阅读说明：本技术 产业链识别方法及系统 (Industry chain identification method and system ) 是由 刘颖 邓飞飏 吴倩倩 聂宇达 兰舒 黄儒宁 于 2021-09-15 设计创作，主要内容包括：本发明提供一种产业链识别方法及系统,通过获取交易流水数据,对交易流水数据进行预处理,得到经营性交易流水数据和构建资金图谱的关键信息；基于经营性交易流水数据和关键信息构建资金图谱；基于资金图谱,利用关联挖掘算法挖掘资金图谱中满足支持度阈值的行业序列,得到行业频繁序列集；将产业关联度最高的k个行业序列作为成链序列,得到产业链图谱并展示。在本方案中,利用交易流水数据构建资金图谱,并利用关联挖掘算法挖掘资金图谱中满足支持度阈值的行业序列,将得到的产业关联度最高的k个行业序列作为成链序列,得到产业链图谱并展示,从而能够识别行业之间的关联程度,提高识别的完整性和充分性,减少识别时间以及提高通用性。(The invention provides an industrial chain identification method and system, which are characterized in that transaction flow data are acquired and preprocessed to obtain business transaction flow data and key information for constructing a fund map; constructing a fund map based on the operational transaction flow data and the key information; based on the fund map, utilizing an association mining algorithm to mine an industry sequence meeting a support degree threshold value in the fund map to obtain an industry frequent sequence set; and taking the k industry sequences with the highest industry association degree as chain forming sequences to obtain and display an industry chain map. In the scheme, the fund map is constructed by using transaction flow data, the industry sequences meeting the support degree threshold value in the fund map are mined by using an association mining algorithm, the obtained k industry sequences with the highest industry association degree are used as chain forming sequences, and the industry chain map is obtained and displayed, so that the association degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.)

1. An industrial chain identification method, characterized in that the method comprises:

acquiring transaction flow data, preprocessing the transaction flow data to obtain business transaction flow data and key information for constructing a fund map, wherein the key information at least comprises enterprises participating in transaction, transaction directions and industry categories of the enterprises participating in transaction;

constructing a fund map based on the operational transaction flow data and the key information;

based on the fund map, utilizing an association mining algorithm Prefix span to mine an industry sequence meeting a support degree threshold value in the fund map to obtain an industry frequent sequence set, wherein the industry sequence has industry association degree, and the industry association degree is the weight of the industry sequence on an industry chain;

and taking k industry sequences with the highest industry association degree as chaining sequences to obtain and display an industry chain map, wherein k is a positive integer.

2. The method of claim 1, wherein the obtaining transaction flow data, pre-processing the transaction flow data to obtain business transaction flow data and key information for constructing a fund map comprises:

acquiring transaction flow data, and performing word segmentation processing on the transaction flow data to obtain word segmentation results;

determining words representing the purposes in the word segmentation results based on the word segmentation results;

obtaining words representing the use in the transaction flow data of different types to obtain a word set representing the use;

and obtaining business transaction running data and key information for constructing a fund map based on the words with the characteristic use and the word set with the characteristic use in the word segmentation result.

3. The method of claim 1, wherein constructing a fund map based on the business transaction flow data and the key information comprises:

acquiring enterprise and fund outflow relation participating in the transaction in the business transaction pipelining data;

and constructing a directed fund map by taking the enterprises as nodes, taking the fund flow relation as a relation edge and taking the transaction stroke number and the amount as the attributes of the relation edge.

4. The method according to claim 1, wherein mining industry sequences in the fund map, which meet a support threshold, by using a correlation mining algorithm Prefix span based on the fund map to obtain an industry frequent sequence set comprises:

determining an industry chain core and an industry sequence according to the fund map;

taking each node on the fund map as a root node, performing full path search in the fund map, and acquiring fund unidirectional flow relation edges generated by the root node within preset fund transfer times in a preset period to obtain an industry path;

taking the minimum value of the actual fund flow of each hand on the industry path as the weight on the industry path, and obtaining the minimum edge weight on the industry path according to the weight on the industry path;

taking the minimum edge weight on the industry path as the weight multiple of the industry sequence to obtain the industry sequence with industry association degree;

mining an industry sequence which meets the support threshold value in the fund map in the industry sequence with the industry association degree based on a preset support threshold value and an association mining algorithm Prefix span to obtain a frequent industry sequence;

if the frequent industry sequences meet the requirement of displaying an industry chain map, outputting all the frequent industry sequences meeting the requirement of displaying the industry chain map to obtain an industry frequent sequence set;

if the frequent industry sequence does not meet the display requirement of the industry chain map, mining the industry sequence which meets the support threshold value in the fund map in the industry sequence with the industry association degree based on the set support threshold value and the association mining algorithm Prefix span again until the frequent industry sequence meets the display requirement of the industry chain map.

5. The method according to claim 4, wherein the mining of the industry sequences meeting the support threshold in the fund map in the industry sequences with industry association degrees based on a preset support threshold and an association mining algorithm Prefix span, to obtain frequent industry sequences, comprises:

sequencing the industry sequences with the industry association degrees by utilizing an association mining algorithm Prefix span according to the weight multiples of the industry sequences, and determining a prefix sequence and a suffix sequence in the industry sequences with the industry association degrees, wherein each industry sequence with the industry association degrees comprises a plurality of item sets, and each item set comprises 1 or more elements;

and converting the suffix sequence into the prefix sequence based on a recursive algorithm until the industry sequence with the industry association degree does not meet a support degree threshold value to obtain a frequent industry sequence.

6. The method according to claim 1, wherein the step of obtaining and displaying an industry chain map by taking the k industry sequences with the highest industry association degree as chain forming sequences comprises the following steps:

acquiring k industry sequences with the highest industry association degree in the industry frequent sequence set, and taking the k industry sequences with the highest industry association degree as chaining sequences;

performing reverse sequencing on the frequent industry sequences according to the industry association degree of each frequent industry sequence in the frequent industry sequence set to obtain a reverse sequencing result;

and taking the chain forming sequence as a main stem of an industrial chain map, and sequentially adding the frequent industrial sequence to the industrial chain map according to the inverted sorting result to obtain and display the industrial chain map.

