combinations of the input and sales items as abnormal.
Further, the confidence and the support of the commodity combination of the marketing item are respectively obtained by the following formulas:
confidence = number of times a code of an item appears/total number of combinations
The support = number of times of occurrence of the combination in which the input item product code is present/number of times of occurrence of the input item product code.
Further, calculating the deviation degree of the enterprise based on the quantity of the abnormal sale-promoting commodity combinations comprises the following steps:
the degree of the inconsistency of the enterprise marketing items is comprehensively measured by utilizing the deviation degree, the larger the deviation degree value is, the higher the risk representing the inconsistency of the enterprise marketing items is, and the specific calculation formula of the deviation degree is as follows:
divergence = number of combinations of items sold by business exception/total number of combinations of items sold by business.
Further, according to the deviation degree, if it is determined that the enterprise is a high-risk enterprise, mining a combination of high-risk entry commodities, high-risk sale commodities, and high-risk entry commodities of the enterprise, including:
setting a threshold value based on the deviation degree of the enterprise, and identifying the enterprise with the deviation degree greater than the threshold value as a high-risk enterprise;
and mining high-risk entry commodities, high-risk sale commodities and high-risk entry commodity combinations of the high-risk enterprises through an fp-growth algorithm.
The invention also provides a device for constructing the commodity library sensitive to the items sold, which comprises the following steps:
the commodity information acquisition unit is used for acquiring commodity information of the sale and sale items of enterprises;
the device comprises a main commodity selling and purchasing determining unit, a main commodity selling and purchasing determining unit and a main commodity purchasing and purchasing determining unit, wherein the main commodity selling and purchasing determining unit is used for determining the main commodity selling and purchasing of the enterprise according to the commodity information of the sales items of the enterprise;
an abnormal sale item commodity combination obtaining unit for obtaining a plurality of sale item commodity combinations by calculating the Cartesian product of the bought commodities and the bought commodities; acquiring an abnormal commodity combination of the input and sales items according to the commodity code, the confidence coefficient and the support degree of the commodity combination of the input and sales items;
the sensitive commodity library construction unit of the input and output item is used for calculating the deviation degree of the enterprise based on the quantity of the abnormal input and output item commodity combination; according to the deviation degree, if the enterprise is determined to be a high-risk enterprise, mining high-risk entry commodities, high-risk sale commodities and high-risk entry commodity combinations of the enterprise; and constructing the sale item sensitive commodity library by combining the high-risk sale item commodities, the high-risk sale item commodities and the high-risk sale item commodities.
Further, the sensitive commodity library of items sold by the stock-selling unit comprises:
the high-risk enterprise identification subunit is used for setting a threshold value based on the deviation degree of the enterprise and identifying the enterprise with the deviation degree greater than the threshold value as the high-risk enterprise;
and the mining subunit is used for mining the high-risk entry commodities, the high-risk sale commodities and the high-risk entry commodity combination of the high-risk enterprises through an fp-growth algorithm.
By the method and the device for constructing the sensitive commodity library of the import and export items, the potential association rules between the high-risk commodities and the commodities are further mined aiming at the import and export commodities of enterprises with inconsistent import and export items, the sensitive commodity library of the import and export items is constructed, the tax risk is rapidly pre-judged, the knowledge can be shared and reused, and the problems that the risk pre-judgment is strongly dependent on business personnel and the efficiency is low are solved.
Drawings
FIG. 1 is a schematic flow chart diagram of a method for constructing a sensitive commodity library for sale items according to the present invention;
FIG. 2 is a flow chart of the construction of the sensitive commodity library for the entries to be sold provided by the present invention;
fig. 3 is a schematic structural diagram of an apparatus for building a stock of sensitive commodities for sale items according to the present invention.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather construed as limited to the embodiments set forth herein.
Fig. 1 is a schematic flow chart of a method for constructing a sensitive commodity library for sale items according to the present invention, and the method provided by the present invention is described in detail below with reference to fig. 1.
And step S101, acquiring the commodity information of the sale and sale items of the enterprises.
