CN111292116A - Upstream and downstream rebate management, prediction and monitoring method and system for medicine circulation enterprise - Google Patents
Upstream and downstream rebate management, prediction and monitoring method and system for medicine circulation enterprise Download PDFInfo
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Abstract
A method and a system for upstream and downstream rebate management, prediction and monitoring of a medicine circulation enterprise comprise the following steps: s1, acquiring service data, interfacing with an ERP system, and extracting each service record of the ERP in real time through a data interface; s2 rebate prediction, calculating the service record according to the calculation rule to generate rebate prediction data and give receivable/paid data; reading the calculation rule from the rebate protocol by adopting an artificial intelligence technology; s3, forming a settlement plan by combining the return receivable/payment data with the receipt/payment conditions agreed by the agreement; reading the acceptance/payment conditions agreed by the agreement from the rebate agreement by adopting an artificial intelligence technology; and S4, settlement and verification, namely performing settlement and verification on the settlement plan and the actual receiving/paying result to determine the receivable/paid rebate. Based on the agreement signed between enterprises and the existing authority and responsibility generation system, the method establishes upstream and downstream rebate receivable and accounts payable accounts as the basis for enterprise rebate prediction and tracking.
Description
Technical Field
The invention relates to the field of medicine management and monitoring, in particular to a method and a system for upstream and downstream rebate management, prediction and monitoring of a medicine circulation enterprise.
Background
The business mode that the sales enterprise (upstream) returns profits to the purchasing enterprise (downstream) in the circulation process of the rebated commodities can stimulate the purchasing enterprise to strive for rebate to improve the performance, and is indispensable content in the sales policy of the sales enterprise. At present, a large amount of rebate services exist between upstream and downstream enterprises in the medicine circulation industry, the traditional rebate services are managed in a manual mode, the manual workload is large, data are not concentrated, effective supervision cannot be carried out, potential favorable conditions cannot be found, rebate management becomes difficult and painful points in enterprise operation management, and an effective information tool is needed to help the service development and management.
Some enterprises consider the difficulty and pain point in helping the enterprises solve the rebate business through information technology, but the two main problems of rebate calculation and rebate bookkeeping are encountered in the solving process:
(1) the rebate agreement between enterprises has the characteristics of uncertainty, variability, hysteresis and the like, which are also the main reasons of difficult manual calculation and large workload;
(2) traditional financial staff can implement processing of the return related accounts according to the receipt and payment implementation rules, so that return cannot be predicted, monitored and tracked.
Disclosure of Invention
In order to solve the technical problem, the invention provides a method for upstream and downstream rebate management, prediction and monitoring of a medicine circulation enterprise, which comprises the following steps:
and S1, acquiring service data, interfacing with an ERP system, and extracting each service record of the ERP in real time through a data interface.
S2 rebate prediction, calculating the service record according to the calculation rule to generate rebate prediction data, and further providing receivable/paid data; and the calculation rule is read from the rebate protocol by adopting an artificial intelligence technology.
S3, forming a settlement plan by combining the return receivable/payment data with the receipt/payment conditions agreed by the agreement; and reading the acceptance/payment conditions agreed by the agreement from the rebate agreement by adopting an artificial intelligence technology.
And S4, settlement and verification, namely performing settlement and verification on the settlement plan and the actual receiving/paying result to determine the receivable/paid rebate.
Further, the method comprises a step of S5 of predicting profit, wherein the determined receivable/payable rebate is combined with the commodity purchase unit price, the commodity sale unit price and the sale quantity to perform predictive analysis on the real profit of the commodity.
Further, in the step of obtaining the service data in S1, the service record includes business records of business circulation, business sales, and business storage, and the business record is collected from the ERP system through the ETL tool, by docking the system with the ERP system, through the ETL data interface.
