Quantum support vector machine evaluation and forecast method for fire risk in urban public buildings

Abstract

The invention discloses a quantum support vector machine evaluation method for fire risk evaluation of urban public buildings, which comprises the following steps: determining a fire risk index training sample and a test sample of a fire risk evaluation model; Swap test method is used to calculate the inner product operation among the training samples of the fire risk index; After the inner product is obtained, the relaxation variable is introduced, and the HHL algorithm is used to solve the linear equations, and the standard curve of fire risk is obtained; Simulation matrix F is used to carry out the training process of QSVM, the fire risk standard line is constantly adjusted, and then the QSVM fire prediction training model is obtained; The trained QSVM fire prediction training model isused to test the test sample set, and then the QSVM fire prediction training model is evaluated. If the test fails, the parameters are adjusted to continue training. The QSVM fire prediction trainingmodel is used to evaluate and predict the fire risk of urban public buildings, and the revised prediction model is updated according to the actual feedback results.

Description

technical field [0001] The invention relates to the field of fire prediction, in particular to a quantum support vector machine fire evaluation and testing method and system for urban public buildings. Background technique [0002] With the rapid development of China's economy and society, the process of urbanization has accelerated, the scale of cities has continued to expand, the population density has continued to increase, and the types of buildings have become diversified. The number of public buildings with complex structures, special functions and high population density is also increasing. In the event of a fire, these high fire hazard units result in significant casualties and property damage. Fire risk assessment is an effective way to improve building fire resistance and reduce the possibility of fire, and the fire risk assessment of large public buildings has become a research hotspot. [0003] Rapid detection and prediction are key measures to control this phe...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to apply quantum acceleration technology to fire prediction and risk assessment, and solve the problem of traditional SVM algorithm consuming a lot of computing resources and computing time

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