Rural house identification grade classification method based on Bayesian network

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...

AI Technical Summary

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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