Further, the method for reading the calculation rule from the rebate protocol by using the artificial intelligence technology in the step of S2 rebate prediction comprises the following steps:
the calculation rule comprises a calculation range, a calculation method and a calculation object;
first, the calculated objects are determined: acquiring enterprise names of both sides of the protocol from the protocol;
secondly, determining the calculation range including the name or type of the commodity and the type of the operation business;
thirdly, determining a calculation method, extracting the contents in the rebate clauses in the protocol, carrying out syntactic analysis, selecting a suitable analysis template according to the analysis result, selecting a corresponding mathematical function from a mathematical function library according to the analysis template, and constructing a calculation formula;
finally, performing lexical analysis on the keywords from the contents of the rebate clauses, and completely supplementing the variables and constants related to the mathematical functions;
the analysis template is used for decomposing the sentence type structure and extracting the characteristics; variables related in the mathematical function corresponding to the analysis template refer to data sources, and the data sources are from results of business occurrence; the constant is a fixed numerical value agreed by the protocol;
the mathematical function library comprises mathematical functions and is created manually; the analysis template is artificially created and is composed of structures of natural language.
Further, the step of S3 forming a settlement plan, wherein the step of reading the agreement agreed receipt/payment condition from the rebate agreement using artificial intelligence technology comprises the steps of:
the calculation conditions comprise time requirements of rebate payment and specific forms of rebate payment;
the time requirement of rebate payment refers to the time period for the first party to pay rebates according to agreement; the specific form of the rebate payment is the mode adopted by the first party to pay the rebate according to the agreement;
firstly, extracting keywords of payment time from agreement terms, and converting the keywords into predicted and calculated time parameters;
and thirdly, extracting keywords of the payment mode from the agreement clause, and converting the keywords into dictionary options of the payment mode, wherein the dictionary of the payment mode is preset.
Further, the actual receiving/paying result comes from a data report provided by an external system.
The invention also provides a system for upstream and downstream rebate management, prediction and monitoring of the medicine circulation enterprise, which is characterized by comprising the following modules:
the module for acquiring the service data is in butt joint with the ERP system, and each service record of the ERP is extracted in real time through a data interface;
the rebate prediction module is used for calculating the service records according to the calculation rule to generate rebate prediction data and further give receivable/paid data; the calculation rule is read from the rebate protocol by adopting an artificial intelligence technology;
a module for forming a settlement plan, wherein the rebate receivable/payment data is combined with the receipt/payment conditions agreed by the agreement to form the settlement plan; reading the acceptance/payment conditions agreed by the agreement from the rebate agreement by adopting an artificial intelligence technology;
and the module for settlement and verification, namely, the settlement plan and the actual receiving/paying result carry out settlement and verification and determine the receivable/paid rebate.
The system also comprises a profit prediction module which combines the determined receivable/payable rebate with the commodity purchasing unit price, the commodity selling unit price and the selling quantity to predict and analyze the real profit of the commodity.
And in the module for acquiring the service data, the service record comprises business records of business input, business sales and business storage of the circulation service, and the service record is acquired from the ERP system through an ETL data interface by enabling the system to be in butt joint with the ERP system through a data ETL tool.
