38352 results about "Control signal" patented technology

An electric motor based system includes an electric motor; an electric motor power controller for controlling the electric motor; a first control unit, adapted to provide first control signals for the electric motor power controller; an electric power storage device adapted to power the electric motor power controller; a first sensor adapted to determine information related to power exchange between the electric power storage device and the electric motor power controller; and a second control unit, wherein the second control unit is adapted to pass the first control signals, received from the first control unit, to the electric motor power controller in a first mode and adapted to provide second control signals for the electric motor power controller in a second mode; and the second control unit inputs and exploits the information.

One or more data streams may be used for controlling multi-system interaction. For example, a data stream may be received, and a surgical option associated with a surgical instrument may be selected based on the data stream. A control signal associated with the surgical instrument may be generated based on the selected surgical option. A surgical device may receive an external data stream from a source external to the surgical system. The device may derive, based at least on the external data stream, decision contextual information. The device may select a surgical option associated with a surgical instrument based on the decision context information. The device may generate a visual indication of the decision context information associated with selecting the surgical option. The device may generate a control signal associated with the surgical instrument based on the selected surgical option.

The invention provides an intelligent temperature adjusting method and system for an injection molding mold. The method comprises the steps that real-time multi-source sensor data flow is generated by collecting temperature data and pressure data of all areas of the injection molding mold; performing feature extraction on the real-time multi-source sensor data flow to obtain temperature field distribution features; a temperature field distribution prediction model is obtained through training based on the temperature field distribution characteristics and a differential private Bayesian optimization algorithm, and then a mold temperature prediction result is output through the model; performing feature fusion on the mold temperature prediction result and the pressure data, constructing input features of a neural network controller, training based on the input features and a positive definite curvature learning method to obtain the neural network controller, and generating a power control signal of a heater and a flow control instruction of a water cooling system through the neural network controller, and based on the control signal and the instruction, the temperature field of the mold is dynamically controlled through the adjustable clearance compensation mechanism. According to the invention, the precision and response speed of temperature adjustment are improved.

A multi-modal articulation system translates human gestures into control commands for digital and automation devices. The system integrates a camera module, which captures facial and hand movements, with an image processing unit that extracts facial features, such as eyebrow movement, lip curvature, eyelid motion, and eye trajectory. These extracted features are analyzed in real time by an articulation recognition module that compares them against a database to recognize specific gestures. The system tracks and processes air-drawn hand gestures using a gesture trajectory tracking unit, which converts the motions into digital representations and classifies them using deep learning algorithms. The recognized gestures are converted into machine-readable control signals and executed on various connected devices via a relay control unit. The system incorporates adaptive algorithms to improve accuracy, account for environmental conditions, and stabilize hand tremors, providing a gesture-based control interface for applications ranging from smart home systems to multimedia devices.

An example method can include providing a natural language instruction and user interface image data to a machine-learned sequence processing model that is configured to process image data and generate commands for controlling the target computing device, wherein the machine-learned sequence processing model has parameters learned using an interface recognition objective based on an evaluation of an interface recognition output generated based on processing a rendered training interface from a pre-training dataset and an interface navigation objective based on an evaluation of a user interface command generated based on processing a rendered training interface from a fine-tuning dataset; receiving, from the machine-learned sequence processing model, a command indicating an interaction with the user interface to implement the natural language instruction; and generating, based on the command, a control signal configured to initiate the interaction.

The invention relates to the technical field of fault detection control, and discloses an automobile fault detection method and system fusing sensor data. The method comprises the following steps: modeling a multi-system sensor network through an automobile system topology mapping algorithm to obtain a sensor topology incidence matrix; performing synchronous processing on the sensor topological incidence matrix according to a vehicle dynamics constraint equation to obtain a synchronous multi-sensor data set; inputting the synchronous multi-sensor data set into a fault propagation path sensing network for fusion to obtain a vehicle system fault feature vector; detecting the fault feature vector based on an automobile physical model constraint optimizer to obtain a fault state recognition result; and tracing and controlling a fault state identification result according to the fault propagation atlas model to obtain a system adjustment control signal. According to the invention, the source identification accuracy of the complex coupling fault and the pertinence of control and adjustment are improved.

