19640 results about "Frequency band" patented technology

A headset antenna and a connector for the same are provided. The headset antenna includes an audio signal line, an antenna and a high impedance element in specified application frequency ranges. The audio signal line is adapted for transmitting an audio signal and the antenna is adapted for receiving an RF signal. The high impedance element is disposed on a transmission path of the audio signal and generates a high impedance at a specified frequency band of the RF signal, so that the audio signal line is equivalent to an open circuit and the antenna obtains a better receiving capability.

The invention relates to a power supply switching control optimization method and system, and the method comprises the steps: constructing a multi-dimensional data fusion monitoring system, integrating a real-time voltage ripple coefficient detection module of a main power supply, a load transient response analysis module and an environment temperature and humidity sensor array, and generating a dynamic health degree index; the method comprises the following steps: establishing a fault prediction model based on transfer learning, pre-training through multi-scene failure data in a historical database, receiving a health degree index in real time, outputting a reliability attenuation curve, and activating a dual verification mechanism when the slope of the attenuation curve exceeds a preset threshold; extracting characteristic energy distribution of a frequency band of 0.1-10 kHz; executing a transient load impact test, and measuring dynamic response parameters of voltage recovery time and overshoot amplitude; and generating a switching confidence score based on cross processing of the dual verification result and the attenuation curve, and triggering a hierarchical switching instruction. The accuracy of the switching time of the main power supply and the standby power supply can be improved.

The invention relates to the field of power equipment data processing, in particular to a steam turbine vibration fault diagnosis system fused with deep learning. Comprising a dynamic knowledge base construction module, a working condition adaptive data synchronization module, a knowledge-guided heterogeneous feature fusion module, a dynamic structure neural network module, an online self-optimization weight distribution module and a knowledge-enhanced coupling fault reasoning module. The dynamic knowledge base construction module updates the multi-modal knowledge graph through an incremental knowledge distillation mechanism, and generates an interpolation strategy template and a frequency band sensitivity matrix; the working condition self-adaptive data synchronization module dynamically calls an interpolation algorithm based on a rotating speed fluctuation mode to realize time sequence alignment optimization of multi-source sensor data; the system effectively solves the problems of time scale asynchronism and feature heterogeneity in multi-source heterogeneous data fusion through a knowledge-driven and data-driven closed-loop interaction mechanism, and realizes accurate diagnosis and early warning of steam turbine vibration faults under complex working conditions.

The invention provides an equipment supervision data real-time analysis method and system, and the method comprises the steps: calculating an energy value of a frequency band corresponding to a sub-band signal, determining the energy characteristics of the surface roughness and dust deposition distribution uniformity of a blade in each sub-band in a square sum form, and generating energy distribution vectors which are arranged according to a frequency band sequence; performing time-frequency analysis on the vibration signal by using the acquired real-time rotating speed information of the gas turbine, identifying a frequency component corresponding to the eccentric distance value of the rotor and the vibration frequency offset, and determining the amplitude of the frequency component; according to the classification result and the real-time rotating speed information, the comprehensive influence index of the material density change rate and the ash deposition layer thickness difference is calculated, the fault early warning level is determined through a weighted average method, and an early warning signal is generated; and according to the change trend of the early warning signal and the blade edge wear degree, a random forest algorithm is adopted to predict a development curve of the vibration frequency offset in the future target time, and real-time equipment supervision analysis data are obtained.

A power distribution network cable fault point location method and system, a computer device and a medium, belonging to the technical field of power distribution networks. The location method comprises: S101, when a cable has a fault, a transient signal of a wide-area frequency band is generated, a fault traveling wave containing high-frequency components; a traveling wave line mode component having a relatively small chromatic dispersion is used to perform measurement; decoupling an extracted fault traveling wave signal; S102: the traveling wave voltage and current will have a sharp change when the traveling wave generated by the fault point reaches a measurement end, and the traveling wave head can appear as a high-frequency sudden change in a time-frequency graph, the sudden change point being the position of the wave head; S103: performing EEMD decomposition on the fault traveling wave line mode component, and extracting a first IMF component for Hilbert transform to obtain a time-frequency graph thereof, a sampling moment corresponding to the position of a first frequency sudden change point on the time-frequency graph being the arrival time of the fault traveling wave head; and using a double-end fault location algorithm to calculate a distance with respect to the fault point. The result of the location method has relatively high precision, and, compared with the actual fault position, has a relative error not more than 4%.

The invention relates to a multi-microphone array beam forming signal enhancement method and device, and the method comprises the steps: obtaining a multi-channel time domain signal collected by a multi-microphone array, and carrying out the time-frequency conversion, and obtaining a plurality of time-frequency domain signals; acquiring a spatial distribution vector of the multi-microphone array, and performing deformation compensation processing on the complex number of time-frequency domain signals to obtain sound field consistency signals; performing sound source direction feature extraction on the sound field consistency signal, and performing complex time-space attention beam forming network processing to obtain a beam forming signal; performing beam direction and gain parameter optimization on the beam forming signal through a preset adversarial environment simulator to obtain a dynamic frequency band enhanced signal; and performing inverse time-frequency conversion on the dynamic frequency band enhanced signal, and performing low-delay optimization to obtain a target quality voice output signal. According to the invention, signal deformation compensation can be carried out more accurately.

The invention provides an intelligent image signal processing method and system based on multi-modal fusion. According to the invention, a multi-mode input signal is received, is divided into a spatial distribution feature extraction region and a dynamic change trajectory capture region, and is decomposed into a penetrability feature layer and a substance reflection feature layer; carrying out association mapping on missing pixel information in the dynamic change trajectory capture region and energy distribution of the penetrability feature layer to generate enhanced dynamic trajectory data, identifying a determined reflection mode in the substance reflection feature layer, and carrying out frequency domain superposition on corresponding frequency band response and the spatial distribution feature extraction region; generating a composite spatial feature, then constructing a multi-modal joint optimization model, then adjusting the contribution ratio of the two data, and generating a fused image signal; the technical scheme provided by the invention not only solves the problems of detail loss, artifact generation and poor dynamic adaptability caused by single-mode limitation, but also improves the spatial resolution and tracking precision of the image signal.

The invention relates to a high-altitude large steel structure corridor safety risk monitoring method and system and a medium, and belongs to the technical field of civil engineering structure health monitoring, and the monitoring method comprises the steps: collecting original monitoring data through a multi-source sensor group disposed at each node of a steel structure corridor; performing space-time alignment processing on the original monitoring data to generate a sensor data matrix; performing dynamic noise suppression on the sensor data matrix based on an environmental noise transfer function model, and outputting a pure signal matrix and a damage characteristic frequency band identifier; calculating a thermal stress sensitive factor, extracting a multi-physical field coupling feature associated with the damage feature frequency band identifier in the pure signal matrix, fusing to generate a high-dimensional damage feature tensor, inputting the high-dimensional damage feature tensor into a pre-constructed digital twinborn risk assessment model, and outputting an assessment result including a risk level label and a damage position coordinate; and matching the regulation and control strategy according to the risk level label, and generating a corresponding equipment control instruction. According to the invention, the scientificity and accuracy of risk decision can be improved.

The invention provides a multi-means fusion unmanned aerial vehicle countering method and system, and the method comprises the steps: obtaining three-dimensional topographic data, atmospheric turbulence features and electromagnetic spectrum features in real time through deploying a distributed sensing array in a target region, constructing a multi-layer virtual signal barrier boundary, and generating a frequency band interference parameter set; when it is detected that the unmanned aerial vehicle passes through the barrier boundary, the cooperative interference module is triggered to generate an initial interference signal weight, polarization direction distribution and power density distribution are dynamically corrected in combination with electromagnetic scattering characteristics fed back by the unmanned aerial vehicle in real time, and the initial interference signal weight serves as flight path modeling input and is used for generating a three-dimensional dynamic flight path model; according to the method, the azimuth angle correction, the elevation angle compensation coefficient and the energy gradient parameter of the ground interception equipment are calculated, and then the dynamic interception strategy is generated through the dynamic strategy fuser, so that the interference and interception efficiency of the unmanned aerial vehicle is improved through fusion and dynamic closed-loop control of excessive means, and the full-link closed loop from environmental perception to interception control is realized.

