66 results about "Neural control" patented technology

The invention discloses an Alzheimer's disease long-term prediction and dynamic intervention method based on deep learning. Accurate dynamic intervention from group statistics to individual dynamics is realized through time sequence alignment, multi-scale time sequence feature extraction, dynamic risk assessment and personalized intervention. Through time sequence alignment and multi-scale time sequence feature extraction, a time-sensitive personalized intervention scheme can be generated. And carrying out continuous time modeling by adopting a cubic Hermite interpolation method and a neural control differential equation, and predicting the long-term risk. By establishing a double-track interaction model, a pathological track and a functional track of a user are analyzed, so that time-varying association among multi-modal data can be dynamically captured. A personalized intervention strategy is provided through reinforcement learning, the intervention strategy is dynamically adjusted in combination with risk reduction amplitude, intervention measure compliance and physiological index change, the effect of short-term behavior change and long-term prediction is balanced, and the Alzheimer's disease is further promoted to be converted from passive treatment to active intervention.

The embodiment of the invention relates to the technical field of lighting control, and provides a lighting control method and device, equipment and a storage medium, and the method comprises the steps: obtaining environment perception data and physiological influence data; generating an energy efficiency distribution strategy according to the environmental perception data through a metabolism decision branch; optimizing the control weight of each lighting node through a neural decision branch according to an energy efficiency distribution strategy to obtain a preliminary control strategy; in a virtual simulation environment, performing parameter optimization on the preliminary control strategy through a metabolic path reconstruction unit and a neural topology optimization unit to obtain an optimized control strategy; obtaining an illumination parameter adjustment strategy according to the physiological influence data through the physiological response prediction model; and generating a control instruction through the lighting controller according to the optimized control strategy and the adjustment instruction, and performing corresponding lighting control on the lighting terminal cluster in response to the control instruction. According to the embodiment of the invention, efficient cooperation of metabolic energy efficiency distribution and neural control decision can be realized.

The invention discloses an industrial AI assistant cross-modal interaction method based on a dynamic knowledge graph, and the method comprises the following steps: collecting texts, voices, images and multi-source sensor data in an industrial system, carrying out the modal recognition, feature extraction and time alignment, and generating an event feature set and a state feature set with timestamps. Modeling a time dependency relationship between event types by constructing a multivariable Hawkes model, and outputting an event trigger sequence and trigger strength; and in combination with a neural controlled differential equation model, guiding the state to evolve along with time and jump at a specific moment to form a state evolution trajectory. Performing fusion coding on the event and the state, constructing a dynamic knowledge graph with a causal structure and semantic continuity, and generating interactive output based on context reasoning; and after system feedback is received, the triggering strength and the state track are updated, and continuous evolution of the knowledge graph and reverse optimization of model parameters are achieved.

The invention relates to the technical field of neural engineering and brain-computer interfaces, in particular to a closed-loop multi-modal nerve stimulation system and method based on time interference, and the system comprises a multi-modal stimulation module which is used for integrating electrical stimulation, magnetic stimulation and optical genetic stimulation, and generating a time interference field domain; the neural state sensing module is used for collecting real-time electroencephalogram signals, blood oxygen concentration and neural metabolite level data; the neural control center is used for fusing neural state data and stimulation parameters, and dynamically adjusting time interference frequency and stimulation intensity through an adaptive algorithm; the time sequence cooperation engine predicts a neural response time phase based on a deep learning model, and optimizes a stimulation time sequence and a mode switching strategy; and the visual interaction platform is used for rendering the nerve activation thermodynamic diagram and the stimulation parameter adjustment curve in real time. Therefore, the problems of adjustment strategy solidification, low adjustment precision, insufficient energy conversion efficiency and the like in the prior art are solved.

