Abnormal sound detection method based on edge cloud intelligent architecture

A technology of abnormal sound and intelligent architecture, applied in speech analysis, transmission system, electrical components, etc., can solve the problems of high transmission delay, difficult to guarantee data security, storage and network pressure, etc., to achieve reduced transmission delay and safety. Sexual protection, the effect of improving processing power

Inactive Publication Date: 2019-12-06
江苏慧中数据科技有限公司
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Problems solved by technology

[0004] In order to overcome the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide an abnormal sound detection method based on the edge-cloud intelligent architecture, which solves the problem of high storage and network pressure and time-consuming transmission in the traditional audio detection system centered on cloud computing. Problems such as height extension and data security are di

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  • Abnormal sound detection method based on edge cloud intelligent architecture
  • Abnormal sound detection method based on edge cloud intelligent architecture

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[0021] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention is not limited to this embodiment. In order to provide the public with a thorough understanding of the present invention, specific details are set forth in the following preferred embodiments of the present invention.

[0022] see Figure 1-2 , using three Raspberry Pis at the edge to collect audio data from different subway stations at the same time, taking glass breaking and screaming as abnormal sound events, and using Docker container technology in the cloud to encapsulate task processing operators to realize computing tasks. Migrate and store audio detection results, including a variety of different sound feature extraction methods and abnormal sound detection deep neural network models, deploy sound feature extraction and abnormal sound model judgment tasks to edge devices for execution, and perform between diff...

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Abstract

An abnormal sound detection method based on an edge cloud intelligent architecture is provided. The method comprises the following steps: collecting audio data on an edge end; deploying tasks that canbe processed by the edge end to an edge device as much as possible; using the Docker container technology to perform encapsulation on task processing operators on a cloud end to realize migration ofcomputing tasks, and storing an audio detection result; using a deep neural network model to perform abnormal sound determination; and performing message communication between different devices through the MTZT protocol. According to the method provided by the present invention, the pressures on the cloud computing center and the network bandwidth are alleviated, the system real-time performance and responsiveness are improved, and data security can be better protected.

Description

technical field [0001] The invention belongs to the technical field of edge-cloud intelligence, and in particular relates to an abnormal sound detection method based on the edge-cloud intelligence architecture. Background technique [0002] In recent years, with the development of audio and video surveillance, audio surveillance systems have been widely used in urban public safety management. Audio detection and classification has become an important research direction in the research field of computational auditory scene analysis. With the further application of the audio monitoring system based on sound event detection and classification, some new problems also arise. Applications in the field of public safety require audio surveillance systems to provide real-time and efficient audio processing capabilities. The built-in computing power of the audio collection equipment of the traditional audio monitoring system is low, while the intelligent processing ability of the ex...

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Application Information

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IPC IPC(8): G10L25/18G10L25/30H04L29/08
CPCG10L25/18G10L25/30H04L67/1004H04L67/1095
Inventor 杨树森张红惠永昌
Owner 江苏慧中数据科技有限公司
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