7. An industrial chain identification system, the system comprising:

the system comprises a preprocessing module, a data processing module and a data processing module, wherein the preprocessing module is used for acquiring transaction flow data, preprocessing the transaction flow data to obtain business transaction flow data and key information for constructing a fund map, and the key information at least comprises enterprises participating in transaction, transaction directions and industry categories to which the enterprises participating in transaction belong;

a construction module for constructing a fund map based on the business transaction flow data and the key information;

the mining module is used for mining an industry sequence meeting a support degree threshold value in the fund map by utilizing an association mining algorithm Prefix span based on the fund map to obtain an industry frequent sequence set, wherein the industry sequence has industry association degree, and the industry association degree is the weight of the industry sequence on an industry chain;

and the obtaining module is used for taking the k industry sequences with the highest industry association degree as chaining sequences, obtaining and displaying an industry chain map, wherein k is a positive integer.

8. The system of claim 7, wherein the pre-processing module comprises:

the word segmentation processing unit is used for acquiring transaction flow data and performing word segmentation processing on the transaction flow data to obtain word segmentation results;

the determining unit is used for determining words representing purposes in the word segmentation results based on the word segmentation results;

the acquisition unit is used for acquiring words with characteristic purposes in the transaction flow data of different types to obtain a word set with characteristic purposes;

and the obtaining unit is used for obtaining the business transaction flow data and the key information for constructing the fund map based on the words with the characteristic use and the word set with the characteristic use in the word segmentation result.

9. The system of claim 7, wherein the building module comprises:

the acquiring unit is used for acquiring enterprises and fund outflow relations participating in the transaction in the business transaction running data;

and the construction unit is used for constructing a directed fund map by taking the enterprise as a node, taking the fund flow relation as a relation edge and taking the transaction stroke number and the amount as the attributes of the relation edge.

10. The system of claim 7, wherein the mining module comprises:

the determining unit is used for determining an industry chain core and an industry sequence according to the fund map;

the searching and acquiring unit is used for carrying out full path searching in the fund map by taking each node on the fund map as a root node, and acquiring fund unidirectional flow relation edges generated by the root node within preset fund transfer times in a preset period to obtain an industry path;

the obtaining unit is used for taking the minimum value of the actual fund flow of each hand on the industry path as the weight on the industry path and obtaining the minimum edge weight on the industry path according to the weight on the industry path; taking the minimum edge weight on the industry path as the weight multiple of the industry sequence to obtain the industry sequence with industry association degree;

the mining unit is used for mining an industry sequence which meets the support threshold value in the fund map in the industry sequence with the industry association degree based on a preset support threshold value and an association mining algorithm Prefix span to obtain a frequent industry sequence;

the first mining processing unit is used for outputting all frequent industry sequences meeting the display requirements of the industry chain map to obtain an industry frequent sequence set if the frequent industry sequences meet the display requirements of the industry chain map;

and the second mining processing unit is used for mining the industry sequence which meets the support threshold value in the fund map in the industry sequence with the industry association degree based on the set support threshold value and the association mining algorithm Prefix span again until the frequent industry sequence meets the display requirement of the industry chain map.

Technical Field

The invention relates to the technical field of transaction data processing, in particular to an industrial chain identification method and system.

Background

The industry chain is a relatively macroscopic concept describing the technical economic associations between industry sectors. There is a large amount of upstream and downstream relationships and value exchange in the industry chain. In order to understand the technical and economic association between industrial departments, the association between industries is usually outlined by means of industry reports, media information, enterprise visits, etc. to form an industrial chain.

However, this industrial chain identification method relies on manpower and investigation, and has strong subjective judgment, so that the identification of the industrial chain is not complete and sufficient, and the investigation and carding of the industrial chain also takes a long time, and it is difficult to obtain the industrial revolution data in time. In addition, an industry chain constructed through industry research lacks quantification of industry strength and weakness relation, and the association closeness degree between industries cannot be identified. Different mechanisms have different industry subdivision standards for constructing the industry chain, and different industry systems of the constructed industry chain are different and are implemented to a specific application level, and different industry systems cannot completely correspond to the division of the national standard industry, so that the universality is poor.

Therefore, the industrial chain identification method has the problems that the identification is incomplete, insufficient and long, and the association degree and universality between industries cannot be identified.

Disclosure of Invention

In view of this, embodiments of the present invention provide an industry chain identification method and system, so as to solve the problems of incomplete and insufficient identification, long time, incapability of identifying the association degree between industries, and poor universality existing in the existing industry chain identification method.

In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:

the first aspect of the embodiment of the invention discloses an industrial chain identification method, which comprises the following steps:

acquiring transaction flow data, preprocessing the transaction flow data to obtain business transaction flow data and key information for constructing a fund map, wherein the key information at least comprises enterprises participating in transaction, transaction directions and industry categories of the enterprises participating in transaction;

constructing a fund map based on the operational transaction flow data and the key information;

based on the fund map, utilizing an association mining algorithm Prefix span to mine an industry sequence meeting a support degree threshold value in the fund map to obtain an industry frequent sequence set, wherein the industry sequence has industry association degree, and the industry association degree is the weight of the industry sequence on an industry chain;

and taking k industry sequences with the highest industry association degree as chaining sequences to obtain and display an industry chain map, wherein k is a positive integer.

Optionally, the obtaining of the transaction running data, and the preprocessing of the transaction running data to obtain the business transaction running data and the key information for constructing the fund map include:

acquiring transaction flow data, and performing word segmentation processing on the transaction flow data to obtain word segmentation results;

determining words representing the purposes in the word segmentation results based on the word segmentation results;

obtaining words representing the use in the transaction flow data of different types to obtain a word set representing the use;

and obtaining business transaction running data and key information for constructing a fund map based on the words with the characteristic use and the word set with the characteristic use in the word segmentation result.

