9251 results about "High Frequency Waves" patented technology

The invention relates to the technical field of environmental monitoring, in particular to a stored material controlled atmosphere maintenance early warning method and system based on environmental monitoring, and the method comprises the following steps: collecting real-time gas composition, temperature and humidity data, carrying out the time sorting of the collected data, recognizing the change trend of multiple parameters at each time point, and obtaining a parameter trend prediction result. According to the method, by combining data time sorting and change trend analysis, analyzing the relation between parameters and environment safety values and recognizing the variables exceeding the threshold value, accurate adjustment of gas, temperature and humidity is ensured, pertinence of parameter control is improved, early warning marking is carried out on the high-frequency fluctuation area through continuous monitoring and dynamic data classification, and the accuracy of the high-frequency fluctuation area is improved. The storage article quality guarantee condition monitoring capability is improved, so that article loss caused by environment change is reduced, a data analysis method and a real-time monitoring method are integrated, the operation logic of an environment controller is optimized, and the overall efficiency and the resource utilization efficiency of the system are improved.

The invention relates to an underwater image enhancement method based on frequency domain analysis and visual Mama, comprising the following steps: constructing a multistage wavelet transform network, the network being an encoder-decoder structure, the encoder comprising a space-frequency feature fusion module and discrete wavelet transform; the decoder comprises a space-frequency feature fusion module and inverse discrete wavelet transform; an underwater image is decomposed into a low-frequency part and a high-frequency part through discrete wavelet transform in an encoder, spatial feature extraction and frequency feature extraction are conducted on the low-frequency part and the high-frequency part through corresponding space-frequency feature fusion modules based on Mama and frequency domain convolution respectively, and splicing is conducted on the low-frequency part and the high-frequency part along a channel in the decoder according to the sequence of low frequency and high frequency. Reconstruction is carried out through inverse discrete wavelet transform; and inputting the underwater image data into the multistage wavelet transform network to obtain enhanced underwater image data. Compared with the prior art, the method has the advantages of improving the enhancement quality, reducing the complexity, quickly enhancing and the like.

The invention belongs to the technical field of electromechanical equipment intelligent monitoring and operation and maintenance management, and discloses an electromechanical system health monitoring and optimization method based on artificial intelligence, and the method comprises the steps: building a digital twin architecture based on a simulation model, reasonably arranging a multi-source sensor, and carrying out the optimization of the health of an electromechanical system; key equipment is fully covered; the system acquires operation data at high frequency and synchronously transmits the operation data to the cloud through multiple channels to realize real-time monitoring; the collected data is cleaned and preprocessed to remove abnormities so as to ensure the data quality; inputting the cleaned data into a neural network, extracting features, and identifying an operation trend and a fault mode; establishing a prediction model in combination with historical data, and constructing a maintenance knowledge base; the equipment state is predicted in real time, and early warning is dynamically generated; through virtual-real synchronization, health and carbon emission states are mapped in real time, equipment operation is intelligently optimized, and cloud closed-loop monitoring is continuously iterated.

The invention discloses a GIS partial discharge intelligent diagnosis system and method based on one-dimensional ultrahigh frequency signal analysis, and relates to the technical field of power electrical equipment intelligent monitoring, and the system comprises a signal collection and preprocessing module which is used for collecting ultrahigh frequency signals of GIS equipment and obtaining preprocessed signal data through a dynamic threshold algorithm; the discharge initial judgment module is used for performing multi-dimensional sequential judgment to eliminate interference discharge data so as to obtain effective discharge signal data; the feature extraction module is used for performing time domain kurtosis and pulse width analysis, frequency domain energy distribution analysis and time-frequency domain wavelet entropy calculation based on the multi-dimensional features of GIS partial discharge, and generating an optimized feature subset; and the type identification module is used for identifying the partial discharge type by using the integrated learning model to obtain a diagnosis result. According to the invention, the problem of unstable recognition accuracy caused by insufficient signal preprocessing, single feature representation and single classification algorithm in the prior art is solved.