In a rebate prediction module, a method for reading a calculation rule from a rebate protocol by adopting an artificial intelligence technology comprises the following steps:
the calculation rule comprises a calculation range, a calculation method and a calculation object;
first, the calculated objects are determined: acquiring enterprise names of both sides of the protocol from the protocol;
secondly, the method comprises the following steps: determining a scope of the calculation, including: name or type of goods, type of business;
and thirdly: the method for determining the calculation comprises the following steps: extracting contents in rebate clauses in the protocol, carrying out syntactic analysis, selecting a corresponding analysis template according to an analysis result, selecting a corresponding mathematical function from a mathematical function library according to the analysis template, and constructing a calculation formula;
performing lexical analysis of keywords from the content, and completely supplementing variables and constants related to the mathematical function;
the analysis template is used for decomposing the sentence type structure and extracting the characteristics; variables related in the mathematical function corresponding to the analysis template refer to data sources, and the data sources are from results of business occurrence; the constant is a fixed numerical value agreed by the protocol;
the mathematical function library comprises mathematical functions and is created manually; the analysis template is artificially created and is formed by the structure of natural language;
in the module for forming settlement plans, reading the acceptance/payment conditions agreed by the agreement from the rebate agreement by adopting an artificial intelligence technology comprises the following steps:
the calculation conditions comprise time requirements of rebate payment and specific forms of rebate payment;
the time requirement of rebate payment refers to the time period for the first party to pay rebates according to agreement; the specific form of the rebate payment is the mode adopted by the first party to pay the rebate according to the agreement;
extracting keywords of the payment time from the agreement terms, and converting the keywords into predicted and calculated time parameters;
extracting keywords of a payment mode from the agreement clause, and converting the keywords into dictionary options of the payment mode, wherein the dictionary of the payment mode is preset (such as money giving, ticket giving or goods giving);
the actual receiving/paying result comes from a data report provided by an external system.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that:
(1) based on the agreement signed between enterprises and the existing authority and responsibility generation system, the upstream and downstream rebate receivable and accounts payable are established as the basis for enterprise rebate prediction and tracking;
(2) establishing a rebate agreement calculation system, taking actual business generation as data input, carrying out automatic calculation through a calculation model, and outputting a calculation result to become an account which is returned to the rebate and receivable;
(3) the method can adapt to various agreements among a plurality of enterprises, and can solve the problems of difficult manual calculation and large workload caused by the characteristics of uncertainty, variability, hysteresis and the like of the signed rebate agreement among the enterprises only by inputting the rebate content of the agreement into the system and combining the rebate content with actual service occurrence data;
(4) the system does not need manual calculation, the rebate condition, the realized profit and the profit and loss condition can be visually seen through the system only by importing data, the manpower waste is reduced, the calculation result is rapid and accurate, the data can be well reserved so as to meet the calling of emergency data, and the system has convenience;
(5) performing ETL and analysis on data from a traditional ERP system, and refining the business data of return profit real income and real payment for carrying out verification and cancellation with return profit receivable and accounts payable, wherein the return profit verification and cancellation is used as a monitoring result of return profit business behavior;
(6) financial staff carries out processing of the related accounts of rebates on the client media, the past receipt and payment implementation system is replaced, and the financial staff can predict, monitor and track rebates on the client;
(7) enterprise operation profits can be predicted and analyzed from multiple dimensions through rebate return and return response, rebate and return and verification;
(8) in the rebate calculation, only business records of business entering, selling, storing and the like of the circulation business need to be collected, automatic data acquisition is carried out through a data ETL tool (data extraction, conversion and loading), manual intervention is not needed, and business personnel are concentrated on negotiation and management of rebate business protocols;
(9) the rebate protocol calculation is automatically carried out, once the protocol is effective, the rebate protocol is automatically calculated, and a calculation result is output in a very short time;
(10) the change information generated by the protocol is automatically identified and processed during calculation.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic view of the profit prediction process according to the present invention.
Detailed description of the preferred embodiments
Example 1:
case 1: the agreement A, the two parties sign a rebate agreement, the party A is a rebate paying party, and the party B is a rebate receiving party;
the rebate protocol is: invoicing is carried out according to the price of winning a bid, and the provincial, the provincial and the ministerial, the winning bid commodity is sold to medical institutions to ensure the gross profit of 4.5 percent of the second party;
the payment conditions in the protocol are: payment 90 days later, invoice discount;
the protocol is characterized in that: and performing yielding and point returning according to the guaranteed gross profit rate.
With the above scenario, the embodiment describes how to perform the upstream and downstream rebate management of the pharmaceutical product distribution enterprise in detail.
