CN113850675A - Information processing method and device for enterprise transaction relation data - Google Patents
Information processing method and device for enterprise transaction relation data Download PDFInfo
- Publication number
- CN113850675A CN113850675A CN202010602342.6A CN202010602342A CN113850675A CN 113850675 A CN113850675 A CN 113850675A CN 202010602342 A CN202010602342 A CN 202010602342A CN 113850675 A CN113850675 A CN 113850675A
- Authority
- CN
- China
- Prior art keywords
- enterprise
- target
- enterprises
- information set
- business
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
Abstract
The present disclosure provides an information processing method and apparatus for enterprise transaction relationship data, the method comprising: responding to the enterprise transaction relation processing instruction, and acquiring a specified number of target enterprises according to the association degree of each enterprise; and dividing the target enterprises according to the obtained similarity between the target enterprises to obtain the enterprise information sets to which the target enterprises belong. Therefore, the enterprise information sets are divided according to the sequence of the association degrees, so that the division of the enterprise information sets is more accurate, and the problem of low accuracy of community division results in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to an information processing method and device for enterprise transaction relationship data.
Background
With the rapid development of the network, the community structure in the network reflects the distribution structure of the network, and the nodes are classified according to the structural information of the nodes in the network to form community groups. There are usually many intersections among members in the communities, and a member may have multiple identities in the network belonging to multiple communities at the same time, so that there is a phenomenon of overlapping communities. For example, in a business transaction relationship, different businesses may have a close transaction relationship with other businesses in different domains due to the particularity of the business, and thus the same business may belong to different business communities.
In order to effectively divide the enterprise, a typical method is to use a community discovery algorithm. The community discovery algorithm in the prior art divides communities, so that distinct communities are divided every time, the community division accuracy is low, and the community division algorithm is not beneficial to analyzing the divided communities in the follow-up process. Therefore, a new method is urgently needed to solve the above problems.
Disclosure of Invention
The invention provides an information processing method and device for enterprise transaction relation data, which are used for solving the problem of low community division accuracy in the prior art.
In a first aspect, the present disclosure provides an information processing method for enterprise transaction relationship data, the method comprising:
responding to the enterprise transaction relation processing instruction, and acquiring a specified number of target enterprises according to the association degree of each enterprise;
and dividing the target enterprises according to the obtained similarity between the target enterprises to obtain the enterprise information sets to which the target enterprises belong.
In an embodiment, the dividing the target enterprises according to the obtained similarity between the target enterprises to obtain the enterprise information sets to which the target enterprises belong includes:
determining enterprise similarity among the target enterprises according to the acquired transaction relationship of the target enterprises;
and adding the target enterprises with the enterprise similarity larger than a first designated value into the same enterprise information set, and dividing the target enterprises with the enterprise similarity smaller than the first designated value into different enterprise information sets.
In one embodiment, the determining the business similarity of each target business according to the obtained transaction relationship of each target business includes:
and if each target enterprise is each node in the directed graph and the transaction relationship of each target enterprise is a vector edge in the directed graph, determining the enterprise similarity of each target enterprise according to the cosine similarity and the betweenness ratio of the transaction relationship of each enterprise.
In one embodiment, the determining the business similarity of the target businesses according to the cosine similarity and the betweenness ratio of the transaction relationship of the businesses includes:
determining the business similarity according to the following formula:
wherein v isiRepresenting enterprises i, vjRepresents business j;
representing the cosine similarity of the enterprise i and the enterprise j, wherein delta is the betweenness proportion of the enterprise i and the enterprise j;
wherein, the medium ratio of the enterprise i and the enterprise j is determined according to the following formula:
wherein σ is the total number of shortest paths between any two enterprises in the directed graph; sigma (v)i,vj) The shortest path between any two enterprises in the directed graph includes the number of the shortest paths of enterprise i and enterprise j.
In one embodiment, determining the relevancy of the businesses includes:
the relevancy is in direct proportion to the ratio of the specified number of enterprises of an enterprise to the specified number of enterprises of the neighboring enterprises of the enterprise; wherein the specified number of businesses is the number of businesses that provide products to the target business;
and determining the degree of association according to the following formula:
wherein PR (i) is the association degree of the enterprise i, and N is the total number of the enterprises; d is a damping factor, adj (i) is a set formed by neighbor enterprises of the enterprise i; INi is the degree of entry of business i, which is the number of businesses that provide products to business i;
is the sum of the incomes of the neighbors of business j, where k is the k-th neighbor of business j, k is an element [1, m ]]。
In an embodiment, after the dividing the target enterprises according to the obtained similarity between the target enterprises to obtain the enterprise information sets to which the target enterprises belong, the method further includes:
acquiring enterprises outside the union of the enterprise information sets as to-be-processed enterprises;
and if the variable quantity of the transaction frequency between enterprises in the target enterprise information set is larger than a first specified value before and after the to-be-processed enterprise is merged into the target enterprise information set, merging the to-be-processed enterprise into the target enterprise information set.
In one embodiment, determining the transaction frequency comprises:
determining the transaction frequency according to the following formula:
wherein A represents a target enterprise that is merged into the enterprise information set S;
the sum of the number of connecting edges among all target enterprises in the enterprise information set S is represented;
representing the sum of the number of connecting edges between each target enterprise in the enterprise information set S and other enterprises except the enterprise information set S; α is a specified value; kinner(S, A) is the sum of the number of the target enterprise A and each target enterprise connecting edge in the enterprise information set S;
the sum of the out-degree and the in-degree of the target enterprise a is the out-degree, the number of enterprises purchasing the product from the target enterprise a, and the in-degree, the number of enterprises providing the product to the target enterprise a.
In one embodiment, after merging the pending business into the target business information set, the method further includes:
determining the contact ratio among the enterprise information sets;
and merging the enterprise information sets with the contact ratio larger than the second designated value.
