Data fusion and intelligent searching processing method for multiple data sources

Abstract

The invention relates to the sensor data processing technology field and particularly relates to a data fusion and intelligent searching processing method for multiple data sources, and an application. According to the method, sensors are arranged in a planar layout, the sensors are arranged on one same plane to form a sensing network, data fusion comprises data fusion through multiple same-type sensors and data fusion through multiple different-type sensors, data characteristics and data types of the sensors are acquired by employing a polling mode, redundancy processing on the data is carried out when acquisition is carried out by an acquisition node, and an self-adaptive algorithm based on batched estimation is employed. Through the method, the data of each sensor is dynamically acquired, the identification time of a system for the sensor data is prolonged, data precision is improved, data accuracy is improved, identification reliability of the system is improved, the method is suitable for occasions having a relatively large quantity of same-type sensors, relatively large data amount and same-type-sensor-in-one-same-plane layout, and data fusion in plantation environments and farmland climate monitoring multi-sensor environments can be realized through the method.

Description

[technical field] [0001] The invention relates to the technical field of sensor data processing, in particular to a processing method and application of data fusion and intelligent search from multiple data sources. [Background technique] [0002] The prototype of multi-sensor data fusion technology appeared at the end of World War II. In the sonar signal understanding system funded by the US Department of Defense in the 1970s, the concept of data fusion was formally proposed. Multi-sensing data fusion can comprehensively process and optimize the measurement data provided by various types of sensors in the system at the same time or at different times, in the same form or in different forms, to obtain the internal connections and laws between various data, and eliminate useless data. Combining the complementary or redundant data of multiple sensors in time and space according to certain criteria, and finally realizing the optimization of the data so that it can accurately r...

AI Technical Summary

Problems solved by technology

[0003] In wireless sensor networks, on the one hand, the data collected by a single sensor node has a very high similarity due to the data collected multiple times in a short period of time; on the other hand, the data collected by adjacent sensor nodes at similar times will also be similar With a high degree of similarity, the data collected by all sensor nodes are all transmitted to the sink node, which is not only meaningless, but also consumes too much energy of the sensor nodes and affects the life of the network. And the storage capacity is very limited. When the sensor nodes receive interference, the collected data will produce errors. It is obviously inappropriate to use each node to send the data to the sink node separately when collecting data.

At present, there is no general method for multi-sensor data fusion

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