The invention relates to the technical field of electromagnetic pulses, and discloses an electromagnetic pulse data transmission method. The method comprises the following steps: collecting natural electromagnetic pulse signal data of a detection target area through a six-dimensional complementary electrode array structure, and executing Kalman filtering peak extraction and characteristic frequency locking to obtain frequency-optimized signal data; executing first-order and second-order gradient difference matrix calculation and space vector synthesis to obtain signal source position coordinate data and signal intensity data; performing spectral domain reconstruction and multilayer dynamic threshold analysis to obtain a spatial clustering abnormal data packet; and selecting an optimal transmission channel to transmit the spatial clustering abnormal data packet to a cloud server. According to the invention, the influence of an external interference source is effectively eliminated, accurate extraction and characteristic frequency locking of effective frequency band signals are realized, power frequency and harmonic interference thereof are effectively suppressed, the signal-to-noise ratio of the effective frequency band of the signals is improved, and the reliability and timeliness of data transmission in a severe field environment are ensured.

An electric power assisted steering assembly comprises a steering mechanism which operatively connects a steering wheel to the roadwheels of a vehicle, a torque sensing means adapted to produce a first output signal indicative of the torque carried by a portion of the steering mechanism, a means for producing a second output signal indicative from a signal indicative of the angular velocity of the portion of the steering mechanism, an electric motor operatively connected to the steering mechanism, a signal processing unit adapted to receive the two output signals and to produce therefrom an overall torque demand signal representative of a torque to be applied to the steering mechanism by the motor, and a motor drive stage adapted to provide a drive current to the motor responsive to the torque demand signal. The overall torque demand signal includes a damping component that is dependent upon the second output signal. The means for producing the yaw damping component includes a filter having one pole and one zero, and in use the second signal is passed through the filter to produce the damping component, the frequency of the pole and zero being selected such that the filter provides a band between the pole and zero within which the phase of the filtered second signal and the velocity signal are further out of phase or in phase than within the band.

The invention relates to the technical field of ammeter monitoring, in particular to a detection method and system for ammeter load optimization, and the method comprises the following steps: collecting voltage and current data based on a load access terminal, judging synchronous offset, recognizing a dominant channel change mode, adjusting sampling rhythm configuration, and constructing amplitude mapping between voltage and power; and analyzing a multi-cycle response trend and classifying load states to obtain a load feature distribution label. According to the invention, by constructing a time sequence comparison structure of voltage and current signals in the same period, the identification capability of channel synchronization characteristics is enhanced, the interference of periodic rhythm drift on data sampling is avoided, the channel dominant trend is judged according to the spacing change proportion, the difference source has clear guidance, and the fluctuation identification precision is improved. In combination with a dynamic regulation and control strategy of a sampling rhythm, adaptive distribution of sampling frequency bands of key channels is realized, the detection granularity is improved, linkage expression among multi-channel signals is completed, and the depiction depth of a load response structure is enhanced.

The invention discloses a wide-narrow integrated ad hoc network method, and belongs to the technical field of network communication. The method comprises the following steps: a network node generates dynamic topology information based on a narrow-band communication module periodic broadcast state beacon; according to the link quality and the node load in the topological information, coordinating the resource allocation of broadband and narrowband channels by adopting a load-aware time slot allocation algorithm; data are classified according to service types, broadband data are modulated and transmitted by adopting orthogonal frequency division multiplexing, and narrowband control instructions are modulated and transmitted by adopting differential phase shift keying; when the link quality is deteriorated, the link is switched, and the transmitting power and the frequency band parameters are dynamically adjusted; and adaptive switching and data distribution of wide and narrow band communication modes are realized. According to the method, the problems of difficulty in topology maintenance and low bandwidth and narrowband resource allocation efficiency in a high-dynamic network scene are solved, the network adaptability, the anti-interference capability and the resource utilization rate are remarkably improved through a dynamic cooperative bandwidth and narrowband communication mode, and meanwhile, the protocol switching overhead and energy consumption are reduced.

