As the gig economy continues to grow, behaviors of workers on gig service platforms have an increasing impact on service satisfaction. For example, fatigue driving behaviors of drivers in ride-hailing platforms may cause serious damages, both for individuals and society. Therefore, regulating behaviors of workers is urgent and challenging. A lot of studies are conducted to detect workers' noncompliance behaviors, such as detecting fatigue driving by computer vision or pattern recognition methods. However, few of them indicate how to efficiently exploit the detection results to regulate workers' behaviors. In this paper, we point out that workers' noncompliance behaviors and their incomes should be correlated, and propose a quantifiable computation framework that includes a price-based incentive mechanism and a method to verify the effectiveness of the mechanism. Historical behaviors of workers are summarized as credits and stored in nonfungible token called CreditToken to ensure that it cannot be tampered with. CreditToken will further affect workers' incomes. We abstract the decision-making behavior of workers as a Markov decision process and demonstrate the effectiveness of the incentive mechanism with model checking and formal methods. The analysis shows that our framework is able to provide a rational price strategy formation for gig service platforms, and can be flexibly integrated into existing pricing schemes to maximize the value of the detection results. Extensive experiments illustrate the advanced nature and practicality of our framework.