data acquisition abnormity call supplementing method based on the Internet of Things

Abstract

The invention discloses a data acquisition abnormity call supplementing method based on the Internet of Things. The data acquisition abnormity call supplementing method comprises the following steps:S1, judging the integrity of received data by a platform; S2, analyzing whether the incomplete data meets a call supplementing condition or not, and entering a call supplementing process if the incomplete data meets the call supplementing condition; According to the method, through the complementary recruiting strategy of the concentrator memory and the platform historical database, the problem that due to limitation of the concentrator memory capacity, recruiting data are incomplete, and consequently the accuracy of the power utilization safety analysis model is affected is solved. The methodhas the advantages that the data loss reason is judged, and the practicability of electric power safety system analysis modeling is improved; The system has the functions of judging the accuracy of the supplementary calling data and automatically calibrating the time, the accuracy of modeling analysis of the power utilization safety system is improved, and misjudgment of an electrical fire monitoring system and false processing of operation and maintenance personnel are avoided.

Description

technical field [0001] The invention relates to the technical field of abnormal data processing, in particular to a supplementary recruitment method for abnormal power data based on Internet of Things data collection. Background technique [0002] In recent years, due to factors such as improper use of electrical equipment, aging lines, and inadequate supervision, electrical fires account for more than 70% of the total number of fires in my country, which remains high and poses a great threat to people's lives. Traditional electrical fire prevention measures can no longer meet the safety needs of today's society. The electrical fire monitoring technology based on the Internet of Things and big data has become an inevitable trend to replace the traditional electrical fire prevention technology. By deploying smart meters in the electrical circuit, the leakage current, current, voltage, power, and harmonics of the electrical circuit can be collected. , temperature and other da...

AI Technical Summary

Problems solved by technology

However, the monitoring data will be missing or abnormal due to many monitoring nodes, unstable network transmission or various problems in the environment where the instrument is located, which will lead to misjudgment by the electrical fire monitoring system and mishandling by operation and maintenance personnel

[0003]At present, the method of dealing with missing or abnormal data in the electrical fire monitoring system is generally to supplement the memory data of the terminal collection instrument, and to supplement all missing items The method of supplementary recruitment lacks the judgment of the reasons for the missing items, so that the practicability of power safety system analysis and modeling is not high; moreover, this supplementary recruitment method is often due to the capacity limitation of the instrument memory, resulting in incomplete supplementary recruitment data, thereby reducing the construction cost. The precision of model analysis

The existing supplementary recruiting method for data also has the method of recruiting data from the historical database. However, due to the particularity of the electrical industry, data fluctuations are greatly affected by the data collection environment, and the existing supplementary recruiting method does not fully consider the environmental factors of data collection. Influenced by the impact, when supplementing data, it is necessary to find historical data from the historical data that is closest to the environmental factors collected at the time point where the missing data is located, and the supplementary recruitment of data is scientific and accurate.

Images