5903 results about "Noise reduction" patented technology

A real-time system for the multimodal generation of virtual reality scenes based on artificial intelligence for the creation of immersive three-dimensional environments from natural language narratives, consisting of: a speech capture module configured to continuously record a user's spoken narrative via one or more directional microphones, preprocesses the captured signal by noise reduction and temporal alignment, and outputs a digital speech stream; A speech-to-text processing unit that is operationally coupled to the speech capture module and configured for real-time speech recognition using a continuous neural transformer model. The unit is trained to transcribe natural language utterances into structured text data while maintaining contextual continuity throughout the evolving narrative. a semantic interpretation processing unit that is communicatively linked to the speech recognition unit and configured to perform natural language understanding techniques to extract contextual entities, spatial references, temporal relationships, and object attributes from the transcribed narrative; the engine includes a large language model that is fine-tuned for spatial reasoning tasks; a scene graph generation module configured to transform the interpreted semantic data into a structured, hierarchical representation that defines nodes for identified entities and edges for corresponding relationships, with each node associated with metadata describing geometry, position, orientation, texture, and linking attributes between objects; a multimodal image-language model processor coupled with the scene graph generation module, wherein the processor is configured to retrieve, adapt, or synthesize appropriate three-dimensional elements from a pre-trained visual-lexical embedding space and align these elements with their semantic and spatial definitions derived from the scene graph; a scene assembly and rendering controller configured to create a cohesive virtual scene from the aligned assets, perform real-time rendering using a GPU-accelerated ray tracing pipeline, and produce a stereoscopic visual output that corresponds to the evolving narrative; A head-mounted virtual reality visualization device connected to the rendering engine and configured to display the generated immersive environment to the user in real time. The device features motion sensors and inside-out tracking cameras to detect head and body movements, dynamically updating viewing angles and perspective within the rendered scene; and a bidirectional feedback module integrated into the head-mounted device and connected to the semantic interpretation processing unit; the module is configured to interpret corrective commands, gestures, or supplementary comments from the user to refine or modify specific scene elements without interrupting the real-time visualization; The system continuously updates the virtual scene as the narrative develops, ensuring temporal synchronization between speech input and rendered output below a defined latency threshold, thus enabling a natural, dialogic construction of complex three-dimensional virtual environments.

The invention discloses an attention mechanism and LSTM fused industrial bearing vibration time sequence signal fault prediction method and system. The method comprises the following steps: collecting a bearing vibration signal and carrying out filtering, noise reduction and normalization preprocessing; constructing a deep learning model combining the bidirectional BiLSTM and a coordinate attention mechanism to extract bidirectional time sequence features and enhance key fault features; carrying out model training by adopting a multi-target composite loss function and an Adam optimizer, and introducing an early stop mechanism to prevent overfitting; performing fault type identification and degree evaluation on the real-time vibration signal by using the trained model, and performing quantitative analysis by fusing multi-scale spectrum kurtosis features and nonlinear kinetic parameters; and finally, outputting a fault diagnosis report, and triggering multi-stage early warning based on an adaptive threshold. The method can realize high-precision and high-reliability bearing fault prediction and health state evaluation, and is suitable for intelligent operation and maintenance of industrial equipment.

The invention relates to the technical field of robot decision making, and provides a robot multi-modal fusion autonomous decision making method and system based on a large language model.The method comprises the steps that a robot obtains multi-modal environment information through a visual sensor, a touch sensor, an auditory sensor and a laser radar which are carried by the robot; performing preliminary filtering and noise reduction processing on the original sensor data, and synchronously recording all the sensor data by timestamps; performing space-time semantic alignment on the preprocessed multi-modal data, mapping pixel coordinates of a target in a visual target coordinate quantization original image to a robot coordinate system, performing uncertainty evaluation on a multi-modal signal through a dynamic Bayesian network, and taking entropy or variance as an uncertainty quantitative evaluation index. According to the method, the information quality is improved from a data fusion source, accurate and reliable basic support is provided for subsequent decision making, and decision making errors caused by data deviation are greatly reduced.