The invention discloses a standing and walking test method and system based on an inertial sensor and a myoelectricity technology, and relates to the technical field of gait evaluation and rehabilitation detection, and the method comprises the following steps: completing data acquisition by using the inertial sensor and the myoelectricity technology; comprehensively evaluating the difference between the left and right support phases and the swing phase through a first algorithm, and judging whether the gait is symmetrical and stable or not; and evaluating the muscle coordination of the patient through a second algorithm. According to the method, synchronous data fusion of the behavior layer and the neural control layer is achieved, a data foundation is laid for follow-up construction of an accurate and multi-dimensional gait and myoelectricity evaluation model, joint evaluation of two core indexes of gait symmetry and stability is achieved, the detection capacity of neurological gait disorders is improved, and the method is suitable for being used for the field of neurological gait disorders. Reliable criteria are provided for individualized rehabilitation intervention, modeling and difference measurement of time sequence regulation and control characteristics are achieved, and the recognition capacity for neuromuscular control disorders is improved.

The invention discloses an integrated power box health state assessment method based on AI modeling. The method comprises the following steps: S1, constructing a multivariable time sequence; s2, modeling the multivariable time sequence trajectory by adopting a neural control differential equation model to obtain a hidden space trajectory; s3, performing dynamic modal decomposition on the hidden space trajectory, and constructing a modal health space; s4, adopting a manifold interpolation mixing method in the modal health space to generate an enhanced training sample set; s5, inputting the mixed mode vector in the enhanced training sample set into a health classification module; s6, inputting the hidden space trajectory into a time semantic playback module to generate a final prediction state trajectory; and S7, comparing the final predicted state trajectory with the actual historical state trajectory, and when the semantic difference value exceeds a set tolerance threshold value, generating an abnormal mark and executing a state rollback operation. According to the method, neural differential modeling, interpolation enhancement and time semantic playback are fused, and the health state of the integrated power box is accurately evaluated.

The invention discloses an airspace engine 3D printing interlayer metallurgical bonding enhancing method which comprises the following steps: S1, slicing an airspace engine 3D model, and constructing a printing path diagram structure; s2, constructing a lattice consistency map of the current printing layer; s3, performing graph structure alignment on the lattice consistency graphs of the current printing layer and the previous printing layer to generate a cross-layer lattice graph sequence; s4, inputting the cross-layer lattice atlas sequence into the graph neural control body model, obtaining a disturbance sensitivity vector and a disturbance score value of each node, and outputting a correction control strategy vector; s5, executing a cross-layer disturbance projection operation based on the corrected control strategy vector, and generating a control intervention instruction; and S6, repeatedly executing the steps S2 to S5 to form a complete lattice atlas evolution record sequence. According to the method, the lattice atlas modeling and the graph neural control body model are utilized to realize the metallurgical enhancement between the printing layers of the airspace engine, and the method has the advantages of closed loop regulation and control, structural continuity and the like.

The invention discloses a power control method and device for an industrial forklift charger, and the method comprises the steps: obtaining industrial forklift charging demand data and LLC resonant topology characteristic parameters, and constructing an LLC resonant converter; on the basis of the working frequency range and the power density requirement, through a probability learning algorithm and a random agent model, a random agent model for multi-objective optimization of magnetic element parameters is formed, and a high-frequency low-loss magnetic element is obtained; a high-frequency low-loss magnetic element is integrated into an LLC resonant converter, a random neural control barrier function algorithm is realized, and dynamic power control is performed on a charging process; based on the real-time data, a data-driven hybrid prediction control strategy is realized, and a charging parameter optimization instruction is generated; and in combination with the battery charge and discharge characteristic data and the battery aging state evaluation model, battery state evaluation and charging parameter adaptive adjustment are realized. The problems that a traditional charger is insufficient in control precision, low in efficiency, short in battery life and the like in a complex industrial environment are solved.

The invention discloses a bionic octopus arm simulation method. The bionic octopus arm simulation method comprises the following steps: establishing a variable-curvature kinematics model of the bionic octopus grabbing robot; establishing a bending wave transmission model; and performing a model simulation experiment on the established bending wave transmission model and the variable curvature kinematics model in the Matlab environment so as to obtain a simulation result. Different from a traditional modeling control method, the method is inspired by a neural control method in the octopus capturing process, and bionic octopus arm bending wave transmission capturing is achieved through sequential activation of muscles. Robot body configuration calculation is not needed, bending wave transmission motion definition can be completed in a driving space only through three parameters, and underwater octopus-imitating capturing behaviors can be achieved.