As shown in fig. 1 and 2, the method for upstream and downstream rebate management, prediction and monitoring of a medicine distribution enterprise of the present invention comprises the following steps:
s1, acquiring service data, connecting with ERP system, extracting ERP service records in real time through ETL data interface, wherein the service records comprise sales service, the sales service comprises sales client, sales time, sales commodity, sales price, sales amount and sales cost;
s2 rebate prediction, calculating the service record according to the calculation rule to generate rebate prediction data, and further providing receivable/paid data; the calculation rule is read from the rebate protocol by adopting an artificial intelligence technology;
the calculation rule comprises a calculation range, a calculation method and a calculation object;
first, the calculated objects are determined: acquiring enterprise names of both parties of the protocol from the protocol, wherein the protocol is manually scanned into the system and OCR (optical character recognition) is carried out on the protocol;
example (c): the system judges that the calculation object is the first party, namely the rebate paying party is the first party.
Firstly, the artificial intelligence technology of the invention is adopted to determine the calculation range from the protocol text recognized by OCR, and the method comprises the following steps: name or type of goods, type of business; the agreement includes the name or type of the commodity, the type of the business and other key information.
Example (c): the calculation range in the protocol text is: the provincial and the standard winning commodities are sold to medical institutions to guarantee the gross profit of the second party to be 4.5%;
then: the calculated range is:
(1) the commodity types are: winning a bid;
(2) the types of the operation business are as follows: a sales service;
secondly, the method comprises the following steps: the method for determining the calculation comprises the following steps: the artificial intelligence technology is adopted to extract the contents in the rebate clauses in the agreement from the agreement text recognized by the OCR, carry out word segmentation, carry out syntactic analysis according to the word segmentation result, select a suitable analysis template, select a corresponding mathematical function from a mathematical function library according to the analysis template, and construct a calculation formula;
performing lexical analysis of keywords from the content, and completely supplementing variables and constants related to the mathematical function;
the analysis template is used for decomposing the sentence type structure and extracting the characteristics; variables related in the mathematical function corresponding to the analysis template refer to data sources, and the data sources are from results of business occurrence; the constant is a fixed numerical value agreed by the protocol;
example (c): the contents in the rebate clause are as follows: the gross profit rate of the second prescription is ensured to be 4.5 percent;
the content of the rebate clause in the agreement extracted from the agreement text recognized by the OCR by the artificial intelligence technology is as follows: the gross profit rate of the second prescription is ensured to be 4.5 percent;
word segmentation: the sentence is divided into words according to the word stock and is decomposed into ' guarantee ', ' second square ', ' 4.5% ' gross interest rate '
And (3) performing grammatical analysis according to the word segmentation result to select a suitable analysis template:
determining 'guarantee' as a predicate from a word bank and a language, determining 'gross profit rate' as a noun, and determining '4.5%' as a constant;
thereby to obtain
Determine the predicate from this sentence: "guarantee";
from this sentence, the noun is determined: "second square", "gross rate";
determine the constant from this sentence: 4.5 percent;
the analysis template is artificially created and is formed by the structure of natural language;
selecting a corresponding mathematical function from a mathematical function library according to the analysis template to construct a calculation formula;
the mathematical function library comprises mathematical functions and is created manually;
example (c): the mathematical function library already contains a guaranteed interest rate formula which is manually created and stored in the mathematical function library. The guaranteed gross benefit ratio formula is as follows:
rebate amount = (sale price x (1- [ guaranteed gross profit rate) -purchase offer) x number of sales;
selecting the rebate amount formula from the mathematical function library according to the 'guarantee' predicate and the 'gross interest rate' noun;
determining the constants as: 4.5 percent
The variables were determined to be: selling price and selling cost, wherein the selling price and the selling cost are from selling business, and the selling business is from data obtained by ERP;
thereby, a calculation formula of the rebate amount is generated.