The invention belongs to the field of data processing, and particularly relates to a dynamic cooperative control system and method for a gas turbine and a micro-grid, and the method comprises the steps: constructing a micro-grid real-time monitoring module, continuously collecting distributed energy real-time output, controllable load demands, bus voltage frequency and equipment state parameters, and carrying out the filtering and noise reduction through a preprocessing unit, thereby guaranteeing the data precision; calculating a real-time power difference value based on the preprocessed data, calling an adaptive neural fuzzy inference system, taking the power difference value, the bus voltage deviation and the frequency deviation as input, and judging whether the power difference value, the bus voltage deviation and the frequency deviation exceed a preset threshold value by means of a fuzzy rule base and a neural network model; if the threshold values are not exceeded, the current states of the gas turbine and the energy storage system are maintained; if any one exceeds the threshold value, a dynamic cooperative control instruction is triggered, precise cooperative control of the gas turbine and the micro-grid is achieved, and the operation stability, the operation efficiency and the reliability of the micro-grid are improved.

The invention relates to the technical field of artificial intelligence, and discloses an AI-based composite insulator internal defect ultrasonic detection method, which comprises a multi-mode ultrasonic probe array module, a signal preprocessing module, an AI defect analysis module, a dynamic parameter optimization module, an edge calculation module and a visual report module, the method comprises the following steps: acquiring a full-dimensional signal through a multi-modal ultrasonic probe array, and inputting the full-dimensional signal into a deep space-time convolutional neural network for defect recognition after adaptive noise reduction and feature fusion; the detection precision is improved by combining dynamic waveform matching and multi-physics coupling analysis; model lightweight and real-time processing are realized by adopting transfer learning and edge calculation. The system integrates the functions of parameter adaptive optimization, three-dimensional visualization and Internet of Things cooperation, solves the problems of low efficiency and high false detection rate of a traditional detection method, and improves the intelligent level and engineering applicability of composite insulator defect detection.

The invention discloses an automatic anchor point searching and processing method for grid-connected test data of a photovoltaic inverter, and belongs to the technical field of automatic test of a power system. According to the method, a three-phase voltage and current signal output by a power grid simulator and an inverter power instruction signal are aligned through a high-precision time synchronization device; wavelet transform multi-scale noise reduction and moving average filtering combined preprocessing is adopted to improve the signal-to-noise ratio; identifying a voltage zero crossing point candidate set, a drop starting point candidate set and a recovery termination point candidate set based on a self-adaptive dynamic threshold value; transient energy characteristic verification is introduced for a voltage drop starting point; effective anchor points are confirmed through time window association of power instruction step changes. According to the method, the problems of low efficiency of manual key event point identification, misjudgment caused by noise interference, grid event and inverter response time sequence correlation missing and the like are solved, and the automation degree of test data analysis, anchor point positioning precision and control response time sequence analysis reliability are remarkably improved.

The invention discloses an acoustic emission intelligent detection method and system for hydrogen-induced damage of a high-pressure hydrogen system, and the method comprises the steps: collecting a system operation signal through an acoustic emission sensor, carrying out the combined preprocessing of variational mode decomposition and adaptive wavelet threshold noise reduction, restraining noise, constructing a lightweight MobileNet-TCN network, carrying out the deep feature extraction, and carrying out the detection of the hydrogen-induced damage of the high-pressure hydrogen system. GRU and a three-dimensional point cloud technology are fused to realize submillimeter-level positioning of an acoustic emission source, a big data damage case library is associated based on an acoustic emission parameter accumulation model, dynamic assessment and early warning of damage risks are realized, multi-physics field monitoring data are combined, damage classification is optimized through a GCNs (Graph Convolutional Networks), and the above processes are systematically integrated. And full-chain intelligent processing from signal acquisition to risk early warning is completed. The scheme has the advantages of strong anti-interference capability, submillimeter positioning precision, high edge end reasoning efficiency, high damage classification accuracy and dynamic early warning capability, and is suitable for safety monitoring of hydrogen energy storage and transportation equipment.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a task instruction generation method, device and equipment based on cross-modal fusion, and a medium, and the method comprises the steps: carrying out the decoding and noise reduction of an input video, generating a frame sequence, and recognizing a plurality of key frames based on the inter-frame similarity; extracting spatial features of the key frames to form a sequence, and generating video spatial-temporal features in combination with time features; performing semantic preprocessing on the input text to obtain text semantic features, and acquiring motion sensor signals to obtain motion features; fusing the video spatio-temporal features, the text semantic features and the action features to generate fused features; and generating a perception vector based on the fusion feature and outputting a task instruction. According to the method, multi-modal fusion is realized through key frame extraction and space-time fusion mechanisms in combination with text semantic features and action features, and the perception expression ability and the task instruction generation accuracy are improved by using time sequence information and multi-source perception input of the video.