In one embodiment, the determining the degree of overlap between the enterprise information sets comprises:
the contact ratio O is calculated according to the following formula:
wherein, C1,C2Respectively represent different enterprise information sets, min (| C)1|,|C2|) represents an enterprise information set C1And enterprise information set C2And the enterprise information set with less medium enterprises.
In one embodiment, before the obtaining the specified number of target businesses according to the association degree of each business in response to the business transaction relation processing instruction, the method further includes:
acquiring transaction data of each enterprise;
acquiring a knowledge graph constructed based on an enterprise transaction relation according to the enterprise transaction data, and storing the knowledge graph into a distributed graph database;
and carrying out distributed computation on the knowledge graph by using a spark component on a Hadoop platform of a distributed system infrastructure to obtain the transaction relationship of each enterprise supporting spark computation.
In a second aspect, the present disclosure provides an information processing apparatus for enterprise transaction relationship data, the apparatus comprising:
the target enterprise acquisition module is used for responding to the enterprise transaction relation processing instruction and acquiring a specified number of target enterprises according to the association degree of each enterprise;
and the enterprise information set determining module is used for dividing the target enterprises according to the acquired similarity between the target enterprises to obtain the enterprise information sets to which the target enterprises belong.
In one embodiment, the enterprise information set determination module is further configured to:
determining enterprise similarity among the target enterprises according to the acquired transaction relationship of the target enterprises;
and adding the target enterprises with the enterprise similarity larger than a first designated value into the same enterprise information set, and dividing the target enterprises with the enterprise similarity smaller than the first designated value into different enterprise information sets.
In one embodiment, the enterprise information set determination module is further configured to:
and if each target enterprise is each node in the directed graph and the transaction relationship of each target enterprise is a vector edge in the directed graph, determining the enterprise similarity of each target enterprise according to the cosine similarity and the betweenness ratio of the transaction relationship of each enterprise.
In one embodiment, the enterprise information set determination module is further configured to:
determining the business similarity according to the following formula:
wherein v isiRepresenting enterprises i, vjRepresents business j;
representing the cosine similarity of the enterprise i and the enterprise j, wherein delta is the betweenness proportion of the enterprise i and the enterprise j;
wherein, the medium ratio of the enterprise i and the enterprise j is determined according to the following formula:
wherein σ is the total number of shortest paths between any two enterprises in the directed graph; sigma (v)i,vj) The shortest path between any two enterprises in the directed graph includes the number of the shortest paths of enterprise i and enterprise j.
In one embodiment, the apparatus further comprises:
the enterprise association degree determining module is used for determining the association degree in direct proportion to the ratio of the specified enterprise number of the enterprise to the specified enterprise number of the neighboring enterprise of the enterprise; wherein the specified number of businesses is the number of businesses that provide products to the target business;
and determining the degree of association according to the following formula:
wherein PR (i) is the association degree of the enterprise i, and N is the total number of the enterprises; d is a damping factor, adj (i) is a set formed by neighbor enterprises of the enterprise i; INi is the degree of entry of business i, which is the number of businesses that provide products to business i;
is the sum of the incomes of the neighboring enterprises of enterprise j, where k is denoted as that of enterprise jThe k-th neighbor, k ∈ [1, m ]]。
In one embodiment, the apparatus further comprises:
the to-be-processed enterprise acquisition module is used for acquiring enterprises outside the union set of the enterprise information sets as to-be-processed enterprises after the target enterprises are divided according to the acquired similarity among the target enterprises to obtain the enterprise information sets to which the target enterprises belong;
and the merging module is used for merging the to-be-processed enterprise into the target enterprise information set if the variation of the transaction frequency between the enterprises in the target enterprise information set is larger than a first specified value before and after the to-be-processed enterprise is merged into the target enterprise information set.
In one embodiment, the apparatus further comprises:
a transaction frequency determination module for determining the transaction frequency according to the following formula:
wherein A represents a target enterprise that is merged into the enterprise information set S;
the sum of the number of connecting edges among all target enterprises in the enterprise information set S is represented;
representing the sum of the number of connecting edges between each target enterprise in the enterprise information set S and other enterprises except the enterprise information set S; α is a specified value; kinner(S, A) is the sum of the number of the target enterprise A and each target enterprise connecting edge in the enterprise information set S;
the sum of the out-degree and in-degree of the target enterprise a.
In one embodiment, the apparatus further comprises:
the contact ratio determining module is used for determining the contact ratio among the enterprise information sets after the to-be-processed enterprises are merged into the target enterprise information set;
and the merging module is used for merging the enterprise information sets with the contact ratio larger than the second specified value.
In an embodiment, the contact ratio determining module is specifically configured to:
the contact ratio O is calculated according to the following formula:
wherein, C1,C2Respectively represent different enterprise information sets, min (| C)1|,|C2|) represents an enterprise information set C1And enterprise information set C2And the enterprise information set with less medium enterprises.
In one embodiment, the apparatus further comprises:
the enterprise transaction data acquisition module is used for acquiring enterprise transaction data before acquiring a specified number of target enterprises according to the association degree of each enterprise in response to the enterprise transaction relation processing instruction;
the knowledge map construction module is used for obtaining a knowledge map constructed based on enterprise transaction relations according to the enterprise transaction data and storing the knowledge map into the distributed map database;
and the transaction relationship determining module is used for performing distributed calculation on the knowledge graph by using the spark component on a Hadoop platform of a distributed system infrastructure to obtain the transaction relationship of each enterprise supporting spark calculation.