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 power flow generating capacity prediction method based on STL decomposition and multi-model fusion, which adopts an STL decomposition method to perform trend term, season term and residual term decomposition on historical generating capacity data, optimizes the decomposition process through a Bisquare weighting function and an internal loop iteration mechanism, and enhances the processing capacity for abnormal values and high-frequency fluctuations. The method comprises the following steps: extracting multi-scale frequency domain features by using a TimeNet model in combination with fast Fourier transform (FFT), predicting a trend term and a seasonal term, and performing accurate modeling on a residual term by using an Itransform model in combination with a self-attention mechanism and gating residual connection. Meteorological factors related to the tidal current generating capacity are screened, prediction is carried out in combination with historical operation data, and high-precision prediction of the tidal current generating capacity is achieved. The method has high adaptability and robustness, can effectively deal with multi-source influence and high-frequency fluctuation in tidal current power generation data, and provides an accurate and stable prediction result.

The invention discloses a safety risk assessment system based on slope construction, relates to the technical field of slope construction, constructs a complete system composed of a data acquisition module, a dynamic disturbance identification module, a risk assessment module and an intelligent early warning and decision-making module, and acquires multi-source data such as displacement, cracks, pore water pressure, seismic disturbance and blasting vibration at high frequency. The time sequence analysis and disturbance feature extraction algorithm is combined to realize the rapid identification of the sudden disturbance event; risk score prediction is further carried out based on a polynomial regression model, and multi-level early warning is generated through comparison with a set threshold value; and finally, by combining the construction state, the support parameters and the disturbance intensity, a safety control suggestion is dynamically generated, so that the response speed and prediction precision of the system to the sudden slope instability risk are effectively improved, and real-time perception and intelligent management and control of the risk in the slope construction process are realized.

The invention discloses a metal 3D printing forming precision monitoring method and device and electronic equipment thereof, and relates to the technical field of metal 3D printing, and the method comprises the following steps: obtaining multi-modal sensor data, collecting multi-dimensional data in the metal 3D printing process based on the multi-modal sensor data, determining an optical sensor to record the molten pool form and powder distribution, and determining the metal 3D printing forming precision. The thermal infrared camera is determined to monitor temperature field distribution, the acoustic sensor is determined to collect and print medium-high frequency sound wave signals, and defect signals are identified; extracting boundary and geometric features from the optical image based on a convolutional neural network according to local preprocessing data of an edge computing device, and extracting a key temperature change area in response to thermal field gradient analysis; spectral features are extracted from the acoustic data based on time-frequency analysis. According to the invention, through synchronous acquisition of optical, thermal and acoustic multi-modal data, data fusion and defect detection are realized by using an advanced deep learning technology.

The invention relates to the technical field of power distribution system reliability analysis, in particular to a power distribution system reliability analysis method and system. The method comprises the following steps: performing multi-dimensional data acquisition on a power distribution system through a sensor network, and performing multi-dimensional data fusion to obtain multi-dimensional associated data; performing comprehensive operation data set construction on the multi-dimensional associated data to obtain a comprehensive operation data set; performing time sequence data segmentation according to the comprehensive operation data set to obtain a rapid fluctuation characteristic sequence and a slow change long-term trend sequence; performing time sequence decomposition on the rapid fluctuation characteristic sequence and the slow change long-term trend sequence to obtain high-frequency fluctuation characteristic data and long-term trend characteristic data; and performing feature component analysis on the high-frequency fluctuation feature data and the long-term trend feature data to obtain a multi-scale feature set. Through risk factor correlation analysis and reliability network model construction, the reliability analysis precision of the power distribution system is improved.

The invention discloses a drilling multi-source sensing signal denoising method based on dynamic noise decoupling and a self-adaptive mode, and belongs to the technical field of underground processing. The method comprises the following steps of: separating common noise of a noise energy distribution matrix of a sensor and separating specific noise; carrying out adaptive noise set empirical mode decomposition on the separated signals, carrying out variational mode decomposition on residual signals in the signals, and screening effective modes through a kurtosis-entropy joint criterion to obtain signals with effective characteristic components reserved; high-frequency fluctuation of the signal is punished through total variation regularization, an improved alternating direction multiplier method algorithm is used for solving, short-time Fourier transform is carried out on the solved signal, low-frequency and high-frequency features are extracted through a multi-scale convolutional network, and a final denoised signal is output through gating weight fusion. According to the method, the signal denoising precision in a complex noise environment is remarkably improved, the processing time is shortened, and the resource consumption is reduced.