The invention discloses a scoliosis neuromuscular rehabilitation system based on a brain-computer interface and digital twinning, and the system comprises a multi-mode sensing module which is used for synchronously collecting a neurophysiological signal, a muscle electrical activity signal and a motion posture signal of a user; the intelligent processing and simulation module is used for carrying out fusion processing on the multi-modal signals, decoding the motion intention of the user and carrying out real-time simulation prediction on a biomechanical effect generated by the motion intention based on a personalized digital twinborn model of the user; and the decision and feedback module is used for generating and executing a personalized feedback instruction according to the decoding result and the simulation prediction result of the motion intention. The invention belongs to the technical field of medical rehabilitation engineering and biomedical engineering, and particularly provides a neuromuscular function remodeling method which is capable of directly providing a scoliosis patient with biomechanical accuracy, real-time interactivity and individual adaptability from a neural control source by constructing an integrated rehabilitation system.

The invention discloses an SCR denitration ammonia escape and NOx intelligent control method based on machine learning. The method comprises the following steps that boiler operation data are collected and preprocessed; constructing a feature input tensor set; inputting the feature input tensor into a prediction model based on a neural control differential equation, and outputting a predicted NOx and ammonia escape concentration sequence in combination with a Koopman observable space and an energy gating mechanism; constructing a candidate ammonia injection distribution set meeting the valve constraint based on the prediction result; multi-objective optimization is carried out under emission limitation and ammonia injection stability constraint, and an optimal ammonia injection distribution vector is generated; projecting to a feasible region barrier set and then generating an ammonia spraying execution instruction; and collecting feedback data for incremental training and model adaptive updating. The ammonia spraying control precision and the denitration efficiency are improved, and intelligent prediction of ammonia escape and self-adaptive control of NOx are achieved.

The invention discloses a multi-dimensional network intrusion behavior intelligent identification method based on deep learning. The method comprises the following steps: collecting network multi-dimensional data and generating a standardized network event set and a network control path input set; establishing a neural controlled differential equation model, and generating a continuous time context representation set through hidden state evolution; establishing a neuro-hox process identification model, performing intensity function modeling, and generating an event intensity prediction sequence set and an identification intermediate representation set; forming an intrusion behavior decision rule set and a reasoning configuration set through joint training; new network multi-dimensional data are collected, the reasoning configuration set operation model is loaded, and a new event intensity prediction sequence set and a new candidate trigger time set are output; and generating a network intrusion behavior recognition result set in combination with the intrusion behavior decision rule set. According to the method, time modeling and logical reasoning are fused, and high-precision intrusion identification is realized.

The invention discloses a carbon footprint real-time evaluation system based on the Internet of Things, and the system comprises the steps: collecting the high-frequency energy consumption and carbon emission activity data of industrial production equipment and environment monitoring equipment in real time, and forming a data sequence with a timestamp; performing anomaly detection, denoising and interpolation preprocessing on the data sequence; extracting high-dimensional continuous time features of the data sequence; predicting a carbon emission sequence based on a neural controlled differential equation model; time-varying carbon emission factors corresponding to the enterprise area and the power grid nodes are obtained in real time; and coupling the carbon emission sequence and the time-varying carbon emission factor sequence in a continuous time domain, calculating a real-time carbon footprint, and performing dynamic monitoring and visual display. According to the invention, high-precision real-time prediction and monitoring of the carbon footprint data are realized, and the real-time performance and accuracy of evaluation are improved.

The invention discloses a humanoid robot control method based on a neural circuit, and belongs to the technical field of robot control, and the method comprises the steps: S1, constructing a three-layer pulse neural control architecture comprising spinal cord reflex, brainstem balance and cortex decision, and carrying out the weight adaptive distribution and cooperative operation between layers through the dynamic adjustment of neurotransmitter concentration, organic cooperation of prediction and reflection is realized through a three-stage control system; the cerebellar prediction network executes attitude adjustment in advance through a delay compensation algorithm, and compared with pure feedback, the control energy consumption is reduced; the parallel reflex pathway of the spinal cord layer establishes a nerve bypass mechanism, and the emergency response is realized by bypassing high-level processing in an emergency; the multi-layer safety monitoring improves the triggering accuracy of a protection mechanism through error confidence evaluation and contact stability analysis; a weight transfer mechanism of pulse frequency modulation avoids sudden movement change during control mode switching; by means of the design, the stability retention rate of the robot walking on the inclined plane is remarkably improved compared with a traditional method.