The invention discloses a smart power grid line loss intelligent detection method based on Internet of Things big data, and relates to the technical field of smart power grid electric energy metering, and the method comprises the following steps: S1, extracting a voltage signal and a current signal of each electric energy metering node in a set time window, carrying out the frequency domain transformation to obtain a frequency spectrum, calculating the energy concentration index of a high-frequency band, and calculating the energy concentration index of the high-frequency band; and identifying a high-frequency harmonic interference time section according to the change of the energy concentration degree. According to the method, a closed-loop mechanism of high-frequency harmonic interference identification, error traceability, cause discrimination and dynamic correction is constructed, fragment-level and node-level error compensation is realized, and the accuracy and the stability of line loss anomaly identification of the smart grid are improved by combining electric quantity consistency verification and trend model updating; and the reliability and the intelligent level of the system in a complex interference environment are obviously enhanced.

The invention discloses a method for realizing data communication by a multi-band adaptive antenna based on 5G communication. The method comprises the steps of dynamic sensing and multi-mode signal fusion, multi-band intelligent analysis, hybrid beam forming and dynamic reconstruction, cross-layer parameter joint tuning, real-time monitoring and self-learning compensation, multi-band adaptive switching and fault diagnosis and error calculation. The problems that in traditional 5G communication, the antenna frequency band is fixed, the beam adjusting capacity is limited, the anti-interference performance is poor, and the communication performance is unstable in a complex environment are solved. According to the method, the channel state is accurately evaluated and predicted through fusion of the extended Kalman filtering algorithm and the convolutional neural network, and frequency band resources are intelligently allocated and optimized through the deep Q network DQN and the non-orthogonal multiple access NOMA principle, so that omnibearing optimization and management of the antenna are realized, the communication efficiency and stability are improved, and the method is suitable for large-scale popularization and application. And the efficient and stable operation of the communication system in various complex environments is ensured.

The invention relates to the technical field of ship communication, in particular to a multi-link converged communication method and system for a ship. The method comprises the following steps: receiving navigation information broadcasted by other ships in a preset range, evaluating the availability of a communication frequency band, and generating sea area communication channel state data; acquiring geographic position data of each ship; space communication link potential analysis is carried out according to the geographic position data of each ship and the sea area communication channel state data, and cross-chain space communication potential data is generated; constructing a multi-link ad hoc network topological structure according to the cross-chain space communication potential data; and task adaptation communication link allocation is carried out based on a multi-link ad hoc network topological structure, and a multi-link fusion transmission strategy is constructed, so that real-time communication link safe switching management is realized. According to the invention, on the basis of communication service adaptation and a multi-link fusion transmission strategy, real-time communication safe switching and high-reliability transmission of the marine formation are realized.

The invention relates to the technical field of medical image processing, and provides a medical image segmentation method based on wavelet enhancement and multi-scale feature fusion. According to the method, a CNN-Transform double-branch coding structure is combined, a multi-scale wavelet fusion module is provided, from the perspective of a frequency domain, Haar wavelet transform is adopted to extract an image high-frequency sub-band so as to enhance edge and texture detail expression, dynamic weighting is performed on different frequency band features through grouping convolution and a sub-band attention mechanism, and the discrimination capability is improved; meanwhile, a multi-scale cavity pyramid structure is fused in a spatial domain, and after cross attention dynamic fusion is introduced, a feature alignment mechanism of a wavelet domain and the spatial domain is established; and collaborative fusion of frequency domain and space domain features is realized. The method effectively improves the segmentation precision of the fuzzy boundary and the fine-grained structure under the complex background, has good universality and adaptability, and is suitable for various medical image segmentation tasks.