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 relates to an unmanned aerial vehicle multi-modal visual positioning method and system in a GNSS denial environment, and the method comprises the steps: constructing a visual inertial mileage calculation method based on deep learning feature extraction and matching, calculating the relative positioning of an unmanned aerial vehicle, and outputting to obtain high-frequency 6-DoF pose information; performing scene matching of infrared RGB image multi-modal fusion based on the Officient-LoFTR, and obtaining absolute positioning in a world system by solving a relative pose between a current key frame picture and a matched satellite image; a factor graph optimization mode is adopted to carry out self-adaptive weight information fusion according to the confidence coefficient of absolute positioning and relative positioning under a world system, and 6-DoF pose information with high frequency and higher precision is output; based on high-frequency and higher-precision 6-DoF pose information, after the unmanned aerial vehicle is recovered, all aerial images and satellite images are fused and updated. The system comprises a calculation module and a fusion updating module. According to the invention, more accurate and high-frequency pose output can be provided.

A base station may transmit, to a wireless device, configuration parameters for semi-persistent scheduling (SPS), indicating periodic downlink resources for SPS physical downlink shared channel (PDSCH) transmissions. The base station may transmit, to the wireless device, a downlink control information (DCI). The DCI may indicate: an activation of the periodic downlink resources, a PUCCH resource for receiving feedback information corresponding to a plurality of SPS PDSCH transmissions via the periodic downlink resources, and a total downlink assignment index (DAI) incremented based on a number of the plurality of SPS PDSCH transmissions corresponding to the PUCCH resource. The base station may receive, from the wireless device via the PUCCH resource, feedback information bits for the plurality of SPS PDSCH transmissions based on the total DAI.

The invention relates to the technical field of cable partial discharge detection and diagnosis, provides a high-voltage cable multi-parameter intelligent diagnosis method, system and equipment and a storage medium, and aims to solve the problems of low accuracy and poor reliability of high-voltage cable multi-parameter diagnosis caused by multiple factors. The method comprises the following steps: decomposing a partial discharge signal after interference filtering, extracting a time domain amplitude change rate and an energy distribution parameter, and generating a multi-dimensional time-frequency domain feature vector; calibrating a multi-dimensional time-frequency domain feature vector, carrying out nonlinear manifold mapping, generating a low-dimensional feature vector matched with dielectric spectrum characteristics, matching a partial discharge type with the highest similarity with an identification result, judging an insulation degradation level in combination with a final loss threshold interval, and generating a comprehensive diagnosis report. According to the invention, the ultrahigh-frequency sensor array captures the partial discharge signal matched with the dielectric spectrum, precise identification of the discharge type and dynamic evaluation of the insulation degradation level are realized through multi-scale analysis and deep learning fusion, and the reliability of high-voltage cable diagnosis is improved.

The invention provides an open earphone audio optimization method based on a self-adaptive compensation algorithm, and aims to improve the audio auditory experience of a user. The method comprises the following steps: firstly, acquiring an environmental noise signal through an earphone microphone, receiving a target audio signal, analyzing auditory perception of a user in a current environment by combining an ISO226 loudness curve and the like, and determining auditory sensitivity of the user to different frequency bands (including high frequency, intermediate frequency and low frequency); and then, enhancing the high-frequency part, deepening the low-frequency part and performing smooth transition compensation on the intermediate-frequency part by using a self-adaptive compensation algorithm. And then, dynamically adjusting the low-frequency part by using a hyperbolic filter, generating an adjusted audio signal, and generating a harmonic signal based on the amplitude and frequency characteristics to further optimize the tone quality. Finally, the optimized audio signal and the generated harmonic signal are provided for a user together, and the effects of automatically optimizing audio output in different environments and enhancing tone quality performance are achieved.

A distance measuring device includes: a pixel array unit that performs a light receiving operation for time-of-flight distance measurement using a modulation frequency; a distance measurement processing unit that performs dual mode distance measurement that causes the pixel array unit to alternately perform a light receiving operation using a first frequency as the modulation frequency and a row receiving operation using a second frequency having a higher frequency than the first frequency as the modulation frequency, and single mode distance measurement that causes the pixel array unit to perform only a light receiving operation using a predetermined frequency as the modulation frequency; and a switching processing unit that switches the distance measurement in the dual mode and the distance measurement in the single mode according to a predetermined condition.

