24745 results about "Signal processing" patented technology

The invention provides an intelligent real-time interactive question-answering system based on a virtual digital human, and belongs to the technical field of voice signal processing and voice recognition, and the system comprises a data acquisition module which receives a voice or text interaction request input by a user, collects the expression dynamic parameter sequence and limb movement sequence data of the user in real time, and transmits the data to a user interaction module; obtaining a standardized voice feature vector and structured text data; the cross-modal fusion module is used for constructing an interactive feature matrix; the behavior decision module outputs a decision instruction set; the knowledge retrieval module is used for generating an answer text with emotional adaptability and voice features; and the voice generation module is used for generating a mouth shape animation key frame, a micro expression parameter sequence and a limb action track of the virtual digital human, generating a voice response in combination with the answer text and the voice characteristics, and pushing the voice response to the user terminal. According to the method, the interaction experience and adaptability of the virtual digital human are remarkably improved.

The present invention relates to the field of industrial intelligent predictive maintenance, and in particular to an interpretable deep feature fusion network-based industrial intelligent predictive maintenance method, comprising: acquiring gearbox vibration data comprising noise; performing preliminary extraction and noise suppression on features of the acquired data by establishing an interpretable feature extraction module having a physical information constraint; integrating multi-scale features comprising long-distance and local dependencies by means of a dual-branch feature fusion module having global and local feature fusion capabilities; performing dimensionality reduction on a high-dimensional feature and generating an output by means of a classifier to obtain a final fault identification result; and performing interpretability analysis on a diagnosis process of a model. In the present invention, by embedding the signal processing technology having a well-defined physical theory support into a deep neural network, the interpretability and reliability of model inference results are effectively improved while the fault identification accuracy of the model is improved.

The invention discloses a ppb-grade methane gas online detection method, equipment, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring an infrared absorption spectrum signal, and obtaining original spectrum data; performing multi-scale noise separation and baseline correction on the original spectral data to obtain de-noised effective spectral features, and extracting methane specific absorption peaks from the de-noised effective spectral features; comparing the methane specific absorption peak with the interference gas spectral line through a multi-spectral line weight matching algorithm to obtain methane characteristic intensity; carrying out self-adaptive calibration on the methane characteristic intensity according to the drift compensation model to obtain calibrated characteristic intensity; obtaining a ppb-level methane concentration value through a concentration inversion model; and inputting the sorted concentration time sequence data into an anomaly detection model to detect a methane leakage event. By designing signal processing and algorithm analysis, the dependence on equipment hardware performance is reduced, and the use cost of ppb-level methane gas detection is reduced.

The invention discloses a mixed tower structure full-life damage intelligent sensing array early warning system, which relates to the field of mixed tower structure detection and comprises a multi-modal data collection module, an array topology optimization module, a self-adaptive signal processing module, a digital twin life prediction module, a grading early warning module and a visualization system. The multi-modal data collection module comprises a multi-modal sensor array, a self-powered module and a wireless transmission module. According to the invention, a full-scale sensing network is constructed, full-dimension damage perception from distributed monitoring to sudden damage capture and structural modal analysis is realized, wavelet transform and blind source separation are combined to eliminate environmental noise interference, a damage characteristic ultrasonic attenuation coefficient, an acoustic emission energy spectrum peak value, optical fiber strain gradient anomaly and vibration modal frequency deviation are extracted, and the detection accuracy is improved. And classification and positioning of damage types and intelligent diagnosis of severity levels are realized through a convolutional neural network and long and short memory neural network hybrid model, and a closed-loop processing flow from data acquisition to feature analysis is formed.

