CN107886359A - A kind of price quoting method based on machine learning - Google Patents
A kind of price quoting method based on machine learning Download PDFInfo
- Publication number
- CN107886359A CN107886359A CN201711114410.9A CN201711114410A CN107886359A CN 107886359 A CN107886359 A CN 107886359A CN 201711114410 A CN201711114410 A CN 201711114410A CN 107886359 A CN107886359 A CN 107886359A
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- Prior art keywords
- quotation
- machine learning
- fitting function
- information
- quoting method
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
Abstract
The invention discloses a kind of price quoting method based on machine learning, comprise the steps of：A, all types of product contract parameter sets are obtained；B, the quotation information of conventional product is obtained；C, system learns to the information of typing；D, fitting function is updated；E, new quotation is carried out using the fitting function after renewal.Price quoting method of the invention based on machine learning can be according to the quotation information of existing procucts, automatically from learning to the quotation rules of correlation, and in quotation activity afterwards, recommend suitable price automatically for quotation personnel according to the quotation rules that the Contract parameters and study that newly input arrive, so as to drastically increase the accuracy of quotation.
Description
Technical field
The present invention relates to field of artificial intelligence, specifically a kind of price quoting method based on machine learning.
Background technology
Quotation is a ring important in business activity.At present, when enterprise offers, based on contract joined by quotation personnel
Number, based on there is the quotation of the product of identical parameters in the past, generate new quotation and be presented to user again.
Quotation personnel based on contract parameter, based on there is the quotation of the product of identical parameters in the past, generate new report
Valency is presented to user again.This bid mode need to rely on the products quotation of parameter as before.If the ginseng of current production
Several classes or number change, and certainly will influence whether to offer accordingly.For quotation personnel, if the parameter after changing
Combination and conventional any successful quotes case are all different, then the change for being difficult accurate judgement parameter current can be to product price
What kind of, which is produced, influences, therefore it is too high or too low very likely to cause to offer, so that enterprise suffers a loss.
The content of the invention
It is an object of the invention to provide a kind of intelligent price quoting method based on machine learning, to solve above-mentioned background technology
The problem of middle proposition.
To achieve the above object, the present invention provides following technical scheme：
A kind of intelligent price quoting method based on machine learning, it is characterised in that comprise the steps of：
A, the set of all types of product contract parameters is obtained；
B, the quotation information of conventional product is obtained；
C, system learns to the information of typing；
D, fitting function is updated；
E, new quotation is carried out using the fitting function after renewal.
Further scheme as the present invention：The step C is specifically：Using the data of quotation personnel's input as study sample
This, utilizes sample data, the output vector got, and suitable network structure（Perceptron, multilayer neural network）, choose
Suitable activation primitive（Such as Linear, Tanh, Sigmoid, ReLu）, pass through calculating（BP neural network）Obtain corresponding weights
Matrix, finally give and meet expected network structure, i.e. fitting function.
Compared with prior art, the beneficial effects of the invention are as follows：Intelligent price quoting method of the invention based on machine learning can
With the quotation information according to existing procucts, automatically from learning to the quotation rules of correlation, and in quotation activity afterwards, according to
The quotation rules arrived according to the Contract parameters and study that newly input recommend suitable price for quotation personnel automatically, so as to greatly carry
The high accuracy of quotation.
Brief description of the drawings
Fig. 1 is the schematic diagram of the intelligent price quoting method based on machine learning.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other under the premise of creative work is not made
Embodiment, belong to the scope of protection of the invention.
Referring to Fig. 1, in the embodiment of the present invention, a kind of intelligent price quoting method based on machine learning, comprise the steps of：
A. certain type products Contract parameters set is obtained.The Contract parameters species and number that different types of product is included are not
Identical to the greatest extent, that to be obtained here is the complete or collected works for the Contract parameters that certain type of product is included；
B, the quotation information of conventional product is obtained.Quotation information before obtaining, using passing contract information as input sample, report
Valency information is used as output to this, for the training sample set in machine learning；
C, the information of typing is learnt.Using the data got as learning sample, the sample data, defeated got is utilized
Outgoing vector（Price）, and suitable network structure（Perceptron, multilayer neural network etc.）, choose suitable activation primitive（Such as
Linear, Tanh, Sigmoid, ReLu etc.）, pass through calculating（BP neural network etc.）Corresponding weight matrix is obtained, is finally given
Meet expected network structure, i.e. fitting function（Equation）；
D, fitting function is updated（Equation）.Its fitting function is all updated for every a kind of product；
E, new quotation is carried out using the fitting function after renewal.For new Contract parameters value, it can directly utilize and obtain
To fitting function calculated, so as to recommend suitable price to quotation personnel.
Claims (2)
1. a kind of price quoting method based on machine learning, it is characterised in that comprise the steps of：
A, all types of product contract parameter sets are obtained；
B, the quotation information of conventional product is obtained；
C, system learns to the information of typing；
D, fitting function is updated；
E, new quotation is carried out using the fitting function after renewal.
2. the intelligent price quoting method according to claim 1 based on machine learning, it is characterised in that the step C is specific
It is：Using the data of quotation personnel's input as learning sample, using sample data, the output vector got, and suitably
Network structure（Perceptron, multilayer neural network）, choose suitable activation primitive（Such as Linear, Tanh, Sigmoid, ReLu）,
Pass through calculating（BP neural network）Corresponding weight matrix is obtained, finally gives and meets expected network structure, i.e. fitting function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711114410.9A CN107886359A (en) | 2017-11-13 | 2017-11-13 | A kind of price quoting method based on machine learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711114410.9A CN107886359A (en) | 2017-11-13 | 2017-11-13 | A kind of price quoting method based on machine learning |
Publications (1)
| Publication Number | Publication Date |
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| CN107886359A true CN107886359A (en) | 2018-04-06 |
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| Application Number | Title | Priority Date | Filing Date |
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| CN201711114410.9A Pending CN107886359A (en) | 2017-11-13 | 2017-11-13 | A kind of price quoting method based on machine learning |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109816480A (en) * | 2018-12-29 | 2019-05-28 | 广州兴森快捷电路科技有限公司 | It is a kind of to promote the method and device for extracting quotation parameter accuracy rate |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101578618A (en) * | 2006-10-11 | 2009-11-11 | 玫瑰蓝公司 | Diamond valuation method, apparatus and computer readable medium product |
| CN103578057A (en) * | 2012-08-10 | 2014-02-12 | 北京奥齐都市网络科技有限公司 | Real estate value estimation method based on artificial neural network statistic model |
| CN106104615A (en) * | 2013-12-11 | 2016-11-09 | 天巡有限公司 | For providing method and the server of one group of price evaluation value, such as air fare price evaluation value |
-
2017
- 2017-11-13 CN CN201711114410.9A patent/CN107886359A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101578618A (en) * | 2006-10-11 | 2009-11-11 | 玫瑰蓝公司 | Diamond valuation method, apparatus and computer readable medium product |
| CN103578057A (en) * | 2012-08-10 | 2014-02-12 | 北京奥齐都市网络科技有限公司 | Real estate value estimation method based on artificial neural network statistic model |
| CN106104615A (en) * | 2013-12-11 | 2016-11-09 | 天巡有限公司 | For providing method and the server of one group of price evaluation value, such as air fare price evaluation value |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109816480A (en) * | 2018-12-29 | 2019-05-28 | 广州兴森快捷电路科技有限公司 | It is a kind of to promote the method and device for extracting quotation parameter accuracy rate |
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Application publication date: 20180406 |