The invention discloses a nursing data sharing method and system based on the Internet of Things. The method comprises the steps that S1, multi-modal nursing data are collected in real time through a distributed Internet of Things terminal cluster, and a feature signal set is formed; s2, constructing a data coordination engine, executing federal learning preprocessing operation on the feature signal set, and forming a coordination signal flow; s3, dynamically calculating a data contribution degree weight and generating a verification signal chain; s4, monitoring vital sign abnormal indexes in the coordination signal flow in real time, and generating a trigger signal vector; and S5, updating incentive mechanism parameters according to the contribution degree voucher in the verification signal chain, optimizing a service quality strategy based on a network resource allocation scheme in the trigger signal vector, and forming a closed-loop control signal. According to the invention, the problem of data islanding effect in the medical care data sharing process can be solved.

The invention provides a color steel plate coating flatness evaluation method and system based on artificial intelligence. According to the method, the three-dimensional point cloud data is generated by collecting the interference fringe image, and the surface fluctuation characteristics are quantified; capturing a multi-dimensional vibration spectrum of the transmission roller shaft, generating a servo motor compensation control signal, driving a multispectral scanning head to perform reverse displacement compensation, and generating real-time compensation data; inputting the surface topography features in the three-dimensional point cloud and the real-time compensation data into a lightweight convolutional neural network, and outputting fusion features; and dynamically classifying and identifying surface defects and uneven areas based on the fusion result, adjusting a classification threshold in combination with the speed of the production line, outputting a flatness evaluation result, and synchronizing the flatness evaluation result to a speed regulation system of the production line to realize closed-loop optimization. According to the method, laser interference, vibration compensation and lightweight AI technologies are fused, dynamic high-precision evaluation of the surface flatness of the color steel plate of the high-speed production line is achieved, and the problems of defect misjudgment and measurement distortion caused by vibration interference are solved.

The invention relates to a coal mine safety production intelligent decision-making system based on digital twinning, and the system comprises a physical sensing layer which collects coal mine environment parameters, equipment states and personnel positioning data through the deployment of a multi-mode sensor network, and generates a structured data flow; the edge calculation layer is used for operating an incremental multi-objective evolutionary algorithm, quickly generating a cache strategy in combination with a strategy cache pool preloading mechanism, uploading the processed data to the digital twinborn layer, receiving a global instruction of the intelligent decision-making layer and decomposing the global instruction into a device-level control signal; the digital twinborn layer is used for receiving the real-time data uploaded by the edge calculation layer, updating the state of a digital twinborn body and feeding back an optimization demand to the intelligent decision-making layer; and the intelligent decision-making layer is used for generating a global strategy by means of digital twin-guided hybrid optimization and a special FPGA acceleration card for a coal mine, and fusing the cache strategy of the edge calculation layer and the global strategy of the intelligent decision-making layer to generate a global instruction.

The invention discloses a large model reasoning efficiency dynamic optimization and hardware sensing compression method. The method comprises the following five steps: S1, generating an input complexity signal representing calculation complexity; s2, synchronously monitoring a hardware resource index of the operation platform, and generating a hardware state signal reflecting a real-time load; s3, inputting the input complexity signal and the hardware state signal into a dynamic strategy selector, and generating a compression control signal through a pre-trained decision model; s4, according to the compression control signal, dynamic reconfiguration operation is executed on the large model weight and the activation value of the current reasoning task; and S5, performing reasoning calculation by using the reconfigured large model, and feeding back a hardware resource index to the step S2 in real time in the calculation process to form a closed-loop optimization link. According to the large model reasoning efficiency dynamic optimization and hardware perception compression method, the problems of low resource utilization rate, delay fluctuation and energy efficiency imbalance caused by a static compression method in dynamic input and heterogeneous hardware environments can be solved.

