34 results about "Human–machine system" patented technology

A system and method for strategic airspace deconfliction is disclosed, centered on a Cooperative Multi-Agent Platform (CMAP) embedded within an Automated Data Service Provider (ADSP) operating within a federated Unmanned Aircraft Systems (UAS) Traffic Management (UTM) network. The system's inventive feature resides in a specific technical architecture that synergistically integrates a real-time safety constraint into a strategic negotiation engine. A Regulatory Compliance Monitor (RCM) generates a real-time Conflict Risk Score that is incorporated directly into the Local Observation Vector of a Multi-Agent Reinforcement Learning (MARL) policy network. The MARL engine is thereby configured to select a strategic negotiation primitive (e.g., BID, YIELD, TRADE) that is dynamically determined based on this real-time Conflict Risk Score. This transforms abstract resource allocation into a safety-aware, risk-adaptive protocol, allowing an ADSP agent to maximize fleet efficiency while guaranteeing collective safe separation

The invention discloses a dual-scale resource optimization method for a multi-unmanned aerial vehicle auxiliary edge computing system. The method comprises the following steps: establishing a dynamic mobile edge computing system model comprising multiple unmanned aerial vehicles and ground mobile users; establishing a joint optimization problem of task unloading, computing resource allocation and unmanned aerial vehicle trajectory planning by taking minimization of total energy consumption of an unmanned aerial vehicle system as an optimization target; based on a designed double-time-scale layered optimization framework, aiming at a joint optimization problem, on a small time scale, an improved clustering algorithm and a closed solution method are adopted, and a real-time optimal task unloading decision and a computing resource allocation strategy are efficiently obtained; and on a large time scale, a near-end strategy optimization algorithm in deep reinforcement learning is used to carry out autonomous learning optimization, and an optimized unmanned aerial vehicle flight path is obtained. The invention provides a resource optimization method which can give consideration to dynamic adaptability, multi-time scale collaboration, global energy efficiency optimality and low calculation complexity.

This invention discloses a spatial domain impedance learning control method for a five-bar parallel robot, comprising the following steps: Step 1, establishing an Eulerian-Lagrange model of the five-bar parallel robot; Step 2, establishing a human-machine system interaction force model based on the trajectory tracking error of the parallel robot; Step 3, estimating impedance dynamics based on the Eulerian-Lagrange model; Step 4, designing an iterative learning strategy for human-machine interaction force based on the human-machine system interaction force model and impedance dynamics; Step 5, conducting control tests on the five-bar parallel robot using the iterative learning strategy for human-machine interaction force. The learning control method proposed in this invention ensures the convergence of tracking errors, considers the uncertainties in robot modeling, and further guarantees the safety of human-machine interaction.

This disclosure provides a method and apparatus for predicting human-machine conflict, capable of proactively predicting human-machine conflict from multiple dimensions. By acquiring and analyzing various physiological and behavioral data of operators in historical human-machine interaction scenarios, significant characterization factors corresponding to fatigue states are screened, enabling a more accurate quantitative representation of operator status. Simultaneously, the significant characterization factors of operators are combined with environmental and machine characteristic dimensions (including the complexity of interface interaction) to establish a human-machine conflict prediction model suitable for the target scenario. This method can dynamically reflect changes in multiple factors during system operation, proactively predicting the probability of human-machine conflict occurrence, thereby improving the comprehensiveness, timeliness, and reliability of identification, avoiding lag and bias caused by single data or rules, and ultimately contributing to improving the safety and stability of complex human-machine systems.

The invention provides a method and a device for evaluating the workload of a pilot in a high-dynamic scene, and the method comprises the steps: inputting the audio and video information of a cockpit according to the real-time operation of a computer; the method comprises the following steps: decomposing uncertain actions made by a pilot when executing a task in a high-dynamic scene in a time window with a preset length into basic action elements with fine granularity and certainty; and evaluating the workload of the pilot according to the types of the basic action elements appearing in the time window with the preset length, the duration of each basic action element and the total resource consumption value of each basic action element. The method is real-time, accurate, quantitative and non-invasive, and a certain reference is provided for efficiency improvement and task arrangement optimization of new-generation aviation equipment. The method is suitable for flight training, task optimization and man-machine system design.

