12 results about "Adaptive behavior" patented technology

The invention discloses an education robot teaching strategy generation system based on learning behavior big data analysis, and particularly relates to the technical field of intelligent education. The method comprises the following steps: acquiring learning behavior data of students in an education robot teaching process; identifying the active adaptation behavior of the student, and generating an active adaptation behavior identification result; based on the active adaptation behavior recognition result, analyzing a response relationship between the existing teaching strategy and the student learning behavior, and outputting a teaching strategy adaptability analysis index; according to the teaching strategy adaptability analysis index, the deviation degree of the existing teaching strategy deviating from the real learning requirement is evaluated, and a teaching strategy deviation evaluation result is obtained; an adaptive feedback suppression constraint and a strategy depolarization weight are constructed to generate a teaching strategy candidate set, screening and confirmation are carried out according to a consistency verification rule and a robustness threshold, an education robot teaching strategy is updated, and an education robot teaching execution instruction is formed. And the scientificity and individuation level of the teaching strategy are improved.

According to an embodiment, disclosed is a system comprising, a sensor and a processor; wherein the processor storing instructions in a non-transitory memory that, when executed, cause the processor to detect, via the sensor of a host vehicle, a target vehicle; capture, via the sensor, a first region comprising a first characteristic of the target vehicle; analyze, the first region using an image processing module; determine, an identity of the target vehicle; capture, via the sensor, a second region comprising a second characteristic of the target vehicle; analyze, the second region using the image processing module; and determine, a safety feature that is enabled in the target vehicle.

An adaptive behavior recognition method and apparatus, a device, a medium, and a product. The method comprises: acquiring behavior feature data respectively corresponding to at least two individuals to be recognized (S110), wherein the behavior feature data represents statistical behavior information exhibited by said individuals in response to repetitive threat stimulus signals during repeated threat stimulus experiments; on the basis of at least two pieces of behavior feature data, clustering said at least two individuals to obtain at least two cluster sets (S120); and on the basis of cluster index values respectively corresponding to the at least two cluster sets, determining adaptive behavior results respectively corresponding to said individuals in each cluster set (S130).

The invention discloses a personality self-adaption-based personalized communication scheme generation method and device, and the method comprises the steps: receiving a communication scene inputted by a user, and extracting the personality features of the user and the personality features of an interaction object; according to the personality characteristics of the user and the personality characteristics of the interaction object, a behavior disorder diagnosis report is generated in combination with a personality disorder rule base; generating a scene simulation interaction task according to the behavior disorder diagnosis report, and sending the scene simulation interaction task to the user for interaction; collecting interaction data of finishing the scene simulation interaction task by the user, and comparing the interaction data with risk points in the behavior disorder diagnosis report to obtain a comparison result; and an intervention instruction is generated according to a comparison result, and the non-adaptive behavior or psychological state glide of the user is intervened and corrected in real time according to the intervention instruction, so that a final communication scheme is generated, and the behavior change efficiency and continuous motivation of the user in multiple fields such as interpersonal communication, sales, fitness training, learning and growth and the like are improved.

The invention discloses a refined behavior recognition method and system for a virtual teaching scene, and solves the technical problem that the precision of a final behavior recognition result is poor due to an existing refined behavior recognition method for the virtual teaching scene. The method comprises the steps that a virtual teaching scene behavior image is acquired, the virtual teaching scene behavior image is input into a behavior measurement model based on multilayer perception self-adaption, and the behavior measurement model based on multilayer perception self-adaption comprises a feature extraction network, a feature enhancement network and a posture decoding network; performing multi-scale feature extraction on the virtual teaching scene behavior image through a feature extraction network, and outputting multi-scale fusion features; performing feature enhancement on the multi-scale fusion features by adopting a feature enhancement network, and outputting target enhancement features; and inputting the target enhanced feature into a posture decoding network for posture decoding, and generating a behavior recognition result.

Methods, systems, devices and computer software / program code products enable the generation of synthetic images based on actual images captured by one or more physical cameras. The generated synthetic images provide visual spatial awareness to enable adaptive behavior in a dynamic environment, including robotics and simulation.