A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.

The invention provides an intelligent incubation bin anti-interference control method and system based on error self-learning, and relates to the technical field of intelligent control, and the method comprises the steps: respectively calculating a temperature deviation value and a humidity deviation value according to an optimization parameter sequence of a temperature prediction error and an optimization parameter sequence of a humidity prediction error; performing normalized weighted summation on the temperature deviation value and the humidity deviation value, and performing dynamic scaling through a Gaussian kernel function to obtain a correction factor; dynamically adjusting the activation function slope of a neural network hidden layer according to the correction factor and a BP neural network, reconstructing the weight of the fuzzy rule base based on the numerical distribution characteristics of the correction factor, and generating the corrected weight of the fuzzy rule base; and based on the corrected fuzzy rule base weight, constructing a three-dimensional parameter adjustment curved surface, and dynamically adjusting proportion, integral and differential parameters of a PID controller through a curved surface gradient search algorithm to generate a control signal. According to the invention, the control accuracy is improved.

The invention relates to the technical field of automatic industrial control, and discloses an automatic industrial equipment plc data acquisition monitoring control method and system. The method comprises the following steps: receiving a multi-source PLC data stream, and processing the multi-source PLC data stream through a multi-modal data fusion model to generate an equipment health degree evaluation matrix; constructing a dynamic control strategy generation model, and outputting a multi-target optimization instruction set; simulating the interaction influence of the equipment group by utilizing a process chain collaborative constraint model, and optimizing real-time control parameters of an instruction set; parameters are adjusted in parallel through a distributed edge computing framework, and an equipment cooperative control signal is output to an industrial monitoring center. The system comprises a distributed data acquisition module, a multi-modal fusion calculation module, a dynamic strategy generation module, an edge cooperative control module and an industrial communication gateway. According to the invention, multi-source data can be efficiently processed, equipment health can be accurately evaluated, a control strategy is optimized, equipment collaboration is realized, and industrial production efficiency and energy utilization rate are improved.

The invention provides a single-tube boost PFC circuit control method and system for single-cycle control, and relates to the technical field of data processing, and the method comprises the steps: generating a self-adaptive PWM control signal according to a duty ratio adjustment amount and a frequency compensation factor, and dynamically limiting the pulse current amplitude of a power switch tube through a peak current prediction algorithm, a load current change rate is generated based on the real-time relevance between the pulse current amplitude and the load current; setting a first monitoring point in a dynamic duty ratio change path of the PWM control signal, and extracting a duty ratio change rate and a switching frequency fluctuation parameter in real time; setting a second monitoring point in a feedback path of a load current change rate, and extracting a load current slope characteristic and a transient change amplitude in real time; and based on the parameters of the first monitoring point and the second monitoring point, generating a correction factor through a dynamic response correlation model. According to the invention, a high power factor can be realized under wide input voltage and load fluctuation.

The invention relates to the field of power plant control, discloses a virtual power plant control method, system and equipment based on a neural network, and is used for solving the core problems of high data dependence, low topology safety and difficulty in multi-scale collaboration in traditional virtual power plant control. According to the virtual power plant control method based on the neural network, a correction instruction set, a joint estimation value and a topology constraint matrix are input into a neural network controller, and a cooperative control signal is output through singular perturbation decoupling of a fast-varying subsystem and a slow-varying subsystem. And the cooperative control signal is issued to the distributed power supply inverter, the energy storage converter and the intelligent switch, and meanwhile, an execution result is monitored in real time and fed back to the phase space reconstruction module, so that closed-loop control is formed. By constructing the Lyapunov candidate function and calculating the virtual damping coefficient, the transient stability, real-time persistent homologous analysis and topology self-healing instruction generation of the system are enhanced, and the self-healing capability and the fault-resistant capability of the system are improved.