The application belongs to the field of industrial control network security, and discloses an attack detection method for multi-element sensor data in an incomplete SCADA scene. An interpolation network based on a neural controlled differential equation is used to repair original samples under different missing degrees; a window self-adaptive mechanism is used to solve the problem of unreasonable sample window setting in the preprocessing process without prior knowledge. An adaptive mask mechanism is proposed to guide the detection model from the input side, enhance the understanding of the dependency between time and sensor nodes, and provide a certain degree of interpretability for the model from the mechanism definition angle. In combination with adversarial training, the application defects of the original diffusion in the SCADA anomaly detection field are optimized. Compared with other filling methods, the diffusion step is greatly compressed, and the training and detection efficiency is significantly improved. Full consideration is given to the application requirements in actual production, and the multi-element sensor data anomaly detection in the heterogeneous SCADA scene can be supported without prior knowledge.

The invention provides an intelligent fatigue recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: evaluating a fatigue index of a target user through two dimensions, i.e., a target conduction velocity and a target fractal dimension, the target conduction velocity being capable of reflecting a muscle fiber function, the target fractal dimension being capable of reflecting neural control complexity, and the target fractal dimension being capable of reflecting neural control complexity; one-sidedness of single index evaluation is avoided, and muscle fatigue and central fatigue can be distinguished more accurately; through multi-channel and multi-time-scale analysis, signal characteristics of different muscle areas and different contraction rhythms are covered, and errors of a single channel and a time scale are reduced. Furthermore, the peripheral fatigue index and the central fatigue index of the target user are calculated in combination with the baseline conduction velocity and the baseline fractal dimension obtained based on the steady state surface electromyogram signal of the target user, and the method can adapt to the muscle basic states of different users, so that the evaluation result is more personalized and higher in accuracy.

The invention discloses an intelligent sensor cooperative supervision method and system based on the Internet of Things. The method comprises the following steps: collecting heterogeneous original monitoring data of various intelligent sensors and carrying out unified time sequence alignment; constructing a unified semantic mapping relationship to obtain standardized data; constructing a sensor state representation model based on a neural controlled differential equation, and obtaining a continuous hidden state vector; integrating newly obtained data in real time to update continuous hidden state vectors and calculating fusion feature representation; the current cooperation state is discriminated in real time through an anomaly detection discriminator; and when an exception is detected, starting a cooperative event disposal strategy, generating a supervision linkage disposal instruction, and executing closed-loop supervision. According to the invention, the real-time collaborative supervision of heterogeneous intelligent sensor data is realized, and the efficiency and accuracy of cross-platform data collaborative supervision are improved.

The invention relates to a nerve regulation and control method based on multi-target point cooperation, and the method comprises the following steps: building a dual-channel photocurrent model, building a quick response type photocurrent model for a PV channel, and building a continuous inhibition type photocurrent model for an SOM channel; establishing an electromagnetic coupling model, determining an electromagnetic induction stimulation voltage according to a memory equation of the memristor, and coupling the light current and the electromagnetic induction stimulation voltage determined based on the dual-channel light current model to obtain a total current; performing multi-target cooperative control effect analysis on the electromagnetic coupling model, and optimizing off-line parameters to obtain an off-line optimal parameter combination; and performing nerve regulation based on the electromagnetic coupling model after the offline parameter optimization, and performing online optimization of the operation parameters according to the result of the nerve regulation. Compared with the prior art, the method has the advantages of being capable of achieving accurate regulation and control of different target spots and the like.

