8 results about "Cognitive module" patented technology

The utility model provides a child picture book with a sounder, which relates to the technical field of picture books, and comprises a cover, a plurality of title pages are arranged in the cover, a plurality of detachable animal image cognition modules are arranged on each title page, and the animal image cognition modules are arranged on the title pages. The animal image cognition modules are used for forming images of animals, a plurality of module storage grooves are formed in each title page, and the number of the module storage grooves corresponds to that of the animal image cognition modules; a plurality of animal image cognition modules are arranged on the title page, and the animal image cognition modules are detachable, so that a child can splice an animal image through the animal image cognition modules in the process of teaching cognition of the child, and the thinking, learning and manipulative ability of the child is trained in the process; and moreover, the interactivity in the animal cognition process can be improved, the learning interest of children is improved, and the cognitive learning effect of the children is relatively good.

The invention discloses a human-based interaction system for artificial awareness cognition education. The system comprises an awareness cognition guiding engine, a human teacher cooperative training module, an interpretable behavior demonstration system, a cognition deviation reminding mechanism and a human-based intention driving feedback module. Wherein the guide engine extracts semantic expression, reasoning process and destination driving information of an artificial intelligence system based on a DIKWP multilayer cognitive model, and calculates information integration quantity phi of each cognitive module in combination with an information integration theory (IIT) so as to identify and mark a consciousness response stage with high-integration information and generate visual cognitive path interpretation; the teacher cooperative training module provides an interactive task release and hierarchical result feedback labeling mechanism to realize man-machine cooperative training; the behavior demonstration system synchronously displays cognitive state evolution in a graphical interface through a concept space orbit and a semantic space orbit; the deviation reminding mechanism monitors the logic consistency and the target matching degree and provides deviation correction interaction; the intention-driven feedback module collects the target intention, the interaction rhythm and the emotional response of the user to dynamically adjust the presentation mode and the interaction strategy, so that the interpretability, the trainability and the controllability of the artificial intelligence cognitive process are improved.

The invention relates to the technical field of sensors, in particular to a hazard source identification and guidance system for a disaster site. The system comprises a first cognitive module, a second cognitive module and a risk management module. The first cognitive module monitors smoke dust or burnt smell in the environment, and the second cognitive module monitors personnel distress sound and circuit discharge sound. The system provides visual route guidance away from a high-concentration area for rescue workers by monitoring smoke dust and burnt smell; when the call-for-help sound is detected, generating a route leading to the call-for-help position and avoiding the high-concentration area; and when the discharge sound is detected, generating an electric shock risk prompt. The system can be used for disaster sites such as fire disasters, the problem that rescue workers cannot effectively avoid poison gas, find trapped persons and avoid hidden electric spark dangers in a low-visibility environment is solved, and dangerous source identification and path planning are provided.

The invention provides a large model-agent collaborative dynamic extensible remote sensing target interpretation system and method, and a medium. The target interpretation system comprises a cognition module, a planning module and an execution module. Wherein the cognition module is used for performing task decomposition on a to-be-processed text task through a large language model, obtaining sub-tasks corresponding to the to-be-processed text task, and obtaining a direct dependency relationship between every two sub-tasks; the planning module is used for performing task sequence planning on the subtasks based on the direct dependency relationship between the subtasks and constructing a task execution sequence; wherein the task execution sequence comprises a first subtask for serial execution and a second subtask for parallel execution; and the execution module is used for performing task processing on the to-be-processed multi-mode remote sensing image corresponding to the subtask based on the task execution sequence, and outputting a task execution result.

The invention relates to the technical field of medical education, in particular to a multi-modal data fused craniocerebral trauma education achievement evaluation method and system. The invention discloses a multi-modal data fused craniocerebral trauma education achievement evaluation method. The method comprises the following steps of S1, performing induction association on a craniocerebral trauma cognitive module and a craniocerebral trauma cognitive node of a craniocerebral trauma cognitive map to obtain a first score weight; s2, constructing a craniocerebral trauma induction route through a craniocerebral trauma cognitive module and a craniocerebral trauma cognitive node to obtain a second score weight; s3, face language data information of participants is collected, and a third score weight is obtained through the face coding technology; and S4, setting a first threshold value, a second threshold value and a third threshold value to obtain a final score, and generating a craniocerebral trauma education achievement evaluation report. According to the method, the craniocerebral trauma educational achievement is evaluated through triple evaluation methods of module matching, induction routes and a facial language coding technology.

This invention relates to the field of UAV communication technology, and in particular to an autonomous identification method for multimodal interference in UAVs based on a hierarchical cognitive architecture. By introducing homomorphic encryption and secure multi-party computation, it achieves encrypted domain processing and fusion of multimodal data throughout its entire lifecycle, avoiding the risk of leakage of original information during transmission and computation, and constructing a data security barrier in high-adversarial environments. Furthermore, at the cognitive layer, it integrates an attention mechanism and a causal reasoning engine, elevating multimodal features to a deep semantic understanding level of intent and evolutionary patterns, generating a spatiotemporal interference cognitive situation map, breaking through the limitation of traditional methods that can only identify interference types. Finally, the decision layer adopts hierarchical reinforcement learning to generate the optimal anti-interference strategy in real time based on the dynamic situation, and optimizes the perception and cognition modules through feedback of evaluation results, forming a closed-loop self-evolutionary mechanism.

The application provides a training method, an inference method and a system of a perception-cognition-control multi-modal large model based on a Riemannian manifold. The method comprises the following steps: obtaining Euclidean features corresponding to multi-modal training samples, and inputting the Euclidean features into a perception-cognition-control multi-modal large model to be trained; obtaining feature point representations corresponding to the multi-modal training samples through a multi-modal mapping alignment module in the large model, and determining a multi-modal alignment loss based on the similarity between the feature point representations; taking the feature point representations corresponding to first modal training samples and second modal training positive samples as inputs of a cognition module, and taking the output of the cognition module as an input of a control module; determining a spatial consistency loss based on the input and output of the cognition module and the output of the control module; and iteratively updating based on multiple loss terms to obtain a logically integrated multi-modal large model. The application can overcome the geometric mismatch defect of the Euclidean space.