Large-scale launching equipment sensing data fusion evaluation method based on sensing weight annular density convergence

Abstract

The invention provides a large-scale launching equipment sensing data fusion evaluation method based on sensing weight annular density convergence, and the method achieves the digitalized representation of the required fusion data content, and visually displays the condition of large-scale launching equipment. In the evaluation process, the sensing weight theory is used for reasonably calculatingthe weight indexes of all the sensors, and the sensing weights of some sensors which fail and are low in function are corrected. In addition, the evaluation process not only performs weight evaluationon the required acquisition values, but also pays more attention to the internal aggregation calculation evaluation of the change of the annular density convergence degree of the acquisition values,and performs fusion evaluation on the operation quality of the large-scale launching equipment. The temperature index condition, the humidity index condition, the business volume and the bearing capacity are all qualitative indexes in the operation fusion evaluation of the large-scale launching equipment and are quantified in the evaluation process by utilizing annular density convergence, and thedenser the annular density convergence numerical value is, the closer the evaluation is to the real condition step by step.

Description

technical field [0001] The invention relates to the field of large-scale radio and television and communication systems, and is particularly used in a method for fusion evaluation of sensor data of large-scale transmission equipment in a large-scale radio and television and communication equipment operation and maintenance system. Background technique [0002] Large-scale launch equipment is the core part of large-scale broadcasting, television and communication systems. In recent years, communications, radio and television business exchanges are frequent and large-scale, but lagging data collection, integration, analysis and evaluation methods have caused certain challenges to this field. At present, there are (1) the accuracy of sensor data collection in the daily operation and maintenance of large-scale launch equipment is poor; the data detection and collection processes of equipment are independent of each other, so the difficulty of data fusion is very large; The grea...

AI Technical Summary

Problems solved by technology

In recent years, communications, radio and television business exchanges are frequent and large-scale, but lagging data collection, integration, analysis and evaluation methods have caused certain challenges to this field

At present, there are (1) the accuracy of sensor data collection in the daily operation and maintenance of large-scale launch equipment is poor; the data detection and collection processes of equipment are independent of each other, so the difficulty of data fusion is very large; The greater the number of sensors, the more complex the control program will be

The existing data information system lacks unified planning, making it difficult to effectively search for data sources

(2) Partially applied to large-scale launch equipment, the sensing data fusion method is simple and the effect is poor. The core value of data fusion is to ensure the normal operation of the business system, and the environment supporting data fusion is very complex, involving equipment, equipment, manpower, network, Servers, various applications and other resources

The lack of existing fusion methods affects the stable operation of large-scale radio and television communication systems, and the lack of display of data fusion results leads to low efficiency in data analysis and emergency event handling

(3) The fusion and concentration of each data performance is not enough. In major key equipment, each operation and maintenance sensor is too scattered, and it is difficult to comprehensive performance in the first time

Relevant staff cannot fully understand the operating status of the network, and it is difficult to conduct scientific assessments, resulting in the lack of unified caliber data in application system interfaces, alarm information correlation analysis, alarm detection rule definition, hidden fault analysis and prediction, etc.

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