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We report the measurement of total molybdenum ion density for L-mode and H-mode plasmas on EAST using spectral lines observation and calculation based on an impurity transport code. A flat-filed extreme ultraviolet spectrometer with some spatial resolution is used to obtain the radial profiles of molybdenum spectral line emissions. The absolute calibration for the extreme ultraviolet spectrometer is finished by comparing the calculated bremsstrahlung intensity with the readings of CCD detector. Molybdenum ion transport study is performed using the radial ion density profiles and one-dimensional impurity transport code STRAHL. The total molybdenum density profiles are determined from the transport analysis. The molybdenum density during L-mode and H-mode phases are obtained, which are about 3 and 4 orders of magnitude smaller than the electron density, respectively. An inward pinch is found during the H-mode phase that leads to the peaked profile of molybdenum density.

To solve the problem of load unbalance in the case of few users and multi-task, a fuzzy inference PSO algorithm (FPSO) crowd sensing single objective task assignment method is proposed. With task completion time, user load balancing and perceived cost as the optimization goals, the fuzzy learning algorithm dynamically adjusts the learning factor in the PSO algorithm, so that the PSO algorithm can perform global search in the scope of the task space, thus obtaining the optimal task assignment solution set. Finally, the FPSO algorithm is compared with the PSO, GA and ABC algorithms on the optimization objectives, such as the algorithm convergence, task completion time, perceived cost and load balance. The experimental results show that the FPSO algorithm not only has faster convergence rate than the other algorithms, and shorten the task completion time, reduce the platform’s perceived cost, improve the user’s load balance, and have a good application effect in the crowd sensing task assignment.