Enterprises with only entries or only sales can be directly judged as abnormal enterprises, and are out of the scope of the invention. Therefore, the data is preprocessed, specifically including:
(1) The sample is locked. And screening out enterprises with both sales invoices and incoming invoices, and eliminating small-scale taxpayers.
(2) And (5) filtering data. And rejecting obsolete invoices, invoices with zero tax rate, and data with lease, real estate and rent in the names of the commodities.
And then, extracting the commodity information of the business for the sale-in item from the value-added tax invoice and the goods detail data of the business.
The diversity of the commodity name description causes the difficulty of identifying the same commodity entity, so that the analysis of the consistence of the sale items based on the commodity codes is more scientific. And in view of the problems of irregular filling and inaccuracy of the commodity codes in the invoice data, determining the commodity codes of each commodity through an open-source text classification tool fastText.
And S102, determining the bought commodities and the bought commodities of the enterprise according to the purchase and sale item commodity information of the enterprise.
The number of the commodities sold or purchased by each enterprise is often more than one, and only the bought commodities and the bought commodities of the enterprise are analyzed to reduce the calculated amount and improve the analysis efficiency. After data of commodity codes beginning with '4', '5' and '6' are removed, the following steps are respectively executed based on sales data and entry data of enterprises:
respectively summarizing the total expense amount and/or the total entrance amount of the enterprise;
calculating the proportion of the sum of the corresponding commodities to the total sales and/or the total entrance sum for each commodity code, and arranging the sum in a descending order;
respectively accumulating the total sales item and/or the total entry amount of the commodities one by one according to the sequence, and stopping accumulation when the proportion is greater than a preset threshold value;
and determining the accumulated commodities as the main sales commodities and/or the main purchase commodities of the enterprise.
Step S103, obtaining a plurality of commodity combinations of the sale-in items by calculating Cartesian products of the bought commodities and the bought commodities; and acquiring an abnormal commodity combination of the input and sales items according to the commodity code, the confidence coefficient and the support degree of the commodity combination of the input and sales items.
And acquiring a plurality of commodity combinations of the sale-entering items for the Cartesian product of the bought commodities and the bought commodities of the enterprise. A judgment rule is set, and the judgment rule is set,
if the front n bits of the commodity code of the input commodity and the commodity code of the sales commodity in the input and sales commodity combination are the same, judging that the input and sales commodity combination is normal, otherwise, judging that the input and sales commodity combination is in a state to be determined;
and calculating the confidence coefficient and the support degree of the commodity combination of the inlet and outlet items to be determined with the judged state, judging the commodity combination of the inlet and outlet items with the support degree and the confidence coefficient both larger than a preset threshold value as normal, and judging the commodity combinations of the other inlet and outlet items as abnormal. In the present invention, n is 3.
The confidence and the support degree of the commodity combination of the marketing item are respectively obtained by the following formulas:
confidence = number of times a code appears in a given item/total number of combinations
The support = number of times of occurrence of the combination in which the input item product code is present/number of times of occurrence of the input item product code.
In the invention, the confidence threshold is 0.004, and the support threshold is 0.1.
Step S104, calculating the deviation degree of the enterprise based on the quantity of the abnormal sale-promoting item commodity combinations; according to the deviation degree, if the enterprise is determined to be a high-risk enterprise, mining high-risk entry commodities, high-risk sale commodities and high-risk entry commodity combinations of the enterprise; and constructing the sale item sensitive commodity library by combining the high-risk sale item commodities, the high-risk sale item commodities and the high-risk sale item commodities.
The degree of inconsistency of the enterprise sale-in items is comprehensively measured by utilizing the deviation degree, the larger the deviation degree value is, the higher the risk representing the inconsistency of the enterprise sale-in items is, and the specific calculation formula of the deviation degree is as follows:
the degree of deviation = number of business exceptional incoming and outgoing item commodity combinations/total number of business incoming and outgoing item commodity combinations.
