Method and system for realizing dialogue based on correlation analysis of big data and small data
A technology of correlation analysis and small data, applied in the direction of electronic digital data processing, digital data information retrieval, special data processing applications, etc., can solve the problems of low humor level and humor level, limited number of humorous sentences or humorous components, etc.
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Embodiment 1
[0118] Such as figure 1 As shown, this embodiment provides a method for implementing dialogue based on big data and small data correlation analysis, which can be applied to robots, and includes the following steps:
[0119] S101. Obtain user data.
[0120] In this embodiment, a robot refers to an intelligent robot, a machine that can simulate human behavior or thought, which includes some computer programs, such as chat programs, etc.; during the dialogue process between the robot and the user, the robot's sense organs can obtain multiple small data, For example, the robot’s eyes are equipped with a camera, which can obtain the user’s video image data, the robot’s ear is equipped with a pickup, which can obtain the user’s audio data, and other various sensors obtain sensing data (such as the user’s body temperature, heart rate, etc.), Various terminals obtain text data (such as user attributes, words, etc.) input by users; "throw" multiple small data (stones) obtained into bi...
Embodiment 2
[0179] Such as Figure 10 As shown, on the basis of Embodiment 1, the method for implementing dialogue based on the correlation analysis between big data and small data in this embodiment also includes:
[0180] S107. Select the humorous statement with the largest degree of humor from the set of humorous statements, and output it, specifically: the robot selects the humorous statement with the greatest degree of humor from the set of humorous statements, and outputs it to the user through text and / or voice, so that the user A humorous statement with a high degree of humor and a high level of humor can be obtained.
Embodiment 3
[0182] Such as Figure 11 As shown, the difference between this embodiment and Embodiment 1 or 2 is: step S1044, when the matching degree of the sentence with the first small data is greater than the preset matching degree threshold, the matching degree of the second small data is greater than the preset matching When a statement in the statements of degree threshold is not marked, the statement is deleted; otherwise, the statement is kept.
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