CN107368917A - A kind of Power Material inventory optimization system and method based on KNN algorithms - Google Patents
A kind of Power Material inventory optimization system and method based on KNN algorithms Download PDFInfo
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Abstract
The present invention realizes the stock in storage chronological classification of Utilities Electric Co.'s magnanimity in goods and materials storage process using KNN algorithms as core.According to power network goods and materials in storehouse situation, pass through the dimensions such as districts and cities' unit, goods and materials type, mobile type, factory types, storage batch number, storage time, unit price, total amount, current date and materials warehousing date.By the sorting technique of the present invention, the characteristic classification deposited to Power Material in Kuku is realized, goods and materials storage time region divide, has effectively obtained the normal of inventory time, expires and exceeds the time limit to identify.
Description
Technical field
The present invention relates to power production management and materials management technique field, more particularly to KNN algorithms are in Power Material storehouse
Deposit the sorting technique of time.
Background technology
The scope of State Grid Corporation of China's centralized purchasing at present constantly expands, and extra-high voltage builds speed-raising comprehensively, safe and reliable power supply
The rigidity lifting of ability and clean energy resource digestion capability, the work of goods and materials intensive management be need to strengthen Whole Course Management, carried out energetically
Goods and materials standardize, optimization purchases strategy, strengthen information-based management and control, General Promotion log equipment quality, are protected to build sturdy power grid
Drive convoy.
But the relevant information of traditional Power Material stock control, based on manual entry, prior art realizes information
Synchronized update efficiency is very low, and manual operation is error-prone.Because logistics can not timely be connected with information flow, there is the time
Difference, cause substantial amounts of state's net goods and materials and overstock in warehouse, state's net corporate resource be present and waste and support the norm of material reserve not
The problem of reasonable.
The content of the invention
The purpose of the present invention be exactly in order to solve to difficult present in Power Material stock, propose one kind be easily achieved,
Simple and quick, the efficient Power Material inventory optimization system and method based on KNN algorithms.This method using KNN algorithms as core,
For realizing the stock in storage chronological classification of Utilities Electric Co.'s magnanimity in goods and materials storage process.According to power network goods and materials in storehouse situation,
By districts and cities' unit, goods and materials type, mobile type, factory types, storage batch number, storage time, unit price, total amount, current
The dimension such as date and materials warehousing date.By the sorting technique of the present invention, the characteristic deposited to Power Material in Kuku is realized
According to classification, goods and materials storage time region divide, has effectively obtained the normal of inventory time, expires and exceeds the time limit to identify.
To achieve the above object, the system flow is as shown in Figure 1.The present invention adopts the following technical scheme that：
First, Power Material inventory optimization Establishing, according to the selection of rule requirement, respectively to districts and cities' unit, goods and materials
Type, mobile type, factory types, storage batch number, storage time, unit price, total amount, current date and materials warehousing date
Etc. historical data from the extraction of full-service data center, it is labeled and forms the wide table of data.
Secondly, stock in storage data compliance inspection, the data scrubbing of early stage is carried out to the wide table of stock in storage data, eliminated
Repetition values, missing values, exceptional value.
Further, stock in storage data characteristics is created, and data standard standardization is carried out to the wide table of stock in storage data, will
Each data dimension carries out standardization processing according to evaluation.
3rd, the Data Modeling Method in stock in storage selects use, implementation includes following content：
The problem of going to be classified using characteristic value distance metric can be run into material storage chronological classification problem.
Using KNN algorithm models, stock in storage test data is inputted, the feature of test data is corresponding with training set
Feature is compared to each other, and finds stock in storage in training set the most similar preceding K data therewith, then the test data pair
The stock in storage time answered is exactly that chronological classification that occurrence number is most in K data.
Stock in storage chronological classification model evaluation after use is embodied as described below：
The stock in storage preference pattern of a high quality has been established from the angle of data analysis, KNN algorithms have been carried out
Stock in storage chronological classification result carries out the contrast of accuracy rate with the real grouped data result of history.
The problem of going to be classified using characteristic value distance metric can be run into material storage chronological classification problem.If
In the sample of k of one sample in feature space most like (i.e. closest in feature space) most of belong to some
Classification, then the sample fall within this classification.The grader of most simple initial stage is the class corresponding to by the training data of whole
All do not record, can be to divide it when the attribute of the attribute of test object and some training object matches completely
Class, in KNN algorithms, selected neighbours are the objects correctly classified.This method is on class decision-making is determined only according to most adjacent
The classifications of one or several near samples determines the classification belonging to sample to be divided.