According to a third aspect of embodiments of the present disclosure, there is provided an electronic device comprising at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
According to a fourth aspect provided by an embodiment of the present disclosure, there is provided a computer storage medium storing a computer program for executing the method according to the first aspect.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:
the present disclosure provides an information processing method and apparatus for enterprise transaction relationship data. The method comprises the following steps: responding to the enterprise transaction relation processing instruction, and acquiring a specified number of target enterprises according to the association degree of each enterprise; and dividing the target enterprises according to the obtained similarity between the target enterprises to obtain the enterprise information sets to which the target enterprises belong.
It should be noted that the enterprise information set in the present disclosure corresponds to a community in the background art, and each target enterprise corresponds to a core node in the background art. In the present disclosure, when selecting a target company, the selection is performed according to the relevance of each target company. Therefore, the enterprise information sets are divided according to the sequence of the association degrees, so that the division of the enterprise information sets is more accurate, and the problem of low accuracy of community division results in the prior art is solved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure and are not to be construed as limiting the disclosure.
FIG. 1 is a schematic diagram of a suitable scenario in accordance with an embodiment of the present disclosure;
FIG. 2 is one of a flow chart of an information processing method for enterprise transaction relationship data according to one embodiment of the present disclosure;
FIG. 3 is a second flow chart of an information processing method for enterprise transaction relationship data according to an embodiment of the present disclosure;
FIG. 4 is an information processing apparatus for enterprise transaction relationship data according to one embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
To further illustrate the technical solutions provided by the embodiments of the present disclosure, the following detailed description is made with reference to the accompanying drawings and the specific embodiments. Although the disclosed embodiments provide method steps as shown in the following embodiments or figures, more or fewer steps may be included in the method based on conventional or non-inventive efforts. In steps where no necessary causal relationship exists logically, the order of execution of the steps is not limited to that provided by the disclosed embodiments. The method can be executed in the order of the embodiments or the method shown in the drawings or in parallel in the actual process or the control device.
The term "plurality" in the embodiments of the present disclosure means two or more, and other terms are used similarly, it being understood that the preferred embodiments described herein are only for illustrating and explaining the present disclosure, and are not intended to limit the present disclosure, and that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
The inventor researches and discovers that in the existing community division method, the core nodes are randomly selected to divide the communities every time, so that the divided communities are quite different when the communities of enterprises are divided every time, the problem of low accuracy of community division is caused, and the divided communities are not easy to analyze subsequently. Therefore, the present disclosure proposes an information processing method and apparatus for enterprise transaction relationship data. The present disclosure is described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an applicable scenario in the embodiment of the present disclosure. The application scenario includes a terminal device 10 and a server 11, and fig. 1 illustrates one terminal device 10 as an example, and the number of terminal devices 10 is not limited in practice. The terminal device 10 and the server 11 can communicate with each other via a communication network. The terminal device 10 is, for example, a mobile phone, a tablet computer, a personal computer, or the like. The server 11 may be implemented by a single server or may be implemented by a plurality of servers. The server 11 may be implemented by a physical server or may be implemented by a virtual server.
In one possible application scenario, the server 11, in response to the business transaction relationship processing instruction, obtains a specified number of target businesses according to the association degree of each business. And then dividing the target enterprises according to the obtained similarity between the target enterprises to obtain enterprise information sets to which the target enterprises belong. And finally, sending each divided enterprise information set to the terminal equipment 10 for display.
In the embodiment of the application, the target enterprises are selected according to the sequence of the relevancy of the enterprises, so that the consistency of the result of community division each time is ensured, and the problem of low accuracy of community division caused by randomly selecting the core nodes to divide the community in the prior art is solved. The present disclosure will be described in detail below with reference to the accompanying drawings.
As shown in fig. 2, fig. 2 is a flowchart illustrating an information processing method for enterprise transaction relationship data, which may include the following steps:
step 201: responding to the enterprise transaction relation processing instruction, and acquiring a specified number of target enterprises according to the association degree of each enterprise;
for example, the target enterprises may be ranked in the order of decreasing enterprise relevance, and the specified number of target enterprises may be obtained in sequence according to the ranking order.
Step 202: and dividing the target enterprises according to the obtained similarity between the target enterprises to obtain the enterprise information sets to which the target enterprises belong.
Therefore, the enterprise information set division is realized by acquiring a specified number of target enterprises according to the association degree of each enterprise and taking each target enterprise as a core enterprise. Therefore, the problem that the accuracy of community division is low due to the fact that the core nodes are randomly selected to conduct the community division in the prior art is solved, and noisy enterprises can be filtered when the association degree is set to be low.
In order to obtain the transaction relationship of each enterprise and improve the operation efficiency of information processing of the enterprise transaction relationship data, before step 201 is executed, in one embodiment, each enterprise transaction data is obtained; acquiring a knowledge graph constructed based on an enterprise transaction relation according to the enterprise transaction data, and storing the knowledge graph into a distributed graph database; and carrying out distributed computation on the knowledge graph by using a spark component on a Hadoop platform of a distributed system infrastructure to obtain the transaction relationship of each enterprise supporting spark computation.
In the present disclosure, the transaction data of each company is acquired from the Hbase-based database. And the transaction data of each enterprise in the Hbase column database is obtained by analyzing each enterprise transaction file in the distributed file system HDFS.
Therefore, distributed computation is performed by using the Spark component on the Hadoop platform, the computation efficiency of the graph data can be effectively improved, distributed computation of enterprises can be rapidly completed, and the transaction relation among the enterprises can be obtained. And the operation efficiency of information processing of the enterprise transaction relationship data is improved.
To further improve the accuracy of enterprise information set partitioning, in one embodiment, the degree of overlap between enterprise information sets is determined; and merging the enterprise information sets with the contact ratio larger than the second designated value.
For example, the degree of overlap between business information set a and business information set B is 30%, and the degree of overlap between business information set B and business information set C is 60%. If the second specified threshold is 50%, then it is determined that the degree of overlap between the enterprise information set B and the enterprise information set C is greater than the second specified threshold. The enterprise information set B is merged with the enterprise information set C.