Setting a threshold value based on the deviation degree of the enterprise, wherein the threshold value of the deviation degree is generally 0.95, and identifying the enterprise with the deviation degree larger than the threshold value as a high-risk enterprise, wherein the high-risk enterprise has high probability of inconsistent behaviors of the purchase and sale items, and the higher the threshold value of the deviation degree is, the higher the probability of inconsistent behaviors of the purchase and sale items of the high-risk enterprise is; and mining high-risk entry commodities, high-risk sale commodities and high-risk entry commodity combinations of the high-risk enterprises through an fp-growth algorithm.
And constructing the sale item sensitive commodity library by combining the high-risk sale item commodities, the high-risk sale item commodities and the high-risk sale item commodities. Meanwhile, the sensitive commodity library of the items to be sold supports custom addition, modification and deletion of business personnel.
As shown in fig. 2, the process of constructing the sensitive commodity library for the sale entry specifically includes:
firstly, preprocessing the value-added tax invoice data and the goods detail data, and eliminating data which do not meet the requirements, such as small-scale taxpayers, invoices which are invalid and invoices with zero tax rate. And then, extracting commodity information of the sale and sale items of the enterprise from the value-added tax invoice and the cargo detail data of the enterprise. And determining the main commodity and the main commodity according to the commodity information of the marketing item of the enterprise.
And acquiring a plurality of sale-entering item commodity combinations for Cartesian products of the bought commodities and the bought commodities of the enterprise. If the front n bits of the commodity code of the input commodity and the commodity code of the sales commodity in the input and sales commodity combination are the same, judging that the input and sales commodity combination is normal, otherwise, judging that the input and sales commodity combination is in a state to be determined; and calculating the confidence coefficient and the support degree of the commodity combination of the input and sales items to be determined as the state, judging the commodity combination of the input and sales items with the support degree and the confidence coefficient both larger than a preset threshold value as normal, and judging the rest commodity combinations of the input and sales items as abnormal.
Comprehensively measuring the degree of inconsistency of the enterprise sales items by utilizing the deviation degree, setting a threshold value based on the deviation degree of the enterprise when the deviation degree is larger, identifying the enterprise with the deviation degree larger than the threshold value as a high-risk enterprise, excavating a combination of the high-risk sales item, the high-risk sales item and the high-risk sales item of the high-risk enterprise, and constructing the sales item sensitive commodity library by combining the high-risk sales item, the high-risk sales item and the high-risk sales item.
Based on the same inventive concept, the present invention also provides an apparatus 300 for constructing a commodity library sensitive to items to be sold, as shown in fig. 3, comprising:
a commodity information acquiring unit 310 for acquiring commodity information of an entry and sale item of an enterprise;
a merchandize and bought commodity determining unit 320, configured to determine a merchandize and a bought commodity of the enterprise according to the purchase and sale item commodity information of the enterprise;
an abnormal sale-in item commodity combination obtaining unit 330, configured to obtain a plurality of sale-in item commodity combinations by calculating cartesian products of the bought commodities and the bought commodities; according to the commodity code, the confidence coefficient and the support degree of the commodity combination with the input and sale items, obtaining an abnormal commodity combination with the input and sale items;
the import and export item sensitive commodity library construction unit 340 is used for calculating the deviation degree of the enterprise based on the number of the abnormal import and export item commodity combinations; according to the deviation degree, if the enterprise is determined to be a high-risk enterprise, mining high-risk entry commodities, high-risk sale commodities and high-risk entry commodity combinations of the enterprise; and constructing the sensitive commodity library of the input and sales items by combining the high-risk input commodity, the high-risk sales item commodity and the high-risk input and sales item commodity.
Further, the sensitive commodity library of items sold by the stock-selling unit comprises:
the high-risk enterprise identification subunit is used for setting a threshold value based on the deviation degree of the enterprise and identifying the enterprise with the deviation degree greater than the threshold value as the high-risk enterprise;
and the mining subunit is used for mining the high-risk entry commodity, the high-risk sale commodity and the high-risk entry commodity combination of the high-risk enterprise through the fp-growth algorithm.
By the method and the device for constructing the sensitive commodity library of the import and export items, the potential association rules between the high-risk commodities and the commodities are further mined aiming at the import and export commodities of enterprises with inconsistent import and export items, the sensitive commodity library of the import and export items is constructed, the tax risk is rapidly pre-judged, the knowledge can be shared and reused, and the problems that the risk pre-judgment is strongly dependent on business personnel and the efficiency is low are solved.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the invention.
