CN112749237A - Personnel relationship construction and analysis method based on graph calculation - Google Patents
Personnel relationship construction and analysis method based on graph calculation Download PDFInfo
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- CN112749237A CN112749237A CN202011618324.3A CN202011618324A CN112749237A CN 112749237 A CN112749237 A CN 112749237A CN 202011618324 A CN202011618324 A CN 202011618324A CN 112749237 A CN112749237 A CN 112749237A
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- 238000004458 analytical method Methods 0.000 title claims abstract description 28
- 238000004364 calculation method Methods 0.000 title claims abstract description 13
- 238000010276 construction Methods 0.000 title claims abstract description 9
- 238000000034 method Methods 0.000 claims abstract description 18
- 230000002688 persistence Effects 0.000 claims description 7
- 230000000694 effects Effects 0.000 claims description 3
- 238000009960 carding Methods 0.000 abstract 1
- 238000005215 recombination Methods 0.000 abstract 1
- 230000006798 recombination Effects 0.000 abstract 1
- 238000013499 data model Methods 0.000 description 3
- 102100038367 Gremlin-1 Human genes 0.000 description 1
- 101001032872 Homo sapiens Gremlin-1 Proteins 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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- 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
- G06F16/288—Entity relationship models
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
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Abstract
The invention discloses a personnel relationship construction based on graph calculation, which comprises the steps of extracting relationship data, extracting finest granularity data of relationship information by integrating internal and external data, and storing the finest granularity data in a graph database; and the second step is the construction of a relation model, and the relationship recombination is realized by traversing the top points and the edges stored in the graph database through the carding relationship types to form the relation model. As mentioned in the background section, the relationship model is changed to "the target mobile phone contacts the mobile phone N days before and after the riding of the target mobile phone (default time), and the identity card of the owner of the mobile phone also subscribes to the secondary car". Due to the fact that the concept of the relational model is introduced, one or more relational models can be flexibly called in the relational analysis process, and different relational analysis results can be obtained by introducing different parameters.
Description
Technical Field
The invention relates to a personnel relationship map construction and analysis method, in particular to a personnel relationship construction and analysis method based on map calculation.
Background
The existing method for building and analyzing the relationship graph is generally extracted based on a fixed data model, and the following extraction mode for the relationship between people and vehicles based on the fixed data model is explained:
the first method is as follows: the target mobile phone contacts the mobile phone 3 days (default time) before and after the riding of the mobile phone, and the identity card of the mobile phone owner orders the secondary car;
the second method comprises the following steps: the person on the train who has the same origin as the target enters the same tourism with the target;
the third method comprises the following steps: a person who has sat the same trip (same flight) with the target within 3 months (default time) except this time;
the method is as follows: the fuzzy query mode is that the first six digits of the ID number of the personnel on the train number, which are the same as the target starting place, are the same as the target;
the fifth mode is as follows: fuzzy query mode, target near seat, same start and close time (e.g. within 10 minutes) of buying tickets.
At present, a method for extracting relationship data based on a fixed data model is a method for persisting relationship data and providing query service for the outside, and is difficult to support a complex and changeable relationship analysis scene. For example, in the first mode, when "3 days before and after riding" needs to be changed into "5 days before and after riding", the relational data needs to be re-extracted, which causes a large amount of relational operation and storage overhead; for another example, in the third embodiment, "within 3 months" is replaced with "within 1 month", there is also a problem that the relationship data needs to be regenerated.
Therefore, the present invention provides a method for constructing and analyzing a human relationship based on graph computation, so as to solve the problems in the background art.
Disclosure of Invention
Aiming at the defects of the existing method, the method utilizes the inherent advantages of a graph database in graph operation, converts the relation analysis into graph calculation, and realizes the real-time relation analysis based on a depth-first traversal algorithm and an breadth-first traversal algorithm, so that the relation data which needs to be presented finally is obtained through the graph calculation, and the problem of analysis limitation caused by the persistence of the relation data is solved.
In order to achieve the purpose, the invention provides the following technical scheme:
the scheme adopts hundredth open source HugeGraph as a Graph Database, the HugeGraph is an easy-to-use, efficient and universal open source Graph Database system (Graph Database, GitHub project address), an Apache TinkerPop3 framework and complete compatibility with Gremlin query language are realized, a complete tool chain assembly is provided, and a user is assisted in easily constructing applications and products based on the Graph Database.
The HugeGraph supports the rapid import of more than one billion vertexes and edges, provides an incidence relation query capability (OLTP) in a millisecond level, and can be integrated with a large data platform such as Hadoop, Spark and the like for offline analysis (OLAP).