And forming a settlement plan. The rebate receivable/payment data is combined with the receipt/payment conditions agreed by the agreement to form a settlement plan; reading the acceptance/payment conditions agreed by the agreement from the rebate agreement by adopting an artificial intelligence technology;
the calculation conditions comprise time requirements of rebate payment and specific forms of rebate payment;
the time requirement of rebate payment refers to the time period for the first party to pay rebates according to agreement; the specific form of the rebate payment is the mode adopted by the first party to pay the rebate according to the agreement;
extracting keywords of the payment time from the agreement terms, and converting the keywords into predicted and calculated time parameters;
extracting keywords of a payment mode from the agreement clause, and converting the keywords into dictionary options of the payment mode, wherein the dictionary of the payment mode is preset (such as money giving, ticket giving or goods giving);
thereby, a settlement report is generated.
Example (c): the sale conditions are as follows: 90 days; discount of invoice;
then, the system receives/pays the condition that the value is derived from the sales time collected from ERP 90 days from the sales business occurrence date; example (c): the first selling service selling time collected from the ERP is 7, 31 and 2019, and the second selling service selling time is 8, 1 and 2019; then the first rebate amount is generated at 30 days (31 days at 7 and 31 months in 2019 +90 days) in 2019 and the second rebate amount is generated at 31 days at 10 and 31 months in 2019 (1 day at 8 and 1 +90 days in 2019)
The settlement report also includes a payment mode column filled with 'invoice discount'.
And (5) settling the verification and cancellation. And carrying out manual settlement and verification according to the settlement report and the actual receiving/paying result to determine the receivable/paid rebate.
The actual receiving/paying result comes from a data report provided by an external system, the data report is generally provided by the second-party accounting, the first-party checks and compares the data report calculated by the system with the data report provided by the second-party accounting one by one, the settlement and the verification are correctly performed, and if the error occurs, manual processing, query and analysis are performed until the verification and the verification are successful.
Further comprising the step of S5 estimating profit. And (4) predicting the profit, namely combining the determined receivable/payable rebate with the commodity purchasing unit price and the commodity selling unit price to predict and analyze the real profit of the commodity.
Estimated profit = (commodity sales unit price-commodity purchase unit price) commodity sales number + return for receipt/payment;
in the estimated profit formula, the receivable rebates are represented by positive values in the formula and the payable rebates are represented by negative values in the formula.
The invention also provides a system for upstream and downstream rebate management, prediction and monitoring of the medicine circulation enterprise, which comprises the following modules:
the system comprises a module for acquiring service data, an ETL data interface, a database and a database, wherein the module is in butt joint with an ERP system and extracts various service records of the ERP in real time through the ETL data interface, the service records comprise sales services, and the sales services comprise sales customers, sales time, sales commodities, sales price, sales quantity, sales amount and sales cost;
the rebate prediction module is used for calculating the service records according to the calculation rule to generate rebate prediction data and further give receivable/paid data; the calculation rule is read from the rebate protocol by adopting an artificial intelligence technology;
the calculation rule comprises a calculation range, a calculation method and a calculation object;
first, the calculated objects are determined: acquiring enterprise names of both parties of the protocol from the protocol, wherein the protocol is manually scanned into the system and OCR (optical character recognition) is carried out on the protocol;
example (c): the system judges that the calculation object is the first party, namely the rebate paying party is the first party.
Firstly, the artificial intelligence technology of the invention is adopted to determine the calculation range from the protocol text recognized by OCR, and the method comprises the following steps: name or type of goods, type of business; the agreement includes the name or type of the commodity, the type of the business and other key information.