The invention relates to the technical field of geometric quantity precision measurement based on satellite positioning, in particular to a pumped storage power station dam safety monitoring method based on Beidou positioning, and the method comprises the steps: arranging a Beidou monitoring station and base station array in a dam body and a slope region, synchronously receiving multi-band satellite signals, and carrying out the edge calculation processing; and the effective frequency band data are screened and fused by using a signal-to-noise ratio threshold to generate preprocessed observation data, and the preprocessed observation data are uploaded to a cloud server in real time through a 4G network and a Beidou short message dual-channel transmission link. A cloud server constructs a local enhanced network, ionosphere and troposphere errors are corrected by adopting a multi-base-station joint error modeling method, a closed-loop mechanism dynamically optimizes monitoring point distribution density, signal processing parameters and atmospheric refraction compensation coefficients, and data integrity is guaranteed in combination with a dual-channel redundancy check and interpolation completion technology. According to the invention, full-domain millimeter-level deformation dynamic monitoring of the dam under the complex terrain is realized, and the space-time reference uniformity, the data real-time performance and the structure safety evaluation precision are improved.

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 discloses a power transformer partial discharge positioning method based on multi-sensor array fusion, and the method comprises the following steps: S1, selecting a sensor installation point, and laying a multi-sensor array structure; s2, partial discharge signals of the three types of sensors are collected, and primary signal processing is carried out; s3, calculating propagation time differences between the reference channel and other channels by adopting a generalized cross-correlation weighting algorithm, and generating a time difference matrix; s4, constructing a TDOA model in combination with the layout coordinates and the time difference matrix, and solving three-dimensional initial coordinates of a power supply; s5, establishing a structure correction model, compensating the path deviation, and outputting corrected positioning coordinates; s6, calculating an error and generating a confidence score; s7, mapping a positioning result to the three-dimensional model and generating an image; and S8, writing the positioning information into a database for filing management. According to the method, the multi-frequency sensor and the path correction model are fused, and high-precision three-dimensional positioning of partial discharge of the transformer is realized.

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 discloses a power supply real-time compensation technology and application based on digital signal processing, and relates to the technical field of power electronics, and the power supply real-time compensation technology comprises a split-phase parallel processing architecture: a three-phase independently configured DSP core, each core integration comprises a real-time harmonic detection module, a dynamic sliding window FFT-RT algorithm is adopted, and the window length is adjustable in a set range; the bandwidth of the self-adaptive variable parameter filter can be dynamically adjusted in a set range; the FPGA coprocessor is specially used for PWM generation; an input stage of the harmonic prediction and decomposition module is synchronously acquired by a multi-source sensor; the processing layer comprises an improved VMD decomposition unit and a variational mode decomposition order; the edge prediction network is used for deploying a lightweight LSTM model, embedding a DSP core and predicting a harmonic spectrum in a future short period; and the multi-target dynamic optimization layer is used for setting a dynamic weight distributor and performing real-time adjustment according to the load sudden change rate. According to the invention, through split-phase parallel architecture, harmonic prediction and dynamic optimization, the response speed, multi-target cooperation and reliability improvement in the field of power supply real-time compensation are realized.

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.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention discloses a self-adaptive filtering and intelligent separation method for a multi-mode partial discharge signal, and relates to the technical field of insulation state monitoring and signal processing of power equipment, and the method comprises the following steps: S1, collecting the partial discharge signal of the power equipment in real time, and synchronously obtaining an equipment operation parameter and an environment noise signal; according to the adaptive filtering and intelligent separation method for the multi-modal partial discharge signal, the problems of modal aliasing and feature distortion caused by signal and noise time-frequency coupling in a dynamic noise environment in a traditional method are effectively solved through a cooperative mechanism of dynamic coupling degree modeling and dual-channel adversarial decoupling. The dynamic coupling path sensing module quantifies interaction characteristics of noise and signals in real time, and realizes accurate suppression of high-frequency transient interference and low-frequency periodic noise in combination with a double-channel architecture of complex field phase sensitive filtering and vibration trajectory matched filtering.