Therefore, the redundant communities are determined by determining the contact ratio among the communities and are combined, and the accuracy of social information set division is further improved.
In one embodiment, the degree of overlap O may be calculated according to equation (1):
wherein, C1,C2Respectively represent different enterprise information sets, min (| C)1|,|C2|) represents an enterprise information set C1And enterprise information set C2And the enterprise information set with less medium enterprises.
Thus, the contact degree of the enterprise information set can be determined through the formula.
In one embodiment, step 202 may be embodied as: determining enterprise similarity among the target enterprises according to the acquired transaction relationship of each target enterprise; and adding the target enterprises with the enterprise similarity larger than a first designated value into the same enterprise information set, and dividing the target enterprises with the enterprise similarity smaller than the first designated value into different enterprise information sets.
For example, the target businesses obtained include target business a, target business B, target business C, target business D, and target business E. The following two cases may be included:
(1) and if the similarity between the target enterprise A and the target enterprise B is greater than a first specified value and the similarity between the target enterprise B and the target enterprise C is also greater than the first specified value, adding the target enterprise A, the target enterprise B and the target enterprise C into the same enterprise information set. And if the similarity between the target enterprise D and the target enterprise F is smaller than the first specified value, adding the target enterprise D and the target enterprise F into different enterprise information sets.
(2) If the similarity between the target business A and the target business B is less than the first specified value. If the similarity between target business B and target business C is greater than the first specified value. Target business a and target business B are added to different sets of business information. And add target business B and target business C to the same business information set. And if the similarity between the target enterprise D and the target enterprise F is smaller than the first specified value, adding the target enterprise D and the target enterprise F into different enterprise information sets.
Therefore, the enterprise information set which needs to be added by each target enterprise is determined by determining the similarity among the target enterprises, so that the accuracy and the efficiency of enterprise information set division are improved.
In one embodiment, if each target enterprise is a node in a directed graph and the transaction relationship of each target enterprise is a vector edge in the directed graph, the enterprise similarity of each target enterprise is determined according to the cosine similarity and the betweenness ratio example of the transaction relationship of each enterprise.
Specifically, the enterprise similarity of each target enterprise can be determined according to the formula (2):
wherein v isiRepresenting enterprises i, vjRepresents business j;
representing the cosine similarity of the enterprise i and the enterprise j, wherein delta is the betweenness proportion of the enterprise i and the enterprise j;
wherein the betweenness ratio of the enterprise i and the enterprise j can be determined according to formula (3):
wherein σ is the total number of shortest paths between any two enterprises in the directed graph; sigma (v)i,vj) The shortest path between any two enterprises in the directed graph includes the number of the shortest paths of enterprise i and enterprise j.
Therefore, the enterprise similarity of each target enterprise can be determined according to the cosine similarity and the betweenness ratio of the transaction relationship of each enterprise, so that redundant enterprises are merged.
In one embodiment, the degree of association is proportional to a ratio of a specified number of businesses of an enterprise to a specified number of businesses of a neighboring enterprise of the enterprise; wherein the specified number of businesses is the number of businesses that provide products to the target business;
the degree of association may be determined according to equation (4):
wherein PR (i) is the association degree of the enterprise i, and N is the total number of the enterprises; d is a damping factor, adj (i) is a set formed by neighbor enterprises of the enterprise i; INi is the degree of entry of business i, which is the number of businesses that provide products to business i;
is the sum of the incomes of the neighbors of business j, where k is the k-th neighbor of business j, k is an element [1, m ]]I is included in the value range of k.
Therefore, the association degree of each enterprise can be determined through the formula so as to select a specified number of target enterprises.
In one embodiment, an enterprise outside the union of the enterprise information sets is obtained as a to-be-processed enterprise;
and if the variable quantity of the transaction frequency between the enterprises in the enterprise information set is larger than a first specified value before and after the to-be-processed enterprise is merged into the target enterprise information set, merging the to-be-processed enterprise into the target enterprise information set.
For example, before the to-be-processed enterprise a is merged into the target enterprise information set, the transaction frequency of the target enterprise information set is a, and after the to-be-processed enterprise a is merged into the target enterprise information set, the transaction frequency of the target enterprise information set is b. Determining that the variable quantity of the transaction frequency of the target enterprise is a-b, and if the a-b is larger than a first specified value, merging the enterprise A to be processed into the target enterprise information set.
Therefore, the to-be-processed enterprises which can be combined into the enterprise information set can be determined through the transaction frequency.
The determination of transaction frequency, in one embodiment, may be determined according to the determination of equation (5):
wherein A represents a target enterprise that is merged into the enterprise information set S;
the sum of the number of connecting edges among all target enterprises in the enterprise information set S is represented;
representing the sum of the number of connecting edges between each target enterprise in the enterprise information set S and other enterprises except the enterprise information set S; α is a specified value; kinner(S, A) is the sum of the number of the target enterprise A and each target enterprise connecting edge in the enterprise information set;
the sum of the out-degree and the in-degree of the target enterprise a is the out-degree, the number of enterprises purchasing the product from the target enterprise a, and the in-degree, the number of enterprises providing the product to the target enterprise a.
It should be noted that, the formula (5) is obtained by decomposing and simplifying the formula (6), where the formula (6) is:
the specific decomposition and simplification process can comprise the following steps:
to avoid redundancy due to the need to recalculate the transaction frequency of the enterprise information set after each addition of a pending enterprise, the numerator of equation (6) is reduced to equation (7):
wherein, Kin(S U v) represents the connecting edge between each target enterprise in the enterprise information set S after the target enterprise v is merged into the enterprise information set SThe sum of the amounts of (a) and (b).
The undirected, unauthorized graph represents the sum of the number of connecting edges between the target enterprise v and each target enterprise in the enterprise information set S.
And reduces the denominator of equation (6) to equation (8):
wherein, Kout(S £ v) represents the sum of the number of connecting edges between each target enterprise and other enterprises except the enterprise information set S in the enterprise information set S after the target enterprise v is merged into the enterprise information set S. Kouter(G-S, V) is expressed as the sum of the number of connecting edges of other businesses in the unlicensed undirected graph G, except for the target businesses in the business information set S, with the target business V.
The above equation (6) can be obtained by simplifying the equation (7) and the equation (8).
Therefore, the transaction frequency before and after the enterprise is added to the enterprise information set can be directly determined through the formula.
For further understanding of the technical solution of the present disclosure, the following detailed description with reference to fig. 3 may include the following steps:
step 301: acquiring transaction data of each enterprise;
step 302: acquiring a knowledge graph constructed based on an enterprise transaction relation according to the enterprise transaction data, and storing the knowledge graph into a distributed graph database;
step 303: carrying out distributed computation on the knowledge graph by using a spark component on a Hadoop platform of a distributed system infrastructure to obtain transaction relations of enterprises supporting spark computation;
step 304: responding to the enterprise transaction relation processing instruction, and acquiring a specified number of target enterprises according to the association degree of each enterprise;
step 305: determining enterprise similarity among the target enterprises according to the acquired transaction relationship of each target enterprise;
step 306: adding target enterprises with the enterprise similarity larger than a first designated value into the same enterprise information set, and dividing the target enterprises with the enterprise similarity smaller than the first designated value into different enterprise information sets;
step 307: sequencing the number of target enterprises in each enterprise information set according to a specified sequence, and sequentially executing the operation of dividing the enterprise information sets on each target enterprise information set according to the sequence;
step 308: determining the contact ratio among the enterprise information sets;
step 309: and merging the enterprise information sets with the contact ratio larger than the second designated value.
Based on the same inventive concept, an information processing method for business transaction relationship data as described above in the present disclosure can also be implemented by an information processing apparatus for business transaction relationship data. The effect of the device is similar to that of the method, and is not repeated herein.
Fig. 4 is a schematic structural diagram of an information processing apparatus for enterprise transaction relationship data according to an embodiment of the present disclosure.
As shown in fig. 4, the information processing apparatus 400 for enterprise transaction relationship data of the present disclosure may include a target enterprise acquisition module 410, and an enterprise information set determination module 420.
A target enterprise obtaining module 410, configured to, in response to the enterprise transaction relationship processing instruction, obtain a specified number of target enterprises according to the association degrees of the enterprises;
and the enterprise information set determining module 420 is configured to divide the target enterprises according to the obtained similarity between the target enterprises to obtain enterprise information sets to which the target enterprises belong.
In one embodiment, the enterprise information set determining module 420 is further configured to:
determining enterprise similarity among the target enterprises according to the acquired transaction relationship of the target enterprises;
and adding the target enterprises with the enterprise similarity larger than a first designated value into the same enterprise information set, and dividing the target enterprises with the enterprise similarity smaller than the first designated value into different enterprise information sets.
In one embodiment, the enterprise information set determining module 420 is further configured to:
and if each target enterprise is each node in the directed graph and the transaction relationship of each target enterprise is a vector edge in the directed graph, determining the enterprise similarity of each target enterprise according to the cosine similarity and the betweenness ratio of the transaction relationship of each enterprise.
In one embodiment, the enterprise information set determining module 420 is further configured to:
determining the business similarity according to the following formula:
wherein v isiRepresenting enterprises i, vjRepresents business j;
representing the cosine similarity of the enterprise i and the enterprise j, wherein delta is the betweenness proportion of the enterprise i and the enterprise j;
wherein, the medium ratio of the enterprise i and the enterprise j is determined according to the following formula:
wherein σ is the total number of shortest paths between any two enterprises in the directed graph; sigma (v)i,vj) The shortest path between any two enterprises in the directed graph includes the number of the shortest paths of enterprise i and enterprise j.
In one embodiment, the apparatus further comprises:
an enterprise association degree determination module 430, configured to determine the association degree in proportion to a ratio of a specified number of enterprises of an enterprise to a specified number of enterprises of neighboring enterprises of the enterprise; wherein the specified number of businesses is the number of businesses that provide products to the target business;
and determining the degree of association according to the following formula:
wherein PR (i) is the association degree of the enterprise i, and N is the total number of the enterprises; d is a damping factor, adj (i) is a set formed by neighbor enterprises of the enterprise i; INi is the degree of entry of business i, which is the number of businesses that provide products to business i;
is the sum of the incomes of the neighbors of business j, where k is the k-th neighbor of business j, k is an element [1, m ]]。
In one embodiment, the apparatus further comprises:
the to-be-processed enterprise obtaining module 440, configured to obtain, as the to-be-processed enterprise, an enterprise outside the union of the enterprise information sets after obtaining the enterprise information sets to which the target enterprises belong by dividing the target enterprises according to the obtained similarity between the target enterprises;
a merging module 450, configured to merge the to-be-processed enterprise into the target enterprise information set if a variation of the transaction frequency between enterprises in the target enterprise information set is greater than a first specified value before and after the to-be-processed enterprise is merged into the target enterprise information set.
In one embodiment, the apparatus further comprises:
a transaction frequency determination module 460 for determining the transaction frequency according to the following formula:
wherein A represents a target enterprise that is merged into the enterprise information set S;
the sum of the number of connecting edges among all target enterprises in the enterprise information set S is represented;
representing the sum of the number of connecting edges between each target enterprise in the enterprise information set S and other enterprises except the enterprise information set S; α is a specified value; kinner(S, A) is the sum of the number of the target enterprise A and each target enterprise connecting edge in the enterprise information set S;
the sum of the out-degree and in-degree of the target enterprise a.