Claims (10)
1. A method for building a library of sensitive items for sale, comprising:
acquiring commodity information of sale and sale items of enterprises;
determining the bought commodities and the bought commodities of the enterprise according to the purchase and sale item commodity information of the enterprise;
obtaining a plurality of sale-in item commodity combinations by calculating the Cartesian product of the bought commodities and the bought commodities; according to the commodity code, the confidence coefficient and the support degree of the commodity combination with the input and sale items, obtaining an abnormal commodity combination with the input and sale items;
calculating the deviation degree of the enterprise based on the quantity of the abnormal commodity combinations for sale items; according to the deviation degree, if the enterprise is determined to be a high-risk enterprise, mining high-risk entry commodities, high-risk sale commodities and high-risk entry commodity combinations of the enterprise; and constructing the sensitive commodity library of the input and sales items by combining the high-risk input commodity, the high-risk sales item commodity and the high-risk input and sales item commodity.
2. The method of claim 1, wherein obtaining the marketing item merchandise information for the business comprises:
and extracting the commodity information of the sale and sale items of the enterprise from the value-added tax invoice and the cargo detail data of the enterprise.
3. The method of claim 1, further comprising, after the step of obtaining information about the items sold by the enterprise:
the item code for each item is determined by a text classification tool.
4. The method of claim 1, wherein determining the merchandize and the bought-order goods of the business from the merchandize information of the business comprises:
respectively summarizing the total expense amount and/or the total entrance amount of the enterprise;
calculating the proportion of the sum of the corresponding commodities to the total sales and/or the total entrance sum for each commodity code, and arranging the sum in a descending order;
respectively accumulating the total sales item and/or the total entry amount of the commodities one by one according to the sequence, and stopping accumulation when the proportion is greater than a preset threshold value;
and determining the accumulated commodities as the main sales commodities and/or the main purchase commodities of the enterprise.
5. The method of claim 1, wherein obtaining abnormal commodity combinations for marketing items according to the commodity codes, the confidence degrees and the support degrees of the commodity combinations for marketing items comprises:
if the front n bits of the commodity code of the input commodity and the commodity code of the sales commodity in the input and sales commodity combination are the same, judging that the input and sales commodity combination is normal, otherwise, judging that the input and sales commodity combination is in a state to be determined;
and calculating the confidence coefficient and the support degree of the commodity combination of the inlet and outlet items to be determined with the judged state, judging the commodity combination of the inlet and outlet items with the support degree and the confidence coefficient both larger than a preset threshold value as normal, and judging the commodity combinations of the other inlet and outlet items as abnormal.
6. The method of claim 5, wherein the confidence level and the support level of the combination of the items to be sold are obtained by the following formulas:
confidence = number of times a code appears in a given item/total number of combinations
The support = number of times of occurrence of the combination in which the input item product code is present/number of times of occurrence of the input item product code.
7. The method of claim 1, wherein calculating the degree of divergence of the business based on the number of unusual merchandize combinations comprises:
the degree of the inconsistency of the enterprise marketing items is comprehensively measured by utilizing the deviation degree, the larger the deviation degree value is, the higher the risk representing the inconsistency of the enterprise marketing items is, and the specific calculation formula of the deviation degree is as follows:
the degree of deviation = number of business exceptional incoming and outgoing item commodity combinations/total number of business incoming and outgoing item commodity combinations.
8. The method of claim 1, wherein mining high-risk incoming commodities, high-risk marketing commodities, and high-risk incoming marketing commodity combinations of the enterprise if the enterprise is determined to be a high-risk enterprise according to the deviation degree comprises:
setting a threshold value based on the deviation degree of the enterprise, and identifying the enterprise with the deviation degree greater than the threshold value as a high-risk enterprise;
and mining high-risk entry commodities, high-risk sale commodities and high-risk entry commodity combinations of the high-risk enterprises through an fp-growth algorithm.