In KNN, as the non-similarity index between each object, object is avoided by calculating between object distance
Between matching problem.
Meanwhile KNN is by carrying out decision-making, rather than single object type decision-making according to the classification being dominant in k object.
This 2 points be exactly KNN algorithms advantage.Using classification of the KNN algorithms to the stock in storage time：Be exactly training intensive data and
In the case of label is known, stock in storage test data is inputted, the feature of test data feature corresponding with training set is entered
Row is compared to each other, and finds stock in storage in training set the most similar preceding K data therewith, then storehouse corresponding to the test data
The storage money time is exactly that chronological classification that occurrence number is most in K data.
4th, stock in storage chronological classification model evaluation after include it is several it is lower for the use of：
The stock in storage preference pattern of a high quality is established from the angle of data analysis, stock is carried out to KNN algorithms
Goods and materials chronological classification result carries out the contrast of accuracy rate with the real grouped data result of history.
To the fuzzy comprehensive evaluation of chronological classification, further determining whether that important service problem is not examined sufficiently
Consider.After this stage terminates, the use for reaching a stock in storage chronological classification determines.
5th, whether it is optimization and concrete condition according to system, judges whether to issue regulating command, if this cycle need not
Regulating command is issued, then waits next controlling cycle.
The advantages of on the one hand beneficial effects of the present invention are to have merged KNN algorithms, efficiently carry out inventory time region
Division；Further aspect is that model has effectively carried out the extraction of stock in storage data characteristics, according to districts and cities, material type, thing
Expect group dimension, grasp the inventory time situation of prefectures and cities' different material type, inventory time classification is carried out to goods and materials.
Brief description of the drawings
The general frame figure of the sorting technique of Fig. 1 Power Material inventory times
Fig. 2 material storage time data access technology route maps.
The data flow figure of Fig. 3 material storage time datas.
Embodiment
1 further explanation is made to the present invention with embodiment below in conjunction with the accompanying drawings, propose a kind of electric power based on KNN algorithms
Material storage optimizes system and method, using following steps：
S1. Power Material inventory optimization Establishing, such as accompanying drawing 2, accompanying drawing 3.
S1.1 combs data present situation and state's nets such as goods and materials ERP operation systems data storage, data frequency, data growth rate
Goods and materials platform hardware environment present situation, form system description investigation report.
S1.2 is with reference to goods and materials ERP system Current Situation Investigation situation, and current goods and materials ERP system moon incremental data is larger, renewal
Table is more, and a big chunk table does not have increment control algorithm field, therefore incremental data cut-in operation will be carried out using OGG modes.
S1.3 the goods and materials ERP data of access are put first carry out data calamity it is standby, formed database backup file, then by number
ODS data buffer zones are placed according to reduction.
S1.4 includes the database for having built goods and materials supplier selection in ODS buffering areas, is required according to the selection of rule,
Respectively to districts and cities' unit, goods and materials type, mobile type, factory types, storage batch number, storage time, unit price, total amount, when
The historical data such as preceding date and materials warehousing date extracts from full-service data center, is labeled and forms the wide table of data.
S2. electric power stock in storage data compliance inspection, the data that early stage is carried out to the wide table of electric power stock in storage data are clear
Reason, eliminate repetition values, missing values, exceptional value.
Further, electric power stock in storage data characteristics is created, and data standard is carried out to the wide table of electric power stock in storage data
Standardization, each data dimension is subjected to standardization processing according to evaluation.
S3. the Data Modeling Method in stock in storage selects use, implementation include following content：
The problem of going to be classified using characteristic value distance metric can be run into material storage chronological classification problem.If
In the sample of k of one sample in feature space most like (i.e. closest in feature space) most of belong to some
Classification, then the sample fall within this classification.The grader of most simple initial stage is the class corresponding to by the training data of whole
All do not record, can be to divide it when the attribute of the attribute of test object and some training object matches completely
Class, in KNN algorithms, selected neighbours are the objects correctly classified.This method is on class decision-making is determined only according to most adjacent
The classifications of one or several near samples determines the classification belonging to sample to be divided.
In KNN, as the non-similarity index between each object, object is avoided by calculating between object distance
Between matching problem, herein distance typically use Euclidean distance：
Meanwhile KNN is by carrying out decision-making, rather than single object type decision-making according to the classification being dominant in k object.
This 2 points be exactly KNN algorithms advantage.Using classification of the KNN algorithms to the stock in storage time：Be exactly training intensive data and
In the case of label is known, stock in storage test data is inputted, the feature of test data feature corresponding with training set is entered
Row is compared to each other, and finds stock in storage in training set the most similar preceding K data therewith, then storehouse corresponding to the test data
The storage money time is exactly that chronological classification that occurrence number is most in K data.
