A sensor data fusion evaluation method for large launch equipment

Abstract

The invention provides a sensor data fusion evaluation method for large-scale launch equipment, which digitally represents the required content of the fusion data, and intuitively shows the status of the large-scale launch equipment. In the evaluation process, the sensor weight theory is used to reasonably calculate the weight indicators of each sensor, and the sensor weights of some failed and low-function sensors are corrected. In addition, the evaluation process not only evaluates the weight of each required acquisition value, but also pays more attention to the internal aggregation calculation evaluation of the change of the annular density convergence degree of the acquisition value, and makes a fusion evaluation of the operation quality of the large-scale launch equipment. In the evaluation of large-scale launch equipment operation integration, such as temperature indicators, humidity indicators, business volume, and carrying capacity are qualitative indicators, and the annular density aggregation is used to quantify them in the evaluation process. The denser the annular density aggregation value, the better the evaluation. to gradually approach the real situation.

Description

technical field [0001] The invention relates to the field of large-scale radio, television and communication systems, in particular to a method for fusion evaluation of sensor data of large-scale transmission equipment in large-scale radio, television and communication equipment operation and maintenance systems. Background technique [0002] Large-scale launch equipment is the core part of large-scale broadcasting and communication systems. In recent years, the communication, radio and television business has frequent and large-scale business exchanges, but the lagging data collection, integration, analysis and evaluation methods have caused certain challenges to this field. At present, (1) the accuracy of sensor data collection in 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 data fusion is very difficult; in a general distributed system, a single subject ...

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.

Images