CN113642957A - Material 'checking, storing and matching' analysis method and device based on big data - Google Patents

Material 'checking, storing and matching' analysis method and device based on big data Download PDF

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CN113642957A
CN113642957A CN202110890602.9A CN202110890602A CN113642957A CN 113642957 A CN113642957 A CN 113642957A CN 202110890602 A CN202110890602 A CN 202110890602A CN 113642957 A CN113642957 A CN 113642957A
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高琪
石海春
谢先锋
孟威
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HEFEI YOUO ELECTRONIC TECHNOLOGY CO LTD
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Abstract

The invention relates to a material 'checking, storing and matching' analysis method and device based on big data, comprising the following steps: s1, acquiring and storing data; s2, cleaning data based on the data cleaning method of the knowledge graph; s3, acquiring supplier evaluation information by utilizing a supplier evaluation model of an entropy weight method based on the cleaned data; s4, acquiring an optimal bin to store materials; and S5, scheduling the goods and materials based on the supplier evaluation information and the bin position information. The invention greatly shortens the logistics transfer detection period, generally takes 15 days from material sampling to detection report issuing under the traditional centralized inspection mode, obtains supplier evaluation information by cleaning data and obtains the optimal bin position to schedule materials, so that the period from delivery to detection is reduced to 7-10 days, and the working efficiency is improved by 1 time.

Description

Material 'checking, storing and matching' analysis method and device based on big data
Technical Field
The invention relates to the technical field of production logistics, in particular to a material 'checking, storing and matching' analysis method and device based on big data.
Background
In 2020, national grid companies propose strategic measures of modern intelligent supply chains, and require provinces and companies to build a material supply system with the characteristics of the times. In order to further improve the management level of a warehouse, power supply companies in various places actively adapt to the construction requirement of a modern intelligent supply chain according to the working requirement of 'the idea of comprehensively enhancing the construction and the improvement work of a material supply system by the State grid Material department', create a quality inspection, storage and distribution integrated management center, actively promote the standardization, the standardization and the operation of inspection, storage and distribution construction, the standardization and the operation standardization of management informatization, the automation of operation and the collaborative interaction promotion, increase the development and the application of the Internet of things technology, the informatization technology and new logistics products, improve the inspection, storage and distribution construction and the operation level, and realize the intelligent and intensive management of the inspection, storage and distribution system of the companies.
Along with the continuous promotion of electric wire netting construction, all kinds of goods and materials supply and demand volume are showing and are increasing, lead to that quality control ability is not enough, turnover goods and materials are frequent, delivery efficiency is not high in each city and county warehouse.
For example, the invention patent application with the application number of 'CN202010810135. X' discloses a supplier classification and classification model and a method based on big data analysis, which comprises the steps of dividing suppliers into four categories of strategic materials, bottleneck materials, lever materials and common materials; carrying out grading management according to five parts of qualification capability evaluation, operation evaluation, performance evaluation, enterprise reputation and quality control management by combining the classification of suppliers, and grading various suppliers; according to the comprehensive scoring condition of the suppliers, the suppliers are classified into nine grades and the like; constructing a big data analysis technology platform according to the classification and classification standards and models of suppliers; the classified grading of the suppliers obtained according to the big data analysis platform truly reflects the overall condition of the suppliers, and the classified grading result is applied to the purchasing field, so that the excellence and the inferiority of the suppliers are realized. The patent scheme only realizes the classification of suppliers and cannot fundamentally overcome the problem that the period from the distribution to the detection of each warehouse is long when the supply demand of materials is increased.
Disclosure of Invention
The invention aims to solve the technical problem that the period from the distribution to the detection of each warehouse is long when the material supply demand is increased.
The invention solves the technical problems through the following technical means: a material 'checking, storing and matching' analysis method based on big data comprises the following steps:
s1, acquiring and storing data;
s2, cleaning data based on the data cleaning method of the knowledge graph;
s3, acquiring supplier evaluation information by using a supplier evaluation model of an entropy weight method based on the cleaned data, wherein the supplier evaluation information comprises the following specific steps:
s31, obtaining the proportion of the ith material sample value in the j index, including:
s311, normalizing the forward index by using a formula (1);
Figure RE-GDA0003274440740000021
the cleaned data comprises n material objects to be evaluated and m evaluation indexes, wherein the evaluation indexes comprise positive indexes and negative indexes, and xijIs the value of the j index of the i sample (i ═ l., n;, j ═ l., m),
normalizing the negative direction index by using a formula (2);
Figure RE-GDA0003274440740000022
for n samples, m indices, xijThe value of the j index of the ith sample (i ═ l., n: ═ l., m);
s312, calculating the proportion of the ith material sample value in the j index by using a formula (3):
Figure BDA0003195740590000031
pijthe sample value of the ith material under the jth index accounts for the proportion of the index;
s32, acquiring the comprehensive score of each material sample i based on the proportion of the ith material sample value in the jth index, wherein the comprehensive score comprises the following steps:
s321, calculating an entropy value of the j-th index by using a formula (4):
Figure BDA0003195740590000032
wherein k is 1/ln (n)>0, satisfies ej≥0；j＝1,2,3...,m；ejEntropy value of j index;
s322, obtaining information entropy redundancy (difference), and calculating the information entropy redundancy (difference) by using a formula (5):
dj＝1-ej (5)
djinformation entropy redundancy is obtained;
s323, acquiring the weight of each index j, and calculating the weight of each index j by using a formula (6):
Figure BDA0003195740590000033
wjis the weight of each index j;
s324, calculating the comprehensive score of each material sample i by using the formula (7):
Figure BDA0003195740590000034
wherein s isiThe final score of the quality inspection of the ith material item is represented;
s4, acquiring an optimal bin to store materials;
and S5, scheduling the goods and materials based on the supplier evaluation information and the bin position information.
