A method for constructing an artificial intelligence super depth learning model includes inputting by an objective function to input into each node of an input layer by interposing a no-teacher machine learning, connecting the no-teacher machine learning mutually between each node of the input layer and a nerve layer, calculating output reference values based on a learning value obtained by the no-teacher machine learning, a trigger threshold of a cranial nerve, or a sampling learning value, and determining an excitation level according to output reference values of all the nerve layers.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for constructing an artificial intelligence super depth learning model, comprising: inputting by an objective function to input into each node of an input layer by interposing a no-teacher machine learning; connecting the no-teacher machine learning mutually between each node of the input layer and a nerve layer; calculating the output reference value, wherein the output reference value is at least one learning result obtained from no-teacher machine learning, including learning values, brain neural triggering thresholds, or sampling values; and determining an excitation level according to output reference values of all the nerve layers. 2. The method of claim 1, wherein the no-teacher machine learning is characterized by a self-organization algorithm for obtaining a probability scale, a fuzzy event probability measure, or a center value by repeated processing on a basis of a probability scale or a fuzzy event probability measure. 3. The method of claim 2, wherein data belonging to a probability distribution of the probability scale has a scale, which is a value that measures a maximum probability distribution among the data. 4. The method of claim 2, wherein in probability information and data belonging to ambiguous information, the fuzzy event probability measure has a scale which is a value that measures a maximum fuzzy event probability measure among the data. 5. The method of claim 4, wherein the probability scale or the fuzzy event probability measure is a threshold of the trigger values of the brain neural. 6. A device of an artificial intelligence super depth learning model, comprising a memory containing executable code having stored thereon instructions, and a processor coupled to the memory configures to execute the executable code to: extract feature information on a feature value of an objective function information from input information by non-teacher machine learning and input the feature information to an input layer; obtain a feature value of an input data, a probability scale, or a fuzzy event probability measure through the no-teacher machine learning connected between each node of the input layer and a nerve layer, the learning data of the probability scale or the fuzzy event probability measure are logged and used as a trigger threshold of a cranial nerve; a calculated output reference value is at least one learning result obtained from no-teacher machine learning, including learning values, brain neural triggering thresholds, or sampling values; and determine the degree of excitation of the brain and determine the final result by a number of neural signals triggered from the nerve layer. 7. A general purpose mobile terminal equipment equipped with an artificial intelligence super deep layer learning model, comprising a memory containing executable code having stored thereon instructions, and a processor coupled to the memory configures to execute the executable code to: mutually connect with a teacher machine learning between the input information and each node of an input layer, and between each node of the input layer and a nerve layer; and process a self-organization algorithm based on a probability scale or a fuzzy event probability measure. 8. A software program of an artificial intelligence super deep layer learning model characterized by generating an algorithm comprising; mutually connecting with the teacher machine learning respectively between the input information and each node of an input layer, and between each node of the input layer and a hidden layer, the hidden layer being a nerve layer; and processing a self-organization algorithm based on a probability scale or a fuzzy event probability measure.