Urban self-learning sensing network adapted to sensor

A sensor, sensor-type technology, applied in the field of deep learning comprehensive perception system, can solve the problems of neglecting humanization and livability, and not being able to bring a sense of belonging to citizens

Pending Publication Date: 2021-11-09
上海吾城微脑数字技术有限公司
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  • Summary
  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

Expert design solutions focus on technical considerations, often ignoring humanity and livability, and fail to bring a sense of belonging to citizens

Method used

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  • Urban self-learning sensing network adapted to sensor
  • Urban self-learning sensing network adapted to sensor

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Embodiment Construction

[0017] figure 1 Explain how to build a neighborhood network and how to extend the service area. Computing power node 1 is composed of an edge computer and a wireless module. The edge computer is used for platform computing and data storage. The wireless module establishes two communication links, uplink and downlink. The sensing device is connected to the computing power node 1 by the downlink, and its service radius is 20 to 400 meters, and a cellular service area 5 connected end to end is established. Sensing equipment includes fixed equipment and scattered equipment. The fixed equipment in each service area includes at least one camera 2 and one audio device 3. The camera is used for image analysis, and the audio device is used for sound wave collection in different frequency bands. They are basic sensors and scattered equipment. It is a citizen's handheld terminal 6, typically a mobile phone, with a special application installed, which can take pictures and upload, and ca...

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Abstract

The invention relates to an urban self-learning sensing system with an automatically adjusted sensor. According to the method, computing power nodes are arranged on street lamps, honeycomb service areas connected end to end are established, an urban private network covering the whole block is formed, new computing power nodes extend from the block network, and the corresponding service areas are expanded to the city area outside the block. Each service area at least comprises a camera and an audio device, and furthermore, the private network accesses scattered equipment of citizens and serves as an auxiliary sensor and edge computing power. During operation of the system, the basic sensor and the auxiliary sensor work cooperatively, sensing data of each service area is acquired for a long time, sensing requirements are continuously analyzed and deduced by the computing power nodes, type adaptation of the sensors and the edge computing power is completed, and partition sensing is enabled to achieve the optimal function step by step. According to the invention, the government and citizens can be fully mobilized to participate in digital city construction together, and the people-oriented goal of the smart city is realized.

Description

(1) Technical field [0001] The invention is a deep learning comprehensive perception system with sensor type self-adjustment, which is used for automatic adjustment of multi-mode sensors and computing power construction in urban areas. The system sets computing power nodes on street lamps, establishes end-to-end cellular service areas, forms an urban private network covering the entire block, and extends new computing power nodes from the block network to expand the corresponding service area to the city outside the block. Each service area contains at least one camera and one audio device as basic sensors. Further, the private network is connected to citizens' scattered devices as auxiliary sensors and edge computing power. During the operation of the system, the basic sensor and the auxiliary sensor work together to collect the sensing data of each service area for a long time, and the computing power node will continue to analyze, infer the sensing demand, push the selecti...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/08H04W4/38H04W84/18H04N7/18G06F16/583G06F16/587G16Y40/10
CPCH04L67/12H04W4/38H04W84/18H04N7/181H04L67/06H04L67/1097G06F16/5838G06F16/5854G06F16/587G16Y40/10Y02D30/70
Inventor 尹梦寒徐君龙
Owner 上海吾城微脑数字技术有限公司
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