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Freight supervision abnormal information collection method and device, server and storage medium

A technology of abnormal information and acquisition methods, which is applied in the direction of instruments, relational databases, data processing applications, etc., can solve problems such as ignoring relationships, affecting the mining of abnormal behavior patterns of enterprises, and appearing meaningless, achieving high efficiency and intuitive and accurate results. sex high effect

Pending Publication Date: 2020-11-13
中华人民共和国深圳海关 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Although there are many extended models based on frequent pattern mining, they often ignore the relationship between entities in frequent itemsets.
Taking the mining of freight supervision behavior patterns as an example, in the mining of association rules based on frequent itemsets, there will be multiple highly correlated companies in a frequent itemset, and their appearance is meaningless. relationships between businesses, businesses and other entities
In addition, the phenomenon that multiple entities of the same type appear in the same frequent itemset will affect the mining of abnormal behavior patterns of the final enterprise

Method used

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  • Freight supervision abnormal information collection method and device, server and storage medium
  • Freight supervision abnormal information collection method and device, server and storage medium
  • Freight supervision abnormal information collection method and device, server and storage medium

Examples

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Embodiment 1

[0032] figure 1 It is a flow chart of the method for obtaining freight supervision abnormal information in Embodiment 1 of the present invention. The method can be executed by a terminal or a server. In this embodiment, a terminal is taken as an example. The method specifically includes:

[0033] S110, extracting triplets based on freight supervision data to generate a knowledge network graph.

[0034] Freight supervision data includes text data and table data recorded in the supervision of the cargo transportation process. Triplets refer to data sets in the form of "entity A-[relation R]->entity B" and "entity A-attribute category-attribute value", which are suitable for mining relational networks, where "entity A-[relation R] ]->Entity B" indicates the relationship between entities, and "Entity A-Attribute Category-Attribute Value" indicates the attribute of a single entity. The knowledge network graph is a graph structure that represents the entity attributes and the rela...

Embodiment 2

[0047] This embodiment further supplements some content on the basis of Embodiment 1 to further explain some steps, specifically including:

[0048] Such as figure 2 As shown, step S110 specifically includes steps S111-112:

[0049] S111. Extract entities based on freight supervision data, and create nodes according to the entities.

[0050] In this embodiment, the data stored in the knowledge network map is the NEO4J graph database. For the initial full amount of data, NEO4J-impot will be used to import the node attribute and association relationship triplet data using csv. Each column of the node attribute class csv contains all the attribute types of the entity that need to be imported into NEO4J. The first column has a mark like ":ID(Entity)", ID represents the primary key of the column, and the value of the Entity in the brackets is the The entity name of the class entity. The csv file of the association relationship type contains at least two columns, which need to c...

Embodiment 3

[0077] Figure 6 The device 300 for obtaining freight supervision abnormality information provided by Embodiment 3 of the present invention specifically includes the following modules:

[0078]A data extraction module 310, configured to extract triples based on freight supervision data to generate a knowledge network map;

[0079] The subgraph query module 320 is used to obtain corresponding multiple subgraph query results through multiple preset subgraph query models based on the knowledge network map, and each subgraph query result includes one or more subgraphs;

[0080] A support calculation module 330, configured to determine the support of each subgraph query result;

[0081] A subgraph query result screening module 340, configured to determine that the subgraph query result whose support degree is greater than a support threshold is a target subgraph query result;

[0082] The abnormal information determination module 350 is configured to determine the confidence leve...

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Abstract

The invention discloses a freight supervision abnormal information collection method and device, a server and a storage medium. The method comprises the steps that triples are extracted based on freight supervision data to generate a knowledge network graph; obtaining a plurality of corresponding sub-graph query results through a plurality of preset sub-graph query models based on the knowledge network graph; determining the support degree of each sub-graph query result; determining the sub-graph query result with the support degree greater than a support degree threshold as a target sub-graphquery result; and determining a confidence coefficient of a preset association rule according to the target sub-graph query result, and determining abnormal information of the freight supervision process based on the confidence coefficient and the preset association rule. Compared with a traditional method, the method pays more attention to the association relationship between entities, each preset association rule has practical significance in a specific behavior mode, two entities without the practical association relationship cannot be mined, the efficiency and the accuracy are higher, andmeanwhile, the result is displayed more visually by utilizing a calculation mode of a graph structure.

Description

technical field [0001] The present invention relates to the technical field of data processing, and in particular to a method, device, server and storage medium for acquiring abnormal freight supervision information. Background technique [0002] As an important unit of the country's foreign trade import and export, the customs has the responsibility to supervise the whole process of import, export and transportation of goods. What we need to pay attention to is whether there is any internal and external collusion in the whole process of customs freight supervision. [0003] Pattern mining based on frequent itemsets is an important method in the field of "follow subjects and people" data mining. Multiple entities are included in the customs freight supervision process, such as customs declaration goods, enterprises, customs officials and so on. Each entity is an element, each customs declaration record is a collection, and all customs declarations form a large data set. By...

Claims

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Application Information

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IPC IPC(8): G06Q10/08G06F16/28
CPCG06Q10/0831G06F16/288
Inventor 栗晋斌张鑫华胡泽纯孙体东张书启王昊雷敏黄哲学李俊杰吴定明王旭
Owner 中华人民共和国深圳海关
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