The invention greatly shortens the logistics transfer detection period, generally takes 15 days from material sampling to detection report issuing under the traditional centralized inspection mode, obtains supplier evaluation information by cleaning data and obtains the optimal bin position to schedule materials, so that the period from delivery to detection is reduced to 7-10 days, and the working efficiency is improved by 1 time.
As an optimized technical solution of the present invention, in step S1, the data includes:
material list, material inventory list, equipment information, material information, inventory information, supplier information and bin position information; item details, payment lists; material information, detection plan, warehouse information, bin position information, inventory information, unit information and department information.
As an optimized technical solution of the present invention, the step S2 includes:
s21, combing and summarizing the acquired complete data by using a normal data processing flow;
and S22, searching and matching the 'bad data' with missing part information by using a preset database.
As a technical solution for the optimization of the present invention, in step S4, a systematic clustering method is adopted to obtain the most optimal bin for storing the materials.
As an optimized technical solution of the present invention, the step S5 includes:
s51, setting a mark number group book [ ]: dividing vertexes of all paths into two parts, namely a vertex set P with a known shortest path and a vertex set Q with an unknown shortest path, wherein the initial set P only has one vertex of a source point, and a book [ i ] is 1 and is shown in the set P;
wherein, the top point of the content is the tail end of the path;
s52, setting the shortest path array dst [ ] and continuously updating: in an initial state, let dst [ i ] ═ edge [ s ] [ i ], s be a source point, edge be an adjacency matrix, at this time, dst [ s ] ═ 0, book [ s ] ═ 1, at this time, a vertex u closest to the source point s in the set Q can be selected and added to P, and each edge is subjected to a relaxation operation according to a new central point with u, where the relaxation refers to a point u that can pass through on the way of the node s- - > j, and let dst [ j ] ═ min { dst [ j ], dst [ u ] + edge [ u ] [ j ] }), and let book [ u ] ═ 1;
s53, selecting a vertex v closest to the source point S from the set Q again, adding the vertex v into P, and performing a relaxation operation on each edge according to v as a new center point, that is, dst [ j ] ═ min { dst [ j ], dst [ v ] + edge [ v ] [ j ] }, and making book [ v ] ═ 1;
and S54, repeating the step S53 until the set Q is empty, obtaining a warehouse with the least time cost, selecting an optimal route according to a warehouse transportation route, and finally improving the material use experience.
As the optimized technical scheme of the invention, the material list and the material inventory list are derived from an ERP system, the equipment information is derived from a PMS2.0 system, the material information, the inventory information, the supplier information and the position information are derived from an IWMS storage management system, the project details and the payment list are derived from a financial management and control system, and the detection plan, the warehouse information, the position information, the unit information and the department information are derived from a self-built system.