9. An apparatus for constructing a library of sensitive items for sale, comprising:
the commodity information acquisition unit is used for acquiring commodity information of sales items of enterprises;
the device comprises a unit for determining the bought commodities and the bought commodities of the enterprise, a unit for determining the bought commodities and the bought commodities of the enterprise according to the information of the bought commodities of the enterprise;
an abnormal sale item commodity combination obtaining unit, configured to obtain a plurality of sale item commodity combinations by calculating cartesian products of the bought commodities and the bought commodities; acquiring an abnormal commodity combination of the input and sales items according to the commodity code, the confidence coefficient and the support degree of the commodity combination of the input and sales items;
the purchase and sale item sensitive commodity library construction unit is used for calculating the deviation degree of the enterprise based on the number of the abnormal purchase and sale item commodity combinations; according to the deviation degree, if the enterprise is determined to be a high-risk enterprise, mining high-risk entry commodities, high-risk sale commodities and high-risk entry commodity combinations of the enterprise; and constructing the sensitive commodity library of the input and sales items by combining the high-risk input commodity, the high-risk sales item commodity and the high-risk input and sales item commodity.
10. The apparatus of claim 9, wherein the promotion sensitive merchandise repository construction unit comprises:
the high-risk enterprise identification subunit is used for setting a threshold value based on the deviation degree of the enterprise and identifying the enterprise with the deviation degree greater than the threshold value as the high-risk enterprise;
and the mining subunit is used for mining the high-risk entry commodity, the high-risk sale commodity and the high-risk entry commodity combination of the high-risk enterprise through the fp-growth algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211448114.3A CN115775093A (en) | 2022-11-18 | 2022-11-18 | Method and device for constructing sensitive commodity library of sale and sale items |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211448114.3A CN115775093A (en) | 2022-11-18 | 2022-11-18 | Method and device for constructing sensitive commodity library of sale and sale items |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115775093A true CN115775093A (en) | 2023-03-10 |
Family
ID=85389491
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211448114.3A Pending CN115775093A (en) | 2022-11-18 | 2022-11-18 | Method and device for constructing sensitive commodity library of sale and sale items |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115775093A (en) |
-
2022
- 2022-11-18 CN CN202211448114.3A patent/CN115775093A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10504167B2 (en) | Evaluating public records of supply transactions | |
| AU2002353396B2 (en) | Sales optimization | |
| JP5337174B2 (en) | Demand prediction device and program thereof | |
| US8429018B2 (en) | System and method for detecting a possible error in a customer provided product order quantity | |
| EP2524299A1 (en) | Evaluating public records of supply transactions for financial investment decisions | |
| EP3637347B1 (en) | Method and system for processing environmental impact | |
| CN110019798B (en) | Method and system for measuring commodity type difference of sale and sale items | |
| US20180253711A1 (en) | Inventory management system and method | |
| US7487109B2 (en) | Method and apparatus for optimizing a security database for a self-service checkout system | |
| US7805334B1 (en) | Method and system for processing retail data | |
| CN114723492A (en) | Enterprise portrait generation method and equipment | |
| CN114912858A (en) | Method, apparatus and storage medium for inventory management system | |
| CN116611796A (en) | Exception detection method and device for store transaction data | |
| CN115775093A (en) | Method and device for constructing sensitive commodity library of sale and sale items | |
| CN115775094A (en) | Method and device for constructing commodity library with abnormal sale entries | |
| US7970711B2 (en) | Warranty management system and method | |
| US11935054B2 (en) | Systems and methods for automatically generating fraud strategies | |
| CN115170032A (en) | Storage and transportation management system for pharmaceutical industry | |
| CN115062687A (en) | Enterprise credit monitoring method, device, equipment and storage medium | |
| CN112258253A (en) | Cost accounting method and device, electronic equipment and storage medium | |
| Gonçalves | Nowcasting Brazilian GDP with Eletronic Payments Data | |
| CN115048355B (en) | Updating method, device, equipment and medium of identification model | |
| Samal et al. | An EOQ model for Inventory System dependent upon on hand inventory without shortages | |
| CN115131070B (en) | Online commodity predetermined quantity virtual mark identification and processing method and device | |
| CN113901103B (en) | Financial data processing method and system for digital catering |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |