10 results about "Human decision" patented technology

The invention relates to the technical field of coal mining, in particular to an ultra-thick coal seam fully mechanized caving face mine pressure regulation and control optimization method. The method is used for solving the problems that in the mine pressure detection process, response to mine pressure fluctuation emergencies is slow, multi-dimensional data cannot be effectively combined for comprehensive analysis, and manual judgment errors are caused by excessive manual intervention. By monitoring the mine pressure of the fully mechanized caving face of the coal seam, displacement of a supporting structure and soil settlement data in real time and combining a Kalman filter and a neural network deep learning algorithm, the stability of a mine pressure system is accurately analyzed, the change trend of the mine pressure is predicted, early warning is conducted in advance, mine pressure regulation measures are optimized, and coal mine operation safety is ensured; a convolutional neural network and a recurrent neural network are adopted to process time domain and frequency domain feature data, automatic signal matching and control decision are achieved, the operation modes of a supporting structure and mechanical equipment are dynamically adjusted, the human decision complexity is reduced, and mine safety is ensured.

The invention relates to the technical field of robot intelligent control, and discloses a table tennis playing robot preset performance control method based on an optimal human decision, which comprises the following steps: reconstructing a kinetic model of a table tennis playing robot system, and obtaining servo constraints after state transformation; constructing a hierarchical hybrid controller of a robot preemption algorithm and a human decision control algorithm, integrating a human fuzzy intention with mechanical control to optimize a ball hitting strategy, and converting a decision problem into a functional optimization problem; and obtaining an analytical expression of the optimal membership function to obtain weights or priorities of human decisions in different states. According to the method, machine motion guided by a clear physical law is combined with fuzzy representation of human behaviors and intentions to form a coherent framework, optimization of system performance is achieved by integrating a preemption algorithm and a human decision algorithm, and meanwhile safety and robustness are ensured.

A computing device determines a relative weightage of human-in-the-loop component usage to artificial intelligence component usage in a computing process that includes human decision-making and artificial intelligence decision-making. When the relative weightage is below a given range, such that the human-in-the-loop component usage is low relative to the artificial intelligence component usage: the computing device adjusts the computing process to increase the human-in-the-loop component usage relative to the artificial intelligence component usage. When the relative weightage is above the given range, such that the human-in-the-loop component usage is high relative to the artificial intelligence component usage: the computing device adjusts the computing process to decrease the human-in-the-loop component usage relative to the artificial intelligence component usage

The application provides a kind of automatic driving model verification method, device, controller, vehicle and medium, method includes: in the process of vehicle driving, make the automatic driving model to be verified based on shadow mode operation, collect first environment data and human decision planning result, obtain the risk scene and corresponding data segment of model decision deviation;Human decision planning segment is used as supervision label, and the model is incrementally trained;Control vehicle to run on open road, collect second environment data to obtain model decision planning result and driving scene;Statistical model and driver's feature parameters under each scene category;According to the comparison result of feature parameter, calculate the comprehensive confidence index of model;If the index is greater than the preset threshold, determine that the model passes the verification.Based on this, the application realizes hierarchical, closed-loop model verification method, realizes the quantitative evaluation of the uncertainty of automatic driving model, improves the pertinence and effectiveness of test verification.

The invention relates to the field of robot control, in particular to a robot adaptive control method based on a human-in-the-loop and an asymmetric barrier, and the method comprises the following steps: obtaining a reconstructed decoupling constraint based on a U-K principle; asymmetric logarithmic barrier transformation based on a dynamic skew factor is constructed, and bounded constraints are mapped to an unbounded virtual space; an ideal constraint force without a Lagrange multiplier is constructed based on decoupling constraint and a U-K principle, and a self-adaptive robust controller and an optimal decision model based on a fuzzy theory are constructed; and constructing a dynamic adjustment mechanism of a basic weight based on a constraint following error norm and an internal force safety index, and combining a smooth regional transition mechanism with a human decision based on a fuzzy theory to synthesize final control input. By orthogonally decomposing the task space, it is ensured that the robot can independently and strictly maintain the clamping internal force between the two arms while responding to a human random motion instruction, and objects are effectively prevented from sliding off in the cooperation process.