The invention provides a trajectory control video generation method and device based on depth information and time-frequency optimization, and relates to the technical field of image processing, and the method comprises the steps: optimizing a 3D trajectory through multi-entity segmentation, depth estimation and time-frequency decomposition in combination with a user instruction, and generating a control signal through a multi-scale fusion network; finally, the signals and original images are input into an improved Stable Video Diffusion model to generate a video potential representation sequence, the problems that an existing video generation method is insufficient in dynamic entity motion control precision and poor in cross-frame consistency are solved, and through 3D trajectory modeling guided by depth information and a time-frequency joint optimization mechanism, the video potential representation sequence is generated. And the motion smoothness, the space authenticity and the time-frequency stability of the generated video are obviously improved.

The invention provides a robot obstacle avoidance and navigation method based on multi-modal fusion and a visual language model, and the method comprises the steps: collecting different modal data in real time through a plurality of sensors, and carrying out time synchronization processing and normalization processing; extracting features of different modal data and fusing the features through an intermediate layer; and inputting the fused multi-modal data into a visual language model, generating a semantic map of the environment by utilizing semantic segmentation of the model and a target detection result, generating an action strategy in combination with a natural language instruction and a visual analysis result, and converting the generated action strategy into a control signal which can be executed by the robot to realize closed-loop control. According to the method, the visual language model and the multi-modal sensor fusion technology are combined, and the perception, decision making and real-time response capabilities of the mobile robot in a complex dynamic environment are improved.

The invention discloses a machine tool control method and system based on mechatronics, and the method comprises the steps: outputting an optimal cutting track sequence according to the three-dimensional model features and material attribute parameters of a machined workpiece; based on the optimal cutting track sequence, motion interference among shafts of the machine tool is eliminated through dynamic weight distribution, and an anti-interference optimization machining instruction is generated; constructing a nonlinear vibration wave propagation model according to the coupling relation between the spindle rotating speed and the feeding speed, and generating a steady speed regulation and control signal; generating an energy efficiency optimal parameter set including motor torque, cooling power and lubricating frequency based on the energy consumption efficiency constraint condition and the steady speed regulation and control signal; and outputting a closed-loop control signal and synchronously updating the three-dimensional machining precision thermodynamic diagram according to the real-time data in the machining process and the predicted deviation of the digital twin model. According to the embodiment of the invention, global optimization, dynamic adaptation and efficient operation of the machine tool can be realized, and technical support is provided for intelligent upgrading of the modern manufacturing industry.

The invention relates to the technical field of monitoring and early warning, in particular to a fire hydrant monitoring intelligent early warning system based on an anomaly analysis technology, which comprises a flow velocity anomaly identification module, a node collaborative pressure difference detection module, a pressure difference trend independence judgment module, a node degradation feature extraction module and a risk level generation module. According to the method, through correlation judgment of flow velocity deviation and control signals, no-signal recognition of abnormal water taking behaviors and a cooperative analysis mechanism of pressure change of adjacent nodes, a hydraulic disturbance area under non-manual control can be accurately recognized, and through analysis of spatial independence of a pressure response trend, a water flow disturbance area under non-manual control can be accurately recognized. The function degradation level of the device is extracted according to historical data of on-off time delay and response performance, the quantitative evaluation capability of the node function state is enhanced, the capability of finely dividing the risk level is achieved when risk early warning is given out, the risk identification accuracy is improved, and the active discovery capability of early fault hidden dangers is enhanced.

The invention discloses a self-adaptive nanosecond driving protection method for a high-voltage IGBT power module. The method comprises the steps that data such as high-voltage side voltage, collector current, grid voltage and junction temperature are collected and preprocessed, and standardized feature vectors are obtained; extracting statistical features, frequency domain features and trend features to obtain a comprehensive feature vector; density clustering is carried out based on historical fault data, and an extreme value model is established by using generalized Pareto distribution to generate an optimized threshold value; constructing a time sequence feature matrix, predicting a fault probability by adopting a time sequence convolutional network and a long-short term memory network, and generating a protection strategy parameter; and generating an optimal driving waveform and a control signal, and updating parameters according to an actual response effect. According to the invention, predictive protection and nanosecond-level precise control of faults are realized, and the reliability and performance of the high-voltage IGBT module are remarkably improved.

