Hydrological monitoring station image identification monitoring system based on edge intelligence

Abstract

The invention relates to the technical field of hydrological monitoring stations, and discloses an edge intelligence-based hydrological monitoring station image recognition monitoring system, which comprises an intelligent camera, an edge node, a macro base station, an intelligent mobile terminal, a cloud center and a visualization center, and is characterized in that the output end of the intelligent camera is electrically connected with the input end of the edge node; the output end of the edge node is electrically connected with the input end of the macro base station, the output end of the macro base station is electrically connected with the input end of the cloud center, and the output end of the cloud center is electrically connected with the input end of the visualization center; and the output end of the macro base station is electrically connected with the intelligent mobile terminal. According to the hydrological monitoring station operation management target, the hydrological monitoring station intelligent operation management quality evaluation standard and the monitoring evaluation technical standard are researched and formulated, so that a unified hydrological monitoring station operation management monitoring evaluation system is established, and the effect of convenient monitoring is achieved.

Description

technical field [0001] The invention relates to the technical field of hydrological monitoring stations, in particular to an image recognition and monitoring system for hydrological monitoring stations based on edge intelligence. Background technique [0002] Edge computing is an inevitable technology for the evolution of computing systems from flat to edge and 5G network architecture, and it also provides a new ecosystem and value chain. IDC, a third-party data analysis agency, predicts that by 2020, there will be about 50 billion smart devices connected to the Internet worldwide, mainly involving smartphones, wearable devices, and personal vehicles, among which 40% of the data will require edge computing services. Edge computing has strong market potential and has attracted great attention from various research institutions, standard organizations, service providers and the industry. For example, the mobile phone is the edge node between people and the cloud center, and t...

AI Technical Summary

Problems solved by technology

[0003] At present, the existing hydrological monitoring stations on the market mainly rely on manual monitoring, which leads to increased labor costs; manual long-term monitoring is prone to fatigue, resulting in negligence, omission or misjudgment of potential safety hazards in monitoring; manual monitoring and personnel emotions, status, work experience, Influenced by personality and living conditions, safety judgment has a strong subjective consciousness and lacks objectivity

Therefore, manual inspection has a series of problems such as high supervision costs, large subjective interference, and inability to monitor the whole process.

Lack of professional hydrological monitoring team, the smooth development of hydrological monitoring work is affected by the comprehensive quality of hydrological monitoring staff

The professionalism of the staff is directly linked to the quality of monitoring. The lack of a professional hydrological monitoring team will lead to more quality problems when hydrological monitoring serves other industries.

In actual work, if the quality of monitoring personnel is too low and lacks a positive working attitude, it will affect the validity and availability of hydrological monitoring data, and the value of hydrological monitoring work will not be reflected

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