4 results about "Habit" patented technology

Embodiments of the present application provide a method for prompting deviation of an intended behavior habit based on a behavior habit, comprising: predicting an intended behavior habit of a user according to an actual environment state generated by an actual behavior habit of the user, using a pre-constructed behavior habit model; predicting a predicted behavior habit of the user according to an obtained current environment state, using the behavior habit model; determining a difference between the predicted behavior habit and the intended behavior habit; and issuing a prompt information when the difference meets a set threshold. Embodiments of the present application also provide an apparatus for prompting deviation of an intended behavior habit based on a behavior habit, a computer readable storage medium and an electronic device.

The system according to the embodiment aims to accurately read the habits and psychological state of the other person from their words, actions and facial expressions, and make effective suggestions. [Solution] A system according to an embodiment includes a sensor unit, an analysis unit, an estimation unit, and a proposal unit. The sensor unit collects the other person's words, actions, or facial expressions using a camera or microphone. The analysis unit analyzes the data collected by the sensor unit using deep learning. The estimation unit estimates the other person's habits and psychological state based on the data analyzed by the analysis unit. The proposal unit makes a proposal based on the habits and psychological state estimated by the estimation unit.

The application provides a campus abnormal behavior analysis method based on position information, and the specific steps are as follows: according to the work and rest schedule, a time period in which the campus abnormal behavior is prone to occur is divided, and a weight coefficient is set for each time period; according to the campus layout, a space region is divided, and a space weight coefficient is set for each space region; student position information is collected and obtained, the daily behavior habits of each student are statistically analyzed according to the collected student position information in a period of time, and the relationship closeness of all students is calculated; the abnormal behavior that may occur to the members in the clustering group is analyzed by fusion in combination with auxiliary information such as the time-space function attribute, the daily behavior habits of students, the relationship closeness of students and the like, and an alarm prompt is given. The campus abnormal behavior analysis method can better adapt to various occasions, and effectively avoid the exposure of student privacy.

The application provides an indoor personnel environmental behavior mode recognition method, aiming at the preference type behavior, first, the collected data is cleaned, then the data compression is carried out according to the characteristics of time series data, including segmenting the data according to important points and classifying the data segments by using a clustering algorithm, so as to realize the symbolic representation of time series, finally, the environment-behavior sequence is constructed by using the processed data, the time series association rules are formulated and the main association rules are extracted. The main association rules express that a behavior usually occurs after a series of state changes, so as to describe the long-time influence of environmental parameters on personnel environmental behavior. For the habit type behavior, only the collected data is cleaned. According to the characteristics of the behavior data, the behavior sequence is constructed and the main association rules are extracted. The corresponding main association rules express a series of behaviors produced by the personnel continuously, so as to describe the long-time influence of behavior factors on personnel environmental behavior.