Optionally, the constructing a fund map based on the business transaction flow data and the key information includes:

acquiring enterprise and fund outflow relation participating in the transaction in the business transaction pipelining data;

and constructing a directed fund map by taking the enterprises as nodes, taking the fund flow relation as a relation edge and taking the transaction stroke number and the amount as the attributes of the relation edge.

Optionally, the mining, based on the fund map, an industry sequence meeting a support threshold in the fund map by using a correlation mining algorithm, namely PrefixSpan, to obtain an industry frequent sequence set, includes:

determining an industry chain core and an industry sequence according to the fund map;

taking each node on the fund map as a root node, performing full path search in the fund map, and acquiring fund unidirectional flow relation edges generated by the root node within preset fund transfer times in a preset period to obtain an industry path;

taking the minimum value of the actual fund flow of each hand on the industry path as the weight on the industry path, and obtaining the minimum edge weight on the industry path according to the weight on the industry path;

taking the minimum edge weight on the industry path as the weight multiple of the industry sequence to obtain the industry sequence with industry association degree;

mining an industry sequence which meets the support threshold value in the fund map in the industry sequence with the industry association degree based on a preset support threshold value and an association mining algorithm Prefix span to obtain a frequent industry sequence;

if the frequent industry sequences meet the requirement of displaying an industry chain map, outputting all the frequent industry sequences meeting the requirement of displaying the industry chain map to obtain an industry frequent sequence set;

if the frequent industry sequence does not meet the display requirement of the industry chain map, mining the industry sequence which meets the support threshold value in the fund map in the industry sequence with the industry association degree based on the set support threshold value and the association mining algorithm Prefix span again until the frequent industry sequence meets the display requirement of the industry chain map.

Optionally, the mining, in the industry sequence with industry association, an industry sequence that meets the support threshold in the fund map based on a preset support threshold and an association mining algorithm PrefixSpan to obtain a frequent industry sequence includes:

sequencing the industry sequences with the industry association degrees by utilizing an association mining algorithm Prefix span according to the weight multiples of the industry sequences, and determining a prefix sequence and a suffix sequence in the industry sequences with the industry association degrees, wherein each industry sequence with the industry association degrees comprises a plurality of item sets, and each item set comprises 1 or more elements;

and converting the suffix sequence into the prefix sequence based on a recursive algorithm until the industry sequence with the industry association degree does not meet a support degree threshold value to obtain a frequent industry sequence.

Optionally, the step of taking the k industry sequences with the highest industry association degree as chaining sequences to obtain and display an industry chain map includes:

acquiring k industry sequences with the highest industry association degree in the industry frequent sequence set, and taking the k industry sequences with the highest industry association degree as chaining sequences;

performing reverse sequencing on the frequent industry sequences according to the industry association degree of each frequent industry sequence in the frequent industry sequence set to obtain a reverse sequencing result;

and taking the chain forming sequence as a main stem of an industrial chain map, and sequentially adding the frequent industrial sequence to the industrial chain map according to the inverted sorting result to obtain and display the industrial chain map.

The second aspect of the embodiments of the present invention discloses an industry chain identification system, which includes:

the system comprises a preprocessing module, a data processing module and a data processing module, wherein the preprocessing module is used for acquiring transaction flow data, preprocessing the transaction flow data to obtain business transaction flow data and key information for constructing a fund map, and the key information at least comprises enterprises participating in transaction, transaction directions and industry categories to which the enterprises participating in transaction belong;

a construction module for constructing a fund map based on the business transaction flow data and the key information;

the mining module is used for mining an industry sequence meeting a support degree threshold value in the fund map by utilizing an association mining algorithm Prefix span based on the fund map to obtain an industry frequent sequence set, wherein the industry sequence has industry association degree, and the industry association degree is the weight of the industry sequence on an industry chain;

and the obtaining module is used for taking the k industry sequences with the highest industry association degree as chaining sequences, obtaining and displaying an industry chain map, wherein k is a positive integer.

Optionally, the preprocessing module includes:

the word segmentation processing unit is used for acquiring transaction flow data and performing word segmentation processing on the transaction flow data to obtain word segmentation results;

the determining unit is used for determining words representing purposes in the word segmentation results based on the word segmentation results;

the acquisition unit is used for acquiring words with characteristic purposes in the transaction flow data of different types to obtain a word set with characteristic purposes;

and the obtaining unit is used for obtaining the business transaction flow data and the key information for constructing the fund map based on the words with the characteristic use and the word set with the characteristic use in the word segmentation result.

Optionally, the building module includes:

the acquiring unit is used for acquiring enterprises and fund outflow relations participating in the transaction in the business transaction running data;

and the construction unit is used for constructing a directed fund map by taking the enterprise as a node, taking the fund flow relation as a relation edge and taking the transaction stroke number and the amount as the attributes of the relation edge.

Optionally, the excavation module includes:

the determining unit is used for determining an industry chain core and an industry sequence according to the fund map;

the searching and acquiring unit is used for carrying out full path searching in the fund map by taking each node on the fund map as a root node, and acquiring fund unidirectional flow relation edges generated by the root node within preset fund transfer times in a preset period to obtain an industry path;

the obtaining unit is used for taking the minimum value of the actual fund flow of each hand on the industry path as the weight on the industry path and obtaining the minimum edge weight on the industry path according to the weight on the industry path; taking the minimum edge weight on the industry path as the weight multiple of the industry sequence to obtain the industry sequence with industry association degree;

the mining unit is used for mining an industry sequence which meets the support threshold value in the fund map in the industry sequence with the industry association degree based on a preset support threshold value and an association mining algorithm Prefix span to obtain a frequent industry sequence;

the first mining processing unit is used for outputting all frequent industry sequences meeting the display requirements of the industry chain map to obtain an industry frequent sequence set if the frequent industry sequences meet the display requirements of the industry chain map;

and the second mining processing unit is used for mining the industry sequence which meets the support threshold value in the fund map in the industry sequence with the industry association degree based on the set support threshold value and the association mining algorithm Prefix span again until the frequent industry sequence meets the display requirement of the industry chain map.

