Fire detection method, device and equipment based on neural network and storage medium

A fire detection and neural network technology, applied in neural learning methods, biological neural network models, fire alarms, etc., can solve problems such as high requirements for software and hardware, complex fire recognition algorithms, and complex test data, etc., to reduce hardware performance Requirements, recognition algorithm is simple, the effect of small amount of data processing

Active Publication Date: 2020-06-02
SHANGHAI AEGIS IND SAFETY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This patented technology allows for quick identification between different types of fires based upon their characteristics or properties like color temperature (C), location relative to an object being tested, etc., without requiring extensive human effort. By learning from this trained dataset, it becomes possible to create models that recognize both normal smoke patterns and abnormal smoking conditions effectively. These systems have been shown to perform well even when there may exist multiple sources of noise interference affecting them.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving the efficiency and effectiveness of detecting fires with neural networks trained from images obtained during normal conditions (such as rain). Current solutions require expensive equipment that requires installation and maintenance, making it difficult to implement them over longer periods without being able to accurately identify fires quickly enough.

Method used

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  • Fire detection method, device and equipment based on neural network and storage medium
  • Fire detection method, device and equipment based on neural network and storage medium
  • Fire detection method, device and equipment based on neural network and storage medium

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Embodiment 1

[0034] The fire detection method provided in this embodiment is applicable to an application scenario where a neural network model is used for flame identification. In this embodiment, the characteristic parameters of the training light source, such as spectral signal parameters, time signal parameters and spatial signal parameters, are used as the input parameters of the neural network model, and the preset output parameters are set according to the type of the training light source, and the input parameters and the preset Output parameters, train the neural network model, and build a neural network model for fire detection. figure 1 It is a flow chart of the neural network-based fire detection method provided by Embodiment 1 of the present invention, and the method can be executed by a fire detection device storing a neural network model. Such as figure 1 As shown, the fire detection method based on neural network specifically includes the following steps:

[0035] Step S101:

Embodiment 2

[0156] The fire detection device provided by the embodiment of the present invention can execute the fire detection method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. Figure 6 It is a structural schematic diagram of the neural network-based fire detection device provided by Embodiment 2 of the present invention.

[0157] Such as Figure 6 As shown, the fire detection device 100 of the embodiment of the present invention includes: a signal processing unit 110 and a control unit 120 .

[0158] Wherein, the signal processing unit 110 includes a filtering unit 101, an optical sensor 102, and an analog-to-digital converter 103. The filtering unit 101 is used to receive an incident optical signal, and perform filtering processing on the incident optical signal to obtain optical signals of at least two preset wavelengths. The optical sensor 102 is used to convert the optical signal into

Embodiment 3

[0172] Figure 7 It is a schematic structural diagram of a device provided in Embodiment 3 of the present invention. Figure 7 A block diagram of an exemplary device 12 suitable for use in implementing embodiments of the invention is shown. Figure 7 The shown device 12 is only an example and should not impose any limitation on the functions and scope of use of the embodiments of the present invention.

[0173] Such as Figure 7 As shown, device 12 takes the form of a general purpose computing device. Components of device 12 may include, but are not limited to: one or more processors or processing unit 16, system memory 28 for storing one or more programs; a bus connecting various system components including system memory 28 and processing unit 16 18. When one or more programs are executed by one or more processors, the one or more processors implement the fire detection method of the embodiment of the present invention.

[0174] Bus 18 represents one or more of several types

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Abstract

The invention discloses a fire detection method, device and equipment based on a neural network, and a storage medium. The method comprises the following steps: obtaining incident light signals of a plurality of training light sources; performing signal processing and analysis on the incident light signal of each training light source to obtain a spectral signal parameter, a time signal parameterand a space signal parameter of each training light source; establishing a training data set according to the spectral signal parameters, the time signal parameters and the space signal parameters ofthe training light source, and constructing a neural network model for fire detection according to the training data set; determining output parameters of a light source of the to-be-detected site according to the neural network model, and determining whether a fire occurs in the to-be-detected site according to the output parameters. According to the fire detection method provided by the embodiment of the invention, the neural network model is optimized by training the characteristic parameters of the light source, and the neural network model is used for fire identification, so the identification algorithm is simple, the data processing amount is small, the fire detection response speed is high, and the accuracy is high.

Description

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Claims

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Application Information

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Owner SHANGHAI AEGIS IND SAFETY
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