The invention also provides a material 'checking, storing and matching' analysis device based on any one of the schemes, which comprises:
the storage module is used for acquiring and storing data;
the cleaning module is used for cleaning data based on a data cleaning method of the knowledge graph;
the first obtaining module is used for obtaining supplier evaluation information based on a supplier evaluation model of an entropy weight method, and comprises the following steps:
the proportion obtaining unit of the ith material sample value occupying the index under the j index executes the following operations:
normalizing the forward direction index by using a formula (1);
Figure RE-GDA0003274440740000061
the cleaned data comprises n material objects to be evaluated and m evaluation indexes, wherein the evaluation indexes comprise positive indexes and negative indexes, and xijIs the value of the j index of the i sample (i ═ l., n;, j ═ l., m),
normalizing the negative direction index by using a formula (2);
Figure RE-GDA0003274440740000062
for n samples, m indices, xijThe value of the j index of the ith sample (i ═ l., n: ═ l., m);
calculating the proportion of the ith material sample value in the j index by using a formula (3):
Figure BDA0003195740590000063
pijthe sample value of the ith material under the jth index accounts for the proportion of the index;
the comprehensive score obtaining unit of each goods and materials sample i executes the following operations:
calculating the entropy value of the j index by using formula (4):
Figure BDA0003195740590000064
wherein k is 1/ln (n)>0, satisfies ej≥0；j＝1,2,3...,m；ejEntropy value of j index;
obtaining information entropy redundancy (difference), and calculating the information entropy redundancy (difference) by using a formula (5):
dj＝1-ej (5)
djinformation entropy redundancy is obtained;
s323, acquiring the weight of each index j, and calculating the weight of each index j by using a formula (6):
Figure BDA0003195740590000071
wjis the weight of each index j;
calculating the comprehensive score of each material item sample i by using the formula (7):
Figure BDA0003195740590000072
wherein s isiThe final score of the quality inspection of the ith material item is represented;
the second acquisition module is used for acquiring the optimal bin to store materials;
and the scheduling module is used for scheduling the materials and solving the problem of timeliness of the use of the materials.
As the optimized technical scheme of the invention, the cleaning module comprises:
a carding induction unit: combing and summarizing the acquired complete data by using a normal data processing flow;
a retrieval matching unit: for 'bad data' with missing part information, searching and matching by using a preset database;
in the second acquisition module, the optimal bin position is acquired by adopting a system clustering method so as to store materials.
As the optimized technical scheme of the invention, the scheduling module comprises:
a flag array setting unit: set number group book [ ]: dividing the vertexes of all paths into two parts, namely a vertex set P with a known shortest path and a vertex set Q with an unknown shortest path, wherein the initial set P only has one vertex of a source point, and a book [ i ] is 1 and is expressed in the set P, wherein the vertexes of the contents are the tail ends of the paths;
the shortest path array setting unit: setting a shortest path array dst [ ] and continuously updating: in an initial state, let dst [ i ] ═ edge [ s ] [ i ], s be a source point, edge be an adjacency matrix, at this time, dst [ s ] ═ 0, book [ s ] ═ 1, at this time, a vertex u closest to the source point s in the set Q can be selected and added into P, and each edge is subjected to a relaxation operation according to u as a new central point, the relaxation refers to a passing point u passing through the middle of a node s- - > j, let dst [ j ] ═ min { dst [ j ], dst [ u ] + edge [ u ] [ j ] }), and let book [ u ] ═ 1;
a relaxation operation unit: selecting a vertex v nearest to the source point s from the set Q again to be added into P, and performing relaxation operation on each edge according to the fact that v is a new central point, namely dst [ j ] ═ min { dst [ j ], dst [ v ] + edge [ v ] [ j ] }, and enabling book [ v ] ═ 1;
an optimal route selection unit: and repeating the operation of the relaxation operation unit until the set Q is empty, obtaining the warehouse with the minimum time cost, selecting an optimal route according to the transportation route of the warehouse, and finally improving the material use experience.
As the optimized technical scheme of the invention, the material list and the material inventory list are derived from an ERP system, the equipment information is derived from a PMS2.0 system, the material information, the inventory information, the supplier information and the position information are derived from an IWMS storage management system, the project details and the payment list are derived from a financial management and control system, and the detection plan, the warehouse information, the position information, the unit information and the department information are derived from a self-built system.
The invention has the advantages that:
1. the invention greatly shortens the logistics transfer detection period, generally takes 15 days from material sampling to detection report issuing under the traditional centralized inspection mode, obtains supplier evaluation information by cleaning data and obtains the optimal bin position to schedule materials, so that the period from delivery to detection is reduced to 7 to 10 days, and the working efficiency is improved by 1 time.
2. The invention can obviously reduce the transportation cost, according to the statistics of practical application data, before the integrated operation of detection, storage and distribution is implemented, the monthly sampling, inspection and distribution average transportation mileage of a company is about 1400 kilometers, the monthly sampling, inspection and distribution average transportation mileage is reduced to within 600 kilometers at present, and the related logistics cost is reduced by more than 60%.
3. According to the invention, the supply capacity, the supply quality and the supply service of a provider are analyzed by using a data mining technology through mass material data, the application value of big data is really exerted, and the mutual relation among a transit warehouse of a city company, a terminal warehouse of a county company and each professional warehouse in material storage management is realized by acquiring data, so that cross-unit information sharing is realized, and the warehouse resources of a material department can be comprehensively staged.