Based on the above method and system for identifying an industrial chain provided by the embodiments of the present invention, the method includes: acquiring transaction flow data, preprocessing the transaction flow data to obtain business transaction flow data and key information for constructing a fund map, wherein the key information at least comprises enterprises participating in transaction, transaction directions and industry categories of the enterprises participating in transaction; constructing a fund map based on the operational transaction flow data and the key information; based on the fund map, utilizing an association mining algorithm Prefix span to mine an industry sequence meeting a support degree threshold value in the fund map to obtain an industry frequent sequence set, wherein the industry sequence has industry association degree, and the industry association degree is the weight of the industry sequence on an industry chain; and taking the k industry sequences with the highest industry association degree as chain forming sequences to obtain and display an industry chain map. In the scheme, the fund map is constructed by using transaction flow data, industry sequences meeting a support degree threshold value in the fund map are mined by using an association mining algorithm Prefix span, and the obtained k industry sequences with the highest industry association degree are used as chaining sequences to obtain and display the industry chain map, so that the association degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

Fig. 1 is a schematic flow chart illustrating an industrial chain identification method according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart of a pre-treatment process according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a process for constructing a fund map according to an embodiment of the present invention;

FIG. 4 is a diagram of an application scenario for constructing a fund map according to an embodiment of the present invention;

FIG. 5 is a schematic flow chart illustrating a process of mining an industry sequence satisfying a support threshold in a fund map according to an embodiment of the present invention;

FIG. 6 is a schematic flow chart illustrating a frequent industry sequence according to an embodiment of the present invention;

FIG. 7 is a schematic diagram of a process for obtaining an industry chain map according to an embodiment of the present invention;

fig. 8 is a schematic structural diagram of an industrial chain identification system according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein.

In order to facilitate understanding of the technical solution of the present invention, technical terms appearing in the present invention are explained:

chain nucleus: the core industry in the industry chain.

An industrial chain: the industrial chain is a concept of industrial economics, and is a chain type incidence relation form objectively formed based on certain technical and economic relations among all industrial departments and according to a specific logic relation and a space-time layout relation.

Prefix span (Prefix-Projected Pattern Growth, Pattern mining of Prefix projections) algorithm: frequent sequences meeting the minimum support can be mined.

The support degree is as follows: means that the number of times several associated data appear in the data set accounts for the weight of the total data set.

As is clear from the background art, the industrial chain identification using the conventional industrial chain identification method has problems of incomplete and insufficient identification, long time, and inability to identify the degree of association between industries and poor versatility.

In the scheme, the fund map is constructed by using transaction flow data, the industry sequences meeting the support threshold in the fund map are mined by using the association mining algorithm Prefix span, the obtained industry sequences with the highest industry association are used as chaining sequences, and the industry chain map is obtained and displayed, so that the association degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

As shown in fig. 1, a schematic flow chart of an industrial chain identification method according to an embodiment of the present invention is provided, and the method mainly includes the following steps:

step S101: and acquiring transaction flow data, and preprocessing the transaction flow data to obtain the business transaction flow data and the key information for constructing the fund map.

In step S101, the key information at least includes the business involved in the transaction, the transaction direction, and the business category to which the business involved in the transaction belongs.

The industry category of enterprises participating in trading uses the national economic industry Classification.

In the process of implementing step S101 specifically, when the fund transaction is performed between enterprises, a large amount of transaction flow data is generated, and the transaction flow data is obtained and integrated, and is preprocessed, so as to obtain the business transaction flow data and the key information of the fund map constructed by the enterprises participating in the transaction, the transaction directions, the industry categories to which the enterprises participating in the transaction belong, and the like.

Step S102: and constructing a fund map based on the operational transaction flow data and the key information.

In the specific implementation process of step S102, a fund map is constructed based on the operational transaction flow data and key information such as the enterprises participating in the transaction, the transaction directions, and the industry categories to which the enterprises participating in the transaction belong.

Step S103: and mining an industry sequence meeting a support degree threshold value in the fund map by using a correlation mining algorithm Prefix span based on the fund map to obtain an industry frequent sequence set.

In step S103, the industry sequence has an industry association degree, which is a weight of the industry sequence on the industry chain.

The threshold value of the degree of support may be set to 0.001 or 0.002, but the present invention is not limited thereto.

In the process of specifically implementing the step S103, a target industry to be mined is determined according to the fund map, an industry chain core and an industry sequence of the target industry are determined according to the target industry, a support threshold is set, and an industry sequence satisfying the set support threshold in the fund map is mined by using the associated mining algorithm PrefixSpan based on the set support threshold, so as to obtain an industry frequent sequence set.

Step S104: and taking the k industry sequences with the highest industry association degree as chain forming sequences to obtain and display an industry chain map.

Wherein k is a positive integer.

In step S104, an industry chain map may be presented using NetworkX.

In the process of specifically implementing the step S104, the k industry sequences with the highest industrial relevance in the obtained industry frequent sequence set are obtained, the k industry sequences with the highest industrial relevance are used as chaining sequences, and an industry chain map is obtained and input to NetworkX for displaying.

For example, the first 100 industry sequences of the industry association degree in the obtained industry frequent sequence set are obtained, the first 100 industry sequences of the industry association degree are used as chaining sequences, and an industry chain map is obtained and input into NetworkX for displaying.

