3 results about "Complete sequence" patented technology

The invention discloses a distributed power supply access unit abnormity judgment method and system. The method comprises the following steps: carrying out missing value interpolation on missing values in data obtained by preprocessing to obtain a complete sequence predicted value after interpolation; a depth model is constructed, the depth model comprises a plurality of parallel HTC-Mama coding blocks, and after output of each HTC-Mama coding block is fused, abnormal category probability distribution is obtained by using a classification head; according to the maximum probability value in the abnormal category probability distribution, obtaining an abnormal score and recording a corresponding category label, then according to the abnormal score, using a DSPOT algorithm to set a dynamic threshold value used for judging the abnormality, when the abnormal score is greater than the corresponding dynamic threshold value, representing the abnormality, and outputting the abnormal score and the corresponding category label, otherwise, determining the abnormality; the method has the advantages that the accuracy, the robustness and the real-time performance of abnormity judgment of the distributed power supply access unit are improved.

This invention discloses a method and system for calculating non-causal magnetic field strength based on an analytical model and a neural network. The method first obtains a fixed parameter set by fitting the equivalent permeability polynomial parameters of the analytical model based on training samples. Then, it calculates the analytical baseline sequence Ha(t) based on the input magnetic flux density sequence B(t) and the fixed parameters. B(t) and Ha(t) are then constructed as feature sequences containing the original sequence, first-order difference, second-order difference, first-order cumulative sum, and inflection point indicators, respectively. These are concatenated with temperature features copied to the sequence length and input into a trained non-causal sequence neural network model, outputting a residual sequence ΔH(t). The final predicted magnetic field strength value is H(t) = Ha(t) + ΔH(t). This invention performs learning in the residual domain, reducing the difficulty of network modeling, and utilizes a bidirectional long short-term memory network to achieve non-causal inference in offline scenarios with known complete sequences, significantly improving prediction accuracy and generalization ability.

The disclosure discloses a method for expanding a sequence data set, comprising: obtaining a distance matrix of two-by-two sequences in an original sequence data set; obtaining an initial average sequence and an initialized weight array based on the original sequence data set and the distance matrix; obtaining a generated sequence based on the initial average sequence and the initialized weight array; constructing a sequence observation value generation model, and taking the generated sequence as an input of the model, and outputting an observation value corresponding to the generated sequence, so that the generated sequence and the observation value corresponding thereto form a complete sequence data set; and combining the complete sequence data set with the original sequence data set, and performing deduplication to complete expansion of the sequence data set.