S4. the stock in storage chronological classification model evaluation after include it is several it is lower for the use of：
The stock in storage preference pattern of a high quality is established from the angle of data analysis, stock is carried out to KNN algorithms
Goods and materials chronological classification result carries out the contrast of accuracy rate with the real grouped data result of history.
To the fuzzy comprehensive evaluation of chronological classification, further determining whether that important service problem is not examined sufficiently
Consider.After this stage terminates, the use for reaching a stock in storage chronological classification determines.
S5. whether it is optimization and concrete condition according to system, judges whether to issue regulating command, if this cycle is without under
Regulating command is sent out, then waits next controlling cycle.
Claims (6)
1. a kind of Power Material inventory optimization system and method based on KNN algorithms, it is characterised in that this method step is：
Step 1: Power Material inventory optimization Establishing, index includes districts and cities' unit, goods and materials type, mobile type, factory
Type, storage batch number, storage time, unit price, total amount, current date and materials warehousing date；
Step 2: to Power Material inventory data compliance inspection, data correction model is established, in achievement data in step 1
Bad value or uncorrelated numerical value is deleted or correcting process, eliminate repetition values, missing values, exceptional value；
Step 3: carried out electric power chronological classification based on KNN algorithms, and the model method to being reminded beyond inventory time.Profit
With KNN algorithm models, stock in storage test data is inputted, the feature of test data feature corresponding with training set is subjected to phase
Mutually relatively, according to the distance of distance, the ballot to neighbour is weighted, and distance is more near, and weight is bigger, and (weight is square distance
Inverse).Stock in storage in training set the most similar preceding K data therewith are found, then stock corresponding to the test data
The goods and materials time is exactly that chronological classification that occurrence number is most in K data；
Step 4: the stock in storage chronological classification model evaluation after use；
Step 5: whether being optimization and concrete condition according to system, judge whether to issue regulating command, if this cycle is without under
Regulating command is sent out, then waits next controlling cycle.
A kind of 2. Power Material inventory optimization system and method based on KNN algorithms as claimed in claim 1, it is characterised in that
The step 1 Power Material inventory optimization Establishing includes combing goods and materials ERP operation systems data storage, data frequency, number
According to the data such as growth rate present situation and state's net goods and materials platform hardware environment present situation, system description investigation report is formed.
A kind of 3. Power Material inventory optimization system and method based on KNN algorithms as claimed in claim 2, it is characterised in that
Step 3 Power Material inventory optimization Establishing is included with reference to goods and materials ERP system Current Situation Investigation situation, current goods and materials ERP system
The moon, incremental data was larger, and the table of renewal is more, and a big chunk table does not have increment control algorithm field, therefore incremental data cut-in operation will
Carried out using OGG modes.
A kind of 4. Power Material inventory optimization system and method based on KNN algorithms as claimed in claim 3, it is characterised in that
Step 1 Power Material inventory optimization Establishing is standby including the goods and materials ERP data of access are put with progress data calamity first, is formed
Database backup file, data convert is then placed in ODS data buffer zones, is included in ODS buffering areas and built goods and materials
The database of supplier's selection.
A kind of 5. Power Material inventory optimization system and method based on KNN algorithms as claimed in claim 1, it is characterised in that
Step 4 includes establishing the stock in storage preference pattern of a high quality from the angle of data analysis, and storehouse is carried out to KNN algorithms
Storage money chronological classification result carries out the contrast of accuracy rate with the real grouped data result of history.
A kind of 6. Power Material inventory optimization system and method based on KNN algorithms as claimed in claim 1, it is characterised in that
Step 4 includes the fuzzy comprehensive evaluation to chronological classification, is further determining whether that important service problem is not sufficient
Consider, after this stage terminates, the use for reaching a stock in storage chronological classification determines.
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| CN109902850A (en) * | 2018-08-24 | 2019-06-18 | 华为技术有限公司 | Determine the method, apparatus and storage medium of Strategy of Inventory Control |
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| 杨学军 等著: "《分布并行图像处理技术》", 30 June 2005 * |
Cited By (2)
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|---|---|---|---|---|
| CN109902850A (en) * | 2018-08-24 | 2019-06-18 | 华为技术有限公司 | Determine the method, apparatus and storage medium of Strategy of Inventory Control |
| CN109902850B (en) * | 2018-08-24 | 2021-08-20 | 华为技术有限公司 | Method, device and storage medium for determining inventory control strategy |
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Application publication date: 20171121 |