The invention belongs to the technical field of concrete crack detection, and particularly discloses and provides a high-strength concrete construction crack detection system and method.The system comprises a multi-parameter cooperative sensing module, a construction crack recognition module, a crack risk analysis module and a crack early warning processing module. According to the invention, the temperature, humidity, strain and multispectral image data from a plasticity stage to a hardening stage are collected in real time through the multi-parameter cooperative sensing module, and crack characteristics are extracted by combining staged differential signal processing and a deep convolutional neural network, so that the detection precision of micro cracks and internal defects is remarkably improved. Meanwhile, multi-physics field data are fused, a crack evolution risk rate model is innovatively constructed, dynamic probability prediction before crack initiation is achieved, a grading disposal scheme is automatically triggered through risk grade division and a progressive early warning mechanism, drying shrinkage and temperature crack causes are effectively distinguished, the prevention and control efficiency is remarkably improved, and the method is suitable for large-scale popularization and application. And full-life-cycle accurate monitoring of the high-strength concrete is supported.

According to an aspect of the present invention, there is provided an electrical audio signal processing system and device, comprising: a computer graphics processing and selective visual display system with a screen; an eye tracking device; a processor; one or more computer memory devices; wherein the processor is arranged for operations comprising: measuring the user's eye movements to ascertain the specific word on which the user is fixated, by the eye tracking device; modifying the display at the user's current fixation point; applying a delay between the presentation of successive graphic elements based on the user's calculated rate to accommodate the user's required time; and presenting elements to the user at a rate based upon the user's required time.

The invention discloses a precise interference avoidance method and device in a radio system, and relates to the field of signal processing, and the method comprises the steps: constructing a sensing matrix through sensing node data, and capturing a transient interference signal through aperiodic scanning; performing tensor decomposition on the signal data to extract time domain, frequency domain, space domain and modulation domain features, constructing a dual-mode spectrum analysis model, reconstructing an instantaneous spectrogram by using compressed sensing, and predicting an interference mode through LSTM; after the instantaneous spectrogram and the predicted interference graph are fused, threat assessment is carried out through a multi-stage interference classification model; according to the interference category and the threat level, beam forming is optimized, adaptive null is generated, a power density optimization model is constructed, and the transmitting power is dynamically adjusted; an anti-interference frequency hopping sequence is generated based on a chaotic mapping algorithm, and spectrum camouflage and tracking interference resistance are realized. The method has the advantages that accurate identification and dynamic avoidance of interference are realized through multi-dimensional perception, intelligent prediction and adaptive beam forming, and the interference avoidance capability of a wireless system is improved.

The invention relates to the technical field of spectral signal processing, in particular to a high-precision spectral signal peak detection method and system. According to the technical scheme, the method comprises the following steps: denoising and smoothing spectral data; dividing a spectrum curve by adopting a dynamic partitioning strategy; generating candidate partitioning points based on various local features, and generating feature blocks through optimization processing; according to the method, the influence of feature differences of different regions on a detection algorithm is reduced through a dynamic partitioning strategy, a multi-scale morphological feature system is constructed to comprehensively describe spectral curve morphological characteristics, potential peaks are gradually mined by applying a layered iteration peak detection algorithm, and local characteristics of spectral signals are adapted by means of an adaptive parameter adjustment mechanism, so that the spectral curve morphological characteristics are accurately detected. The peak value correction and fusion mechanism is utilized to improve the quality and reliability of a detection result, spectral data with a complex structure and a high noise level can be effectively dealt with, an efficient and reliable solution is provided for the field of spectral analysis, and the accuracy and robustness of peak value detection and the conciseness and reliability of the result are improved.