The invention discloses a temperature control and noise reduction method for an analog optical module, and relates to the technical field of data processing. The method comprises the following steps: collecting and preprocessing an original temperature signal, environment temperature time sequence data and an optical wavelength original detection value of an optical module; improving the LSTM model, constructing a junction temperature dynamic prediction model, and outputting prediction data; a pre-trained BP neural network is utilized to calculate the temperature compensation amount according to the real-time wavelength offset, and a PPO reinforcement learning algorithm is adopted to optimize with the temperature stability, the wavelength offset and the TEC power consumption as multiple targets to obtain an optimal PID parameter mapping table; and finally, predicting, compensating and optimizing parameters are fused, and control output for driving the TEC is generated through a multi-mode intelligent decision. The problems of insufficient control precision and response lag are effectively solved, cooperative high-precision control over the junction temperature and the emission wavelength of the laser is achieved, transmission noise is remarkably reduced, and meanwhile system energy consumption and the self-adaptive capacity are considered.

The invention relates to the technical field of gas detection, in particular to a multi-gas accurate detection method and system.The method comprises the following steps that gas to be detected flows through a miniature gas enrichment device prepared through an MEMS technology under driving of a miniature gas pump, and target gas is adsorbed and enriched through graphene and metal oxide composite sensitive materials on the surface of a spiral gas inlet flow channel in the miniature gas enrichment device; the desorption concentrated gas is heated to the multi-gas composite detector, and the sensor array reacts with the gas to generate an electric signal; the data acquisition unit performs noise reduction, amplification and analog-to-digital conversion preprocessing on the electric signal; the data processing unit extracts characteristic vectors such as a response curve peak value and a rising slope; inputting the feature vector into a pre-trained machine learning classification-regression combined model, and outputting a gas type identifier and an initial concentration; error compensation is carried out in combination with environment temperature and humidity and a cross sensitivity interference mode library, and finally accurate information of at least nine gases is output. The device has the advantages of high sensitivity, strong anti-interference performance and miniaturization.

The invention relates to an aeration fan predictive maintenance method, system and equipment based on multi-modal perception and adaptive learning and a medium. The method comprises the following steps: generating a time sequence data set through synchronous acquisition and combined noise reduction processing of a sensor group; generating a multi-dimensional feature vector through time-frequency feature spectrum characterization and interpretability contribution analysis in combination with dynamic weight distribution coupled by environmental factors; on the basis of the multi-dimensional feature vectors, real-time anomaly detection is carried out at the edge end through a lightweight model, and abnormal data fragments are uploaded to the cloud end; and performing cross-sensor bidirectional reasoning on abnormal data fragments through a reasoning model deployed at the cloud, reconstructing a sensor topological graph, intelligently triggering elastic incremental learning, cooperatively processing equipment degradation trend analysis, multi-source evidence fusion and space calibration, and outputting a life prediction result and a fault thermodynamic diagram. According to the method, the core pain points of high early fault omission ratio, insufficient model robustness and the like are solved, and cost reduction, efficiency improvement and equipment life prolonging are realized while the diagnosis precision is maintained.

The invention discloses a defect identification system of an ultrasonic flaw detector, and relates to the technical field of nondestructive testing, the system comprises a signal acquisition and preprocessing module, a defect feature identification module, a physical modeling analysis module, a life prediction and evaluation module and an intelligent decision visualization module; the signal acquisition and preprocessing module adopts a multi-frequency-point phased array transducer array, obtains an original signal through low-noise amplification, band-pass filtering and analog-to-digital conversion, and outputs a time domain signal matrix through adaptive noise reduction and gain compensation processing; the multi-frequency-point phased array transducer array and the advanced signal processing technology are integrated, the defect recognition precision and efficiency are remarkably improved, the system obtains high-quality original signals through low-noise amplification, band-pass filtering and analog-to-digital conversion technologies at first, then the high-quality original signals are subjected to self-adaptive noise reduction and gain compensation processing, and the defect recognition accuracy is improved. The background noise interference is effectively eliminated, and the purity of the signal is ensured.

