CN108764991A - Information of supply chain analysis method based on K-means algorithms - Google Patents
Information of supply chain analysis method based on K-means algorithms Download PDFInfo
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- CN108764991A CN108764991A CN201810495753.2A CN201810495753A CN108764991A CN 108764991 A CN108764991 A CN 108764991A CN 201810495753 A CN201810495753 A CN 201810495753A CN 108764991 A CN108764991 A CN 108764991A
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- 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
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- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- G—PHYSICS
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- 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
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Abstract
The present invention relates to a kind of information of supply chain analysis methods based on K-means algorithms, including：Data cleansing is carried out to supply chain data, extracts the first matrix ordered series of numbers, first matrix is business revenue-Cost matrix ordered series of numbers；The first matrix ordered series of numbers is clustered using the K-means algorithms of the first cluster number K1, and generates the first image of scatterplot diagram form；First reference line and the second reference line are added to described first image；According to first reference line and second reference line, described first image is cut into three parts, middle section region indicates that the steady situation of enterprise operation, upper and lower two parts then show the case where enterprise manages extremely.The above-mentioned information of supply chain analysis method based on K-means algorithms carries out K-means algorithm clusterings to business revenue, cost, gross profit, by establishing reference line, improves K-means algorithms so that the algorithm is more applicable for supply chain FIELD Data.
Description
Technical field
The present invention relates to information of supply chain analyses, more particularly to the information of supply chain analysis side based on K-means algorithms
Method.
Background technology
It is current researched and proposed much the numerous and jumbled information of supply chain is extracted, the related algorithm analyzed and experiment side
Case.On the basis of the supply chain data based on individual enterprise, most typical is exactly that traditional routine data statistics is calculated
Method, the algorithm are very easy to realize.In addition on this basis, there is intersection date comprision algorithm, data are cut into
Multiple segments realize data analysis by the comparison of different data segment.It is most of in lengthy and jumbled and huge supply chain data
Using the data statistics stage is rested on, by being integrated, being counted and being visualized the logistics data in supply chain, to promote more
Into the Effec-tive Function of entire supply chains process.Conventional data statistical approach focuses primarily upon descriptive statistic, Cross Report, vacation
If some problems such as inspections.Gradually also someone uses data mining algorithm for information of supply chain, using Naive Bayes Classification
The information that algorithm can effectively classify to supply chain data, but be excavated really has the embodiment of enterprise operation present situation
Significant limitations.
There are following technical problems for traditional technology：
Data mining is carried out to information of supply chain at present and analysis is very universal, common data analysing method concentrates on uniting
On meter and sorting algorithm, but the result of this kind of algorithm is only confined in the efficiency for improving entire supply chains process, it is difficult to pair
The whole current operation situation and future services decision of company make analysis.
Invention content
Based on this, it is necessary in view of the above technical problems, provide a kind of information of supply chain analysis based on K-means algorithms
Method carries out K-means algorithm clusterings to business revenue, cost, gross profit, by establishing reference line, improves K-means algorithms,
So that the algorithm is more applicable for supply chain FIELD Data.
A kind of information of supply chain analysis method based on K-means algorithms, including：
Data cleansing is carried out to supply chain data, extracts the first matrix ordered series of numbers, first matrix is business revenue-cost square
Battle array ordered series of numbers；
The first matrix ordered series of numbers is clustered using the K-means algorithms of the first cluster number K1, and generates scatterplot
First image of diagram form；
First reference line and the second reference line are added to described first image；
According to first reference line and second reference line, described first image is cut into three parts, middle part
The steady situation of subregion domain representation enterprise operation, upper and lower two parts then show the case where enterprise manages extremely；
Wherein, first reference line is y=ax, tan50 °<a<tan60°；
Second reference line is y=bx, tan30 °<b<tan40°.
In other one embodiment, a=tan55 °.
In other one embodiment, b=tan35 °.