In one embodiment, the apparatus further comprises:
a contact ratio determining module 470, configured to determine contact ratios among the enterprise information sets after merging the to-be-processed enterprise into the target enterprise information set;
and a merging module 480, configured to merge the enterprise information sets with the contact ratio greater than the second specified value.
In an embodiment, the contact ratio determining module 470 is specifically configured to:
the contact ratio O is calculated according to the following formula:
wherein, C1,C2Respectively represent different enterprise information sets, min (| C)1|,|C2|) represents an enterprise information set C1And enterprise information set C2And the enterprise information set with less medium enterprises.
In one embodiment, the apparatus further comprises:
the enterprise transaction data acquisition module 490 is configured to acquire enterprise transaction data before acquiring a specified number of target enterprises according to the association degrees of the enterprises in response to the enterprise transaction relationship processing instruction;
the knowledge graph building module 491 is used for obtaining a knowledge graph built based on the enterprise transaction relation according to the enterprise transaction data and storing the knowledge graph into the distributed graph database;
and the transaction relationship determination module 492 is used for performing distributed computation on the knowledge graph by using a spark component on a distributed system infrastructure Hadoop platform to obtain the transaction relationship of each enterprise supporting spark computation.
Having described a method and apparatus for information processing of business transaction relationship data according to an exemplary embodiment of the present application, an electronic device according to another exemplary embodiment of the present application is next described.
As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method or program product. Accordingly, various aspects of the present application may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
In some possible implementations, an electronic device in accordance with the present application may include at least one processor, and at least one computer storage medium. The computer storage medium stores program code, which when executed by a processor, causes the processor to perform the steps of the information processing method for enterprise transaction relationship data according to various exemplary embodiments of the present application described above in the present specification. For example, the processor may perform step 201 and 202 as shown in FIG. 2.
An electronic device 500 according to this embodiment of the present application is described below with reference to fig. 5. The electronic device 500 shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 5, the electronic device 500 is represented in the form of a general electronic device. The components of the electronic device 500 may include, but are not limited to: the at least one processor 501, the at least one computer storage medium 502, and the bus 503 connecting the various system components (including the computer storage medium 502 and the processor 501).
The computer storage media 502 may include readable media in the form of volatile computer storage media, such as random access computer storage media (RAM)521 and/or cache storage media 522, and may further include read-only computer storage media (ROM) 523.
The electronic device 500 may also communicate with one or more external devices 505 (e.g., keyboard, pointing device, etc.), with one or more devices that enable a user to interact with the electronic device 500, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 500 to communicate with one or more other electronic devices. Such communication may be through input/output (I/O) interfaces 505. Also, the electronic device 500 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 506. As shown, the network adapter 506 communicates with other modules for the electronic device 500 over the bus 503. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
In some possible embodiments, aspects of an information processing method for enterprise transaction relationship data provided by the present application may also be implemented in the form of a program product including program code for causing a computer device to perform the steps of an information processing method for enterprise transaction relationship data according to various exemplary embodiments of the present application described above in this specification when the program product is run on the computer device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable diskette, a hard disk, a random access computer storage media (RAM), a read-only computer storage media (ROM), an erasable programmable read-only computer storage media (EPROM or flash memory), an optical fiber, a portable compact disc read-only computer storage media (CD-ROM), an optical computer storage media piece, a magnetic computer storage media piece, or any suitable combination of the foregoing.
The program product for information processing of enterprise transaction relationship data of an embodiment of the present application may employ a portable compact disc read-only computer storage medium (CD-ROM) and include program code, and may be executable on an electronic device. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the consumer electronic device, partly on the consumer electronic device, as a stand-alone software package, partly on the consumer electronic device and partly on a remote electronic device, or entirely on the remote electronic device or server. In the case of remote electronic devices, the remote electronic devices may be connected to the consumer electronic device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external electronic device (e.g., through the internet using an internet service provider).
It should be noted that although several modules of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the modules described above may be embodied in one module according to embodiments of the application. Conversely, the features and functions of one module described above may be further divided into embodiments by a plurality of modules.
Further, while the operations of the methods of the present application are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk computer storage media, CD-ROMs, optical computer storage media, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable computer storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable computer storage medium produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (22)
1. An information processing method for enterprise transaction relationship data, the method comprising:
responding to the enterprise transaction relation processing instruction, and acquiring a specified number of target enterprises according to the association degree of each enterprise;
and dividing the target enterprises according to the obtained similarity between the target enterprises to obtain the enterprise information sets to which the target enterprises belong.
2. The method according to claim 1, wherein the dividing the target enterprises to obtain the enterprise information sets of the target enterprises according to the obtained similarities between the target enterprises comprises:
determining enterprise similarity among the target enterprises according to the acquired transaction relationship of the target enterprises;
and adding the target enterprises with the enterprise similarity larger than a first designated value into the same enterprise information set, and dividing the target enterprises with the enterprise similarity smaller than the first designated value into different enterprise information sets.
3. The method of claim 2, wherein determining the business similarity of each target business according to the obtained transaction relationship of each target business comprises:
and if each target enterprise is each node in the directed graph and the transaction relationship of each target enterprise is a vector edge in the directed graph, determining the enterprise similarity of each target enterprise according to the cosine similarity and the betweenness ratio of the transaction relationship of each enterprise.
4. The method of claim 3, wherein determining the business similarity of the target businesses according to the cosine similarity and the betweenness ratio of the transaction relationship of the businesses comprises:
determining the business similarity according to the following formula:
wherein v isiRepresenting enterprises i, vjRepresents business j;
representing the cosine similarity of the enterprise i and the enterprise j, wherein delta is the betweenness proportion of the enterprise i and the enterprise j;
wherein, the medium ratio of the enterprise i and the enterprise j is determined according to the following formula:
wherein σ is the total number of shortest paths between any two enterprises in the directed graph; sigma (v)i,vj) The shortest path between any two enterprises in the directed graph includes the number of the shortest paths of enterprise i and enterprise j.