Example (c): the calculation range in the protocol text is: the provincial and the standard winning commodities are sold to medical institutions to guarantee the gross profit of the second party to be 4.5%;
then: the calculated range is:
(1) the commodity types are: winning a bid;
(2) the types of the operation business are as follows: a sales service;
secondly, the method comprises the following steps: the method for determining the calculation comprises the following steps: the artificial intelligence technology is adopted to extract the contents in the rebate clauses in the agreement from the agreement text recognized by the OCR, carry out word segmentation, carry out syntactic analysis according to the word segmentation result, select a suitable analysis template, select a corresponding mathematical function from a mathematical function library according to the analysis template, and construct a calculation formula;
performing lexical analysis of keywords from the content, and completely supplementing variables and constants related to the mathematical function;
the analysis template is used for decomposing the sentence type structure and extracting the characteristics; variables related in the mathematical function corresponding to the analysis template refer to data sources, and the data sources are from results of business occurrence; the constant is a fixed numerical value agreed by the protocol;
example (c): the contents in the rebate clause are as follows: the gross profit rate of the second prescription is ensured to be 4.5 percent;
the content of the rebate clause in the agreement extracted from the agreement text recognized by the OCR by the artificial intelligence technology is as follows: the gross profit rate of the second prescription is ensured to be 4.5 percent;
word segmentation: the sentence is divided into words according to the word stock and is decomposed into ' guarantee ', ' second square ', ' 4.5% ' gross interest rate '
And (3) performing grammatical analysis according to the word segmentation result to select a suitable analysis template:
determining 'guarantee' as a predicate from a word bank and a language, determining 'gross profit rate' as a noun, and determining '4.5%' as a constant;
thereby to obtain
Determine the predicate from this sentence: "guarantee";
from this sentence, the noun is determined: "second square", "gross rate";
determine the constant from this sentence: 4.5 percent;
the analysis template is artificially created and is formed by the structure of natural language;
selecting a corresponding mathematical function from a mathematical function library according to the analysis template to construct a calculation formula;
the mathematical function library comprises mathematical functions and is created manually;
example (c): the mathematical function library already contains a guaranteed interest rate formula which is manually created and stored in the mathematical function library. The guaranteed gross benefit ratio formula is as follows:
rebate amount = (sale price x (1- [ guaranteed gross profit rate) -purchase offer) x number of sales;
selecting the rebate amount formula from the mathematical function library according to the 'guarantee' predicate and the 'gross interest rate' noun;
determining the constants as: 4.5 percent
The variables were determined to be: selling price and selling cost, wherein the selling price and the selling cost are from selling business, and the selling business is from data obtained by ERP;
thereby, a calculation formula of the rebate amount is generated.
A module for forming a settlement plan, wherein the rebate receivable/payment data is combined with the receipt/payment conditions agreed by the agreement to form the settlement plan; reading the acceptance/payment conditions agreed by the agreement from the rebate agreement by adopting an artificial intelligence technology;
the calculation conditions comprise time requirements of rebate payment and specific forms of rebate payment;
the time requirement of rebate payment refers to the time period for the first party to pay rebates according to agreement; the specific form of the rebate payment is the mode adopted by the first party to pay the rebate according to the agreement;
extracting keywords of the payment time from the agreement terms, and converting the keywords into predicted and calculated time parameters;
extracting keywords of a payment mode from the agreement clause, and converting the keywords into dictionary options of the payment mode, wherein the dictionary of the payment mode is preset (such as money giving, ticket giving or goods giving);
thereby, a settlement report is generated.
Example (c): the sale conditions are as follows: 90 days; discount of invoice;
then, the system receives/pays the condition that the value is derived from the sales time collected from ERP 90 days from the sales business occurrence date; example (c): the first selling service selling time collected from the ERP is 7, 31 and 2019, and the second selling service selling time is 8, 1 and 2019; then the first rebate amount is generated at 30 days 10 and 30 months in 2019 (31 days 31 and 90 days 31 and 7 months in 2019), and the second rebate amount is generated at 31 days 10 and 31 months in 2019 (1 day 1 and 90 days 8 and 8 months in 2019);
the settlement report also includes a payment mode column filled with 'invoice discount'.
And (5) clearing and checking. And the settlement and verification module is used for manually settling and verifying according to the settlement report and the actual receiving/paying result and determining the receivable/paid rebate.
The actual receiving/paying result comes from a data report provided by an external system, the data report is generally provided by the second-party accounting, the first-party checks and compares the data report calculated by the system with the data report provided by the second-party accounting one by one, the settlement and the verification are correctly performed, and if the error occurs, manual processing, query and analysis are performed until the verification and the verification are successful.