The invention provides a double-pulse high-frequency switching power supply control method and system for precise electroplating, and relates to the field of electroplating, and the method comprises the steps: collecting area surface data through a three-dimensional scanner, and obtaining a three-dimensional model; extracting a high-curvature area grid according to the three-dimensional model, and calculating to obtain a surface concave-convex degree index; performing current field simulation, and calculating current density vector distribution; when the deviation of the current density vector distribution exceeds a preset deviation threshold value, pulse parameters are optimized through a genetic algorithm; selecting a pulse parameter which is most matched with the surface concave-convex degree index, and carrying out iterative calculation to obtain updated current density vector distribution; based on the difference between the updated current density vector distribution and the initial distribution, predicting a coating thickness value, and determining a preliminary evaluation index; and optimizing pulse parameters to obtain a final control scheme of uniform current distribution. According to the method, pulse parameters can be dynamically optimized according to the geometrical shape of the workpiece, and uniform current distribution and stable plating quality are realized.

The invention provides an unmanned aerial vehicle obstacle avoidance method and system based on inertial navigation, and belongs to the technical field of unmanned aerial vehicle obstacle avoidance systems. The problem that an existing unmanned aerial vehicle is insufficient in obstacle avoidance capacity under the environment that GPS signals are weak, vision is limited or a sensor detection blind area exists is solved. Acquiring broadband high-frequency dynamic response data output by the inertial navigation system of the unmanned aerial vehicle in real time; establishing or acquiring a reference high-frequency dynamic response model when the unmanned aerial vehicle is in a specific flight state and a power system working mode and is not influenced by an external obstacle; the signal is dynamically responded according to high frequency; identifying whether a specific air medium disturbance echo feature formed by disturbance generated by motion of the unmanned aerial vehicle through an external obstacle exists or not; and finally, generating and executing an obstacle avoidance flight instruction according to the estimation information. The method does not depend on a traditional external environment sensor for initial detection, physical interaction is sensed through the INS, and the autonomous obstacle avoidance capacity and safety of the unmanned aerial vehicle in the complex and challenging environment are improved.

The invention belongs to the technical field of connectors and discloses a design method for a low-loss cable connector without resonance points, comprising: designing a spiral gradient groove structure on the surface of an inner conductor based on the impedance continuity principle, establishing a composite gradient impedance theoretical model of the connector, constructing a dielectric constant gradient distribution model of a double insulating layer, and setting a transition function between an inner layer and an outer layer; solving the S parameters and electric field distribution of the connector through electromagnetic-thermal coupling finite element simulation, optimizing the spiral angle θ and the gradient index n, establishing a quantitative model of the surface roughness of the conductor and high-frequency loss, and determining a threshold range of the surface roughness through calculation of the quantitative model; performing global optimization of structural parameters based on a multi-objective genetic algorithm, outputting a theoretical design parameter set and a report collection of performance verification, eliminating the resonance point problem of traditional connectors, and providing excellent low-loss performance in the full frequency band, which is particularly suitable for high-frequency and high-power transmission environments.

The invention relates to the field of motor health management and predictive maintenance, in particular to a motor stator winding insulation state evaluation method and system based on a digital model. Comprising the following steps: S1, collecting high frequency of a motor stator winding, and generating multi-physical-quantity real-time data; s2, calculating a dynamic capacitance reference value according to real-time data of multiple physical quantities; s3, performing subtraction operation on the high-frequency equivalent capacitance measurement value and the dynamic capacitance reference value, and extracting an insulation degradation residual signal; s4, constructing a self-adaptive dynamic detection threshold according to multi-physical-quantity real-time data; s5, judging whether the absolute value of the insulation degradation residual signal is greater than a self-adaptive dynamic detection threshold or not: if so, judging that an insulation degradation event occurs; if not, judging that the operation state is a normal operation state; and S6, in response to the insulation degradation event, updating the insulation degradation index, and generating insulation state evaluation based on the updated insulation degradation index. According to the invention, false alarm under severe load fluctuation is avoided, and the accuracy and reliability of evaluation are significantly improved.

