Token-based multi-class anomaly detection method

An anomaly detection and multi-category technology, applied in character and pattern recognition, instruments, biological neural network models, etc., can solve problems such as failure to function, failure to output probability, low precision, etc., to reduce the complexity of decision-making space and simplify the process , the effect of improving efficiency

CN111242215APending Publication Date: 2020-06-05杭州朗阳科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2020-06-05

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Abstract

The invention discloses a Token-based multi-class anomaly detection method. The method comprises the following steps of 1) obtaining a data source, 2) judging whether the data source is a continuous number type data source or a continuous character type data source, 3) if the data source is judged to be the continuous number type data source, dividing the continuous number type data source into ngroups to obtain token mapping values, and if the data source is judged to be the continuous character type data source, directly obtaining a token mapping value, 4) constructing a deep learning network model, 5) accessing the data features of the multiple groups of tokens into the deep learning network model for training, and obtaining a prediction value of the target by an output layer through an activation function, 6) comparing the predicted value with the actual label to construct a ternary cross entropy loss function, and optimizing an output layer by using the ternary cross entropy lossfunction, and 7) using the deep learning network model to output an approximate value for judgment to obtain the result. According to the method, the complexity of the model is reduced by reducing the complexity of the decision-making space, the requirement for the number of training samples is reduced, and the amount of calculation parameters is small.
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Description

technical field

[0001] The invention belongs to the field of predictive data digitization, and in particular relates to a Token-based multi-category anomaly detection method. Background technique

[0002] In existing applications such as machine abnormality monitoring and predictive maintenance, compared with public data such as image digital recognition and image data, the digitization process of these complex operation data requires high precision and takes a long time. It is still a huge challenge to build these learning models with only a small amount of high-quality data and obtain high recognition accuracy.

[0003] In addition, the selection of the training time window in repetitive data has a great influence on the final result. In order to weaken or eliminate such influence, we introduce the concept of Token in the NLP language, discretize the continuous input data, and The overall model has a certain fault tolerance to the input data, and reduces the quality requi...

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Embodiment Construction

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0025] A Token-based multi-category anomaly detection method, comprising the following steps:

[0026] 1) Obtain the data source to be analyzed;

[0027] 2) Training data set, test data set data source vectorization

[0028] The data source type of abnormal machine is digital, such as sound, vibration, temperature, sensor, and logfile records o...