The present invention provides a soft neural prosthetic hand with both myoelectric control and tactile feedback, comprising a soft humanoid hand-like mechanical body, a hand body drive control system, a myoelectric sensing system, and a tactile sensing and feedback system. The hand body drive control system, myoelectric sensing system, and tactile sensing and feedback system are installed in the soft humanoid hand-like mechanical body, which is provided with a receiving cavity. The hand body drive control system has two types of pneumatic control methods: multi-pump multi-valve and single-pump multi-valve. It also has either a drag-line mode or a lightweight mode for installing, reproducing the dexterous grasping movements and intelligent and compliant characteristics of the human hand. It also relies on a tactile sensor and feedback system based on soft materials to achieve closed-loop neural control of a human in the loop. The present invention reduces the load on the patient's arm end, overcomes the significant shortcomings of traditional prosthetic hands in this regard, and also has the advantages of low cost, light weight, and ease of manufacturing.

The invention discloses a primitive milling track generation method based on path learning, which comprises the following steps of: acquiring primitive feature data and historical milling track data, and respectively preprocessing the primitive feature data and the historical milling track data; inputting to a primitive feature coding module of the improved NCDE model, coding, setting an initial hidden state, and driving time evolution calculation; inputting to a multi-head output module for decoding, constructing a multi-target joint loss function and iteratively updating; inputting to an improved NCDE inference model, and carrying out coding and hidden state evolution decoding; weighting calculation is carried out, and a candidate milling track sequence corresponding to the highest value is screened out; and the optimal milling track sequence is converted into a track instruction file, and a numerical control machine tool is driven to execute milling machining. According to the method, the improved neural controlled differential equation and primitive feature learning are fused, intelligent optimization of the milling track is achieved, and the method has the advantages of being high in efficiency, long in cutter service life and excellent in quality.

This invention discloses an AI-based modeling method for assessing the health status of integrated power supply boxes, comprising the following steps: S1, constructing a multivariate time series; S2, modeling the multivariate time series trajectory using a neural control differential equation model to obtain the latent space trajectory; S3, performing dynamic modal decomposition on the latent space trajectory to construct a modal health space; S4, generating an enhanced training sample set using a manifold interpolation hybrid method in the modal health space; S5, inputting the hybrid modal vectors from the enhanced training sample set into a health classification module; S6, inputting the latent space trajectory into a temporal semantic playback module to generate the final predicted state trajectory; S7, comparing the final predicted state trajectory with the actual historical state trajectory, and generating an anomaly marker and performing a state rollback operation when the semantic difference exceeds a set tolerance threshold. This invention integrates neural differential modeling, interpolation enhancement, and temporal semantic playback to achieve accurate assessment of the health status of integrated power supply boxes.

Systems, devices, and methods for treating drug-refractory epilepsy are disclosed. [Solution] In one embodiment, a method for treating epilepsy is disclosed, comprising detecting electrophysiological signals of a subject using a first electrode array coupled to a first intravascular carrier. The method further comprises analyzing the electrophysiological signals using a neuromodulator electrically coupled to the first electrode array, and stimulating a target site in the subject's body using a second electrode array coupled to a second intravascular carrier implanted in a portion of a body blood vessel above the base of the subject's skull.

This invention provides an advanced AGI (Awareness, Intelligence, and Knowledge) mind guidance system and method that solves problems such as complex consciousness content analysis. It includes: S1: Neural control decoding; S2: Developmental gap analysis; S3: Strategy generation and ethical review; S4: Multi-channel dynamic guidance; S5: Performance retrospection and evolution. This invention has advantages such as good consciousness content analysis effect and high level of intelligence.

The invention discloses a control method, system and equipment for anesthesia prognosis optimization and a storage medium, and relates to the technical field of biomedical engineering. The cognitive function evaluation data, the brain structure image data and the brain function image data of the patient before the operation are calculated, and a cognitive expression index and a nerve image index are obtained; calculating the neural image index and the cognitive performance index to obtain a cognitive reserve index; determining the risk level of the target patient according to the cognitive reserve index, and inputting the physiological data of the patient and the risk level into a technology library for query to obtain a core regulation and control technology; adjusting the stimulation parameters of the core regulation and control technology according to the cognitive reserve index, and generating a noninvasive nerve regulation and control scheme according to the core regulation and control technology, the adjusted stimulation parameters and the stimulation target; and sending the non-invasive nerve regulation scheme to non-invasive nerve regulation equipment to control the non-invasive nerve regulation equipment to intervene the target patient before the operation. By implementing the technical scheme provided by the invention, the stress ability of the brain of the patient to surgical anesthesia is improved.

