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Crime prediction method and system based on knowledge graph fusion

A technology of knowledge graph and prediction method, applied in prediction, biological neural network model, instrument, etc., can solve problems such as inability to accurately predict results, and achieve the effect of convenient use

Inactive Publication Date: 2019-11-15
电子科技大学成都学院
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AI Technical Summary

Problems solved by technology

The experiment, which is localized and cannot accurately predict the outcome, only identifies the possibility of crime in the area

Method used

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  • Crime prediction method and system based on knowledge graph fusion
  • Crime prediction method and system based on knowledge graph fusion
  • Crime prediction method and system based on knowledge graph fusion

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Embodiment Construction

[0042] Below in conjunction with the appendix of the present invention Figure 1-10 , clearly and completely describe the technical solutions in the embodiments of the present invention, obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0043] refer to figure 1 , a crime prediction system based on knowledge graph fusion, including:

[0044] An information acquisition unit, configured to acquire multi-dimensional information of the object to be tested and generate an individual knowledge map image of the object to be tested; combined with image 3 , in this embodiment, the information acquisition unit extracts various data entities such as the name, height and weight of the subject to be tested from the m...

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Abstract

The invention relates to the technical field of neural networks, and aims to provide a crime prediction method and system based on knowledge graph fusion, and the system comprises: an information obtaining unit which is used for obtaining the multi-dimensional information of a to-be-tested object and generating an individual knowledge graph portrait of the to-be-tested object; a word stock librarywhich is used for vectorizing the individual knowledge graph portrait of the to-be-tested object, converting the multi-dimensional information into text information composed of a plurality of standard words, and converting the individual knowledge graph portrait of the to-be-tested object into a plurality of vectorization matrixes; and a comprehensive decision neural network which judges whetherthe to-be-tested object has criminal suspicion according to the plurality of vectorized matrix decision neural networks converted by the word stock library. The method has the advantage that the crimeprobability of the object can be predicted according to the multi-dimensional information of the object.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a crime prediction method and system based on knowledge map fusion. Background technique [0002] With the development of the big data era, most human behaviors can be predicted. Crime prevention has also become one of the big data applications. Potential criminals can be predicted when enough latitude information and valid data are collected. Big data is integrated into the process of public security investigation, using massive data to assist in the investigation, changing the investigation mode, or analyzing the suspect's natural attributes and other characteristic symbols, and at the same time using the big data thinking model to analyze cases to effectively combat and reduce crimes, but this cannot restore what has happened. The loss caused by a criminal act will not prevent the potential criminal act that is about to occur. [0003] In the field of crime predicti...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26G06F16/36G06F16/35G06N3/04
CPCG06Q10/04G06Q50/26G06F16/367G06F16/355G06N3/045
Inventor 邹倩颖王小芳刘俸宇
Owner 电子科技大学成都学院
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