The invention discloses a student psychological assessment early warning method and system based on group behavior clustering analysis, and the method comprises the steps: obtaining multi-source campus behavior time series data, extracting group behavior feature vectors, and dynamically dividing a plurality of behavior normal form groups; calculating a multi-dimensional behavior baseline of each behavior normal form group; calculating a group relative deviation degree between the current behavior feature vector of the target student and the multi-dimensional behavior baseline of the behavior normal form group to which the target student belongs; analyzing the continuous increasing trend of the relative deviation degree of the group based on a sliding time window to judge adaptive behavior deviation; and performing attribution analysis on the target student with adaptive behavior deviation, generating a semantic risk early warning signal, and outputting targeted assistance suggestions according to the association intervention strategy library. According to the invention, through dynamic group division and relative deviation analysis, accurate and explainable early warning and effective intervention guidance of student behavior risks are realized.

Embodiments of the present disclosure provide a method and apparatus for determining a PDCCH monitoring adaptive behavior. The method comprises: receiving PDCCH monitoring adaptive indication information sent by a network side device, the PDCCH monitoring adaptive indication information comprising first indication information; and determining a PDCCH monitoring adaptive behavior on the basis of the PDCCH monitoring adaptive indication information. In the method for determining the PDCCH monitoring adaptive behavior provided in the embodiments of the present disclosure, different configurations of the PDCCH monitoring adaptive indication information, the PDCCH monitoring adaptive behavior is determined, such that different PDCCH monitoring behaviors of a terminal device can be flexibly indicated, or a PDCCH monitoring behavior can be switched, thereby saving the monitoring power consumption of the terminal device and ensuring the timely transmission of data.

The application discloses a human-computer interaction method and system based on character emotion coupling and adaptive behavior rendering, relates to the technical field of robots, and comprises the following steps: receiving and simultaneously inputting signals, performing priority arbitration after fusion, and converting into an interactive dose vector IDV; performing character-emotion double-layer coupling calculation on the IDV, obtaining an original increment, and performing emotion mutual exclusion and attenuation constraint to obtain a final emotion state and character parameter; acquiring a basic action template, correcting the atomic action frame of the basic action template by using a four-dimensional dynamic correction coefficient to obtain final execution parameters, and delivering the final execution parameters to an execution mechanism. The application solves the response conflict problem of multi-sensor concurrent input, realizes that the character determines the emotion sensitivity and reflects individual differences, introduces a mutual exclusion constraint mechanism to avoid unreasonable emotion state superposition, and maps and penetrates the internal character and emotion parameters into the physical execution of each action frame in real time to solve the action mechanical problem.

The invention belongs to the technical field of data processing, and particularly relates to an abnormal behavior recognition data processing method based on a skeleton key point topological graph, which comprises the following steps of: S1, accessing a real-time video stream of a field high-position camera, performing attitude estimation on each frame of image in the video stream, extracting two-dimensional skeleton key points of an operator in a scene, and extracting the two-dimensional skeleton key points of the operator; the adjacent key points are connected according to a human anatomy structure to construct a skeleton topological structure; s2, obtaining the observation length of each limb in the skeleton topological structure on the current frame image, and determining the standard reference length corresponding to each limb in combination with a preset standard human body prior library. According to the method, the problem that human body behaviors are difficult to recognize under high-angle monitoring is solved from the mathematical principle, meanwhile, zero-sample self-adaption is achieved, and the method has the advantages of being high in sensitivity and low in false alarm rate balance.

Methods, systems, devices and computer software / program code products enable the generation of synthetic images based on actual images captured by one or more physical cameras. The generated synthetic images provide visual spatial awareness to enable adaptive behavior in a dynamic environment, including robotics and simulation.

This invention discloses a refined behavior recognition method and system for virtual teaching scenarios, solving the technical problem that existing refined behavior recognition methods for virtual teaching scenarios result in poor accuracy of the final behavior recognition results. The method includes acquiring behavior images of a virtual teaching scenario and inputting these images into a multi-layered perception adaptive behavior measurement model, which includes a feature extraction network, a feature enhancement network, and a pose decoding network. The feature extraction network extracts multi-scale features from the behavior images of the virtual teaching scenario, outputting multi-scale fused features. The feature enhancement network enhances the multi-scale fused features, outputting target enhanced features. The target enhanced features are then input into the pose decoding network for pose decoding to generate the behavior recognition result.