Fire dynamic risk assessment method of super high-rise building based on fuzzy analytic hierarchy process

Abstract

The invention relates to a fire dynamic risk assessment method of super high-rise building based on fuzzy analytic hierarchy process. The method maintains a dynamic risk assessment system based on fire-fighting internet of things, obtains the real-time weights of each index in the dynamic risk assessment system by combining the analytic hierarchy process and the fuzzy mathematics theory, and obtains the fire dynamic risk assessment result of the super high-rise building based on the real-time data collection and the real-time weights. Compared with the prior art, the invention has the advantages of high accuracy, low complexity, strong universality and the like, makes automatic fire risk prediction possible, further provides scientific basis for preventing and controlling the occurrence offire, and reduces subjectivity and blindness in fire prevention.

Description

technical field [0001] The invention relates to the technical field of building risk assessment, in particular to a dynamic risk assessment method for super high-rise building fire based on fuzzy analytic hierarchy process. Background technique [0002] At present, in terms of the construction risk assessment system, the framework of assessment theoretical indicators is not uniform, and most of them are static assessments, that is, multiple experts jointly determine the weight of scoring indicators in the framework, and score each indicator score, which relies too much on expert experience, and Only the fire risk at a certain moment can be assessed, and the fire risk is a dynamic indicator. This creates a shortage of historical data on fire risk, and there is currently no precedent for building fire predictions. [0003] At present, in terms of risk assessment methods, the Analytic Hierarchy Process is mostly used at present. The traditional AHP has the following deficienc...

AI Technical Summary

Problems solved by technology

The traditional AHP has the following deficiencies: (1) The traditional AHP adopts the 1-9 scale method to construct the judgment matrix, which cannot reflect the ambiguity of human judgment; (2) The eigenvalue algorithm is not clean enough. When the number is very high, the workload of calculating the eigenvector and the maximum eigenvalue is very large; (3) the consistency test standard CR<0.1 lacks scientific basis

The traditional AHP can realize the determination of index weights, but due to the above defects, this method is not suitable for the determination of dynamic index weights

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