The invention relates to the technical field of industrial automation control, in particular to a container liquid level measurement method, system and equipment of a pressure sensor and a medium, and provides a method for eliminating gas phase pressure influence based on a double-sensor differential measurement mechanism and dynamically correcting liquid density and sensor drift errors in combination with a temperature compensation model. The temperature data are linked in real time through a three-dimensional compensation function, density calculation and drift correction are synchronously adjusted, and multi-parameter dynamic decoupling and self-adaptive compensation are achieved; a collaborative measurement mechanism is adopted to integrate bottom and top sensor data, and a built-in temperature probe and a preset threshold are combined to complete multi-source signal fusion and compensation calculation; a dynamic calibration method is designed, and system errors are eliminated through reference pressure point calibration and structure coefficient correction; constructing a closed-loop signal processing flow, and integrating high-precision analog-to-digital conversion, digital filtering and real-time algorithm to output a compensated liquid level value; the method improves the measurement precision and stability, and is suitable for temperature change, gas phase pressure fluctuation and multi-medium scenes.

The invention, which relates to the industrial circuit board monitoring technology field, discloses an industrial circuit board operation abnormity monitoring system comprising a multi-sensor welding spot data acquisition module, a signal processing module, an element welding spot mechanical fatigue monitoring module, a welding spot fatigue fault grading early warning module, and an early warning and response module. Data fusion is carried out on the acoustic emission sensor, the strain gauge sensor and the ultrasonic sensor, the state of the welding spot is monitored synchronously from three dimensions of stress acoustic emission, mechanical deformation and internal defects, the strain gauge sensor captures tiny deformation around the welding spot, and the ultrasonic sensor detects internal potential defects. The acoustic emission sensor senses stress release signals, the three kinds of sensor data are subjected to deep fusion analysis through the D-S evidence theory, the problem of misjudgment caused by one-sided information is effectively avoided, stress waves during crack propagation are captured through the acoustic emission signals, and faults can be diagnosed in time in combination with deformation data of the strain gauge sensor.

The invention discloses a real-time monitoring and fault response control device for an industrial and commercial liquid cooling energy storage system, and particularly relates to the technical field of liquid cooling energy storage fault management. Temperature, pressure, vibration and flow data of key components in a liquid cooling system are collected and preprocessed; a time sequence analysis and signal processing technology is utilized to extract feature vectors reflecting key component states from the multi-dimensional operation data set, the saliency of weak abnormal signals is enhanced, and a key feature set containing weak abnormal features is generated; constructing an anomaly recognition model by adopting a long-short-term memory network, inputting a key feature set, outputting an anomaly probability corresponding to the key component, and setting an early warning threshold value of the anomaly probability to trigger early warning; and predicting a fault maintenance time window of the key component based on a Weibull reliability model, and dynamically adjusting the priority of maintaining the key component and executing preventive maintenance based on a fault maintenance time window prediction result and an abnormal probability.

The invention discloses a portable unmanned aerial vehicle sound wave countering system and method, and relates to the field of radio countermeasure and sound wave interference, and the system comprises a multi-source sensing module which is used for obtaining a multi-modal feature data set of a target unmanned aerial vehicle; the signal processing module is used for carrying out space-time correlation analysis on the multi-modal characteristic data set and generating a threat evaluation parameter set containing an unmanned aerial vehicle type identification result, communication protocol analysis characteristics and navigation mode discrimination indexes; the countering strategy generation module is used for generating a sound wave modulation parameter set, a radio frequency interference waveform sequence and a positioning deception signal template; the cooperative interference module is used for generating a multiband joint countering instruction set according to the improved phase cooperative control strategy; and the execution monitoring module is used for executing the multiband joint countering instruction set in the preset airspace range. According to the portable unmanned aerial vehicle sound wave countering system and method provided by the invention, the multi-level countering capability for different threat levels is improved.

The invention provides a multi-stage embedded control equipment state sensing and energy cascade scheduling system. Comprising a master control decision center module, a distributed edge embedded node module, an equipment full-dimension state sensing module, an energy dynamic optimization scheduling module, a fault prediction and self-healing control module, a cross-protocol communication interconnection module and a man-machine cooperative command module. According to the invention, through constructing a three-layer time domain control chain of edge node nanosecond-level signal processing, cloud second-level optimization scheduling and equipment hour-level strategy presetting, seamless cooperation of turbine bearing pedestal micro-vibration monitoring and a power grid peak regulation strategy is realized, and real-time wavelet noise reduction preprocessing of embedded nodes is combined with cloud LSTM life prediction. A taboo search algorithm is driven to dynamically reconstruct a power supply scheme, and the pain point of control response lag in a high-fluctuation scene is solved.