The invention discloses a super-resolution image enhancement system and method based on a variational mode decomposition algorithm. The system comprises an adaptive decomposition module, an enhancement processing module, a fusion module and an optimization module. The adaptive decomposition module receives low-resolution image signals, generates modal component signals containing different frequency band characteristics, and outputs modal quantity parameter signals according to image frequency domain energy distribution. The enhancement processing module comprises a high-frequency enhancement unit and a low-frequency reconstruction unit, and generates a high-frequency enhancement signal and a low-frequency reconstruction signal. And the fusion module receives the modal quantity parameter signal, the high-frequency enhanced signal and the low-frequency reconstructed signal, and performs spatial adaptive weighted fusion on the high-frequency signal and the low-frequency signal through a dynamic weight coefficient to generate an initial high-resolution signal. And the optimization module carries out adaptive nonlinear filtering processing on the initial high-resolution signal. The super-resolution image enhancement system based on the variational mode decomposition algorithm can solve the problem that the prior art is difficult to adapt to a complex image structure.

The invention discloses a motor fault detection method and system based on voiceprint recognition. According to the method, an annular microphone array is adopted to collect motor sound signals in a non-contact mode, a three-channel time-frequency data set is constructed through empirical mode decomposition (EMD) and a Mel-frequency cepstral coefficient (MFCC), fault diagnosis is carried out in combination with a CNN + ResNet network, and dynamic time warping (DTW) and CNN fusion matching is supported. The system comprises a preprocessing module, a fault template library and a matching algorithm, integrates wavelet denoising and multi-beam acquisition technologies, covers a frequency band of 50Hz-20kHz, can display a fault type and trend analysis in real time, and triggers secondary verification when the confidence coefficient is insufficient. According to the scheme, the anti-interference capability is improved through array signal processing, model parameters are optimized in combination with transfer learning, non-contact detection is achieved, the real-time performance and accuracy of fault diagnosis are remarkably improved, and the method is suitable for industrial motor health monitoring.

An engine for aeromechanical instability abatement includes a sensor configured to capture data from rotating blades of the engine system, a airflow effector device, and an engine controller. The engine controller is configured to control the airflow effector device according to a nominal schedule, detect, based on a signal from the sensor indicating a vibration amplitude of the rotating blades within a frequency band, an incipient instability condition, in response to the incipient instability condition being present, determine a modified control parameter for at least one of the airflow effector device, and control the airflow effector device according to the modified control parameter, deviating from the nominal schedule.

A system for dynamic latent space adaptation using spatiotemporal kernel context for multiscale rendering with hierarchical and Lorentzian autoencoders. The Spatiotemporal Kernel Estimator (SKE) analyzes media through motion field, temporal recurrence, frequency band, and scene semantics analyzers to generate adaptive kernel parameters encoding content-specific importance distributions. The system dynamically adapts latent manifold geometry by modifying metric tensor properties according to kernel context, enabling content-aware compression that allocates representational capacity based on visual significance. A multiscale cache implements kernel-adaptive retention policies prioritizing important regions. An adaptive renderer provides intelligent level-of-detail selection based on zoom level and kernel-estimated importance, optimizing processing allocation. The self-optimizing architecture continuously refines kernel context and geometric adaptation based on user interaction and performance feedback, achieving superior compression ratios and perceptual quality. Applications include bandwidth-efficient video streaming, virtual reality, scientific visualization, and cognitive video analytics requiring intelligent context-aware visual processing.

The invention provides a wireless communication network method and system of a mobile first-aid station. The method comprises the following steps: monitoring and evaluating a surrounding environment in real time, selecting an optimal working frequency band, and adjusting a frequency band switching strategy to obtain a self-adaptive multi-frequency-band access mechanism; the bandwidth and the connection priority of the wireless communication network are dynamically adjusted, the task priority is evaluated, and a dynamic resource allocation strategy is generated; deploying a distributed cache and synchronization system, realizing balanced distribution and rapid positioning of data, and generating a distributed temporary data cache pool; establishing an intelligent routing algorithm of deep reinforcement learning, calculating an optimal transmission path from a source to a destination, and finally generating an intelligent routing planning scheme; a redundant path is preset on the path, and a redundant path planning scheme is generated according to a dynamic resource allocation strategy and an intelligent routing planning result. According to the technical scheme provided by the invention, the reliability and the response speed of the wireless communication network of the mobile first-aid station are improved.