The invention relates to the technical field of robot autonomous navigation and control, and discloses a robot system based on a sensing system, which comprises a multi-mode sensing module for outputting data to a dynamic environment modeling module; the dynamic environment modeling module is used for transmitting the model to the path planning module; the path planning module outputs the path sequence to the neural motion control module; the neural motion control module outputs the control signal to the dynamic execution module; the dynamic execution module is used for receiving the pulse control signal and driving each joint of the robot to execute the path in combination with inverse kinematics solution and a feedback control mechanism; and the collaborative optimization module is in two-way communication with the modeling module, the path planning module and the neural motion control module. According to the method, the contradiction between real-time performance and integrity of environment modeling in a complex scene is solved by fusing cross-modal information complementary characteristics of binocular vision, millimeter-wave radar and inertial data and combining mathematical representation of a hypergraph topology model on a dynamic obstacle interaction relationship.

The invention discloses an electromechanical equipment noise reduction method based on machine learning, and relates to the field of electromechanical equipment noise reduction, and the method comprises the steps: collecting sound pressure and vibration signals during the operation of electromechanical equipment, and collecting the operation state data of the electromechanical equipment; sparse reconstruction is carried out on the noise features; carrying out sound source localization on the reconstructed noise features by using a beam forming algorithm to obtain a noise distribution diagram; constructing a noise reduction model based on deep learning to obtain noise signals after noise reduction; extracting the frequency of the noise signal in a preset frequency range through a peak search algorithm; analyzing the acquired running state data by using a time-varying linear predictive coding algorithm TVLPC to obtain a noise frequency offset caused by the abnormal running state of the electromechanical equipment; generating an optimal solution by using a particle swarm algorithm; and according to the optimal solution of noise control, noise dominant frequency components are extracted for separation, and a plurality of phase-reversal noise reduction control signals are obtained. Aiming at low noise reduction precision of electromechanical equipment in the prior art, the noise reduction control precision is improved.

The invention relates to the technical field of intelligent dimming control, and discloses an intelligent dimming control method and system based on an LED driver, and the method comprises the steps: carrying out the current and light intensity data collection of the LED driver under different dimming depth values, and obtaining a response characteristic data set; inputting the response characteristic data set into a convolutional block attention module and convolutional neural network combined architecture for training to obtain an LED response characteristic prediction model; the ratio of the light intensity change rate to the current change rate is calculated, and a piecewise linear dimming sensitivity index is obtained through piecewise linear function fitting; constructing a first-order inertia controller and a brightness feedback loop, and generating a brightness self-compensation control strategy; according to the LED driver, the brightness change response is predicted, and the PWM dimming control signal is generated according to the trajectory smoothing algorithm and the PID controller, so that the adaptive capacity of the LED driver to external interference and parameter change is enhanced, and the stability and reliability of the LED driver are improved.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention provides a servo system optimization method and system based on algorithm control, and relates to a motor control technology, and the method comprises the steps: firstly obtaining a real-time dynamic response parameter set during the dynamic operation of a servo device, covering inertia delay, torque fluctuation and position deviation parameters, and a corresponding optimization demand set, including optimization demands for each parameter; performing multi-level control signal generation on the real-time dynamic response parameter set according to the optimization demand priority, generating a control signal sequence set matched with a first compensation signal, a second compensation signal, a third compensation signal and the like, inputting the control signal sequence set into an execution mechanism, and driving the execution mechanism to dynamically respond according to an updating track, and finally, the control signal sequence set is optimized according to a feedback signal set generated by an execution mechanism, so that operation parameters of the servo device can be comprehensively considered, control signals are generated in a targeted manner, dynamic adjustment and optimization are realized, and the performance of the servo system is effectively improved.

