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Group-oriented rental house multi-dimensional identification method based on knowledge graph and principal component analysis

A technology of principal component analysis and knowledge graph, applied in the field of multi-dimensional identification of group rental housing based on knowledge graph and principal component analysis, can solve the problems of easy to cause missed judgment, single dimension, low efficiency, etc., to avoid arbitrariness and ensure timeliness , to ensure the effect of interpretability

Pending Publication Date: 2022-01-11
南京烽火天地通信科技有限公司
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Problems solved by technology

[0005] Among the existing methods, the first method relies too much on manual work. Although the reliability is high, the efficiency is extremely low. The dimension considered by the second method is relatively single, which is easy to cause missed judgments. Therefore, we improve this and propose a knowledge map-based Multi-dimensional identification method of group rental housing based on principal component analysis

Method used

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  • Group-oriented rental house multi-dimensional identification method based on knowledge graph and principal component analysis
  • Group-oriented rental house multi-dimensional identification method based on knowledge graph and principal component analysis
  • Group-oriented rental house multi-dimensional identification method based on knowledge graph and principal component analysis

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Embodiment

[0039] Example: such as Figure 1-4 As shown, the present invention is based on a knowledge map and principal component analysis multi-dimensional identification method for group rental houses, comprising the following steps:

[0040] Step 1: Construct a human room knowledge graph;

[0041] Step 2: Calculate the weight of each judgment index based on principal component analysis;

[0042] Step 3: Calculate the judgment threshold of group rental housing;

[0043] Step 4: Identify group rental houses.

[0044] Among them, in step 1, constructing the human room knowledge map includes:

[0045] Obtain multi-source heterogeneous data of houses and people as the basic data for building a human house knowledge map, including basic house information, house water use data, house electricity consumption data, house gas use data, express delivery data, character basic information, Person relationship data, person room relationship data, etc.;

[0046] Design the ontology of human ho...

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Abstract

The invention discloses a group-oriented rental house multi-dimensional identification method based on a knowledge graph and principal component analysis. The method comprises the following steps: 1, constructing a human-house knowledge graph; 2, calculating the weight of each judgment index based on principal component analysis; 3, calculating a group-oriented rental house judgment threshold value; and 4, identifying group-oriented rental houses. A to-be-checked house is judged based on a judgment formula and a judgment threshold, and if the to-be-checked house is judged to be a group-rented house, the human-house knowledge graph is queried to mine multi-dimensional data of the to-be-checked house as a judgment basis, so that the interpretability of a judgment result is ensured.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a multi-dimensional identification method for group rental houses based on knowledge graphs and principal component analysis. Background technique [0002] Currently, there are two main methods of identifying group rental housing: [0003] 1. Based on the complaints and feedback information of the masses, organize special personnel to visit and judge whether it is a group rental house; [0004] 2. Based on the housing life data, by mining abnormal data, such as calculating the ratio of water consumption to electricity consumption, the greater the ratio, the more abnormal, and calculate the confidence of suspected group rental houses. [0005] Among the existing methods, the first method relies too much on manual work. Although the reliability is high, the efficiency is extremely low. The dimension considered by the second method is relatively single, which is easy to cause mis...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/16G06F16/36G06N5/02
CPCG06Q10/06393G06Q50/16G06F16/367G06N5/022
Inventor 王峥朱丹梁春陶辉阮祥超
Owner 南京烽火天地通信科技有限公司
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