According to the industrial chain identification method provided by the embodiment of the invention, the transaction flow data is obtained and preprocessed to obtain the business transaction flow data and the key information for constructing the fund map, wherein the key information at least comprises enterprises participating in transaction, transaction directions and industry categories to which the enterprises participating in transaction belong; constructing a fund map based on the operational transaction flow data and the key information; based on the fund map, utilizing an association mining algorithm Prefix span to mine an industry sequence meeting a support degree threshold value in the fund map to obtain an industry frequent sequence set, wherein the industry sequence has industry association degree, and the industry association degree is the weight of the industry sequence on an industry chain; and taking the k industry sequences with the highest industry association degree as chain forming sequences to obtain and display an industry chain map. In the scheme, the fund map is constructed by using transaction flow data, industry sequences meeting a support degree threshold value in the fund map are mined by using an association mining algorithm Prefix span, and the obtained k industry sequences with the highest industry association degree are used as chaining sequences to obtain and display the industry chain map, so that the association degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

Based on the industrial chain identification method provided by the embodiment of the invention, the step S101 is executed to acquire the transaction flow data, and the transaction flow data is preprocessed to obtain the business transaction flow data and the key information for constructing the fund map. As shown in fig. 2, a schematic flow chart for performing preprocessing according to an embodiment of the present invention mainly includes the following steps:

step S201: and acquiring transaction flow data, and performing word segmentation processing on the transaction flow data to obtain word segmentation results.

In step S201, Jieba word segmentation may be performed on the transaction running data, or other word segmentation tools may be used to perform word segmentation on the transaction running data, such as SnowNLP and THULAC, which is not limited in the present invention.

It should be noted that Jieba participle is a Ptyhon Chinese word component, and can perform operations such as participle, part of speech tagging, and keyword extraction on a Chinese text, and support a custom dictionary.

In the process of implementing step S201 specifically, when the fund transaction is performed between enterprises, a large amount of transaction flow data is generated, the transaction flow data is obtained, the Jieba is used to analyze transaction information such as transaction purpose and transaction remark in the transaction flow data, word segmentation is performed on unstructured fields in the transaction flow data, the unstructured data is converted into structured data, and a word segmentation result is obtained.

Step S202: and determining words representing the purposes in the word segmentation results based on the word segmentation results.

In the process of implementing step S202 specifically, words with characteristic usage in the word segmentation result are determined based on the word segmentation result, a fund usage rule base is constructed based on the words with characteristic usage, and the fund usage rule base is used to identify and judge the transaction purpose and the transaction opponent.

Step S203: and obtaining words with characteristic purposes in the transaction flow data of different types to obtain a word set with characteristic purposes.

In step S203, the types of transaction flow data include, but are not limited to, business transaction flow data, financing transaction flow data, investment flow data, and accounting adjustment transaction flow data.

In the process of implementing step S203, the words with characteristic use in different types of transaction flow data are stored in a set, the words with characteristic use are obtained, a word set with characteristic use is obtained,

step S204: and obtaining business transaction running data and key information for constructing a fund map based on the words with the characteristic use and the word set with the characteristic use in the word segmentation result.

In the process of specifically implementing the step S204, based on the words for characterizing use and the word set for characterizing use in the word segmentation result, the business transaction flow data, the financing transaction flow data, the investment transaction flow data, the financial adjustment transaction flow data, and the key information for constructing the fund map are obtained, the supply-demand relationship between industries is identified according to the fund transaction network, and the business transaction flow data is used as a data basis for constructing the fund map.

According to the industrial chain identification method provided by the embodiment of the invention, the transaction flow data is subjected to word segmentation processing, the types of the transaction flow data are distinguished by using the obtained word segmentation result, and the types of the transaction flow data are further determined, so that the association degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

Based on the industrial chain identification method provided by the embodiment of the invention, the process of constructing the fund map based on the business transaction flow data and the key information in the step S102 is executed. As shown in fig. 3, a schematic flow chart for constructing a fund map provided in an embodiment of the present invention mainly includes the following steps:

step S301: and acquiring enterprise and fund outflow relation participating in the transaction in the business transaction flow data.

In the process of implementing step S301 specifically, the enterprises participating in the transaction and the fund flow-out relationship between the enterprises in the fund transaction are obtained from the obtained business transaction flow data.

Step S302: and constructing a directed fund map by taking the enterprises as nodes, taking the fund outflow relation as a relation edge and taking the transaction stroke number and the amount as the attributes of the relation edge.

Note that the fund map is directional. For example, there are two businesses, A and B, each transaction between the two businesses is with the direction of A- > B or B- > A, and it can be determined whether the two businesses are bidirectional according to the actual data situation.

The funding map contains funding payment information for all industries that are available.

In the process of implementing step S302, the enterprises participating in the transaction are used as nodes of the fund map, the fund outflow relationship is used as the relationship edges of the fund map, and the transaction number and amount of money generated during the fund transaction between the enterprises are used as the attributes of the relationship edges of the fund map, so as to construct a directed fund map.

For example, as shown in fig. 4, an application scenario diagram for constructing a fund map is provided in an embodiment of the present invention.

In fig. 4, A, B, C, D and E are nodes of the fund map, i.e. the enterprises participating in the transaction, and the arrows represent the relationship edges of the fund map, i.e. the fund flow relationship between the enterprises participating in the transaction, such as: a- > B represents the fund outflow relation when the enterprise A and the enterprise B carry out fund transaction, in particular, the fund flows from the enterprise A to the enterprise B, the arrow is marked with "(stroke number, amount)", and the attribute of the relation edge of the fund map is represented, so that a directed fund map can be constructed based on the above contents.

According to the industrial chain identification method provided by the embodiment of the invention, the directed fund map is formed by constructing the nodes, the relation edges and the attributes of the relation edges of the fund map, so that the association degree between subsequent identification industries is guaranteed, the condition of low accuracy in weight calculation of a single transaction is avoided, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

Based on the industrial chain identification method provided by the embodiment of the invention, the step S103 is executed to mine the industry sequences meeting the support threshold in the fund map by using the associated mining algorithm Prefix span based on the fund map to obtain the process of industry frequent sequence set. As shown in fig. 5, a schematic flow chart for mining an industry sequence meeting a support threshold in a fund map according to an embodiment of the present invention mainly includes the following steps:

step S501: and determining the industry chain core and the industry sequence according to the fund map.