A personnel relationship construction based on graph calculation comprises the following steps:
firstly, extracting relational data;
a) extracting basic information, track information, ticket information and the like of personnel from internal and external system databases through spark Sql, taking entities such as personnel, trains, hotels, mobile phone numbers and the like as vertexes, taking events or affiliations as sides, converting a relational database into a graph data file, and writing the graph data file into a distributed storage system HDFS;
b) graph data files are imported into graph databases by means of the HugeGraph-Loader tool provided by HugeGraph.
Secondly, constructing a relation model; a relation model is configured at the front end through Vue + ElementUI, and the persistence of the relation model is realized at the rear end through a persistence interface developed by SpringBoot + Vue + ElementUi.
An analysis method for a model constructed based on a person relationship of graph calculation:
a) constructing a display effect of the relation graph by using GoJS;
b) identity information of one or more persons can be input, and the relationship between persons can be inquired or analyzed according to the input conditions by automatically selecting depth traversal priority or breadth traversal priority;
c) and (3) supporting the expansion of the relationship, performing relationship expansion through the relationship model constructed in the step (II), selecting a plurality of different relationship models to form a combined analysis condition, and inputting different parameters in the analysis process to obtain different analysis results.
Compared with the prior art, the invention has the beneficial effects that:
1. the storage overhead and the data utilization rate are improved, and the relational data are generated in real time after the graph is calculated, so that more storage space does not need to be occupied again; the graph database stores the most fine-grained basic information and track information, different relational models can use the same graph data, and the repeated utilization rate of the data is high.
2. Due to the fact that the concept of the relational model is introduced, one or more relational models can be flexibly called in the relational analysis process, and different relational analysis results can be obtained by introducing different parameters.
3. The timeliness of the relation analysis is met, the relation analysis process is a real-time graph calculation process, the analysis result can be presented instantly, and the timeliness is high.
Drawings
FIG. 1 is a method for constructing and analyzing a human relationship based on graph calculation.
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.
As shown in fig. 1, a personnel relationship construction based on graph calculation includes the following steps:
firstly, extracting relational data;
a) extracting basic information, track information, ticket information and the like of personnel from internal and external system databases through spark Sql, taking entities such as personnel, trains, hotels, mobile phone numbers and the like as vertexes, taking events or affiliations as sides, converting a relational database into a graph data file, and writing the graph data file into a distributed storage system HDFS;
b) graph data files are imported into graph databases by means of the HugeGraph-Loader tool provided by HugeGraph.
Secondly, constructing a relation model; a relation model is configured at the front end through Vue + ElementUI, and the persistence of the relation model is realized at the rear end through a persistence interface developed by SpringBoot + Vue + ElementUi.
An analysis method for a model constructed based on a person relationship of graph calculation:
a) constructing a display effect of the relation graph by using GoJS;
b) identity information of one or more persons can be input, and the relationship between persons can be inquired or analyzed according to the input conditions by automatically selecting depth traversal priority or breadth traversal priority;
c) and (3) supporting the expansion of the relationship, performing relationship expansion through the relationship model constructed in the step (II), selecting a plurality of different relationship models to form a combined analysis condition, and inputting different parameters in the analysis process to obtain different analysis results.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (2)
1. A graph computation-based human relationship building, comprising the steps of:
firstly, extracting relational data;
a) extracting basic information, track information, ticket information and the like of personnel from internal and external system databases through spark Sql, taking entities such as personnel, trains, hotels, mobile phone numbers and the like as vertexes, taking events or affiliations as sides, converting a relational database into a graph data file, and writing the graph data file into a distributed storage system HDFS;
b) graph data files are imported into graph databases by means of the HugeGraph-Loader tool provided by HugeGraph.
Secondly, constructing a relation model; a relation model is configured at the front end through Vue + ElementUI, and the persistence of the relation model is realized at the rear end through a persistence interface developed by SpringBoot + Vue + ElementUi.
2. The analysis method for the personnel relationship construction based on the graph calculation is characterized by comprising the following steps:
a) constructing a display effect of the relation graph by using GoJS;
b) identity information of one or more persons can be input, and the relationship between persons can be inquired or analyzed according to the input conditions by automatically selecting depth traversal priority or breadth traversal priority;
c) and (3) supporting the expansion of the relationship, performing relationship expansion through the relationship model constructed in the step (II), selecting a plurality of different relationship models to form a combined analysis condition, and inputting different parameters in the analysis process to obtain different analysis results.
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Application publication date: 20210504 |