The invention discloses a sound acquisition and processing system based on cooperation of multiple microphone arrays. The system comprises a sound acquisition module, a multi-channel signal preprocessing module, a sound signal feature extraction module, an abnormal sound recognition module, a sound source positioning module and an alarm module. A multi-channel mixed data signal is collected through a circularly-arranged multi-microphone array formed by a plurality of microphones, after echo cancellation, wave beam domain noise reduction and multi-sound-source separation, a single-sound-source feature vector is extracted, according to the single-sound-source feature vector, abnormal sound including explosion, screaming or glass breakage is recognized through a BiLSTM and an attention mechanism model, and the abnormal sound is recognized through an attention mechanism model. And the GCC-PHAT and MDS-MUSIC algorithms are combined to position abnormal sound production, and alarm information is generated. According to the invention, accurate identification, positioning and alarm of the abnormal sound can be realized, and the real-time performance, the accuracy and the multi-target processing capability of abnormal sound monitoring in a complex environment can be obviously improved.

The invention provides a partial discharge on-line monitoring method and system based on multi-modal fusion and adaptive noise reduction, and the method comprises the steps: employing an ultrahigh frequency UHF sensor, a miniature ultrasonic sensor, and a miniature detector for gas dissolved in oil, which are disposed on a transformer; synchronously acquiring electric signals, sound signals and characteristic gas concentration data in oil generated in the partial discharge process; performing format unification, abnormal value elimination and time alignment processing on the electric signal, the sound signal and the gas concentration data in an edge calculation unit to obtain aligned multi-source original data; performing adaptive wavelet noise reduction processing on the electric signal to obtain a de-noised UHF signal; respectively extracting time domain, frequency domain and chemical features from the de-noised UHF signal, the aligned sound signal and the gas concentration data to form a multi-dimensional feature vector; and inputting the multi-dimensional feature vector into a pre-trained defect traceability model, and outputting a partial discharge defect type, severity level and development trend prediction result.

The invention relates to an error compensation method of a birefringence self-calibration laser level meter, in particular to the field of laser level meters, and aims to improve the precision of the laser level meter by combining a birefringence effect, physical modeling and neural network optimization. Firstly, temperature data are collected in real time, Kalman filtering is used for noise reduction, and accurate data are provided for physical modeling; then, through physical constraints such as a heat conduction equation and a Jones matrix, a coupling relation between the temperature gradient and optical parameter changes is established; then, dynamically optimizing the refractive index correction by using a physical information neural network to avoid an overfitting problem; and finally, calculating optical path difference compensation by using a hardware accelerator, correcting a system error in real time, and feeding back and adjusting a network weight. According to the method, the precision and the stability of the laser level meter in an environment with relatively large temperature change are effectively improved.

The invention provides a multi-channel contact resistance intelligent test device and method, the contact resistance intelligent test device is integrated with a plurality of different types of resistance test channels, and the resistance test channels comprise a conduction test channel, a static resistance test channel and a dynamic resistance test channel. The device comprises an upper control module used for receiving a channel test parameter of a contact resistor and generating a channel selection test instruction based on the channel test parameter; the multi-channel switching module switches to a target test channel of a corresponding type based on the channel selection test instruction; the resistance measurement module applies current of a corresponding type according to the type of the target test channel, collects the voltage of the target test channel, and obtains a resistance value according to the voltage and the current; and the signal processing module performs noise reduction processing on the resistance value to obtain a target resistance value, and evaluates the change trend of the target resistance value, so that the problems of low test efficiency, dynamic data missing and personal error accumulation of a traditional method are solved, and the subjective deviation of manual interpretation is eliminated.

The invention discloses a tunnel deformation real-time early warning method and system based on an LSTM-CNN model, and belongs to the technical field of tunnel engineering safety monitoring. The method comprises four steps of data acquisition, spatio-temporal feature fusion processing, deformation prediction and risk assessment, and intelligent early warning and decision support: collecting multi-dimensional monitoring data through a distributed sensor network and recording spatio-temporal labels; after standardized noise reduction, extracting time trend and spatial distribution characteristics by using an LSTM-CNN fusion model, and constructing a time-space sequence data set; outputting a deformation prediction value based on the fusion features, and calculating a deviation degree in combination with a dynamic threshold model; and triggering multi-level early warning according to the deviation degree and performing visual display. The system comprises a data acquisition module, an edge calculation module, a cloud analysis module and an intelligent terminal module, and double-channel redundancy transmission is adopted to guarantee data continuity. Through spatio-temporal feature collaborative mining, dynamic threshold value adaptation and graded early warning, the deformation prediction precision and the risk assessment accuracy are improved, the transmission stability in an extreme environment is guaranteed, and the early warning decision efficiency is optimized.