It is right according to the distribution situation of K1 categorical data point in described first image in other one embodiment
Described first image continues to add third reference line；
The third reference line is x=Lk (1≤k≤K1)；Business revenue is divided into K1+1 segment by the third reference line,
Business revenue is divided into the profit and loss situation that K1+1 part carries out analysis enterprise supply chain.
A kind of information of supply chain analysis method based on K-means algorithms, including：
Data cleansing is carried out to supply chain data, extracts the second matrix ordered series of numbers, first matrix is business revenue-gross profit square
Battle array ordered series of numbers；
The second matrix ordered series of numbers is clustered using the K-means algorithms of the second cluster number K2, and generates scatterplot
Second image of diagram form；
4th reference line is added to second image；
4th reference line region below is loss situation, passes through the 4th reference line following region data point point
The dense degree of cloth learns the existing management state of enterprise；
4th reference line is y=0.
It is right according to the distribution situation of K2 categorical data point in second image in other one embodiment
Second image continues to add the 5th reference line；
5th reference line is x=Lm (1≤m≤K2), and business revenue is divided into K2+1 classifications by the 5th reference line
Business revenue is divided into the risk and profit situation that K2+1 categorical data point parts carry out analysis each section by data point segment.
In other one embodiment, the processor realizes the step of any one the method when executing described program
Suddenly.
The step of any one the method is realized in other one embodiment, when which is executed by processor.
The above-mentioned information of supply chain analysis method based on K-means algorithms carries out K-means calculations to business revenue, cost, gross profit
Method clustering improves K-means algorithms so that the algorithm is more applicable for supply chain FIELD Data by establishing reference line.
Description of the drawings
Fig. 1 is a kind of generation of the information of supply chain analysis method based on K-means algorithms provided by the embodiments of the present application
The first image schematic diagram.
Fig. 2 is a kind of generation of the information of supply chain analysis method based on K-means algorithms provided by the embodiments of the present application
The second image schematic diagram.
Fig. 3 is right in a kind of information of supply chain analysis method based on K-means algorithms provided by the embodiments of the present application
The result schematic diagram that first matrix ordered series of numbers is clustered.
Fig. 4 is right in a kind of information of supply chain analysis method based on K-means algorithms provided by the embodiments of the present application
The result schematic diagram that second matrix ordered series of numbers is clustered.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
A kind of information of supply chain analysis method based on K-means algorithms, including：
Data cleansing is carried out to supply chain data, extracts the first matrix ordered series of numbers, first matrix is business revenue-cost square
Battle array ordered series of numbers；
The first matrix ordered series of numbers is clustered using the K-means algorithms of the first cluster number K1, and generates scatterplot
First image of diagram form；
First reference line and the second reference line are added to described first image；
According to first reference line and second reference line, described first image is cut into three parts, middle part
The steady situation of subregion domain representation enterprise operation, upper and lower two parts then show the case where enterprise manages extremely；
Wherein, first reference line is y=ax, tan50 °<a<tan60°；
Second reference line is y=bx, tan30 °<b<tan40°.
In other one embodiment, a=tan55 °.
In other one embodiment, b=tan35 °.
It is right according to the distribution situation of K1 categorical data point in described first image in other one embodiment
Described first image continues to add third reference line；
The third reference line is x=Lk (1≤k≤K1)；Business revenue is divided into K1+1 segment by the third reference line,
Business revenue is divided into the profit and loss situation that K1+1 part carries out analysis enterprise supply chain.
A kind of information of supply chain analysis method based on K-means algorithms, including：
Data cleansing is carried out to supply chain data, extracts the second matrix ordered series of numbers, first matrix is business revenue-gross profit square
Battle array ordered series of numbers；
The second matrix ordered series of numbers is clustered using the K-means algorithms of the second cluster number K2, and generates scatterplot
Second image of diagram form；
4th reference line is added to second image；
4th reference line region below is loss situation, passes through the 4th reference line following region data point point
The dense degree of cloth learns the existing management state of enterprise；
4th reference line is y=0.