5. The method of claim 1, wherein determining the relevancy of the businesses comprises:
the relevancy is in direct proportion to the ratio of the specified number of enterprises of an enterprise to the specified number of enterprises of the neighboring enterprises of the enterprise; wherein the specified number of businesses is the number of businesses that provide products to the target business;
and determining the degree of association according to the following formula:
wherein PR (i) is the association degree of the enterprise i, and N is the total number of the enterprises; d is a damping factor, adj (i) is a set formed by neighbor enterprises of the enterprise i; INi is the degree of entry of business i, which is the number of businesses that provide products to business i;
is the sum of the incomes of the neighbors of business j, where k is the k-th neighbor of business j, k is an element [1, m ]]。
6. The method according to claim 1, wherein after the dividing the target enterprises to obtain the enterprise information sets to which the target enterprises belong according to the obtained similarity between the target enterprises, the method further comprises:
acquiring enterprises outside the union of the enterprise information sets as to-be-processed enterprises;
and if the variable quantity of the transaction frequency between enterprises in the target enterprise information set is larger than a first specified value before and after the to-be-processed enterprise is merged into the target enterprise information set, merging the to-be-processed enterprise into the target enterprise information set.
7. The method of claim 6, wherein determining the transaction frequency comprises:
determining the transaction frequency according to the following formula:
wherein A represents a target enterprise that is merged into the enterprise information set S;
the sum of the number of connecting edges among all target enterprises in the enterprise information set S is represented;
representing the sum of the number of connecting edges between each target enterprise in the enterprise information set S and other enterprises except the enterprise information set S; α is a specified value; kinner(S, A) is the sum of the number of the target enterprise A and each target enterprise connecting edge in the enterprise information set S;
the sum of the out-degree and the in-degree of the target enterprise a is the out-degree, the number of enterprises purchasing the product from the target enterprise a, and the in-degree, the number of enterprises providing the product to the target enterprise a.
8. The method of claim 6, wherein after merging the pending business to the target business information set, further comprising:
determining the contact ratio among the enterprise information sets;
and merging the enterprise information sets with the contact ratio larger than the second designated value.
9. The method of claim 8, wherein determining a degree of overlap between the sets of business information comprises:
the contact ratio O is calculated according to the following formula:
wherein, C1,C2Respectively represent different enterprise information sets, min (| C)1|,|C2|) represents an enterprise information set C1And enterprise information set C2And the enterprise information set with less medium enterprises.
10. The method of claim 1, wherein the obtaining a specified number of target businesses based on the relevancy of each business in response to the business transaction relationship processing instructions further comprises:
acquiring transaction data of each enterprise;
acquiring a knowledge graph constructed based on an enterprise transaction relation according to the enterprise transaction data, and storing the knowledge graph into a distributed graph database;
and carrying out distributed computation on the knowledge graph by using a spark component on a Hadoop platform of a distributed system infrastructure to obtain the transaction relationship of each enterprise supporting spark computation.
11. An information processing apparatus for enterprise transaction relationship data, the apparatus comprising:
the target enterprise acquisition module is used for responding to the enterprise transaction relation processing instruction and acquiring a specified number of target enterprises according to the association degree of each enterprise;
and the enterprise information set determining module is used for dividing the target enterprises according to the acquired similarity between the target enterprises to obtain the enterprise information sets to which the target enterprises belong.
12. The apparatus of claim 11, wherein the enterprise information set determining module is further configured to:
determining enterprise similarity among the target enterprises according to the acquired transaction relationship of the target enterprises;
and adding the target enterprises with the enterprise similarity larger than a first designated value into the same enterprise information set, and dividing the target enterprises with the enterprise similarity smaller than the first designated value into different enterprise information sets.
13. The apparatus of claim 12, wherein the enterprise information set determining module is further configured to:
and if each target enterprise is each node in the directed graph and the transaction relationship of each target enterprise is a vector edge in the directed graph, determining the enterprise similarity of each target enterprise according to the cosine similarity and the betweenness ratio of the transaction relationship of each enterprise.
14. The apparatus of claim 13, wherein the enterprise information set determining module is further configured to:
determining the business similarity according to the following formula:
wherein v isiRepresenting enterprises i, vjRepresents business j;
representing the cosine similarity of the enterprise i and the enterprise j, wherein delta is the betweenness proportion of the enterprise i and the enterprise j;
wherein, the medium ratio of the enterprise i and the enterprise j is determined according to the following formula:
wherein σ is the total number of shortest paths between any two enterprises in the directed graph; sigma (v)i,vj) The shortest path between any two enterprises in the directed graph includes the number of the shortest paths of enterprise i and enterprise j.
15. The apparatus of claim 11, further comprising:
the enterprise association degree determining module is used for determining the association degree in direct proportion to the ratio of the specified enterprise number of the enterprise to the specified enterprise number of the neighboring enterprise of the enterprise; wherein the specified number of businesses is the number of businesses that provide products to the target business;
and determining the degree of association according to the following formula:
wherein PR (i) is the association degree of the enterprise i, and N is the total number of the enterprises; d is a damping factor, adj (i) is a set formed by neighbor enterprises of the enterprise i; INi is the degree of entry of business i, which is the number of businesses that provide products to business i;
is the sum of the incomes of the neighbors of business j, where k is the k-th neighbor of business j, k is an element [1, m ]]。
16. The apparatus of claim 11, further comprising:
the to-be-processed enterprise acquisition module is used for acquiring enterprises outside the union set of the enterprise information sets as to-be-processed enterprises after the target enterprises are divided according to the acquired similarity among the target enterprises to obtain the enterprise information sets to which the target enterprises belong;
and the merging module is used for merging the to-be-processed enterprise into the target enterprise information set if the variation of the transaction frequency between the enterprises in the target enterprise information set is larger than a first specified value before and after the to-be-processed enterprise is merged into the target enterprise information set.