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Fig. 1 is a schematic flow chart of a material "checking, storing and matching" analysis method based on big data according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a material "checking, storing and matching" analysis apparatus based on big data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Example 1
Referring to fig. 1, a material "checking, storing and matching" analysis method based on big data includes the following steps:
s1, acquiring and storing data;
wherein the data comprises: material list, material inventory list, equipment information, material information, inventory information, supplier information and bin information;
item details, payment lists;
material information, detection plans, warehouse information, bin position information, inventory information, unit information and department information;
the system comprises a detection plan, a warehouse information system, a position information system, a supplier information system, a position information system, an IWMS warehouse management system, a project detail and a payment list, wherein the material list and the material inventory list are from an ERP system, the equipment information is from a PMS2.0 system, the material information, the inventory information, the supplier information and the position information are from the IWMS warehouse management system, the project detail and the payment list are from a financial management and control system, and the detection plan, the warehouse information, the position information, the unit information and the department information are from a self-built system.
In addition, in the scheme of the embodiment of the present disclosure, the material information and the bin information may also be from a self-established table, so that the diversity and accuracy of data sources can be ensured.
The so-called self-established table is established manually and will not be described in detail here.
In the above scheme, the acquired data is stored in a storage module, and the storage module uses Hive of a big data platform as a data storage medium and is responsible for storage of mass data.
In addition, the unstructured data (some payment lists are photographed, and the photographed data) are stored in the file system HDFS, and the high-level application of the data processing layer is supported in a multi-dimension mode.
S2, a data cleaning method based on the knowledge map, and a method for cleaning data to improve quality of material data
In multi-system multidimensional data such as ERP (enterprise resource planning), warehouse management system (IWMS) and the like, conditions such as non-standard distribution of suppliers, unfamiliarity of work flows by a material taker and the like exist, and data in the system is often repeated and incomplete. Based on the data, the data needs to be cleaned and sorted, and the cleaning and sorting of the data refers to performing secondary analysis on the data through various data integration technologies under the condition that the original semantics are stored as much as possible, so that the correlation and consistency of the data are ensured.
S21, combing and classifying the collected complete data by using a normal data processing flow;
s22, searching and matching the 'bad data' with missing part information by using a preset database;
such as: when the electric power warehouse enters or exits the warehouse, information is recorded quickly by using a voice recognition method, but the 'hardware fittings' are generally recognized as 'gold sentences' by using engines of mainstream voice recognition factories, such as scientific news, century and the like, and the electric power warehouse stores a large number of hardware fittings of various types, so that the collected error data entries are difficult to tolerate, and therefore a knowledge map repairing method is adopted.
A total of 358223 entries such as main data 20190410 edition of latest materials in national network, main data of an Anhui registration warehouse, and an address book 2019 of a material system of a national network Anhui power-saving limited company are collected in advance.
To facilitate the use and lookup of dictionary data, the power warehouse data knowledge graph is created using unstructured database software neo4 j.
And acquiring complete information by extracting partial characters and entries in the defective data and updating the modified data. From the source of the data, the storage data, the quality inspection data and the distribution logistics data are mainly stored. From a structural perspective, data is primarily structured, semi-structured, and unstructured data.
The data center information is processed and summarized, merging and fusion of multiple data sources are achieved, and multi-level application of big data of material detection, storage and distribution integrated operation is completed based on entropy weight method material quality detection evaluation, system clustering method optimal bin position material storage and the like.
It is to be understood that the modeling is done manually, and the specific search for matches is also a relatively conventional prior art, and therefore will not be described in detail here.
And S3, acquiring supplier evaluation information by utilizing a supplier evaluation model of an entropy weight method based on the cleaned data.
In the early stage of important material purchasing negotiation, how to measure the quality of the material supplied by a provider is an important work of the material department; the regular and irregular sampling inspection of the existing materials in the warehouse is carried out, and the quality and the safety of the materials entering the warehouse are ensured, so that the important work of the material department is also carried out. Therefore, how to select a suitable supplier evaluation method for internal material quality evaluation is a key task of the material department.
In the embodiment, an entropy weight method is adopted to perform material quality inspection and estimation on suppliers with established purchasing contact, and entropy is a measure of the disorder degree of a system; according to the definition of the information entropy, the degree of dispersion of a certain index can be judged by using the entropy value, the smaller the information entropy value is, the larger the degree of dispersion of the index is, the larger the influence (namely weight) of the index on the comprehensive evaluation is, and if the values of the certain index are all equal, the index does not play a role in the comprehensive evaluation. Therefore, the information entropy tool can be used for calculating the weight of each index, and a basis is provided for the comprehensive evaluation of multiple indexes of materials.
In the evaluation of the material entropy weight method, a certain supplier material product is selected, n samples are sampled, and the quality inspection evaluation steps mainly involved are as follows:
s31, acquiring the proportion of the ith material sample value in the j index;
the method specifically comprises the following steps:
s311, normalizing the forward index by using a formula (1);
Figure RE-GDA0003274440740000121
wherein the cleaned data comprises n number of goods and materials objects to be evaluated, m number of evaluation indexes, and xijThe value of the j index of the ith sample (i ═ l., n;. j ═ l., m).