The invention discloses a method for identifying a multi-model fusion signal modulation mode based on a time-frequency diagram, and belongs to the field of signal processing. Preprocessing the original signal to obtain a signal sample; constructing a data set by using the normalized signal samples; constructing a fusion model; training a modulation identification module in the fusion model; performing modulation mode identification on an input unknown signal by using the fusion model to obtain a preliminary identification result; and performing comprehensive judgment on the preliminary recognition result of the fusion model by using a comprehensive judgment device to obtain a final recognition result. According to the invention, by combining a plurality of time-frequency analysis methods, the time-frequency domain feature information of the signal is fully extracted; the reliability and robustness of a final decision are improved by adopting a multi-model fusion framework, and the modulation recognition performance of the system under the condition of a low signal-to-noise ratio is improved.

The invention discloses a pipe network on-line monitoring system, which relates to the field of urban capital construction and comprises a sensing deployment module, a signal acquisition module, a signal processing module, a pipe network modeling module, a leakage positioning module, an edge calculation module, an energy scheduling module, an anomaly prediction module, a deposition evaluation module, an early warning visual module and a center management module. According to the method, the recognition capability of the system on a hidden and complex pipeline structure is improved, accurate and efficient three-dimensional modeling can be carried out, the path deviation resistance capability is improved, and the more comprehensive and robust leakage sensing capability is realized; the method is advantaged in that communication burden and response time delay are substantially reduced, sensitivity and foresight of abnormal evolution trend identification are improved, overall identification accuracy of the system is improved, a global optimal risk solution can be efficiently output, a high-risk area is visually displayed, and rapid pre-judgment and decision making of operation and maintenance personnel are facilitated.

The invention provides a distribution line fault location optimization method based on single-ended traveling wave location, and relates to the technical field of traveling wave detection. The method comprises the following steps: carrying out segmented modeling on a line, calculating the traveling wave propagation speed and wave impedance of each segment, and correcting the traveling wave propagation speed and wave impedance; collecting a voltage / current traveling wave signal of a detection point, and extracting a line mode component; performing wavelet transformation on the line mode component, detecting the wave head position of the initial traveling wave through a modulus maximum search algorithm, and recording the arrival time of the initial traveling wave, a corresponding first energy value and polarity; calculating a reflected wave time window based on segmented modeling parameters, screening reversed polarity waves with qualified energy attenuation, and calculating a fault distance; if no effective reflected wave is detected in the reflected wave time window, triggering a pseudo double-end mode; a result is verified through quadruple constraints; the problems of poor parameter adaptability, low signal processing precision, strong reflected wave dependence and incomplete verification in traveling wave distance measurement of a complex distribution line are solved, and the accuracy and reliability of fault distance measurement are improved.

Systems and apparatus are provided for a high-power microwave sensor using an unmanned aerial vehicle. The unmanned aerial vehicle may include a central body, at least one electric motor, and a barometric pressure feedthrough. The central body may include a first enclosure housing a plurality of low voltage components, a voltage feedthrough connector, and a second enclosure housing a plurality of high voltage components. The low voltage components may include a first signal processing system, a first radio transceiver, a flight controller, a second signal processing system, a second radio transceiver, and a navigation system. The high voltage components may include a plurality of electronic speed controllers, a power distribution module, an input power interface, and a plurality of high power filters. The components of each enclosure may be segregated such that the high voltage components do not create electromagnetic interference with the low voltage components.