The invention discloses a synthetic aperture quantitative phase imaging method based on mixed digital holography and Fourier lamination, which comprises the following steps of: installing a programmable LED (light-emitting diode) array which is annularly arranged on an off-axis digital holography system for providing inclined illumination of angle transformation, obtaining accurate low-frequency information of an object which contains noise through digital holography, and taking the information as initial estimation; and then, object high frequency is reconstructed through a Fourier lamination algorithm based on intensity measurement, so that synthetic aperture quantitative phase imaging is realized. According to the invention, the accurate low frequency provided by holography is used as initial estimation, so that the accuracy of phase recovery and wavefront aberration reconstruction is ensured, a matched illumination condition does not need to be strictly met, and meanwhile, the number of intensity graphs required by Fourier lamination is reduced; the non-interferometric phase recovery method based on Fourier lamination improves the imaging resolution to an incoherent diffraction limit, and effectively solves the inherent speckle noise problem in digital holography.

A terminal includes a reception unit that receives a synchronization signal block in an unlicensed band of a high frequency band higher than or equal to a frequency band of a frequency range 2 (FR2), the FR2 being in a range including a frequency range 1 (FR1) that is a low frequency band and the FR2 that is a high frequency band in a new radio (NR) system; and a control unit that identifies, based on the received synchronization signal block, an index of a candidate position for a transmission corresponding to the received synchronization signal block, from among candidate positions for transmitting a synchronization signal block, the number of the candidate positions being greater than a predetermined number.

The invention relates to the technical field of antenna equipment, in particular to a multi-frequency resonant circularly polarized antenna array and a communication performance test method thereof, and solves the problem that an antenna cannot communicate through different frequencies at the same time. The antenna array comprises an antenna substrate, a circular antenna assembly and a radio frequency signal line; the circular antenna assembly comprises a feeding point, a feeder line and a radiation unit; when the circular antenna assembly works in a low-frequency working frequency band, the first branch knot and the second branch knot in the radiation unit form a first equivalent resonance length, and when the circular antenna assembly works in a high-frequency working frequency band, the part, close to the feeder line, of the second branch knot in the radiation unit and the third branch knot form a second equivalent resonance length. And when the circular antenna assembly works in a working frequency band with higher frequency, the part, close to the feeder line, of the second branch knot in the radiation unit and the fourth branch knot form a third equivalent resonance length. According to the invention, multi-band radiation and receiving can be realized, and the flexibility of the antenna is improved.

The utility model relates to the technical field of water pump desilting, in particular to vertical water pump desilting equipment which comprises a bearing transmission mechanism, a high-frequency vibration mechanism installed on the bearing transmission mechanism and a vertical water pump installed on the high-frequency vibration mechanism. The bearing transmission mechanism comprises a transmission assembly, traction pieces installed on the transmission assembly, two outer frames connected to the top ends of the traction pieces and guide rods installed on the tops of the two traction pieces. After the vertical water pump is suspended and clamped and the vertical water pump in an overhead state is in an inclined state and performs high-frequency vibration, excess materials remaining in the water pump can be shaken off outwards from a filter screen at the moment, so that the water pump does not need to be put into clear water to be washed, and lossless dredging operation can be performed on the water pump; therefore, the inner wall of the used water pump cannot be corroded.

The invention discloses an intelligent fire detection method based on artificial intelligence video analysis, and particularly relates to the technical field of computer vision. The method comprises the following steps: acquiring a video monitoring stream, extracting spatial color features and morphological features of an image, constructing a multi-scale time sequence feature graph group, performing joint modeling on color disturbance and structural disturbance by using a dual-channel attention neural network, and outputting a suspected fire area probability graph; candidate fire source areas are screened in combination with boundary disturbance consistency analysis and red channel high-frequency fluctuation detection; the texture stability, the disturbance directivity and the historical smoke evolution characteristics are integrated through a multi-modal fusion judgment model, the fire state is finally judged, an alarm result is output, meanwhile, the system can mark the fire position and the development trend path, and visual tracking is achieved. The method has the advantages of high robustness, high sensitivity and low false alarm rate, and is suitable for intelligent early warning of early fire in a complex environment.