The invention relates to the technical field of biomedical engineering, in particular to a multi-mode brain anomaly detection method and system based on machine learning. The method comprises the following steps: acquiring brain medical image data of different modalities, and realizing spatial registration and alignment through a multi-modal registration algorithm based on mutual information; a multi-branch feature extraction model including a convolutional neural network, a converter and a state space model is utilized to perform feature embedding on the original image of each modal; performing frequency decoupling on the features of each mode through adaptive approximate wavelet transform, and decomposing the features into high-frequency detail information and low-frequency global information; a frequency band fusion strategy based on an attention mechanism is implemented on high and low frequency features of different modal images, and fused frequency sub-band features are input into a space-frequency Mama module. Through the adaptive frequency domain decomposition and cross-modal fusion mechanism, the multi-modal brain image information is effectively integrated, and the accuracy and robustness of brain anomaly detection are remarkably improved.

The invention relates to the technical field of agricultural science and technology, in particular to a leaf disease and pest identification method and system based on image processing, and the method comprises the following steps: setting multispectral and thermal imaging equipment, collecting multispectral images and thermal imaging data of a target frequency band, deleting images with insufficient definition and data with excessive noise, obtaining optimized multispectral images, and obtaining the leaf disease and pest identification result. And marking and classifying the data according to the collected date to form a time sequence image data set. According to the method, the network parameters are dynamically adjusted to reflect the state of each stage of leaf growth, so that the pest and disease damage prediction is not only based on static data, but also reflects the actual change of plant growth, and the real-time application value and accuracy of the prediction model are improved; the introduction of state transition learning enables the transition from health to each stage of pest and disease damage to be predicted more accurately, carries out early diagnosis and timely intervention, improves the recognition precision of the specific position of the pest and disease damage, can carry out local processing, and effectively improves the pertinence and economic efficiency of the processing.

The invention provides a self-adaptive signal filtering method and system based on multi-band fusion, and the method comprises the steps: firstly obtaining a multi-band signal set containing original signal components of different bands in a target environment, carrying out the frequency band decomposition of the multi-band signal set to obtain a plurality of sub-band signals, and then carrying out the self-adaptive filtering operation of each sub-band signal, the method comprises the following steps: dynamically adjusting a filtering coefficient according to noise characteristics of a sub-band signal, generating a filtered sub-band signal, carrying out multi-band fusion on the filtered sub-band signal to obtain a target filtering signal, and finally adjusting a segmentation strategy of band decomposition or a dynamic adjustment rule of adaptive filtering according to a quality evaluation result of the target filtering signal. And outputting the optimized target filtering signal, thereby effectively coping with a complex multi-band signal, adaptively adjusting the filtering process, and improving the quality and stability of the filtering signal.

The invention discloses a lithium ion battery thermal runaway early warning method, and belongs to the technical field of battery safety monitoring, and the method comprises the steps: synchronously obtaining a low-frequency sound wave signal, a temperature signal, a voltage signal and a stress-strain signal of a lithium ion battery; noise reduction processing is carried out on the low-frequency sound wave signals, time-frequency feature extraction is carried out on the low-frequency sound wave signals after noise reduction processing, and feature frequency band energy corresponding to a thermal runaway early event is obtained; a thermal runaway sensitivity coefficient is calculated based on the characteristic frequency band energy, and when the thermal runaway sensitivity coefficient is larger than a first coefficient threshold value, thermal runaway early warning is triggered; and on the basis of the thermal runaway sensitivity coefficient, the temperature signal, the voltage signal and the stress-strain signal, constructing a feature vector, and inputting the feature vector into a trained long-short-term memory network and attention mechanism hybrid model for classification to obtain a thermal runaway early warning level. The method can solve the problems of installation difficulty, detection lag and high false alarm rate in the prior art.