The invention relates to the technical field of combustion equipment state monitoring, in particular to a combustion state monitoring method and system based on vibration and noise fusion analysis, and the method comprises the steps: collecting a combustion signal in real time through a vibration sensor and a broadband noise sensor, extracting a characteristic frequency band through noise reduction and filtering, and obtaining a combustion state; fPGA hardware-level clock synchronization is adopted to realize time alignment of bimodal signals, a vibration-noise joint feature matrix is constructed and multidimensional coupling is carried out, a combustion state feature vector is generated through a mixed deep learning model, a combustion state is determined based on a bimodal confidence weighting decision, and a linkage control instruction is generated during interruption. The system comprises a signal acquisition synchronization unit, a feature fusion and modeling unit, a state decision and instruction generation unit and a safety linkage execution unit, executes a method and activates a standby safety system, solves the problems of asynchronous bimodal data and insufficient monitoring precision, and improves the combustion state monitoring accuracy and safety response efficiency.

The invention discloses a seal removing and document repairing method based on quantum state collaborative regulation and control, and relates to the field of document image processing and quantum computing cross technology, the method comprises the following steps: obtaining to-be-processed information, and carrying out quantum-classical feature collaborative preparation; the character stroke continuity is guaranteed through quantum entangled state modeling and entanglement degree constraint iteration, texture decoupling is achieved through quantum wavelet transform and Gram-Schmidt orthogonalization, and adaptive filling is conducted in combination with a quantum generative adversarial network; dynamic quantum phase adjustment is used for counteracting superposition interference of stamps with different transparency, and quantum neural network noise reduction and multi-scale quantum Fourier sharpening are used for optimizing image quality; checking the repair result, if the repair result does not reach the standard, returning to the edge sharpening link to perform decoupling and filling the edge sharpening link to readjust the parameter; and for special scenes such as inclination, multi-color overprinting and ultra-thin frames, quantum rotation correction, color channel separation and boundary annihilation operator processing are used, finally, high-precision, high-naturalness and high-adaptability restoration of seal removal is achieved, and high fidelity of results is guaranteed.

The invention relates to a motor power self-adaptive control method and system based on multi-dimensional sensor data, and the method comprises the steps: collecting multi-dimensional signals, such as ground resistance, motor temperature and battery voltage, in real time, carrying out the self-adaptive filtering and noise reduction, analyzing the correlation of all parameters through a multivariable decoupling algorithm, and determining a power demand weight; when it is detected that the ground resistance is a dominant factor, the system generates a load feature vector through weighted fusion, a database is matched to determine an optimal power target value, and PWM output is dynamically adjusted through a fuzzy control algorithm; and a closed-loop feedback mechanism continuously optimizes control parameters to ensure that the system quickly responds to sudden change of ground resistance. According to the method, intelligent adaptation of the motor power is achieved, the problems that adjustment lags behind and multi-parameter coupling processing is insufficient in the traditional technology are effectively solved, the stability of the sweeping robot under the complex ground working condition is remarkably improved, and the service life of the motor and the service life of a battery are remarkably prolonged.

The invention provides a multi-source data fusion self-positioning method and system. The method comprises the following steps: acquiring GPS positioning data, visual image data and laser radar point cloud data of an unmanned aerial vehicle; carrying out noise reduction preprocessing on the GPS positioning data by adopting an unscented Kalman filtering algorithm; performing sensor joint calibration based on a visual image and a laser radar point cloud, and establishing a geometric mapping relation between a camera coordinate system and a world coordinate system by retrieving a preset high-precision tower ledger library and solving a PnP problem; inputting the image target detection data, the GPS state estimation value and the geometric mapping data into a pre-trained auto-encoder regression network; anti-interference potential features are extracted through an encoder of the network, and a target position estimation value of the target space position of the unmanned aerial vehicle is output through a regression head. According to the invention, the problem of positioning drift caused by strong electromagnetic interference and complex landform in electric power inspection is effectively solved.