The embodiment of the invention provides a signal processing circuit, a chip and electronic equipment. The signal processing circuit comprises an analog-to-digital conversion module and a signal amplification module, the first chopping switch module is used for modulating the differential input signal to high frequency under the control of a preset clock signal and inputting the signal to the signal amplification module; the second chopping switch module is used for modulating the differential output signal output by the signal amplification module to a low frequency under the control of a preset control signal and inputting the low frequency to the analog-to-digital conversion module; and the second chopping switch module also enables the analog-to-digital conversion module to establish sampling under the control of a preset control signal. According to the application, the second chopping switch module is multiplexed as a sampling switch of the analog-to-digital conversion module, so that the problem that on-resistance is increased due to the fact that a chopping demodulation switch and the sampling switch are arranged at the same time can be avoided.

The invention provides a high-frequency eddy current-based metal pipeline conductivity measurement method, which comprises the following steps: designing an absolute eddy current sensor, positioning a sensor coil on the inner side of a metal pipeline and coaxially arranging the sensor coil and the pipeline, and introducing high-frequency alternating current into the coil to generate eddy current; establishing a simplified high-frequency eddy current detection analysis theoretical model, and setting geometric dimensions of a sensor and a pipeline; a Bessel function and a spatial resolution expression are used for analyzing the relation between the real part of the eddy current impedance change under high-frequency excitation and the conductivity of the pipeline; setting the conductivity of the metal pipeline according to the analysis result, and fitting the relation between the impedance change and the conductivity through a power function; and according to a coil impedance measurement result, combining the fitted power function curve, and performing inversion to obtain the actual conductivity of the metal pipeline. According to the invention, the conductivity of the metal pipeline can be effectively measured.

The invention discloses an intelligent electromagnetic induction heating method and system for multi-parameter collaborative frequency domain separation, and provides a synchronous parameter acquisition and frequency domain separation processing scheme for solving the problems of temperature detection lag, insufficient frequency domain interference processing and weak dynamic adaptation capability in the prior art. Transient current, voltage and change rate (a first parameter group) of an electromagnetic coil are synchronously acquired, and high-frequency fluctuation (greater than or equal to 10kHz) and low-frequency disturbance (1t, 1t) are converted into low-frequency fluctuation (greater than or equal to 10kHz) by combining environment temperature, cookware materials and user preferences (a second parameter group); 10kHz) is used for frequency spectrum separation. Phase difference features are extracted through band elimination filtering and FFT, heating priorities are corrected in combination with dynamic compensation factors, and a segmented heating curve is generated through time-frequency domain weighted fusion. And the system adjusts PWM parameters in real time, and triggers adaptive protection when detecting that the phase is abnormal or the power frequency exceeds the limit. According to the method, the limitation of single-frequency-band detection is broken through, high-frequency noise suppression and low-frequency disturbance compensation are realized, and accurate temperature control of ferromagnetic / non-ferromagnetic cookware is supported.

The invention discloses a variable-frequency ash removal device for improving photovoltaic power generation efficiency, and a control system of the variable-frequency ash removal device can dynamically adjust the output frequency and amplitude of a signal generator according to cleaning requirements. Particularly, when different types of dust are cleaned, the system can automatically adjust the vibration frequency, so that the cleaning process is more accurate. Large-particle dust is effectively removed through low-frequency and large-amplitude vibration, and small-particle dust is finely cleaned through high-frequency micro-vibration. The dynamic adjustment of the frequency and the amplitude ensures that each dust type can be thoroughly removed, and the problem of poor effect caused by fixed frequency in a traditional cleaning method is avoided.

The invention belongs to the crossing field of artificial intelligence and process control, particularly relates to an intelligent coupling control strategy for chlorine production through seawater electrolysis based on digital twinning, and aims to solve the problems of response lag, weak disturbance suppression and lack of electrode aging early warning in a traditional control method. According to the strategy, a high-fidelity digital twin fusing electrochemistry, fluid and thermodynamics is constructed, characteristics of current, temperature, impedance spectroscopy and the like are collected and fused at high frequency through a multi-source sensor, a state space model driven by a deep recurrent neural network is established, and current efficiency and electrode aging coupling evolution recognition is achieved; when salinization or flow mutation is detected, the twinborn body predicts and dynamically calibrates the current density and the voltage set value in advance, and the safety process window is synchronously scaled. According to the strategy, the chlorine gas yield fluctuation is reduced by 60%, the energy consumption standard deviation is reduced by 45%, the fault early warning is advanced by 2-5 hours, the fault-free operation time of the system is prolonged by 50%, and high-precision, self-adaptive and preventive intelligent control of the electrolysis process is realized.