It is right according to the distribution situation of K2 categorical data point in second image in other one embodiment
Second image continues to add the 5th reference line；
5th reference line is x=Lm (1≤m≤K2), and business revenue is divided into K2+1 classifications by the 5th reference line
Business revenue is divided into the risk and profit situation that K2+1 categorical data point parts carry out analysis each section by data point segment.
In other one embodiment, the processor realizes the step of any one the method when executing described program
Suddenly.
The step of any one the method is realized in other one embodiment, when which is executed by processor.
The above-mentioned information of supply chain analysis method based on K-means algorithms carries out K-means calculations to business revenue, cost, gross profit
Method clustering improves K-means algorithms so that the algorithm is more applicable for supply chain FIELD Data by establishing reference line.
A concrete application scene of the invention is described below：
(1) data cleansing is carried out first to supply chain data, and extracts business revenue, cost, three column data of gross profit, composition battalion
Receipts-cost, business revenue-gross profit totally two matrix ordered series of numbers.
(2) K-means algorithm modelings are carried out respectively to two matrix datas that step (1) obtains, according to the size of manifold
The number of grouping is chosen, here it is K-means cluster algorithms.But the result at this time obtained is to company operation situation
It is embodied there is no apparent.The present invention carries out scatter plot by two matrix ordered series of numbers results for crossing K-means algorithm process, and
By establishing multiple reference lines, the selection of every reference line is all reacted for the different aspect of company operation situation.
(3) current operation situation that company is further analyzed finally by a plurality of reference line combination K-means algorithms, using changing
Into K-means algorithms, the whole current operation situation of company can be obtained, and enterprise is helped more accurately to carry out operational decision making.
By business revenue using in information of supply chain mentioned above, cost, three column data of gross profit as algorithm data collection, it is assumed that
Three column datas are business revenue respectively：(X1, X2, X3..., Xm）, cost：(Y1, Y2, Y3..., Ym), gross profit：(Z1, Z2, Z3..., Zm)。
Three column datas are formed into following two matrix ordered series of numbers (business revenue-cost, business revenue-gross profit)：
It is denoted as (q respectively1, q2, q3..., qm)、(t1, t2, t3..., tm)。
The modeling of K-means algorithms is carried out to first matrix ordered series of numbers (business revenue-cost) first and is calculated.For two matrixes
Ordered series of numbers carries out the selection of initial center point K, is denoted as u respectively1, u2, u3..., ukK < m, specific coordinate points are expressed as：
By Euler's formula：
Selection object function is square error, is shown below：
Following formula can be obtained to u derivations by above formula：
It needs to carry out cluster centre u to remaining each point first in entire algorithm flowjMark：
labeli=argmin | | qi-uj||(1≤j≤k) (4)
U in formula (4)jIt is continually changing, all existing central point K will be traversed.
Then need to adjust and update cluster centre after each traversal, be then employed as the formula (3) of object function into
The update at the centers row K.
By continuous repetitive (4), formula (3), terminated when object function is optimal solution, i.e. maximum iterations,
Algorithm stop condition is as follows：
After completing K-means algorithms and generating image, two reference lines are added：
55 ° of x (6) of y=tan
35 ° of x (7) of y=tan
According to the reference line, image is cut into three parts, middle section region indicates the steady situation of enterprise operation, on
Lower two parts then show the case where enterprise manages extremely.In addition further according to the K values initially chosen, according to K categorical data point
Distribution situation adds K reference line：
X=Lk(1≤k≤K) (8)
Business revenue is divided into K+1 segment by K reference line representated by formula (7), passes through formula (6), formula (7) formula (8) institute structure
Business revenue is divided into the profit and loss situation that K+1 part carries out analysis enterprise supply chain, it can be determined that go out enterprise by the K+2 reference line built
The current business characteristic of industry and the consistent level of operation.
Secondly the modeling of K-means algorithms is carried out to second matrix ordered series of numbers (business revenue-gross profit) and calculated.
By Euler's formula：
Selection object function is square error, is shown below：
Following formula can be obtained to u derivations by above formula：
The mark first to remaining each point progress cluster centre is needed in entire algorithm flow：
labeli=argmin | | ti-uj||(1≤j≤k) (12)
It also needs to adjust and update cluster centre after each traversal, the formula (10) for being employed as object function carries out K
The update at center.
