Multi-model intelligent robot system and construction method

Abstract

The invention discloses a multi-model intelligent robot system and a construction method, and the system comprises a digital operation service layer which comprises a strategy center, an operation center and basic services; the strategy center is used for scheduling and deciding to use various robot models to cope with different customer responses; the operation center is used for data analysis and model training of the multi-modal intelligent robot; the basic services are used for providing standard management function services of the digital operation service; the system also comprises an intelligent middle platform layer used for responding to the digital operation service layer and providing a plurality of robot models supporting semantic comprehension and task type multi-round voice dialogue services; a data service layer used for providing data storage and query services; and an interface service layer is used for integrating with a corresponding standard service interface for calling. The invention can improve the personification service capability of the intelligent robot, supports the multi-model service combination, achieves the efficient optimization operation of the robot, and is suitable for more business scenes.

Description

technical field [0001] The invention relates to the technical field of intelligent robots, in particular to a multi-model intelligent robot system and a construction method. Background technique [0002] In the application of the financial industry, artificial intelligence technology has gradually evolved to more complex scenarios. Among them, intelligent robots based on intelligent voice technology are an important application field of artificial intelligence technology. Through automation and intelligent capabilities, they provide customers with highly personable telephone channels. At the same time, through standardized language communication service advantages, we can more accurately understand customer needs through communication and complete business processing. [0003] However, the application of artificial intelligence in the same business scenario in the existing financial industry relies on a separate model algorithm, resulting in a low degree of anthropomorphic c...

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Problems solved by technology

[0003] However, the application of artificial intelligence in the same business scenario in the existing financial industry relies on a separate model algorithm, resulting in a low degree of anthropomorphic customer interaction, resulting in poor business handling effects. At the same time, due to the lack of digital operation tools, the existing Algorithm model optimization in the technology is more optimized by manual annotation, resulting in a longer model optimization cycle and higher requirements for training data. The application of artificial intelligence in multiple business scenarios is likely to form knowledge islands of business data, business data and scenarios. Lack of connectivity. Algorithmic model optimization in the prior art relies more on manual annotation, which increases the cost of model optimization and operation, prolongs the optimization cycle of the model, and has poor business scalability. Different business scenarios require multiple Set the environment to realize business functions and other issues

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