The invention relates to the technical field of air compressors, and discloses a centrifugal air compressor surge suppression method and device.The method comprises the steps that pressure pulsation feature extraction is conducted on a pressure signal of a centrifugal air compressor, and pressure pulsation features are obtained; extracting a flow statistical feature and a frequency band energy feature of the flow signal to obtain a surge early warning feature space; carrying out characteristic decomposition on the bearing vibration signal, and extracting a characteristic frequency to obtain a surge early warning characteristic set; inputting the surge early warning feature set into a three-channel neural network for surge early warning prediction to obtain a surge early warning level; according to the surge early warning grade, control parameters of a guide vane angle controller, an anti-surge valve opening controller and a return valve opening controller are generated, the guide vane angle, the anti-surge valve opening and the return valve opening are synchronously adjusted, and a surge margin value is calculated. According to the method, accurate control over the guide vane angle, the anti-surge valve opening and the return valve opening is achieved, the surge margin value is calculated, and the control accuracy of the guide vane angle, the anti-surge valve opening and the return valve opening is improved. And real-time optimization and adjustment of the control strategy are ensured.

The invention relates to an intelligent microphone pickup and speech enhancement method, system and device, and the method comprises the steps: obtaining an audio signal collected by a dual-silicon microphone array, and carrying out the cross-correlation analysis of the audio signal, and obtaining a corresponding human voice signal correlation feature; performing sound source positioning analysis on the audio signal based on the human voice signal correlation feature to obtain a corresponding target human voice signal; performing frequency characteristic analysis on the target human voice signal, and performing segmented dynamic gain processing on the signal according to a preset frequency band range to obtain a corresponding voice enhancement signal; performing noise component adaptive filtering processing on the target human voice signal to obtain a corresponding noise suppression parameter; and inputting the speech enhancement signal and the noise suppression parameter into a preset echo cancellation model for joint optimization to obtain a corresponding output human voice signal. According to the invention, the coupling problem of multiple acoustic interferences can be effectively solved.

The invention relates to the field of heart rate monitoring, and discloses a millimeter wave radar breath and heart rate synchronous monitoring method and system, and the method comprises the steps: transmitting a linear frequency modulation continuous wave signal according to a millimeter wave radar, and collecting original echo data reflected by a target region; baseband signal demodulation and phase information extraction are carried out on the original echo data to obtain original phase time sequence data, and the original phase time sequence data comprise thoracic cavity micro-motion features; and according to a Butterworth band-pass filter, preprocessing the original phase time sequence data through human body physiological signal frequency band characteristics to obtain a breathing frequency band signal and a heart rate frequency band signal. According to the method, the apnea event triggering threshold value and the arrhythmia early warning index are updated in real time through Kalman filtering and extended Kalman filtering, so that the monitoring system can dynamically adjust the health parameters, which means that the monitoring system can be optimized in real time and the abnormal health event can be accurately responded in different physiological states.

The invention relates to the technical field of draught fan detection, in particular to a draught fan current detection method and system, computer equipment and a storage medium, and the draught fan current detection method comprises the following steps: collecting draught fan current data, analyzing the time sequence characteristics of the draught fan current data, monitoring and comparing the change trend of the distance between adjacent points, and determining the distance between adjacent points; and determining an optimal embedding dimension and time delay through a phase space reconstruction method, and generating a fan current phase space trajectory. According to the method, the time sequence characteristics of the fan current data are analyzed, the embedded dimensions and the time delay parameters are optimized by adopting the phase-space reconstruction method, and the track point distribution mode is deeply analyzed, so that the capability of capturing abnormal behaviors is effectively improved. The transient characteristic signals are classified and indexed by combining the signal decomposition and frequency band screening technology, the short-time abnormal fluctuation in the current signals is accurately identified, and the response capability to the short-time high-frequency signal change is improved.