In order to solve the "black box" problem of artificial intelligence application in geophysical parameter retrieval, and make artificial intelligence applications have physical significance, interpretability, and universality, the theory and technology of deep learning coupling physical and statistical methods are gradually being developed in various disciplinary fields. This study summarizes the author's more than 20 years of relevant research, and presents the artificial intelligence inversion paradigms and judgment conditions for remote sensing parameters based on the induction and deduction of the theory and judgment conditions of artificial intelligence geophysical parameter inversion paradigms. At present, a common problem encountered in many studies is that many artificial intelligence parameter retrieval uses theoretical simulation data to achieve high retrieval analysis accuracy, but the actual application retrieval accuracy is not ideal. Therefore, deep learning how to couple physical and statistical methods has become an urgent engineering and technical challenge that needs to be addressed. We will take passive microwave soil moisture and surface temperature retrieval as an example to illustrate that the accuracy of the physical model itself still needs to be greatly improved, or the simulated data only represents a small portion of the actual situation. We believe that there are significant limitations in using only physical models to simulate data for direct retrieval, and high-precision multi-source statistical data must be supplemented. At the same time, we can also improve the physical model by directly using deep learning to simulate data training and testing with actual data to verify the gap between the physical model and the actual situation, determine the errors of the physical model, and thus improve the physical model. Statistical methods are the most intuitive description of human beings, while physical methods summarize and generalize statistical methods. However, information or energy transmission in the real world is transmitted in quantum form, and many physical models have made many simplifications without depicting real physical phenomena well. Different neurons in deep learning are more suitable for describing and expressing the transmission methods of quantum information. Understanding the real world through calculus quantum information flow requires improving our cognitive thinking. How to collect data that meets the real situation (quantum information or energy transmission) is very important. We can fully utilize physical logic reasoning to construct physical formulas and statistical methods, and use big data thinking mode to improve the accuracy of geophysical parameter inversion under the guidance of paradigm theory and judgment condition framework. Proving through physical logic reasoning that the input variable can uniquely determine the output variable is a fundamental condition for forming a physically meaningful, interpretable, and universal retrieval or classification or prediction paradigm. Controlling the quality of collected data from the perspective of quantum information (energy) transmission is the key to achieving high-precision inversion engineering and technology for geophysical parameters. Improving the cognitive understanding of quantum information flow in calculus and identifying the limitations of physical models are of milestone significance for achieving high-precision inversion in artificial intelligence.