The invention discloses a three-dimensional modeling unmanned aerial vehicle system and method for dynamic scene change monitoring, and the method comprises the steps: collecting an RGB image, depth data, IMU inertial information and GNSS positioning data of a to-be-monitored scene, and carrying out the standardization processing, so as to output a standardized multi-source data set; calculating a pose corresponding to each frame of data of the unmanned aerial vehicle based on the RGB image after standardization processing, the IMU inertial information and the GNSS positioning data so as to output a continuous pose sequence; processing the RGB image, the depth data and the continuous pose sequence based on an optical flow method-semantic segmentation-geometric consistency verification mechanism to output static data; generating static three-dimensional models at different time points based on the static data and the continuous pose sequence; and generating a monitoring result based on the static three-dimensional model at different time points, wherein the monitoring result comprises a change thermodynamic diagram, a change area quantitative report and an alarm indication signal. The method gives consideration to precision and real-time performance, can be widely applied to the fields of emergency rescue, engineering construction, ecological environment and the like in industrial application, and has remarkable technical innovation and wide market prospects.

The application discloses a kind of based on environmental perception's exoskeleton robot gait autonomous decision-making method, belong to robot automatic control field.It is composed of data acquisition module, environmental perception module, gait switching module three parts, wherein data acquisition module obtains current scene information data by vision sensor, the type information and distance information of ground in front are measured by environmental perception module, gait switching module calculates the landing point of each step according to the track energy of man-machine system, and the end trajectory is planned using the method of dynamic motion primitive, and the step length is dynamically adjusted, so that man-machine system can smoothly reach the terrain in front;And the size information of terrain in front is obtained by environmental perception module, and the gait parameters of exoskeleton robot are dynamically adjusted, to ensure that exoskeleton robot can smoothly, safely and autonomously switch gait;Through motor execution position feedback information, whether the current task of exoskeleton robot is executed is judged in real time.

A human system interface (HSI) for contactlessly controlling a device for a medical procedure is configured to generate a control space, measure a position of a user in the control space, provide a first contactless haptic stimulus in the control space to the user based on the measured position, measure a contactless haptic input from the user in the control space, provide a second haptic stimulus to the user depending on the contactless haptic input, and, based on the contactless haptic input, change a parameter of the HSI and / or provide a parameter to the device connected to the HSI.

The invention provides an unmanned aerial vehicle bridge inspection comprehensive system and a bridge inspection method.The system comprises a hardware layer, a service layer and a user layer, and the hardware layer comprises an unmanned aerial vehicle system and a bridge sensor system and is used for monitoring key parts of a bridge based on the bridge sensor system to obtain multi-dimensional data of the bridge; generating a bridge abnormal area flight path corresponding to the unmanned aerial vehicle system under the condition of determining that abnormal mechanical parameters exist in the bridge multi-dimensional data; the service layer is used for carrying out bridge disease identification on a bridge abnormal area corresponding to the abnormal mechanical parameters according to the unmanned aerial vehicle image data to obtain a bridge disease identification result of the bridge abnormal area; and the user layer is used for generating a corresponding bridge inspection report according to the bridge disease identification result and the bridge multi-dimensional data. According to the invention, the accuracy and inspection efficiency of bridge inspection results are improved.

This invention discloses a multi-level human-computer interaction intent recognition method for complex task scenarios, belonging to the field of human-computer interaction intent recognition technology. It solves the technical problems of low accuracy and low interpretability of multi-level human-computer interaction intent recognition in complex task scenarios. The key technical solution proposes a multi-level intent recognition framework suitable for complex task scenarios, combining task analysis methods and machine learning methods. This framework possesses both sensitivity to human behavior and contextual information, as well as interpretability. Its input data consists of easily captured human behavior data and situational data that can be collected by machine sensors, offering the advantages of not interfering with the working process of the human-computer system and its operability. By simultaneously integrating human behavior, environmental, and task context features, it identifies multi-level human intents layer by layer from the bottom-level data, achieving a more comprehensive understanding of the true human intent. This effectively overcomes the limitations of existing methods that rely on a single level and only on human behavior characteristics for intent recognition, improving the accuracy of multi-level intent recognition while simultaneously achieving interpretable modeling of multi-level intents.

