In this dissertation, taking the magnetic flux leakage (MFL) detection system for longdistance oil pipeline as study object, the online compression algorithm of magnetic flux leakage detection data is studied. Aiming at the problem that traditional data compression method is difficult to apply in the embedded online work environment, compressed sensing (CS) theory is introduced and an online CS compression method for MFL detection data is proposed. The wavelet base is determined as the best sparse representation base of the magnetic flux leakage signal, and the mathematical expression of the wavelet sparse base matrix is derived; A measurement matrix optimization algorithm based on Welch bound and PRP conjugate gradient algorithm is proposed; An important data segment screening method of the MFL detection data is proposed, which greatly reduces the data storage size. The simulation results show that the proposed online compression algorithm greatly reduces the computation complexity of compression encoding in online environment, has the advantages of simple and rapid operation, high compression ratio, high reconstruction precision and etc., and meets the actual requirements of MFL detection data online compression.