Normalizing the negative direction index by using a formula (2);
Figure RE-GDA0003274440740000122
for n samples, m indices, xijThe value of the j index of the ith sample (i ═ l., n;. j ═ l., m).
S312, calculating the proportion of the ith material sample value in the j index by using a formula (3):
Figure BDA0003195740590000131
pijthe sample value of the ith material under the jth index accounts for the proportion of the index。
S32, acquiring the comprehensive score of each material sample i based on the proportion of the ith material sample value in the jth index, wherein the comprehensive score comprises the following steps:
s321, calculating an entropy value of the j-th index by using a formula (4):
Figure BDA0003195740590000132
wherein k is 1/ln (n)>0, satisfies ej≥0；j＝1,2,3...,m；ejEntropy value of j index;
s322, obtaining information entropy redundancy (difference), and calculating the information entropy redundancy (difference) by using a formula (5):
dj＝1-ej(5)
djinformation entropy redundancy is obtained;
s323, acquiring the weight of each index j, and calculating the weight of each index j by using a formula (6):
Figure BDA0003195740590000133
wjis the weight of each index j;
s324, calculating the comprehensive score of each material sample i by using the formula (7):
Figure BDA0003195740590000134
wherein s isiAnd (4) representing the final score of the quality inspection of the ith material item.
The quality inspection final score of the goods and materials is further converted into an evaluation grade.
Furthermore, the principle that the ranking should be satisfied according to the score:
(1) the quality inspection of the goods and materials in the same grade has the minimum difference in the final scoring groups: the quality inspection final score of each material item in each grade and the average value of the quality inspection final scores of all the material items in the grade are made to be as small as possible;
(2) the quality inspection final score group separation of the goods and materials in different grades is as large as possible: the quality inspection final score of each material item in different grades and the average value of the quality inspection final scores of all the material items are as large as possible.
The supplier evaluation quality inspection result based on the entropy weight method provides multi-dimensional all-around evaluation for evaluation of different suppliers, and finally performs key processing on the suppliers with poor grades, so that the reliability of material purchasing evaluation is improved.
And S4, acquiring the optimal bin to store the materials.
The material is of multiple types, different materials have different safety requirements, and the same material has different requirements for storage in different batches, so that how to automatically allocate the optimal storage bin position when the material is put into a warehouse is another problem solved by the application based on the material type. And the optimal bin position is selected for storage based on a system clustering method, so that the manual workload is reduced, and the safety of materials in a warehouse is improved.
It is understood that the systematic clustering method is a relatively mature prior art, so that only a brief introduction description of the systematic clustering method is provided herein.
And when the clustering is initialized, taking each element as one class independently, calculating the distance between any two classes, combining the two classes with the shortest distance into one class, calculating the distance between the new class and the other classes, and repeating the combination until the distance between all the classes is greater than a certain threshold value or the number of the remaining classes is less than a certain value.
In the above system clustering method, the key parameters and methods include: 1. a measure of distance; 2. a threshold in a cluster termination condition; 3. the method used when calculating the distance of a class from other classes when there are multiple elements in a class. The method for measuring the distance selects the pearson coefficient as a measurement standard in the application, and because the pearson coefficient value is opposite to the distance, the cutoff threshold value of the distance is set to indicate that two types larger than the value are not combined into one type, and the two types smaller than a certain value are not combined into one type when the value is transferred to the pearson coefficient, and the value is the value required to be set by the application.
S5, scheduling the materials based on the supplier evaluation information and the bin position information, and solving the aging problem of material use.
When meeting important and scarce supplies needing emergency scheduling, how to realize internal warehouse calling of the supplies, cross-city company calling and how to convey the supplies to the field in the shortest time at the fastest speed and the minimum cost in the first time is another important problem solved by the application. The method is based on the Dijkstra algorithm to optimize material distribution, achieves optimal material distribution with the least cost and the shortest time, and improves the use timeliness of materials. The shortest path from the source point to all the other points is finally obtained by finding a vertex closest to the source point each time and then expanding by taking the vertex as the center. The method mainly comprises the following steps:
s51, setting a mark number group book [ ]: the vertices of all paths (namely warehouse locations for goods and materials delivery) are divided into two parts, namely a vertex set P with a known shortest path and a vertex set Q with an unknown shortest path, and obviously, the initial set P only has one vertex of an active point. book [ i ] is 1 in set P;
wherein, the vertex of the content is the end of the path.