In the process of implementing the step S501 specifically, a target industry to be mined is determined according to the fund map, and an industry chain core and an industry sequence of the target industry are determined according to the target industry.

Step S502: and taking each node on the fund map as a root node, carrying out full path search in the fund map, and acquiring fund unidirectional flow relation edges generated by the root node within the preset fund transfer times in a preset period to obtain an industry path.

In step S502, the preset period is an observation period, which may be one year or half a year, and the present invention is not limited.

The number of transfers of funds refers to how many transfers the funds pass, i.e., the predetermined number of hands.

The number of fund transfer times can be 1-N hands or 2-N hands, and the invention is not limited.

In the process of implementing step S502 specifically, each node on the fund map is taken as a root node, a period is set, global full path search is performed in the fund map, and a fund unidirectional flow relationship edge occurring within a preset fund transfer number of the root node in the set period is obtained to obtain an industry path.

For example, as shown in fig. 4, a node B is used as a root node, a preset period is one year, and a preset fund transfer number is 2 hands, global full-path search is performed in a fund map, and fund unidirectional flow relation sides generated within 2 hands by the root node within one year are obtained to obtain fund unidirectional flow relation sides BD and DE, and an industry path is obtained according to the fund unidirectional flow relation sides.

Step S503: and taking the minimum value of the actual fund flow of each hand on the industry path as the weight on the industry path, and obtaining the minimum edge weight on the industry path according to the weight on the industry path.

In step S503, the fund flow is the number of strokes, i.e. the attribute of the relationship edge in fig. 4.

In the process of the specific implementation step S503, the actual fund flow of each hand on the industry path is obtained, the actual fund flow of each hand is compared to obtain the minimum actual fund flow of one hand, that is, the minimum value of the actual fund flow of one hand is obtained, the minimum value of the actual fund flow of each hand on the industry path is used as the weight on the industry path, and the division operation is performed on the actual fund flow of each hand on the industry path and the minimum value of the actual fund flow of each hand according to the weight on the industry path to obtain the minimum side weight on the industry path.

Step S504: and taking the minimum edge weight on the industry path as the weight multiple of the industry sequence to obtain the industry sequence with the industry association degree.

In the process of implementing step S504 specifically, the obtained minimum edge weight on the industry path is used as the weight multiple of the industry sequence, so as to obtain the industry sequence with the industry association degree.

Step S505: and mining an industry sequence meeting the support degree threshold value in the fund map in the industry sequence with the industry association degree based on a preset support degree threshold value and an association mining algorithm Prefix span to obtain a frequent industry sequence.

In the process of specifically implementing the step S505, a support threshold is preset, and based on the set support threshold, an industry sequence satisfying the set support threshold in the fund map is mined by using a correlation mining algorithm PrefixSpan, so as to obtain a frequent industry sequence.

Step S506: and judging whether the frequent industry sequences meet the requirement of displaying an industry chain map, if so, executing a step S507, and if not, executing a step S508.

In the process of specifically implementing the step S506, it is determined whether the frequent industry sequence meets the requirement of displaying the industry chain map, if so, it is indicated that frequent industry sequence mining is not required again, the step S507 is executed, otherwise, it is indicated that frequent industry sequence mining is required again, and the step S508 is executed.

Step S507: and outputting all frequent industry sequences meeting the display requirements of the industry chain map to obtain an industry frequent sequence set.

In the process of specifically implementing the step S507, it is determined that the obtained frequent industry sequences meet the industry chain map display requirements, the frequent industry sequences meeting the industry chain map display requirements are obtained, and all the frequent industry sequences meeting the industry chain map display requirements are output to obtain an industry frequent sequence set.

Step S508: and mining an industry sequence meeting the support degree threshold value in the fund map in the industry sequence with the industry association degree based on the set support degree threshold value and the association mining algorithm Prefix span again until the frequent industry sequence meets the display requirement of the industry chain map.

In the process of specifically implementing the step S508, it is determined that the mined frequent industry sequence does not meet the display requirement of the industry chain map, a new support threshold is reset, and based on the set new support threshold, the industry sequence meeting the set new support threshold in the fund map is mined by reusing the associated mining algorithm PrefixSpan, so as to obtain the frequent industry sequence.

And if the obtained frequent industry sequences meet the display requirements of the industry chain chart, carrying out the next operation.

And if the obtained frequent industry sequence does not accord with the display requirement of the industry chain map, continuing to execute the operation of mining the industry sequence meeting the support threshold in the fund map in the industry sequence with the industry association degree based on the set support threshold and the association mining algorithm Prefix span again until the obtained frequent industry sequence accords with the display requirement of the industry chain map.

For example, the currently obtained frequent industry sequence is < d > < db > < dc > < dcb >, and if the frequent industry sequence < d > < db > < dc > < dcb > is determined to meet the industry chain map display requirements, the frequent industry sequence < d > < db > < dc > < dcb > is output to obtain an industry frequent sequence set.

For another example, if the currently obtained frequent industry sequence is < b > < bc > < bd > < bdc >, and it is determined that the frequent industry sequence < b > < bc > < bd > < bdc > does not meet the industry chain map display requirements, the operation of mining the industry sequence meeting the support threshold in the fund map in the industry sequence with the industry association degree based on the set support threshold and the association mining algorithm PrefixSpan is continuously executed until the obtained frequent industry sequence meets the industry chain map display requirements.

Based on the industrial chain identification method provided by the embodiment of the invention, the established fund map is used, the association mining algorithm Prefix span is used for mining the industrial sequences meeting the support threshold in the fund map based on the set support threshold, the industrial frequent sequence set is obtained, and preparation is made for obtaining the industrial chain map spectrum subsequently, so that the association degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

Based on the industrial chain identification method provided by the embodiment of the invention, the step S505 is executed to mine the industry sequence meeting the support threshold in the fund map in the industry sequence with the industrial relevance based on the preset support threshold and the association mining algorithm PrefixSpan, so as to obtain the process of the frequent industry sequence. As shown in fig. 6, a schematic flow chart for constructing a frequent industry sequence according to an embodiment of the present invention mainly includes the following steps:

step S601: and sequencing the industry sequences with the industry association degree by using an association mining algorithm Prefix span according to the weight multiple of the industry sequences, and determining a prefix sequence and a suffix sequence in the industry sequences with the industry association degree.