A module for estimating profits is also included. And the profit prediction module is used for combining the determined receivable/payable rebate with the commodity purchasing unit price and the commodity selling unit price and carrying out prediction analysis on the real profit of the commodity.
Estimated profit = (commodity sales unit price-commodity purchase unit price) commodity sales number + return for receipt/payment;
in the estimated profit formula, the receivable rebates are represented by positive values in the formula and the payable rebates are represented by negative values in the formula.
Claims (10)
1. A method for upstream and downstream rebate management, prediction and monitoring of a medicine circulation enterprise is characterized by comprising the following steps:
s1, acquiring service data, interfacing with an ERP system, and extracting each service record of the ERP in real time through a data interface;
s2 rebate prediction, calculating the service record according to the calculation rule to generate rebate prediction data, and further providing receivable/paid data; the calculation rule is read from the rebate protocol by adopting an artificial intelligence technology;
s3, forming a settlement plan by combining the return receivable/payment data with the receipt/payment conditions agreed by the agreement; reading the acceptance/payment conditions agreed by the agreement from the rebate agreement by adopting an artificial intelligence technology;
and S4, settlement and verification, namely performing settlement and verification on the settlement plan and the actual receiving/paying result to determine the receivable/paid rebate.
2. The method for upstream and downstream rebate management, prediction and monitoring of a pharmaceutical dispensing enterprise of claim 1, wherein: and the step of predicting the profit is also included, and the determined receivable/payable rebate is combined with the commodity purchasing unit price, the commodity selling unit price and the selling quantity to carry out prediction analysis on the real profit of the commodity.
3. The method for upstream and downstream rebate management, prediction and monitoring of a pharmaceutical dispensing enterprise of claim 1, wherein: and in the step of acquiring the service data, the service record comprises business records of business input, business sales and business storage of the circulation service, and the service record is acquired from the ERP system through an ETL data interface by enabling the system to be in butt joint with the ERP system through a data ETL tool.
4. The method for upstream and downstream rebate management, prediction and monitoring of a pharmaceutical dispensing enterprise of claim 1, wherein: the method for reading the calculation rule from the rebate protocol by adopting the artificial intelligence technology in the rebate prediction step comprises the following steps:
the calculation rule comprises a calculation range, a calculation method and a calculation object;
first, the calculated objects are determined: acquiring enterprise names of both sides of the protocol from the protocol;
secondly, determining the calculation range including the name or type of the commodity and the type of the operation business;
thirdly, determining a calculation method, extracting the contents in the rebate clauses in the protocol, performing word segmentation and syntax analysis, selecting a corresponding analysis template according to the analysis result, selecting a corresponding mathematical function from a mathematical function library according to the analysis template, and constructing a calculation formula;
finally, performing lexical analysis on the keywords from the contents of the rebate clauses, and completely supplementing the variables and constants related to the mathematical functions;
the analysis template is used for decomposing the sentence type structure and extracting the characteristics; variables related in the mathematical function corresponding to the analysis template refer to data sources, and the data sources are from results of business occurrence; the constant is a fixed numerical value agreed by the protocol;
the mathematical function library comprises mathematical functions and is created manually; the analysis template is artificially created and is composed of structures of natural language.
5. The method for upstream and downstream rebate management, prediction and monitoring of a pharmaceutical dispensing enterprise of claim 1, wherein: the step of forming settlement plan, adopting artificial intelligence technique to read the agreement promissory receipt/payment condition from the rebate agreement includes the following steps:
the calculation conditions comprise time requirements of rebate payment and specific forms of rebate payment;
the time requirement of rebate payment refers to the time period for the first party to pay rebates according to agreement; the specific form of the rebate payment is the mode adopted by the first party to pay the rebate according to the agreement;
firstly, extracting keywords of payment time from agreement terms, and converting the keywords into predicted and calculated time parameters;
thirdly, extracting keywords of the payment mode from the agreement clause, and converting the keywords into dictionary options of the payment mode, wherein the dictionary of the payment mode is preset;
6. the method for upstream and downstream rebate management, prediction and monitoring of a pharmaceutical dispensing enterprise of claim 1, wherein: the actual receiving/paying result comes from a data report provided by an external system;
and step S5, estimating profit, combining the determined receivable/payable rebate with commodity purchasing unit price, commodity selling unit price and selling quantity, and performing prediction analysis on the real profit of the commodity.
