Epidemic situation deduction simulation system and method

Abstract

The invention provides an epidemic situation deduction simulation system and method. The system comprises: a user interface module configured to receive an external input; a control module configuredto control behaviors described in a preset simulation logic program based on the external input; a simulation module configured to run a preset simulation logic program in response to the control of the control module; and an optimization module configured to perform optimization learning on the value of the learnable parameter based on the simulation result data output by the simulation module. The preset simulation logic program describes the state in epidemic situation development and the behavior of driving the change of the state in epidemic situation development in a code form; when theexternal input includes a learnable parameter, the control module acquires an optimization value of the learnable parameter from an optimization module and performs a step of controlling the behaviordescribed in the preset simulation logic program based on the external input by taking the optimization value as a value of the learnable parameter.

Description

technical field [0001] The present invention generally relates to the field of artificial intelligence, and more specifically, relates to a data-driven epidemic deduction simulation system and simulation method. Background technique [0002] Simulation (or simulation) is the approximation or imitation of how a process or system works. A simulator is software that emulates the operation of a process or system over time. The simulator needs to extract the key states involved in the operation of the system, and use the program to simulate the changes of these states (which can be called the behavior of the system). The emulator can be used for system simulation, analysis, tuning, testing, teaching, etc., and has a wide range of applications. [0003] Traditional simulators abstract system behavior into mathematical formulas or solutions to mathematical problems through domain knowledge, and at the same time determine the parameters involved in mathematical formulas or problem...

AI Technical Summary

Problems solved by technology

For example, in the supply chain scenario, it is necessary to simulate the order arrival behavior, but the order arrival is the result of the interaction of multiple factors, the process is too complicated, and it is difficult to abstract it into simple rules

2. Lack of domain knowledge about parameters

Therefore, under the premise of a given level of abstraction, traditional simulators cannot use useful data available in reality, and the accuracy (the ability to reflect the real system) is limited by the accuracy and completeness of domain knowledge

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