The application is directed to the problem of fault-tolerant formation control of fuzzy multi-leader UAV system. A fault-tolerant formation control method based on reduced-order observer is proposed, including: using T-S fuzzy model to model the multi-leader UAV system; transforming the original system into a reduced-order form, and then deriving an augmented form; designing a reduced-order fault observer based on the intermediate variable for the reduced-order augmented system to estimate the state of the follower UAV, process fault and system uncertainty; using the relative state information to design a tracking fault-tolerant formation controller, taking the estimation of process fault and system uncertainty as compensation term; designing adaptive parameters for the reduced-order fault observer and the tracking fault-tolerant formation controller; verifying the performance of the reduced-order fault observer and the tracking fault-tolerant formation controller; the application can more prominently deal with the nonlinear and uncertain control problem, while reducing the calculation amount and improving the efficiency of distributed decision-making.

This invention relates to an ankle joint motion recognition mechanism, comprising a first branch, a second branch, a third branch, a moving platform, and a fixed platform. All three branches are connected to the moving and fixed platforms, with the first and second branches symmetrical about the third branch. Each branch includes a motor and nine links, forming two parallelograms. The first branch has two encoders, and the second branch has one encoder. The mechanism has a fixed center and a moving center. The motors on the three branches control the pose of the moving platform, giving the moving center two degrees of freedom of movement around the fixed center and the moving platform three degrees of freedom of rotation around the moving center, thus achieving high-precision fitting of ankle joint motion. A human-machine motion mapping model is constructed based on the link angles measured by the encoders and the human-machine system mapping parameters. This model identifies the talus posture, effectively recognizing the talus movement patterns hidden within the ankle tissue, providing data for the design and optimization of the motion trajectory and posture transformation trajectory of subsequent ankle joint rehabilitation robots.

The invention provides an unmanned aerial vehicle multivariate heterogeneous virtual-real fusion simulation test method and system, and the method comprises the steps: 1, constructing an unmanned aerial vehicle virtual-real fusion simulation test system which comprises a simulation modeling computer, a simulation calculation node and a storage server; 2, constructing an unmanned aerial vehicle digital prototype model based on an unmanned aerial vehicle virtual-real fusion simulation test system; 3, performing unmanned aerial vehicle virtual prototype model integration on the unmanned aerial vehicle digital prototype model constructed in the step 2; 4, hardware resources are integrated, and virtual-real mapping of all systems and real objects in the unmanned aerial vehicle virtual prototype model is achieved; and 5, full virtual simulation and semi-physical simulation of the unmanned aerial vehicle system are realized through a virtual simulation engine and a real-time simulation engine. By applying the technical scheme of the invention, the technical problem that in the switching process of different fault states of ground simulation test flight in the prior art, the preparation of the physical state of the equipment is relatively time-consuming is solved.

This invention relates to the field of complex human-machine systems technology, and particularly to an early warning method and device for detecting poor mental workload in operators. The method includes: determining the output state of a target mental workload recognition model to obtain a mental workload state result; determining whether the proportion of the same abnormal state in the mental workload state result exceeds a preset threshold; if not, no early warning response is triggered; otherwise, performance monitoring of the target operator is performed to obtain a performance state result; determining whether the performance state result deviates from the individual's historical performance range; if it deviates, an intervention response is triggered; otherwise, a prompt-level response is triggered. This solves the problems of existing early warning methods, such as difficulty in adaptively adjusting to changes in the operational context, leading to a high risk of false alarms or missed alarms, and difficulty in achieving targeted, sensitive, and effective support, thus affecting the intervention effect.

The application discloses a kind of intelligent lower limb prosthesis control mode leading switching method, it is related to rehabilitation robot control technical field, including the following steps: target terrain detection based on monocular vision, and the orientation description of the target terrain is carried out;Shift centroid in horizontal plane pose estimation;Combining the rule determination of motion mode switching with the orientation description and pose estimation.The present application can realize the leading prediction of prosthesis motion mode switching by constructing the effective perception channel of prosthesis to walking movement environment and the motion intention of prosthesis wearer, thereby reducing the delay required for human-machine system interaction communication, which helps to realize the natural switching of different motion modes of prosthesis wearer, and improves the reliability of prosthesis wearer walking.