The embodiment of the application discloses a traffic speed prediction method and device based on self-attention neural control, relates to the technical field of intelligent transportation, and can improve the precision of traffic speed prediction while maintaining low calculation complexity. The application comprises: obtaining historical data of traffic speed, and generating an original input sequence; establishing an AB-NCDE model, and performing a forward propagation process on the original input sequence; establishing a loss function and performing a backward propagation process; and using the trained AB-NCDE model to predict traffic speed. The scheme is used for traffic speed prediction.

The application discloses a kind of based on digital twinning linear control chassis energy consumption simulation method and system, including following steps: S1, constructs digital twinning model, generates state sequence by collecting and processing operation data;S2, using neural control differential equation extraction fusion state;S3, based on fourier neural operator simulation calculation power sequence;S4, through long-term memory network prediction trend, total energy consumption is generated using Simpson integral;S5, disturbance control parameter is generated disturbance energy consumption sequence using SASP algorithm;S6, compare two kinds of energy consumption, and control parameter is optimized using PPO algorithm;S7, according to the total energy consumption of optimization result regeneration, and with actual energy consumption error analysis, update digital twinning model.The application fuses neural control differential equation, fourier neural operator etc., with the advantages of high modeling precision, energy consumption prediction accuracy and high simulation efficiency.

The application discloses an AI pest situation identification and early warning method and system based on multispectral imaging. The method uses a multispectral imaging unit to obtain multi-band images of pests, and synchronously obtains time, point, weather, and crop growth period information. Through correction, registration, and band reliability evaluation, standardized multispectral images are obtained. Based on a background spectrum dictionary and sparse reconstruction residual, the pest area is extracted and the adhered pests are separated. Combined with the prior key parts, the spectral shape part joint feature is constructed. The collaborative network with a neural controlled differential equation spectral line coding branch and a part hypergraph attention branch is used to complete pest identification. Then, the unknown pest is identified in combination with the prototype memory library, and a pest situation spatio-temporal hypergraph is constructed to realize risk early warning. The method can improve the pest identification accuracy, unknown pest identification ability, and pest situation early warning accuracy in complex scenes.

This invention proposes a multi-scale cortical-muscle coupling network analysis method based on ordinal patterns. First, the invention uses multivariate variational mode decomposition to decompose brain electromyography (EMG) signals at the same frequency scale. Then, it uses a multivariate binary temporal partitioning transfer network method to calculate the coupling strength and coupling adjacency matrix of the multi-component signals, constructing a cortical-muscle coupling network. Complex network parameters are then used to analyze the characteristics of this coupling network. This method has advantages in representing the characteristics of information transmission between the cortex and muscle and their variations across different grip force patterns. It also demonstrates the hierarchical nature of causal links between brain EMG signals, confirming the method's potential in deconstructing the intrinsic connections between the cortex and muscles during upper limb movement and the mechanisms of neural control of muscle movement.

The application discloses a world action model enhancement method and device fusing structured prior and continuous dynamics, wherein the method takes human videos in a first view and a third view as input, constructs scene-event graphs and structured priors such as causality, counterfactual constraints and execution constraints; under weak labeling conditions, performs spectrum analysis on local time sequence features of actions, and jointly encodes the time sequence features and space-time features into latent actions; further, continuous time latent dynamics modeling is introduced into a unified world action model through a neural control differential equation, and joint reasoning is performed on video state changes and latent action recovery in combination with task semantics, structured priors and counterfactual conditions. Finally, long-time rolling error accumulation is inhibited through real observation rewriting, action block generation and asynchronous closed-loop execution, so that the feasibility of weakly labeled video action learning, the rationality of action generation and the stability of closed-loop control are improved.