The invention relates to the technical field of signal parameter measurement, and provides a parameter measurement method for a frequency modulation monitoring antenna, which realizes self-adaptive monitoring and accurate measurement of a complex electromagnetic environment through multi-dimensional data acquisition, intelligent analysis and dynamic optimization, and deeply integrates environment perception, signal processing and parameter optimization. A complete technical chain is formed, so that the parameter measurement method can have self-learning and self-adjusting capabilities, can cope with environmental changes and interference challenges in real time, remarkably improves the monitoring efficiency and the measurement precision, and provides a systematic solution for the application of a frequency modulation monitoring antenna in a complex scene.

The invention relates to the technical field of steam turbine testing, in particular to a testing system for mismatch correction of a wide-load-interval thermal power generating unit speed regulation model, which comprises a thermodynamic system component, a speed regulation system device, a dynamic signal acquisition device and a control device, a dynamic signal processing module analyzes the relevance between main steam pressure sudden change and control valve opening oscillation in real time, a dynamic response database including a time sequence is constructed, and a control valve opening feed-forward compensation instruction and a combustor fuel distribution gradient adjustment signal are generated according to correction parameters. Power over-modulation wave crests caused by sudden load change are suppressed, and primary frequency modulation action delay is reduced; and the simulation verification module compares measured data with model output through an error feedback mechanism, dynamically adjusts high-pressure cylinder flow characteristic curve calibration parameters, optimizes a control strategy in combination with a network-related safety margin evaluation result, and realizes improvement of dynamic response precision in a full peak regulation interval and enhancement of power grid frequency stability.

The invention provides an assembly line electronic tag management system based on multi-frequency fusion, and relates to the technical field of the Internet of Things, and the system comprises a dual-band module which is used for integrating a high-frequency and ultrahigh-frequency dual-band radio frequency electronic tag on the surface of a target object, and forming a tag dual-mode function; and the signal processing module is used for synchronously acquiring the high-frequency positioning signal and the ultrahigh-frequency identification signal through the multi-band reader-writer array on the basis of a tag bimodal function so as to obtain a preprocessed signal flow with aligned time domains. According to the invention, the efficiency of label identification, data transmission and production management is improved in a complex scene, errors and cost are reduced, and efficiency and quality are improved.

The invention provides an online calibration method and system for a flowmeter of a natural gas well, and belongs to the technical field of natural gas exploitation and transportation, firstly, a multiphase flow state sensing signal in a transportation pipeline of the natural gas well and a structural deformation monitoring signal of the flowmeter to be calibrated are obtained, and then interactive interference characteristic analysis is conducted on the two kinds of signals; the method comprises the following steps: obtaining a flow state-deformation interaction interference characteristic set, generating a multiphase flow adaptive calibration factor based on the flow state-deformation interaction interference characteristic set, inputting the multiphase flow adaptive calibration factor into a metering signal processing unit to dynamically correct an original metering signal, and obtaining a corrected metering output signal; and finally, collecting matching degree data of the correction signal and the real-time multiphase flow state sensing signal to form a calibration adaptive feedback signal, thereby comprehensively considering the influence of the multiphase flow state and structural deformation, and improving the metering precision and reliability of the flowmeter.

The invention provides a multi-mode sensor fusion algorithm for multi-signal processing and a system thereof, and relates to the technical field of sensor data processing, and the system sequentially generates standardized features through Sp1, generates a fusion feature set through Sp2, optimizes fusion parameters through Sp3, generates compensation features through Sp4, outputs perception information through Sp5, and outputs abnormal conditions such as sensor failure and overload calculation. Through feedback mechanism processing, a termination condition of perception information output or new task triggering, and breakthrough in feature retention and data consistency, a fusion result can still keep high precision in a high noise or complex scene, through failure detection and feature association degree priority ranking, other sensor data with high association degree are used for reconstruction, and a fusion result is obtained. According to the method, the accuracy of feature reconstruction is greatly improved, the high real-time performance of edge equipment and the computing power of the cloud are fully utilized by an edge-cloud cooperation mechanism, the resource utilization rate is optimized, and the stability and adaptability of the system in practical application are greatly improved.