This embodiment discloses an unmanned aerial vehicle (UAV) system optimization method, electronic device, medium, and computer program product. The UAV system optimization method includes: constructing an optimization model for the UAV system based on its secure communication rate and energy consumption; wherein the value of the optimization model is positively correlated with the secure communication rate and negatively correlated with the energy consumption; constructing constraint conditions for the optimization model based on constraint parameters; wherein the constraint parameters include one or more of the following: communication parameters of the UAV system's intelligent reflector (IRS), beam parameters of the UAV base station, and movement parameters of the UAV; combining the constraint conditions, determining the optimal solution of the optimization model with the objective of maximizing its value; and optimizing the UAV system based on the optimal solution.

The invention discloses a scene awareness objective measurement method considering abnormal states, and relates to the field of human-intelligent interaction safety analysis. The method comprises four steps of experiment design and preparation, tested training and pre-experiment, formal data acquisition and monitoring, and data analysis and verification. Eye movement data before and after an abnormal state is collected through an eye movement tracking technology, a second-order transfer entropy index is innovatively introduced, parameters such as pupil variability and gaze entropy are combined, the relevance between eye movement parameters and scene awareness is verified through correlation analysis, and accurate quantitative evaluation of the scene awareness in the abnormal state is achieved. The method breaks through the limitation of a traditional measurement method, can truly reflect the real-time response of an operator to an abnormal state, provides support for the safety design and optimization of a man-machine system in the safety key fields of aviation, nuclear power and the like, and is remarkable in application value.

The application provides a physical human-computer interaction control method based on impedance iterative learning, and comprises the following steps: establishing an Euler-Lagrange dynamics model of a five-link parallel robot; establishing a human-computer system interaction force model according to a trajectory tracking error of the parallel robot; designing a robot model reference adaptive control based on the Euler-Lagrange dynamics model; designing a human-computer interaction force iterative learning strategy based on the human-computer system interaction force model and the robot model reference adaptive control; and testing the control of the five-link parallel robot by using the human-computer interaction force iterative learning strategy. The physical human-computer interaction control method based on impedance iterative learning provided by the application can reduce the dependence on system information and ensure stable and safe human-computer interaction by realizing variable impedance adjustment.

The invention provides a subjective and objective data fused large unmanned aerial vehicle operator situation awareness ability evaluation method and system. The method comprises the steps of collecting face objective data and situation awareness data of a large unmanned aerial vehicle operator in a flight test; performing symmetric point mode polar coordinate fusion on the face objective data to obtain a fusion feature mode; performing grade classification on the situation awareness data to obtain a comprehensive level judgment result; performing correlation judgment on the fusion feature mode and the comprehensive level judgment result, and constructing a situation awareness capability prediction model based on a correlation judgment result; and based on the situation awareness capability prediction model, performing situation awareness capability evaluation on the to-be-evaluated large unmanned aerial vehicle operator to obtain an evaluation result. According to the invention, an efficient and reliable standardized solution is provided for selection and training of unmanned aerial vehicle operators and design optimization of a man-machine system.

The invention discloses a ship task execution efficiency evaluation method and device and electronic equipment, and belongs to the technical field of ship control. The method comprises the following steps: constructing a multi-role network diagram system according to an interaction relationship and a dependency relationship among a plurality of task roles of a ship in task execution, the task roles including crew, equipment and environment, the multi-role network diagram system comprises a man-machine system efficiency evaluation model, a human-ring system efficiency evaluation model and a man-machine-ring collaborative system efficiency evaluation model; analyzing the multi-role network graph system, and identifying an efficiency performance portrait and a key node of each task role in cooperative work; obtaining the operation performance, psychological load and scale data of the sailors according to the performance expression portrait and the key nodes; and according to the job performance, the psychological load and the scale data, obtaining an efficiency evaluation result of the collaborative efficiency of ship task execution. The method can comprehensively and accurately evaluate the overall work efficiency in the ship task execution process.

