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Rural house identification grade classification method based on Bayesian network

A Bayesian network and hierarchical classification technology, applied in the field of hierarchical classification, can solve problems such as villagers' reluctance to live, insufficient lighting and ventilation, poor thermal insulation and waterproof effects, etc.

Active Publication Date: 2022-07-08
JIANGSU OCEAN UNIV
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Problems solved by technology

In reality, there are still a large number of houses that are safe to meet the requirements of the code, but the livability is poor, such as water seepage and rain leakage, insufficient lighting and ventilation, and poor insulation and waterproof effects; eventually, villagers do not want to live in them, resulting in dilapidated or even abandoned houses

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  • Rural house identification grade classification method based on Bayesian network
  • Rural house identification grade classification method based on Bayesian network
  • Rural house identification grade classification method based on Bayesian network

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Embodiment Construction

[0032] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the present invention is not limited to the specific embodiments disclosed below. limit.

[0034] refer to Figure 1-Figure 5 , figure 1 A flowchart of a method for classifying rural houses based on Bayesian network identification provided according to an embodiment of the present disclosure is shown. like figure 1 As shown, the method can include:

[0035] Step 1: Identify relevant factors that constitute the risk of rural housing through expert experience and field visits;

[003...

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Abstract

The invention belongs to the technical field of house intelligent identification, and particularly discloses a rural house danger level intelligent judgment method based on a Bayesian network. The risk factor and livability factor of the house are identified by relying on early-stage expert experience and field investigation. The correlation between the factors is verified through Pearson chi-square test; and constructing a Bayesian network structure model, calculating a prior probability of each factor according to early-stage collected data, and inputting the prior probability to an inference engine to obtain a posterior probability of a final house risk level and a livability level. And finally, model debugging is carried out step by step through accuracy test and sensitivity analysis. According to the method provided by the invention, livability is incorporated into rural house identification, a house evaluation process is visualized, and meanwhile, a method for preliminarily diagnosing the grade of the house by itself is also provided for rural residents, so that the workload of on-site survey of experts for identification one by one from home to home is reduced, and the safety troubleshooting efficiency of the rural houses is improved.

Description

technical field [0001] The invention belongs to the field of house intelligent appraisal, and specifically discloses a Bayesian network-based rural house appraisal grade classification method. Background technique [0002] In the existing rural house appraisal, most of them rely on experts to inspect and inspect one by one from house to house, resulting in time-consuming and labor-intensive inefficiency. There is a lack of a method to determine the danger level of houses for ordinary villagers. [0003] Second, most of the existing rural housing appraisals are based on safety considerations and only meet the basic needs of the households. However, with the continuous development of my country's economy, the people's demand for a better life continues to increase. In reality, there are still a large number of houses that meet the requirements of the regulations, but are not livable, such as water seepage and rain, insufficient lighting and ventilation, and poor thermal insu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/764G06K9/62G06Q50/08
CPCG06Q50/08G06F18/24155
Inventor 朱峥宋明志王佩佩丰村王修华管绍荣陶士恒杨明宇陈敏涵刘钟
Owner JIANGSU OCEAN UNIV
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