This invention discloses an improved cognitive-driven decision-making method for predicting human decisions, comprising: acquiring the current situation; based on the current situation, performing feature recognition based on a preset memory model to obtain a prototype corresponding to the current situation; based on the prototype corresponding to the current situation, performing memory retrieval to obtain memory retrieval information; based on the memory retrieval information, performing an expectation check to obtain memory retrieval information that passes the check; and based on the memory retrieval information that passes the check, performing a simulated action to obtain the final decision action. On the one hand, the preset memory model can effectively store continuous memory information and can match the corresponding prototype based on continuous information in the current situation; on the other hand, this invention uses a joint probability density function, which has the ability to simulate the uncertainty of human decision-making.

The present application belongs to the technical field of artificial intelligence, and provides a kind of man-machine hybrid crowd wisdom knowledge driven decision-making method, device, equipment and medium, comprising: obtaining machine data and processing to obtain machine decision attribute;Acquire expert knowledge and public knowledge, combination to obtain human decision attribute;According to machine decision attribute and human decision attribute, after processing, obtain man-machine hybrid decision attribute weight and human crowd wisdom decision opinion;Build man-machine hybrid social network, determine man-machine hybrid crowd wisdom knowledge value measure through man-machine hybrid social network, information entropy and trust degree;Carry out multi-attribute man-machine knowledge fusion and adjustment, obtain optimized group knowledge value and optimized man-machine hybrid decision subject weight;Further, build man-machine hybrid decision subject weight feedback optimization model, execute decision processing through man-machine hybrid decision subject weight feedback optimization model, obtain decision result.The beneficial effects of the present application are: improve the scientificity and adaptability of decision-making.

Systems, methods, and other embodiments described herein relate to integrating human decision-making into a model-based system. In one embodiment, a method includes acquiring sensor data, including driver data about a driver of a vehicle and driving data about the vehicle and a surrounding environment of the vehicle. The method includes encoding, using a world encoder, the sensor data into a latent representation. The method includes determining human decision-making characteristics according to the latent representation. The method includes generating a control signal for providing shared control of the vehicle according to the human decision-making characteristics and the latent representation.

This application provides a method for determining product disallowance status. It achieves rapid retrieval of configuration information by constructing a quality configuration mapping table, and comprehensively covers the disallowance requirements of cross-border e-commerce products by combining a multi-scenario disallowance determination mechanism (disallowance due to missing information, access-based disallowance, priority-based disallowance, and specific standard-based disallowance). It automatically optimizes product activation strategies using association number grouping and priority sorting algorithms to avoid human decision-making bias. Real-time judgment and batch update operations are triggered by message queues, significantly improving processing efficiency. This solves the problems of low efficiency, high error rate, poor flexibility, and response delay caused by manual maintenance, achieving fully automated, highly accurate, and scalable management of disallowance status.

The invention provides an adaptive task scheduling method for a phased array radar. The method comprises the following steps: acquiring test task parameters, test process parameters and resource parameters of the phased array radar; according to the test task parameters, the test process parameters and the resource parameters, constructing a mathematical model for performing adaptive task scheduling on the phased array radar; based on the mathematical model, constructing a reinforcement learning decision model, and training the reinforcement learning decision model; and inputting a preset monthly test task list into the trained reinforcement learning decision model, executing adaptive task scheduling, and generating a production scheduling scheme of the phased array radar. According to the technical scheme of the embodiment of the invention, the intelligent and automatic production scheduling process is realized, the efficiency of the phased array radar production scheduling plan is improved, the inconsistency and error of human decision are avoided, the self-adaptive optimization production scheduling of the phased array radar test task is realized, and the comprehensive utilization rate of resources is improved.