This invention discloses a method, apparatus, medium, and computer device for task planning in complex human-computer systems, comprising: responding to a planning instruction for a target human-computer interaction task, dividing the target human-computer interaction task into multiple task nodes, and obtaining task attribute parameters, task surrounding environment parameters, and human-computer function allocation core parameters for each task node; determining the multi-dimensional workload of each task node based on the task attribute parameters, wherein the multi-dimensional workload includes at least two of the following: visual workload, auditory workload, cognitive workload, and psychomotor workload; generating multiple candidate task planning schemes using a preset scheme prediction model based on the multi-dimensional workload, task surrounding environment parameters, and human-computer function allocation core parameters; determining the scheme evaluation value of each candidate task planning scheme, and selecting the final task planning scheme for executing the target human-computer interaction task from each candidate task planning scheme based on the scheme evaluation value.

The system according to this embodiment aims to significantly expand the capabilities of human operators in a man-machine system and improve safety and efficiency. [Solution] The system according to the embodiment comprises a glasses unit, a sensor unit, a suit unit, a support unit, and a preparation unit. The glasses unit comprises XR glasses. The sensor unit comprises an electroencephalogram (EEG) sensor. The suit unit comprises a haptic feedback suit. The support unit provides real-time support via a generating AI based on data collected by the glasses unit, sensor unit, and suit unit. The preparation unit anticipates the operator's intentions based on the support provided by the support unit and prepares necessary information and options in advance.

This invention discloses a tracking control method for the cyclical switching of active and passive training modes in a rehabilitation robot. Its features include: constructing a switching dynamics model for the rehabilitation robot based on the different interference environments generated on the human-machine system during active and passive training by the patient; designing a random configuration network with switching characteristics to estimate the interference environment; establishing a tracking error system for active and passive training, enabling the human-machine system to achieve cyclical switching between active and passive training while meeting the condition of minimum average training time; and using an STM32F411 series microcontroller to output PWM signals to the motor drive module, allowing the robot to assist the patient in performing the doctor-specified active-passive cyclical training. This invention not only provides an adaptive walking movement mode for patients to enter comprehensive active training but also avoids the safety impact of frequent manual switching between active and passive training modes.

An electronic device and a method for path optimization for unmanned aerial system (UAS) is provided. The electronic device includes a memory to store a machine learning model to be trained. The electronic device retrieves a first plurality of parameters related to one or more wireless environments to be utilized by the UAS and generates signal-to-interference-plus-noise ratio (SINR) heatmap information. The electronic device further generates state space information based on the generated SINR heatmap information and trains the machine learning model based on the generated state space information. The trained machine learning model indicates one or more movement based actions to be taken by the UAS at a plurality of states defined in the generated state space information. The electronic device further controls the UAS to travel a predefined path in at least one of the one or more wireless environments based on the trained machine learning model.

The embodiment of the application provides a construction site safety supervision unmanned aerial vehicle system based on AI identification and real-time shouting, which comprises: an unmanned aerial vehicle platform, wherein the unmanned aerial vehicle platform is provided with a multi-modal sensor, the multi-modal sensor comprises a visible light camera, a thermal imager and a laser radar; an edge computing module integrated with an identification model, wherein the identification model is trained by combining an improved YOLOv5 algorithm with an attention mechanism, the identification model is used for real-time analysis on collected sensor data, identifies non-wearing of protective equipment, intrusion in a dangerous area and abnormal state of equipment, and generates structured data of a hidden danger type, a position and a risk level based on an identification result; a communication module, which adopts 5G and WiFi6 double-link transmission, synchronizes the structured data and real-time video stream to a cloud management platform, and receives a task instruction issued by the platform; and a directional audio device, which is triggered to project a voice alarm to a hidden danger occurrence area in response to the task instruction.