The invention discloses a server energy-saving system and method based on a lag compensation type dynamic thermal impedance model, and relates to the field of server energy saving.By collecting operation data of a server hardware bottom layer in real time, a multi-dimensional thermal-computing force coupling time sequence atlas is constructed, and based on the multi-dimensional thermal-computing force coupling time sequence atlas, a dynamic thermal impedance model is established. Calculating a heat conduction time constant of hardware in a current environment; establishing a lag compensation type dynamic thermal impedance model based on the heat conduction time constant; constructing a thermodynamic phase space according to operation data; analyzing a noise reduction evolution trajectory in combination with the lag compensation type dynamic thermal impedance model; the method comprises the steps of generating a weighted thermal resonance state vector, activating an adaptive gain MPC predictive damping scheduling strategy based on the weighted thermal resonance state vector, constructing and solving a dynamic weighted multi-objective optimization function, generating an optimal damping scheduling instruction, and thoroughly solving the fan asthma phenomenon that the fan PWM duty ratio fluctuates substantially in a sine mode under the constant computing power load.

The invention relates to a temperature drift compensation method for a pressure instrument, and belongs to the technical field of pressure instruments, and the method comprises the following steps: obtaining output signal values of a pressure sensor at different temperature points, and building a temperature-output signal data set; constructing a polynomial fitting model as a temperature drift compensation model, fitting a temperature coefficient through a least square method, and storing the coefficient into an instrument memory; original values of temperature and pressure are collected in real time and substituted into the model to calculate a compensated pressure value. The method also supports various strategies such as temperature sensor calibration, nonlinear error compensation, Kalman filtering noise reduction, dynamic temperature change rate compensation and neural network compensation, and has self-calibration and remote updating functions. According to the invention, the measurement precision and stability of the pressure instrument in a variable-temperature environment are significantly improved, and the method is suitable for high-precision industrial measurement and intelligent monitoring scenes of the Internet of Things.

The invention relates to a dynamic visual map matching autonomous optimization method based on IMU sensing, and belongs to the technical field of positioning and navigation of autonomous mobile equipment. The method comprises the steps of obtaining IMU high-frequency motion data and visual image data, performing noise reduction to obtain carrier short-time attitude change information, performing semantic segmentation on an image to recognize a dynamic target, extracting feature points and performing dynamic target weight evaluation; calculating confidence degree scores of the feature points, and mapping fusion weights of the IMU and the vision through a self-adaptive credibility distribution mechanism; s3, predicting a trajectory and a potential matching area based on carrier short-time attitude change information, incrementally updating a local map, and matching by taking an IMU trajectory as a constraint to obtain an initial result; and detecting the matching validity, reducing the re-matching range by using an IMU short-time track during mismatching, and correcting an initial value to obtain a final result. The map matching precision and stability in a complex dynamic scene are improved, the real-time performance and continuity are balanced, and the problems that a traditional scheme is redundant in calculation power and slow in mismatch recovery are solved.

The present invention relates to the technical field of signal processing and fault diagnosis. The method of the present invention comprises: collecting a flame chemiluminescence signal, and performing adaptive filtering noise reduction processing; using an improved proper orthogonal decomposition method and a high-order mode to obtain comprehensive combustion feature information; developing a real-time synchrosqueezing transform algorithm to perform time-frequency analysis on data, and updating energy distribution in real time; taking into account sensor data to perform multi-parameter joint analysis to obtain a combustion state evaluation, using a plurality of high-speed cameras to acquire flame images from different angles, reconstructing three-dimensional flame morphology by means of a computer vision technology, and providing fault detection information; and constructing a fault prediction model to give an early warning of a combustion fault. The present method implements nondestructive and rapid detection of oscillating combustion faults, keeps rich information of flame images, reflects overall flame pulsation characteristics, increases the speed and accuracy of detection, and compensates for the defect of low frequency resolution of conventional time-frequency analysis methods.