In step S601, each industry sequence with industry association contains a plurality of item sets, each item set containing 1 or more elements.

In the process of specifically implementing the step S601, the industry sequences with industry association degrees are sorted by using the association mining algorithm PrefixSpan according to the obtained weight multiple of the industry sequences to obtain a sorting result, and a prefix sequence and a suffix sequence in the industry sequences with industry association degrees are determined according to the sorting result.

Step S602: and converting the suffix sequence into a prefix sequence based on a recursive algorithm until the industry sequence with the industry association degree does not meet a support degree threshold value to obtain a frequent industry sequence.

In the process of specifically implementing step S602, the suffix sequence is converted into a prefix sequence, in the conversion process, a first item set is extracted from the suffix sequence, the first item set is added to the prefix sequence, the prefix sequence is scanned from left to right, a second item set corresponding to elements in the first item set in the prefix sequence is determined, the second item set is changed, and according to a conversion rule in the conversion process, recursive processing is continued by using a recursive algorithm until the industry sequence with the industry association does not satisfy the support threshold, and a frequent industry sequence is obtained.

Based on the industrial chain identification method provided by the embodiment of the invention, the frequent industrial sequences are obtained by determining the prefix sequence and the suffix sequence in the industrial sequences with the industrial relevance and converting the suffix sequence into the prefix sequence, so that the preparation is made for subsequently obtaining the industrial chain graph spectrum, the relevance degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

Based on the industrial chain identification method provided by the embodiment of the present invention, step S104 is executed to use the k industrial sequences with the highest industrial relevance as chaining sequences, and obtain and display an industrial chain map. As shown in fig. 7, a schematic flow chart for obtaining an industry chain map according to an embodiment of the present invention mainly includes the following steps:

step S701: and acquiring k industry sequences with the highest industry association degree in the industry frequent sequence set, and taking the k industry sequences with the highest industry association degree as chaining sequences.

In the process of specifically implementing the step S701, k industry sequences with the highest industrial relevance in the obtained industry frequent sequence set are obtained, k industry sequences with the highest industrial relevance are obtained, and the k industry sequences with the highest industrial relevance are used as chaining sequences.

Step S702: and performing reverse sequencing on the frequent industry sequences according to the industry association degree of each frequent industry sequence in the frequent industry sequence set to obtain a reverse sequencing result.

In the process of implementing the step S702 specifically, the frequent industry sequences to be displayed in the industry chain graph are selected, and the frequent industry sequences are inversely ordered according to the industry association degree of each frequent industry sequence in the frequent industry sequence set, so as to obtain an inversely ordered result.

Step S703: and taking the chain sequence as a main stem of the industrial chain map, and sequentially adding the frequent industrial sequence to the industrial chain map according to the inverted sorting result to obtain and display the industrial chain map.

In step S703, an industry chain map may be presented using NetworkX.

In the process of specifically implementing the step S703, the chaining sequence is taken as a main stem of the industrial chain map, according to the inverted sorting result, the frequent industrial sequence with high industrial relevance is sequentially added to the industrial chain map to form a long chain, the frequent industrial sequence with low industrial relevance is sequentially added to the industrial chain map to form a short chain, whether the newly added short chain is included in the long chain is judged, if yes, the support threshold of the long chain is reduced, the support threshold of the short chain is updated, and the industrial chain map is obtained and is input to NetworkX for displaying.

It should be noted that, for example, under a set support threshold, there are two industry sequences a and B, < a, B > is frequent, and < B, a > is also frequent, and corresponding parameters are set in the construction function showing the industry chain map to support the determination of the upstream and downstream directions according to the size of the fund flow.

According to the industrial chain identification method provided by the embodiment of the invention, the k industrial sequences with the highest industrial relevance are used as chain forming sequences, and the processing of ring-shaped links in the industrial chain is avoided, so that the relevance degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

Corresponding to the industrial chain identification method shown in the embodiment of the present invention, an embodiment of the present invention further provides an industrial chain identification system, as shown in fig. 8, where the industrial chain identification system includes: a preprocessing module 81, a construction module 82, a mining module 83, and a get module 84.

The preprocessing module 81 is configured to obtain transaction flow data, preprocess the transaction flow data to obtain business transaction flow data and key information for constructing a fund map, where the key information at least includes an enterprise participating in a transaction, a transaction direction, and an industry category to which the enterprise participating in the transaction belongs.

A construction module 82 for constructing a fund map based on the business transaction flow data and the key information.

And the mining module 83 is configured to mine an industry sequence meeting a support threshold in the fund map by using an association mining algorithm PrefixSpan based on the fund map to obtain an industry frequent sequence set, where the industry sequence has an industry association degree, and the industry association degree is a weight of the industry sequence on an industry chain.

And an obtaining module 84, configured to use the k industry sequences with the highest industry association degree as chaining sequences, obtain and display an industry chain map, where k is a positive integer.

It should be noted that, the specific principle and the implementation process of each module or each unit in the industrial chain identification system disclosed in the embodiment of the present invention are the same as the industrial chain identification method implemented in the present invention, and reference may be made to corresponding parts in the industrial chain identification method disclosed in the embodiment of the present invention, which are not described herein again.