A robotic emulation device includes a processor, memory and multimedia interfaces to capture a video signal from a target device, analyze it, and transmit manipulation control signals to the target device input by emulating a peripheral input device, wherein the manipulation control signals determine manipulation or selection of a target interface control associated with a first user interface encoded and transmitted in the video signal. Furthermore, the robotic emulation device may be connected via an embedded transceiver to at least one computer or computer tenant for relaying the captured video data for analysis, and based on the analysis, applying inferred manipulation semantics.

The invention belongs to the technical field of industrial automation control, and particularly relates to a dynamic optimization control method and system for multi-component batching of a paste making machine, and the method comprises the steps: generating a characteristic waveform spectrum corresponding to a residue molecular structure; dynamically generating electric lubricating medium parameters containing the electroosmotic liquid concentration and the release rate; triggering a low-frequency vibration module of the material layer to generate vibration waves, and enabling the solution and the vibration waves to form a molecular-level synergistic stripping effect; calculating a nanoparticle dispersity index, and generating an inverse gradient mixing instruction when the index exceeds a threshold value; activating a hot vortex ring in a spiral flow guide convex pattern area and establishing a directional heat energy migration channel; and verifying the dynamic equilibrium state of the temperature of the paste core area and the viscoelasticity of the target formula, and triggering a phase lock control signal to terminate the parameter adjustment chain when the energy consumption rate reaches a stable interval. According to the invention, the problems that the transition period during formula switching is prolonged and the phase differences among the execution mechanisms are difficult to match in real time due to a traditional staged regulation and control mode are solved.

The invention relates to the technical field of electrical engineering and sensors, and provides a high-precision current sensor electrical performance detection method, which comprises the steps of collecting an original output signal of a current sensor and performing multi-dimensional signal preprocessing; extracting multi-dimensional feature parameters from the preprocessed signals based on a dynamic feature extraction algorithm; establishing a dynamic compensation model according to the multi-dimensional characteristic parameters, and generating a compensation control signal; a compensation control signal is injected into the detected current sensor to form closed-loop feedback, and a compensated output signal is collected; calculating a sensor performance degradation parameter according to the signal difference before and after compensation; an electrical performance assessment report is generated based on the performance degradation parameter. According to the invention, the measurement precision and reliability of the sensor can be improved, the service life is prolonged, the maintenance cost is reduced, and a powerful guarantee is provided for safe and stable operation of a power system and electronic equipment.

Various embodiments of the present disclosure relate to operator assistance based on operator monitoring. For instance, during long drives, a driver may become drowsy or may not otherwise be alert. As such, particular embodiments have the capability of starting a conversation with the driver based on driver interests and / or detecting that the driver is getting drowsy. In an illustrative example, a Driver Monitoring System (DMS) camera of a vehicle may employ a component that derives pixel-level information showing head nodding, hands dropping, or the like. Based on image pattern characteristics in the image data, particular embodiments generate a score representing an alertness level. A representation of the alertness level can be provided as input to a machine learning model so that the model may generate a suitable natural language or other response, such as starting a conversation with personalized trivia, sending a control signal to honk a horn, or the like.

The invention provides a cardio-pulmonary resuscitation training system based on multi-modal artificial intelligence in combination with a virtual reality technology, and relates to the technical field of virtual reality, and the system comprises a multi-modal sensing module which is used for collecting posture data, mechanical data and environment data; the multi-source data collaboration module is used for generating an operation quality evaluation parameter set and establishing a mapping relation between the operation posture and the pressing efficiency; the virtual scene construction module is used for generating a virtual first-aid environment according to a preset first-aid scene template and the operation quality evaluation parameter set; the decision module is used for generating a feedback control signal; and the interactive closed-loop execution module is used for inputting the feedback control signal into the virtual first-aid environment and updating the operation quality evaluation parameter set according to the operation data after feedback response. According to the invention, standardized, intelligent and scene-diversified cardiopulmonary resuscitation skill training can be realized, and the technical problems of inaccurate posture evaluation, non-personalized feedback, single scene, high resource dependence and the like in traditional training are solved.