S52, setting the shortest path array dst [ ] and continuously updating: in the initial state, dst [ i ] ═ edge [ s ] [ i ] (s is the source point and edge is the adjacency matrix), where dst [ s ] ═ 0 and book [ s ] ═ 1. At this time, a vertex u closest to the source s in the set Q may be selected to be added to P. And performing a relaxation operation on each edge according to the new center point of u (the relaxation refers to that the node s- - > j passes through the point u on the way, and let dst [ j ] ═ min { dst [ j ], dst [ u ] + edge [ u ] [ j ] }, and let book [ u ] ═ 1;
s53, selecting a vertex v closest to the source point S from the set Q again, adding the vertex v to P, and performing a relaxation operation on each edge according to v as a new center point (i.e., dst [ j ] ═ min { dst [ j ], dst [ v ] + edge [ v ] [ j ] }), and making book [ v ] ═ 1;
and S54, repeating the step S53 until the set Q is empty, obtaining the warehouse with the least time cost, selecting the optimal route according to the warehouse transportation route, and finally improving the material use experience.
Example 2
The material 'checking, storing and matching' analysis device based on the big data comprises:
the storage module is used for acquiring and storing data;
the data includes: material list, material inventory list, equipment information, material information, inventory information, supplier information and bin information; item details, payment lists; material information, detection plans, warehouse information, bin position information, inventory information, unit information and department information;
the material list and the material inventory list are from an ERP system, the equipment information is from a PMS2.0 system, the material information, the inventory information, the supplier information and the position information are from an IWMS storage management system, the project detail and the payment list are from a financial management and control system, and the detection plan, the warehouse information, the position information, the unit information and the department information are from a self-built system.
The cleaning module is used for cleaning data based on a data cleaning method of the knowledge graph; further comprising:
s21, combing and summarizing the collected complete data by using a normal data processing flow;
and S22, searching and matching the 'bad data' with missing part information by using a preset database.
The system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring supplier evaluation information based on a supplier evaluation model of an entropy weight method; further comprising: s31, acquiring the proportion of the ith material sample value in the j index;
and S32, acquiring the comprehensive score of each goods and materials sample i based on the proportion of the ith goods and materials sample value in the jth index to the index.
As a further scheme of the invention: the step S31 includes: s311, normalizing the forward index by using a formula (1);
Figure RE-GDA0003274440740000171
wherein the cleaned data comprises n number of goods and materials objects to be evaluated, m number of evaluation indexes and xijThe value of the j index of the ith sample (i ═ l., n;. j ═ l., m).
Normalizing the negative direction index by using a formula (2);
Figure RE-GDA0003274440740000172
for n samples, m indices, xijThe value of the j index of the ith sample (i ═ l., n: ═ l., m);
s312, calculating the proportion of the ith material sample value in the j index by using a formula (3):
Figure BDA0003195740590000173
pijthe sample value of the ith material under the j index accounts for the proportion of the index.
As a further scheme of the invention: the step S32 includes: s321, calculating an entropy value of the j-th index by using a formula (4):
Figure BDA0003195740590000174
wherein k is 1/ln (n)>0, satisfies ej≥0；j＝1,2,3...,m；ejEntropy value of j index;
s322, obtaining information entropy redundancy (difference), and calculating the information entropy redundancy (difference) by using a formula (5):
dj＝1-ej (5)
djinformation entropy redundancy is obtained;
s323, acquiring the weight of each index j, and calculating the weight of each index j by using a formula (6):
Figure BDA0003195740590000181
wjis the weight of each index j;
s324, calculating the comprehensive score of each material sample i by using the formula (7):
Figure BDA0003195740590000182
wherein s isiAnd (4) representing the final score of the quality inspection of the ith material item.
The second acquisition module is used for acquiring the optimal bin to store materials; specifically, a systematic clustering method is adopted to obtain an optimal bin position to store materials.
The scheduling module for the dispatch goods and materials solves the ageing problem that the goods and materials used, still includes: s51, setting a mark number group book [ ]: dividing vertexes of all paths into two parts, namely a vertex set P with a known shortest path and a vertex set Q with an unknown shortest path, wherein the initial set P only has one vertex of a source point, and a book [ i ] is 1 and is represented in the set P;
wherein, the vertex of the content is the end of the path.
S52, setting the shortest path array dst [ ] and continuously updating: in an initial state, let dst [ i ] ═ edge [ s ] [ i ] (s is a source point, edge is an adjacency matrix), at this time, dst [ s ] ═ 0, book [ s ] ═ 1, at this time, a vertex u closest to the source point s in the set Q can be selected and added to P, and a relaxation operation is performed on each edge according to u as a new center point (the relaxation means that a point u can pass through the way of a node s- - > j, and let dst [ j ] ═ min { dst [ j ], dst [ u ] + edge [ u ] [ j ] }), and let book [ u ] ═ 1;
s53, selecting a vertex v closest to the source point S from the set Q again, adding the vertex v to P, and performing a relaxation operation on each edge according to v as a new center point (i.e., dst [ j ] ═ min { dst [ j ], dst [ v ] + edge [ v ] [ j ] }), and making book [ v ] ═ 1;
and S54, repeating the step S53 until the set Q is empty, obtaining a warehouse with the least time cost, selecting an optimal route according to a warehouse transportation route, and finally improving the material use experience.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the present invention as defined by the appended claims.

