Indoor fire source localization method for high-rise buildings based on glass surface temperature

An indoor fire source localization method for high-rise buildings based on glass surface temperature, comprising the following steps:

• step 1:
• arranging an array of m wireless thermocouples on an outer surface of glass in a fire room, with the array distributed on a largest piece of glass in the fire room, wherein each wireless thermocouple is connected to a temperature data signal acquisition module;
• step 2:
• measuring horizontal distances from a centrally positioned wireless thermocouple in the arranged array to two side walls of the fire room, denoted as D1 and D2;
• step 3:
• dividing the fire room into r blocks of an equal area, wherein the quantity of blocks is the same as the quantity of wireless thermocouples, and the blocks are arranged in an array, assigning numbers to the blocks, and using the numbers of the blocks as program-output fire source location categories;
• step 4:
• adjusting the temperature data signal acquisition module to receive temperature data signals from each wireless thermocouple at a frequency q;
• step 5:
• activating a fire source in the fire room and the temperature data signal acquisition module, acquiring and reading temperature data from each wireless thermocouple in real time, saving the acquired temperature data at preset time intervals t, and transmitting the temperature data to a data processing terminal;
• step 6:
• after receiving the temperature data, averaging, by the data processing terminal, the temperature data acquired by each wireless thermocouple over each preset time interval t, to obtain m pieces of average temperature data;
• step 7:
• combining, by the data processing terminal, the obtained m pieces of average temperature data into a 1×m matrix and adding distance data D1 and D2 to the matrix, resulting in a 1×(m+2) matrix;
• step 8:
• inputting the 1×(m+2) matrix obtained in step 7 into a trained prediction program integrating a multilayer perceptron (MLP) and a k-nearest neighbors (KNN) algorithm, and obtaining a predicted location block of the fire source through backpropagation and category voting.