17. The apparatus of claim 16, further comprising:
a transaction frequency determination module for determining the transaction frequency according to the following formula:
wherein A represents a target enterprise that is merged into the enterprise information set S;
representing a Business information set SThe sum of the number of connecting edges among all the target enterprises inside;
representing the sum of the number of connecting edges between each target enterprise in the enterprise information set S and other enterprises except the enterprise information set S; α is a specified value; kinner(S, A) is the sum of the number of the target enterprise A and each target enterprise connecting edge in the enterprise information set S;
the sum of the out-degree and in-degree of the target enterprise a.
18. The apparatus of claim 16, further comprising:
the contact ratio determining module is used for determining the contact ratio among the enterprise information sets after the to-be-processed enterprises are merged into the target enterprise information set;
and the merging module is used for merging the enterprise information sets with the contact ratio larger than the second specified value.
19. The apparatus according to claim 18, wherein the contact ratio determination module is specifically configured to:
the contact ratio O is calculated according to the following formula:
wherein, C1,C2Respectively represent different enterprise information sets, min (| C)1|,|C2|) represents an enterprise information set C1And enterprise information set C2And the enterprise information set with less medium enterprises.
20. The apparatus of claim 11, further comprising:
the enterprise transaction data acquisition module is used for acquiring enterprise transaction data before acquiring a specified number of target enterprises according to the association degree of each enterprise in response to the enterprise transaction relation processing instruction;
the knowledge map construction module is used for obtaining a knowledge map constructed based on enterprise transaction relations according to the enterprise transaction data and storing the knowledge map into the distributed map database;
and the transaction relationship determining module is used for performing distributed calculation on the knowledge graph by using the spark component on a Hadoop platform of a distributed system infrastructure to obtain the transaction relationship of each enterprise supporting spark calculation.
21. An electronic device comprising at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor; the instructions are executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.
22. A computer storage medium, characterized in that the computer storage medium stores a computer program for performing the method according to any one of claims 1-10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010602342.6A CN113850675A (en) | 2020-06-28 | 2020-06-28 | Information processing method and device for enterprise transaction relation data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010602342.6A CN113850675A (en) | 2020-06-28 | 2020-06-28 | Information processing method and device for enterprise transaction relation data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113850675A true CN113850675A (en) | 2021-12-28 |
Family
ID=78972661
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010602342.6A Pending CN113850675A (en) | 2020-06-28 | 2020-06-28 | Information processing method and device for enterprise transaction relation data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113850675A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114615311A (en) * | 2022-03-03 | 2022-06-10 | 平安国际融资租赁有限公司 | Enterprise information processing method, device, equipment and storage medium |
| CN115934963A (en) * | 2022-12-26 | 2023-04-07 | 深度(山东)数字科技集团有限公司 | Business draft big data analysis method and application map for enterprise financial customer acquisition |
-
2020
- 2020-06-28 CN CN202010602342.6A patent/CN113850675A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114615311A (en) * | 2022-03-03 | 2022-06-10 | 平安国际融资租赁有限公司 | Enterprise information processing method, device, equipment and storage medium |
| CN114615311B (en) * | 2022-03-03 | 2024-02-13 | 平安国际融资租赁有限公司 | Enterprise information processing method, device, equipment and storage medium |
| CN115934963A (en) * | 2022-12-26 | 2023-04-07 | 深度(山东)数字科技集团有限公司 | Business draft big data analysis method and application map for enterprise financial customer acquisition |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9870270B2 (en) | Realizing graph processing based on the mapreduce architecture | |
| Kuosmanen et al. | Weak disposability in nonparametric production analysis: reply to Färe and Grosskopf | |
| US20130268913A1 (en) | Managing application template artifacts in a networked computing environment | |
| US10656934B2 (en) | Efficient software testing | |
| CN114357105B (en) | Pre-training method and model fine-tuning method of geographic pre-training model | |
| US11386507B2 (en) | Tensor-based predictions from analysis of time-varying graphs | |
| CN113850675A (en) | Information processing method and device for enterprise transaction relation data | |
| CN116090808A (en) | RPA breakpoint reconstruction method and device, electronic equipment and medium | |
| CN114116065A (en) | Method and device for acquiring topological graph data object and electronic equipment | |
| CN112364185B (en) | Method and device for determining characteristics of multimedia resources, electronic equipment and storage medium | |
| US11212162B2 (en) | Bayesian-based event grouping | |
| US20120041736A1 (en) | System and method for analyzing data | |
| CN115827028A (en) | System upgrading method and device, storage medium and electronic equipment | |
| US20230004750A1 (en) | Abnormal log event detection and prediction | |
| US20230177385A1 (en) | Federated machine learning based on partially secured spatio-temporal data | |
| CN114329058A (en) | Image gathering method and device and electronic equipment | |
| US20130173636A1 (en) | Determining a score for a product based on a location of the product | |
| CN114679468B (en) | File transmission method and device, electronic equipment and computer readable storage medium | |
| CN115658749B (en) | Fund product ordering method and device based on directed acyclic graph and electronic equipment | |
| US20230185791A1 (en) | Prioritized data cleaning | |
| US11853750B2 (en) | Subject matter expert identification and code analysis based on a probabilistic filter | |
| CN117056663B (en) | Data processing method and device, electronic equipment and storage medium | |
| CN112667402B (en) | Method and device for distributively calculating similarity of SimRank single-source nodes | |
| US11340897B1 (en) | Subject matter expert identification for computer software | |
| US20230168884A1 (en) | Extracting entity relationship diagrams from source code |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |