Research On Grassland Environment Monitoring Model And Algorithm Based On Multi-source Data Fusion

The grassland ecological environment of Inner Mongolia has accelerated and deteriorated in a few decades,and its damage degree has seriously affected the ecological security of the north and neighboring countries.According to the survey,there are 260 million hectares of desertification land,80% of which exist in the pastoral area,accounting for 70% of the grassland area,and the trend of accelerated degradation.For this reason,the relevant departments have made great efforts to control the ecological environment of grassland,and at the same time,a series of schemes shall be formulated to monitor the grassland environment in time.However,due to the high requirement for monitoring the grassland environment,the traditional technology is used to monitor the situation of the grassland environment,which lacks some practical effect,and the investment cost is high,and it costs a lot of human and material resources.If the environment of grassland is monitored dynamically by sensors,it is beneficial to supervise the environment from the scientific and technological level.But using of the single sensor alone may have some disadvantages,such as the interference of its own instability,the surrounding environment,etc.,resulting in an error between the monitoring data and the true value.The introduction of data fusion technology into the environmental monitoring of grassland can effectively enhance the accuracy of monitoring results and reduce the probability of false judgment.The main research contents of this paper as follows:Firstly,the basic concept and main fusion method of multi-source data fusion technology are introduced,Besides this paper also expounds the application and application of the relevant fusion methods of scholars at home and abroad.The advantages and disadvantages of the common data fusion method are summarized,and the fusion principle and process of the three fusion methods of the adaptive weighted average,the BP neural network and the D-S evidence theory used in this paper are introduced.Secondly,a two-level data fusion model based on grassland environmental monitoring is proposed.In this model,the data received by each sensor is preprocessed to judge the validity of the data,and the influence of unstable sensor observations on the fusion accuracy is eliminated.Then the adaptive weighted averaging method is used to fuse the same type of sensor data in each region and then the BP neural network is used to fuse the heterogeneous sensor data in the region.After each region sends the fusion result to the gateway node,carries on the secondary fusion.In the second stage fusion,the error between the actual output and the expected output of the BP neural network is calculated,and it is regarded as the basic probability score in the DS.The decision level fusion is carried out by using the DS composition rule.So as to realize the overall decision-making of grassland environment.Finally,aiming at the deficiency of DES evidence theory,the algorithm is improved from evidence distance and conflict factor.Finally,five environmental parameters such as soil temperature,soil humidity,illumination intensity,carbon dioxide concentration and wind velocity were monitored.Then,comparing the improved algorithm with the calculation results of classical algorithm,it is proved that the probability accumulation of this algorithm is more obvious and consistent with the expected results.