The invention relates to a hearing aid intelligent noise reduction and human voice enhancement system based on electroencephalogram signals, and belongs to the field of biomedical engineering and acoustic signal processing. The system comprises an electroencephalogram signal acquisition module, a multi-channel acoustic sensor array, an embedded neural signal processor, an adaptive beam forming module, a dynamic speech enhancement engine and a dual-mode output device, and constructs electroencephalogram-acoustics joint features by extracting an alpha / theta wave power ratio, a P300 component and auditory cortical Gamma phase synchronism. A deep network is driven to separate target voice, a wave beam direction and a frequency response curve are dynamically adjusted based on neural feedback, a closed-loop calibration unit is innovatively adopted, gain is reversely adjusted according to N1-P2 wave amplitude, heart rate variability and eye movement data are fused to optimize decisions, and when the signal-to-noise ratio is-5dB, the voice recognition rate reaches 89%, the auditory fatigue is reduced by 37%, and the decision conflict rate is smaller than 6%. The defects of attention blind area, noise separation failure and physiological adaptation of a traditional hearing aid are overcome. The system is suitable for the fields of hearing impairment rehabilitation, special communication and intelligent cabins.

The invention discloses a wide-frequency-domain weak signal data acquisition method, system, equipment and medium, and relates to the technical field of power system monitoring and fault diagnosis, and the method comprises the steps: collecting an input signal, analyzing the signal characteristics in real time, obtaining the frequency composition and amplitude change information of the signal, and dynamically adjusting the sampling rate according to the signal characteristics. Performing noise reduction processing on the collected signals, eliminating noise interference and retaining effective signal components, performing time alignment processing on the data to form a data set with a unified time reference, extracting multi-category features based on the data set, performing fusion judgment, identifying whether the system is in a fault state, and when it is judged that a fault occurs, judging whether the system is in a fault state or not; if yes, fault analysis and positioning are executed, and an analysis result is generated and output. According to the method, fast Fourier transform analysis is carried out on the signals, the dominant frequency and harmonic components can be accurately recognized, the energy ratio can be calculated, and a reliable data basis is provided for signal feature extraction and fault diagnosis.

The invention relates to a noise damping control system of an offshore wind turbine generator, and belongs to the technical field of offshore wind power generation. The coupling state evaluation module is used for determining a coupling state evaluation index through a preset deep neural network model based on the platform motion information, the vibration spectrum data and the sound pressure signal; the cooperative control strategy generation module is used for determining target rigidity and target damping according to the coupling state evaluation index and the vibration spectrum data; according to the invention, the coupling state evaluation module processes platform motion information, vibration spectrum data and sound pressure signals acquired by the data acquisition module through a preset deep neural network model; the preset model is endowed with strong nonlinear fitting capability through offline supervised learning of massive historical working condition data; and an accurate and quantitative risk basis which cannot be reached by the prior art is provided for subsequent control decisions.

The invention relates to the technical field of audio processing, in particular to a self-adaptive scene sound effect adjusting method and device and a storage medium, and the method comprises the steps: collecting a mixed audio stream and an original playing audio, and extracting an acoustic fingerprint vector through a pre-trained convolutional neural network; performing acoustic feature deconstruction on the acoustic fingerprint vector to obtain a plurality of acoustic feature components, performing scene recognition judgment according to a preset scene judgment rule based on the plurality of acoustic feature components, and determining an audio playing scene; constructing a time-frequency mask according to the audio playing scene, generating a noise reduction gain matrix based on the time-frequency mask, and performing scene sound field enhancement on the audio playing scene to obtain a sound field optimization matrix; and performing noise reduction and sound effect adjustment on the original playing audio according to the noise reduction gain matrix and the sound field optimization matrix, and outputting the optimized playing audio. According to the invention, sound effect adjustment can be dynamically adapted according to different scenes, and the noise reduction accuracy and the multi-scene sound quality adaptability are improved.

The invention discloses a multi-modal dynamic compensation road disease intelligent detection and risk assessment system, and relates to the technical field of artificial intelligence and computer vision, and the system comprises an image collection module which is used for obtaining a road surface image in real time through a camera device, and transmitting the image to a preprocessing module; the preprocessing module is electrically connected with the image acquisition module and is used for carrying out graying, noise reduction, contrast enhancement and geometric correction operation on the image and outputting a standardized image; the feature extraction module is electrically connected with the preprocessing module. According to the road disease detection system provided by the invention, by integrating a plurality of modules, high efficiency and intelligence of road disease detection are realized, compared with traditional manual inspection, the system not only improves the detection efficiency, but also remarkably enhances the objectivity and accuracy of detection, and is particularly suitable for real-time monitoring requirements of a large-scale road network; the image acquisition quality is effectively improved, and the effectiveness of feature extraction can be ensured.