Due to the similarity between storage materials, there are errors and difficulties in controlling efficiency when positioning them. Therefore, a research on accurate positioning methods for intelligent storage materials based on RFID technology and machine vision is proposed. Machine vision technology was introduced, and the RealSenseD435 depth camera was used to identify the target box. The weighted average method was used to process the image, and the distribution of pixel gradient fields in the image was used as a feature parameter for the storage materials. In the positioning stage, RFID technology was introduced, using the extracted pixel gradient field of the warehouse material image as RFID tags. Based on the rigorous relationship between the phase and the distance from the warehouse material, the position of the target material was determined. In the test results, the positioning error of the design method for different types of materials remained stable within 0.15m, and the time cost remained stable within 10.0m.
KEYWORDS: Data modeling, Instrument modeling, Power supplies, Analytical research, Transformers, Electrical breakdown, Inspection, Design and modelling, Power consumption, Telecommunications
In the process of judging the abnormality of power metering devices, the accuracy of identification results is relatively low due to the intersection of abnormal features. Therefore, a study on the abnormality identification of power metering devices based on data driven model is proposed. From the perspective of abnormal parts, the specific manifestations and causes of different abnormal types are analyzed, and the abnormalities of power metering devices are divided into 9 categories. In the process of analyzing the abnormal state of electric energy metering devices by constructing a data-driven model, a loss function is set for the data-driven model, and the interference of the cross identification results of abnormal features is avoided by using the conflict function between the output of the data-driven model and the field experience knowledge. Finally, the abnormal information that meets the requirements of the consistency check of the data-driven model is taken as the identification result of the abnormality of the power metering device. In the test results, the accurate identification of different types of power metering device anomalies by the design method is stable at 83.0%.
KEYWORDS: Data modeling, Data storage, Solar energy, Data conversion, Internet of things, Data compression, Denoising, Data acquisition, Batteries, Education and training
The data of smart watt-hour meter has high-dimensional characteristics, and it is difficult to represent, so it is impossible to ensure high-precision filling of missing data. Therefore, the research on missing data filling of smart watt-hour meter based on variational self-encoder is proposed. Firstly, a filling model based on variational self-encoder is constructed to denoise the measurement data of smart watt-hour meter, then the denoised smart watt-hour meter data is input into the model, and the data missing filling result of smart watt-hour meter is output. Finally, the data missing filling of smart watt-hour meter is realized according to the result. The experimental results show that, after filling, the proposed method is coincident with the original data, and the final filling operation efficiency can reach more than 90%.
With the automatic verification of intelligent power meters, manpower is saved and the verification efficiency is improved. However, the large-scale automatic verification method can not meet the parameter ratio specified in the verification regulation because the verification environment is difficult to control. The environmental factors in the verification are difficult to be accurately controlled, which leads to inaccurate verification results. There are a large number of watt-hour meters in the process of centralized verification. If the verification results are not accurate, it will lead to unnecessary economic losses. In this paper, a T distribution test model is established to consider the influence of the heat of the external shunt and the internal power chip on the temperature of the metering chip in different environments. According to the error fitting curve corresponding to the chip, the measurement error of the DC energy meter in different environments is obtained. The formula is imported into MATLAB for simulation analysis to study the factors affecting the accuracy of the metering algorithm. The error formula and influencing factors are derived when the effective value method is adopted in fast charging mode, and the simulation analysis is carried out.
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