7. A system for upstream and downstream rebate management, prediction and monitoring of a medicine circulation enterprise is characterized by comprising the following modules:
the module for acquiring the service data is in butt joint with the ERP system, and each service record of the ERP is extracted in real time through a data interface;
the rebate prediction module is used for calculating the service records according to the calculation rule to generate rebate prediction data and further give receivable/paid data; the calculation rule is read from the rebate protocol by adopting an artificial intelligence technology;
a module for forming a settlement plan, wherein the rebate receivable/payment data is combined with the receipt/payment conditions agreed by the agreement to form the settlement plan; reading the acceptance/payment conditions agreed by the agreement from the rebate agreement by adopting an artificial intelligence technology;
and the module for settlement and verification, namely, the settlement plan and the actual receiving/paying result carry out settlement and verification and determine the receivable/paid rebate.
8. The system for upstream and downstream rebate management, forecasting and monitoring of a pharmaceutical dispensing enterprise of claim 7, wherein: the system also comprises a profit prediction module which combines the determined receivable/payable rebate with the commodity purchasing unit price, the commodity selling unit price and the selling quantity to predict and analyze the real profit of the commodity.
9. The system for upstream and downstream rebate management, forecasting and monitoring of a pharmaceutical dispensing enterprise of claim 7, wherein:
in a module for acquiring service data, the service record comprises business records of business input, business sales and business storage of the circulation service, and the service record is acquired from an ERP system through an ETL data interface by enabling the system to be in butt joint with the ERP system through a data ETL tool;
in a rebate prediction module, a method for reading a calculation rule from a rebate protocol by adopting an artificial intelligence technology comprises the following steps:
the calculation rule comprises a calculation range, a calculation method and a calculation object;
first, the calculated objects are determined: acquiring enterprise names of both sides of the protocol from the protocol;
secondly, the method comprises the following steps: determining a scope of the calculation, including: name or type of goods, type of business;
and thirdly: the method for determining the calculation comprises the following steps: extracting contents in the rebate clauses in the agreement, performing word segmentation and syntax analysis, selecting a corresponding analysis template according to an analysis result, selecting a corresponding mathematical function from a mathematical function library according to the analysis template, and constructing a calculation formula;
performing lexical analysis of keywords from the content, and completely supplementing variables and constants related to the mathematical function;
the analysis template is used for decomposing the sentence type structure and extracting the characteristics; variables related in the mathematical function corresponding to the analysis template refer to data sources, and the data sources are from results of business occurrence; the constant is a fixed numerical value agreed by the protocol;
the mathematical function library comprises mathematical functions and is created manually; the analysis template is artificially created and is formed by the structure of natural language;
in the module for forming settlement plans, reading the acceptance/payment conditions agreed by the agreement from the rebate agreement by adopting an artificial intelligence technology comprises the following steps:
the calculation conditions comprise time requirements of rebate payment and specific forms of rebate payment;
the time requirement of rebate payment refers to the time period for the first party to pay rebates according to agreement; the specific form of the rebate payment is the mode adopted by the first party to pay the rebate according to the agreement;
extracting keywords of the payment time from the agreement terms, and converting the keywords into predicted and calculated time parameters;
and extracting keywords of the payment mode from the agreement clause, and converting the keywords into dictionary options of the payment mode, wherein the dictionary of the payment mode is preset.
10. The system for upstream and downstream rebate management, forecasting and monitoring of a pharmaceutical dispensing enterprise of claim 1, wherein: the actual receiving/paying result comes from a data report provided by an external system.
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