Claims (10)

1. A material 'checking, storing and matching' analysis method based on big data is characterized by comprising the following steps:
s1, acquiring and storing data;
s2, cleaning data based on the data cleaning method of the knowledge graph;
s3, acquiring supplier evaluation information by using a supplier evaluation model of an entropy weight method based on the cleaned data, wherein the supplier evaluation information comprises the following specific steps:
s31, obtaining the proportion of the ith material sample value in the j index, including:
s311, normalizing the forward index by using a formula (1);
Figure RE-FDA0003274440730000011
the cleaned data comprises n number of goods and materials objects to be evaluated and m number of evaluation indexes, wherein the evaluation indexes comprise positive indexes and negative indexes, and xijIs the value of the j index of the i sample (i ═ l., n;, j ═ l., m),
normalizing the negative direction index by using a formula (2);
Figure RE-FDA0003274440730000012
for n samples, m indices, xijThe value of the j index of the ith sample (i ═ l., n: ═ l., m);
s312, calculating the proportion of the ith material sample value in the j index by using a formula (3):
Figure RE-FDA0003274440730000013
pijthe sample value of the ith material under the jth index accounts for the proportion of the index;
s32, acquiring the comprehensive score of each material sample i based on the proportion of the ith material sample value in the jth index, wherein the comprehensive score comprises the following steps:
s321, calculating an entropy value of the j-th index by using a formula (4):
Figure RE-FDA0003274440730000021
wherein k is 1/ln (n)>0, satisfies ej≥0；j＝1,2,3...,m；ejEntropy value of j index;
s322, obtaining information entropy redundancy (difference), and calculating the information entropy redundancy (difference) by using a formula (5):
dj＝1-ej (5)
djinformation entropy redundancy is obtained;
s323, acquiring the weight of each index j, and calculating the weight of each index j by using a formula (6):
Figure RE-FDA0003274440730000022
wjis the weight of each index j;
s324, calculating the comprehensive score of each material sample i by using the formula (7):
Figure RE-FDA0003274440730000023
wherein s isiThe final score of the quality inspection of the ith material item is represented;
s4, acquiring an optimal bin to store materials;
and S5, scheduling the goods and materials based on the supplier evaluation information and the bin position information.
2. The big data-based material storage and distribution analysis method according to claim 1, wherein in step S1, the data includes:
material list, material inventory list, equipment information, material information, inventory information, supplier information and bin position information; item details, payment lists; material information, detection plan, warehouse information, bin position information, inventory information, unit information and department information.
3. The big data-based material storage and distribution analysis method according to claim 1, wherein the step S2 comprises:
s21, combing and summarizing the acquired complete data by using a normal data processing flow;
and S22, searching and matching the 'bad data' with missing part information by using a preset database.
4. The big data-based material storage and distribution analysis method according to claim 1, wherein in step S4, an optimal bin is obtained by a systematic clustering method to store the material.
5. The big data-based material storage and distribution analysis method according to claim 1, wherein the step S5 comprises:
s51, setting a mark number group book [ ]: dividing vertexes of all paths into two parts, namely a vertex set P with a known shortest path and a vertex set Q with an unknown shortest path, wherein the initial set P only has one vertex of a source point, and a book [ i ] is 1 and is represented in the set P;
wherein, the top point of the content is the tail end of the path;
s52, setting the shortest path array dst [ ] and continuously updating: in an initial state, let dst [ i ] ═ edge [ s ] [ i ], s be a source point, edge be an adjacency matrix, at this time, dst [ s ] ═ 0, book [ s ] ═ 1, at this time, a vertex u closest to the source point s in the set Q can be selected and added into P, and each edge is subjected to a relaxation operation according to u as a new center point, where the relaxation means that a point u can pass through on the way of a node s- - > j, and let dst [ j ] ═ min { dst [ j ], dst [ u ] + edge [ u ] [ j ] }), and let book [ u ] ═ 1;
s53, selecting a vertex v closest to the source point S from the set Q again, adding the vertex v into P, and performing a relaxation operation on each edge according to v as a new center point, that is, dst [ j ] ═ min { dst [ j ], dst [ v ] + edge [ v ] [ j ] }, and making book [ v ] ═ 1;
and S54, repeating the step S53 until the set Q is empty, obtaining a warehouse with the least time cost, selecting an optimal route according to a warehouse transportation route, and finally improving the material use experience.
