Research On Data Processing Of Flexible Packaging Workshop Based On Ubiquitous Perception

With the rapid development of China’s flexible packaging printing industry and the increasing improvement of people’s living conditions,environmental issues in the printing workshop have attracted great attention.Various environmental factors in the print shop directly affect the quality of prints,the comfort of shop workers,and the productivity of the shop.Therefore,a good production operation environment is of great significance to ensure the normal production of the workshop and the quality of printed matter.With the development of network technology,the ubiquitous perception network can monitor and process the environmental data of flexible packaging printing workshops in real time and accurately.However,the more accurate the monitoring,the greater the amount of data that needs to be collected and stored.Therefore,an improved algorithm is proposed for the acquisition and fusion processing of massive data.An improved algorithm is proposed for the acquisition and fusion processing of massive data.On the issue of data collection,according to the number of network operation rounds,the threshold for cluster head election is set based on the remaining energy of the node and the distance from the base station;On the data fusion problem,the compressed sensing theory is used to improve the OMP algorithm according to the reconstruction accuracy and precision.The main research contents of the thesis are as follows:（1）Establish the energy consumption model of the network,analyze the shortcomings of the traditional LEACH protocol,In view of the problem that the randomness of the cluster head node selection leads to the premature termination of the network due to unbalanced energy consumption of each node in the network,In this thesis,the parameters of residual energy and distance from the base station are introduced into the threshold for electing cluster heads,and at the same time,the two parameters are adjusted for different network running rounds.It is proved by experiments from four aspects: clustering structure diagram,number of surviving nodes,network energy consumption,and data transmission volume.The experimental results show that the improved new-LEACH protocol clustering is more reasonable,the network node survival rate is higher,the data transmission efficiency is higher,and the network energy consumption is also reduced.Therefore,the overall performance of the improved new-LEACH protocol is more stable than the original LEACH protocol.（2）The environment of the workshop will always affect the operation status of the production equipment and the comfort of the workers.By analyzing the data of various environmental factors in the flexible packaging printing workshop,it is found that these data have no significant change characteristics,so they have good compressibility.Therefore,for these massive data,this thesis adopts compressed sensing technology to compress and reconstruct to save network storage resources and ensure the stable operation of the network system.Using the OMP algorithm and the improved GMSSWOMP algorithm,data reconstruction experiments are carried out on the data of the simulated printing workshop environment attributes.The algorithm first performs semi-adaptive selection of indeterminate lengths through threshold parameters,continuously updates the support set in the process,and optimizes by approximating the signal sparsity through the constant joint control of fixed-length and indeterminate-length thresholds in the iterative process.The simulation experiment proves that it is feasible to apply the reconstruction algorithm OMP algorithm to the data processing of the environmental attributes of the flexible packaging printing workshop;Through the comparison before and after improvement,the improved GMSSWOMP algorithm is superior to the OMP algorithm in both reconstruction accuracy and reconstruction time.