It is whole when object function is optimal solution, i.e. maximum iterations by continuous repetitive (12), formula (11)
Only, algorithm stop condition is as follows：
At K-means algorithms and after generating image, reference line is added：
Y=0 (14)
Reference line region below is loss situation, passes through the dense degree of the reference line following region data point distribution
It can learn the existing management state of enterprise.In addition further according to the K values initially chosen, according to the distribution situation of K categorical data point,
Add K reference line：
X=Lk(1≤k≤K) (15)
Business revenue is divided into K+1 segment by K reference line representated by formula (15), constructed by formula (14) and formula (15)
K+1 reference line, by business revenue be divided into K+1 part carry out analysis each section risk and profit situation, by business revenue
Sub-category analysis, to obtain the return situation of each business revenue section and the size of risk.To help enterprise's adjustment strategy decision.
Can be the schematic diagram of the information of supply chain of some enterprise analyze referring to figs. 1 to Fig. 4.(it should be noted that
It is since original information of supply chain data are excessive, the present embodiment does not provide)
Each technical characteristic of embodiment described above can be combined arbitrarily, to keep description succinct, not to above-mentioned reality
It applies all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, it is all considered to be the range of this specification record.
Several embodiments of the invention above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the protection of the present invention
Range.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (8)
1. a kind of information of supply chain analysis method based on K-means algorithms, which is characterized in that including：
Data cleansing is carried out to the supply chain data, extracts the first matrix ordered series of numbers, first matrix is business revenue-cost square
Battle array ordered series of numbers；
The first matrix ordered series of numbers is clustered using the K-means algorithms of the first cluster number K1, and generates scatterplot figure
First image of formula；
First reference line and the second reference line are added to described first image；
According to first reference line and second reference line, described first image is cut into three parts, middle part subregion
The steady situation of domain representation enterprise operation, upper and lower two parts then show the case where enterprise manages extremely；
Wherein, first reference line is y=ax, tan50 °<a<tan60°；
Second reference line is y=bx, tan30 °<b<tan40°.
2. the information of supply chain analysis method according to claim 1 based on K-means algorithms, which is characterized in that a=
tan55°。
3. the information of supply chain analysis method according to claim 1 based on K-means algorithms, which is characterized in that b=
tan35°。
4. the information of supply chain analysis method according to claim 1 based on K-means algorithms, which is characterized in that according to
The distribution situation of K1 categorical data point in described first image continues described first image to add third reference line；
The third reference line is x=Lk (1≤k≤K1)；Business revenue is divided into K1+1 segment by the third reference line, will be sought
Contracture carries out the profit and loss situation of analysis enterprise supply chain at K1+1 part.
5. a kind of information of supply chain analysis method based on K-means algorithms, which is characterized in that including：
Data cleansing is carried out to supply chain data, extracts the second matrix ordered series of numbers, first matrix is business revenue-gross profit matrix function
Row；
The second matrix ordered series of numbers is clustered using the K-means algorithms of the second cluster number K2, and generates scatterplot figure
Second image of formula；
4th reference line is added to second image；
4th reference line region below is loss situation, passes through the 4th reference line following region data point distribution
Dense degree learns the existing management state of enterprise；
4th reference line is y=0.
6. the information of supply chain analysis method according to claim 5 based on K-means algorithms, which is characterized in that according to
The distribution situation of K2 categorical data point in second image continues second image to add the 5th reference line；
5th reference line is x=Lm (1≤m≤K2), and business revenue is divided into K2+1 categorical datas by the 5th reference line
Business revenue is divided into the risk and profit situation that K2+1 categorical data point parts carry out analysis each section by point segment.
7. a kind of computer equipment, including memory, processor and storage are on a memory and the meter that can run on a processor
Calculation machine program, which is characterized in that the processor realizes any one of claim 1-6 the methods when executing described program
The step of.
8. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is held by processor
The step of claim 1-6 any one the methods are realized when row.
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