6. The big data-based material checking, storing and matching analysis method according to claim 1, wherein the material list and the material inventory list are derived from an ERP system, the equipment information is derived from a PMS2.0 system, the material information, the inventory information, the supplier information and the position information are derived from an IWMS storage management system, the project details and the payment list are derived from a financial management and control system, and the detection plan, the warehouse information, the position information, the unit information and the department information are derived from a self-built system.
7. The big data-based material checking and storing analysis device as claimed in any one of claims 1 to 6, comprising:
the storage module is used for acquiring and storing data;
the cleaning module is used for cleaning data based on a data cleaning method of the knowledge graph;
the first obtaining module is used for obtaining supplier evaluation information based on a supplier evaluation model of an entropy weight method, and comprises the following steps:
the proportion obtaining unit of the ith material sample value occupying the j index executes the following operations:
normalizing the forward direction index by using a formula (1);
Figure RE-FDA0003274440730000041
the cleaned data comprises n number of goods and materials objects to be evaluated and m number of evaluation indexes, wherein the evaluation indexes comprise positive indexes and negative indexes, and xijIs the value of the j index of the i sample (i ═ l., n;, j ═ l., m),
normalizing the negative direction index by using a formula (2);
Figure RE-FDA0003274440730000042
for n samples, m indices, xijThe value of the j index of the ith sample (i ═ l., n: ═ l., m);
calculating the proportion of the ith material sample value in the j index by using a formula (3):
Figure RE-FDA0003274440730000051
pijthe sample value of the ith material under the jth index accounts for the proportion of the index;
the comprehensive score obtaining unit of each goods and materials sample i executes the following operations:
calculating the entropy value of the j index by using formula (4):
Figure RE-FDA0003274440730000052
wherein k is 1/ln (n)>0, satisfies ej≥0；j＝1,2,3...,m；ejEntropy value of j index;
obtaining information entropy redundancy (difference), and calculating the information entropy redundancy (difference) by using a formula (5):
dj＝1-ej (5)
djinformation entropy redundancy is obtained;
s323, acquiring the weight of each index j, and calculating the weight of each index j by using a formula (6):
Figure RE-FDA0003274440730000053
wjis the weight of each index j;
calculating the comprehensive score of each material item sample i by using the formula (7):
Figure RE-FDA0003274440730000054
wherein s isiThe final score of the quality inspection of the ith material item is represented;
the second acquisition module is used for acquiring the optimal bin to store materials;
and the scheduling module is used for scheduling the materials and solving the problem of timeliness of the use of the materials.
8. The big data-based material storage and distribution analysis device according to claim 7, wherein the cleaning module comprises:
a carding induction unit: combing and summarizing the collected complete data by using a normal data processing flow;
a retrieval matching unit: for 'bad data' with missing part information, searching and matching by using a preset database;
in the second acquisition module, the optimal bin position is acquired by adopting a system clustering method so as to store materials.
9. The big data-based material storage and distribution analysis device according to claim 7, wherein the scheduling module comprises:
a flag array setting unit: set number group book [ ]: dividing the vertexes of all paths into two parts, namely a vertex set P with a known shortest path and a vertex set Q with an unknown shortest path, wherein the initial set P only has one vertex of a source point, and a book [ i ] is 1 and is expressed in the set P, wherein the vertexes of the contents are the tail ends of the paths;
the shortest path array setting unit: setting a shortest path array dst [ ] and continuously updating: in an initial state, let dst [ i ] ═ edge [ s ] [ i ], s be a source point, edge be an adjacency matrix, at this time, dst [ s ] ═ 0, book [ s ] ═ 1, at this time, a vertex u closest to the source point s in the set Q can be selected and added into P, and each edge is subjected to a relaxation operation according to the new center point of u, the relaxation refers to passing through a point u from the way of the node s- - > j, and let dst [ j ] ═ min { dst [ j ], dst [ u ] + edge [ u ] [ j ] }), and let book [ u ] ═ 1;
a relaxation operation unit: selecting a vertex v nearest to the source point s from the set Q again to be added into P, and performing relaxation operation on each edge according to the fact that v is a new central point, namely dst [ j ] ═ min { dst [ j ], dst [ v ] + edge [ v ] [ j ] }, and enabling book [ v ] ═ 1;
an optimal route selection unit: and repeating the operation of the relaxation operation unit until the set Q is empty, obtaining the warehouse with the least time cost, selecting the optimal route according to the warehouse transportation route, and finally improving the material use experience.
10. The big-data-based material checking, storing and matching analysis device according to claim 7, wherein the material list and the material inventory list are derived from an ERP system, the equipment information is derived from a PMS2.0 system, the material information, the inventory information, the supplier information and the position information are derived from an IWMS storage management system, the project details and the payment inventory are derived from a financial management and control system, and the detection plan, the warehouse information, the position information, the unit information and the department information are derived from a self-built system.
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