The invention discloses a ship material autonomous distribution unmanned aerial vehicle, system and method. The system comprises a shore-end UWB base station, an unmanned aerial vehicle-mounted mobile UWB base station and UWB tags configured on inland river or coastal ships, and is used for continuously collecting real-time distance data between the ships and the unmanned aerial vehicle and between the ships and the shore-end base station. And time sequence analysis and processing are carried out on the distance data, the ship navigation direction is determined, the real-time position of the ship is analyzed, and then the unmanned aerial vehicle carries out material delivery. According to the invention, efficient and accurate transportation of inland river or coastal ship materials by the unmanned aerial vehicle is realized, convenience and convenience of material distribution are improved, and reliable technical support is provided for material supply of inland river or coastal ships.

This invention relates to the field of robotics, specifically to an air-ground integrated patrol robot, comprising: a quadruped robot serving as a ground mobility and support platform; an unmanned aerial vehicle (UAV) system integrated on the quadruped robot's back for performing aerial patrol missions; an explosion-proof and emergency response system integrated on the quadruped robot's back for physical control of targets; an environmental perception system mounted on the quadruped robot for sensing external environmental information; an interaction system mounted on the quadruped robot for voice and audio-visual interaction with targets or operators; and a main control system located inside the quadruped robot for coordinating and controlling the UAV system, explosion-proof and emergency response system, environmental perception system, and interaction system. By integrating the UAV system and explosion-proof and emergency response system onto the quadruped robot platform, an integrated air-ground patrol system combining perception, interaction, and response is formed, significantly improving patrol range, response speed, and mission execution capabilities.

The application discloses a method for establishing a complex continuous action iterative dilemma model with incremental dynamics, and belongs to the technical field of artificial intelligence, and comprises the following steps: step 1, establishing an original intelligent agent communication topology; step 2, continuously updating the incremental action iterative dilemma; step 3, all drones containing the incremental intelligent agent after updating continue to carry out the game; and step 4, analyzing the convergence of the value-increment evolution dynamics. Through the above method, the complex continuous action iterative dilemma model with incremental dynamics is used to describe the dynamic dimension change of the drone system, so that the ability of the intelligent agent to face the complex and changeable environment in the drone group combat is improved. Secondly, the incremental updating method is used to avoid invalid refreshing of the original drone group, the relationship between the drones is represented by a weighted adjacency matrix, and the incremental updating method is used to update the state matrix and the adjacency matrix, so that the dynamic of the intelligent agent when the number of the intelligent agent changes can be better analyzed.

The invention provides a power transmission line extreme disaster early warning system and method based on quantum enhanced sensing. The system comprises a quantum sensing microsystem, an unmanned aerial vehicle verification system and an intelligent early warning platform. Wherein the quantum sensing microsystem comprises a quantum illumination radar module, a superconducting single-photon detector, an NV color center sensor and a quantum signal processing unit; the unmanned aerial vehicle system comprises an unmanned aerial vehicle platform, a quantum communication terminal and a multispectral verification module; the intelligent early warning platform comprises a quantum AI analysis device and a multi-dimensional display control and disaster early warning device. The quantum sensing microsystem generates entangled photon pairs through a quantum entangled source, and realizes extremely weak signal detection by using a quantum illumination technology; the unmanned aerial vehicle system transmits verification data to a back-end platform through a quantum encryption channel; and the intelligent early warning platform performs fusion analysis on multi-source data by adopting a quantum machine learning algorithm to realize early warning of extreme disasters.

The application provides a hip exoskeleton control method and system for reaction control and complementary constraint, and the method comprises the following steps: S1, constructing a hip exoskeleton man-machine system dynamics model, and establishing a coupled dynamics equation; S2, establishing a foot-ground contact nonlinear complementary constraint model, and introducing a relaxation parameter for further processing; S3, establishing a multi-objective reaction control cost function, comprehensively considering an exoskeleton space, a human space, an environment space and a complementary constraint violation penalty term, and constructing a quadratic cost function; and S4, solving a quadratic programming problem with a complementary constraint in real time, dynamically adjusting the relaxation parameter according to the real-time estimated stiffness of the ground and the gait phase, and outputting a control instruction. The application is based on the online estimation of the stiffness of the ground and the adaptive parameter adjustment mechanism of the gait phase, can adapt to different ground characteristics and gait stage changes in real time, dynamically adjusts the complementary relaxation parameter, and makes the system show stronger adaptability and robustness in a complex walking environment.