According to the industrial chain identification system provided by the embodiment of the invention, the transaction flow data is obtained and preprocessed to obtain the business transaction flow data and the key information for constructing the fund map, wherein the key information at least comprises enterprises participating in transaction, transaction directions and industry categories to which the enterprises participating in transaction belong; constructing a fund map based on the operational transaction flow data and the key information; based on the fund map, utilizing an association mining algorithm Prefix span to mine an industry sequence meeting a support degree threshold value in the fund map to obtain an industry frequent sequence set, wherein the industry sequence has industry association degree, and the industry association degree is the weight of the industry sequence on an industry chain; and taking the k industry sequences with the highest industry association degree as chain forming sequences to obtain and display an industry chain map. In the scheme, the fund map is constructed by using transaction flow data, industry sequences meeting a support degree threshold value in the fund map are mined by using an association mining algorithm Prefix span, and the obtained k industry sequences with the highest industry association degree are used as chaining sequences to obtain and display the industry chain map, so that the association degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

Optionally, based on the preprocessing module 81 shown in fig. 8, the preprocessing module 81 further includes: the word segmentation device comprises a word segmentation processing unit, a determining unit, an acquiring unit and an obtaining unit.

And the word segmentation processing unit is used for acquiring the transaction flow data and performing word segmentation processing on the transaction flow data to obtain word segmentation results.

And the determining unit is used for determining the words representing the purposes in the word segmentation results based on the word segmentation results.

And the acquisition unit is used for acquiring the words with the characteristic purposes in the different types of transaction flow data to obtain a word set with the characteristic purposes.

And the obtaining unit is used for obtaining the business transaction running data and the key information for constructing the fund map based on the words with the characteristic use and the word set with the characteristic use in the word segmentation result.

According to the industrial chain identification system provided by the embodiment of the invention, the transaction flow data is subjected to word segmentation processing, the types of the transaction flow data are distinguished by using the obtained word segmentation result, and the types of the transaction flow data are further determined, so that the association degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

Optionally, based on the building module 82 shown in fig. 8, the building module 82 further includes: an acquisition unit and a construction unit.

And the acquisition unit is used for acquiring the enterprise and fund outflow relation participating in the transaction in the business transaction flow data.

And the construction unit is used for constructing a directed fund map by taking the enterprise as a node, taking the fund outflow relation as a relation edge and taking the transaction stroke number and the amount as the attributes of the relation edge.

According to the industrial chain identification system provided by the embodiment of the invention, the directed fund map is formed by constructing the nodes, the relation edges and the attributes of the relation edges of the fund map, so that the association degree between subsequent identification industries is guaranteed, the condition of low accuracy in weight calculation of a single transaction is avoided, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

Optionally, based on the digging module 83 shown in fig. 8, the digging module 83 further includes: the device comprises a determining unit, a searching and acquiring unit, an obtaining unit, a mining unit, a first mining processing unit and a second mining processing unit.

And the determining unit is used for determining the industry chain core and the industry sequence according to the fund map.

And the searching and acquiring unit is used for carrying out full path searching in the fund map by taking each node on the fund map as a root node, and acquiring fund unidirectional flow relation edges generated by the root node within the transfer times of preset fund in a preset period to obtain an industry path.

The obtaining unit is used for taking the minimum value of the actual fund flow of each hand on the industry path as the weight on the industry path and obtaining the minimum edge weight on the industry path according to the weight on the industry path; and taking the minimum edge weight on the industry path as the weight multiple of the industry sequence to obtain the industry sequence with the industry association degree.

And the mining unit is used for mining the industry sequences meeting the support degree threshold value in the fund map in the industry sequences with the industry association degree based on the preset support degree threshold value and the associated mining algorithm Prefix span to obtain frequent industry sequences.

And the first mining processing unit is used for outputting all frequent industry sequences meeting the display requirements of the industry chain map to obtain an industry frequent sequence set if the frequent industry sequences meet the display requirements of the industry chain map.

And the second mining processing unit is used for mining the industry sequences meeting the support threshold value in the fund map in the industry sequences with the industry association degree based on the set support threshold value and the association mining algorithm Prefix span again until the frequent industry sequences meet the display requirements of the industry chain map.

Based on the industrial chain identification system provided by the embodiment of the invention, the established fund map is used, the association mining algorithm Prefix span is used for mining the industrial sequences meeting the support threshold in the fund map based on the set support threshold, the industrial frequent sequence set is obtained, and preparation is made for obtaining the industrial chain map spectrum subsequently, so that the association degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

Optionally, based on the excavation module 83 shown in fig. 8, the excavation unit is specifically configured to:

according to the weight multiple of the industry sequences, utilizing an association mining algorithm Prefix span to sequence the industry sequences with the industry association degree, and determining a prefix sequence and a suffix sequence in the industry sequences with the industry association degree, wherein each industry sequence with the industry association degree comprises a plurality of item sets, and each item set comprises 1 or more elements; and converting the suffix sequence into a prefix sequence based on a recursive algorithm until the industry sequence with the industry association degree does not meet a support degree threshold value to obtain a frequent industry sequence.

According to the industrial chain identification system provided by the embodiment of the invention, the prefix sequence and the suffix sequence in the industrial sequence with the industrial relevance are determined, the suffix sequence is converted into the prefix sequence, the frequent industrial sequence is obtained, and preparation is made for obtaining the industrial chain graph spectrum subsequently, so that the relevance degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is reduced, and the universality is improved.

Optionally, based on the obtaining module 84 shown in fig. 8, the obtaining module 84 is specifically configured to:

acquiring k industry sequences with the highest industry association degree in an industry frequent sequence set, and taking the k industry sequences with the highest industry association degree as chaining sequences; reversely ordering the frequent industry sequences according to the industry association degree of each frequent industry sequence in the frequent industry sequence set to obtain a reversely ordered result; and taking the chain sequence as a main stem of the industrial chain map, and sequentially adding the frequent industrial sequence to the industrial chain map according to the inverted sorting result to obtain and display the industrial chain map.

According to the industrial chain identification system provided by the embodiment of the invention, k industrial sequences with the highest industrial relevance are used as chaining sequences, so that a 'ring-shaped' link is avoided from appearing in an industrial chain, the relevance degree between industries can be identified, the integrity and the sufficiency of identification are improved, the identification time is shortened, and the universality is improved.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.