3040 results about "Low frequency" patented technology

The invention discloses a data management system and method based on distributed cloud storage, and relates to the technical field of distributed cloud storage, and the system comprises an access log collection module, a node state monitoring module, a data thermal evaluation module, a redundancy strategy decision module and a storage execution scheduling module. A fragmentation access behavior is recorded through an access log acquisition module, a node state monitoring module acquires a node resource state in real time, and a data thermal evaluation module constructs a three-dimensional thermal model based on an access frequency, a network hop count and a storage cost to calculate a thermal value and supports dynamic weight adjustment; the redundancy strategy decision module divides the fragments into high frequency, medium frequency and low frequency according to the thermodynamic value, and single copy + local cache, erasure code storage and dynamic adjustment strategies are adopted respectively; the storage execution scheduling module executes fragmentation operation and optimizes reading node selection, and data access efficiency is improved through load awareness and a bandwidth-delay joint model; according to the invention, dynamic balance between data access efficiency and storage cost is realized.

The invention belongs to the technical field of remote sensing image segmentation, and particularly relates to a method for generating an adversarial frequency perception image segmentation network, which comprises the following steps of: 1, acquiring a public remote sensing image data set; 2, inputting the remote sensing image data set into a self-adaptive frequency driving feature enhancement module, and decoupling high-frequency features and low-frequency features; the frequency perception adversarial feature enhancement generates more real high and low frequency features through a generative adversarial network, and the resolution capability of the model is improved; and the CLIP text encoder converts the text description into semantic feature vectors to provide support for subsequent cross-modal matching. According to the remote sensing image semantic segmentation method, innovative design is carried out in the aspects of multi-modal fusion, high and low frequency information extraction, adversarial training and the like for remote sensing image semantic segmentation tasks with complex modals, and the segmentation precision, generalization ability and robustness are effectively improved.

The invention discloses a nondestructive testing method and system and a data processing method, and relates to the technical field of nondestructive testing. Comprising the steps of obtaining initial detection data of a detected component; establishing at least one reference model according to the initial detection data, and obtaining simulation data corresponding to each reference model; determining target data according to the initial detection data and simulation data; and obtaining subsequent detection data, and comparing the subsequent detection data with the target data to form an evaluation result. According to the invention, high and low frequency mixed excitation signals are adopted, so that the penetrating power and resolution of detection are improved, surface defects and buried defects can be detected at the same time, and the comprehensiveness and accuracy of detection are remarkably enhanced; a picked complex response signal is decoupled by using an orthogonal phase locking method, a magnetic field signal and an electric field signal are effectively separated, the signal-to-noise ratio and the detection precision are improved, and accurate discrimination of defect types, depths and orientations is realized by establishing a reference model and acquiring analog data.

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 discloses a photovoltaic power generation energy storage device control method based on artificial intelligence, and the method comprises the steps: S1, distributing synchronous sampling nodes at a PV side, a bus side and an energy storage side, collecting six paths of quantities, and obtaining a time sequence matrix through incremental buffering, time correction and abnormity elimination; s2, detecting a locking event through a load power step, performing window interception and differential normalization, and combining reactance and correlation to generate an event space-time diagram; s3, the irradiance and the impact amplitude are superposed, and event features are extracted through multi-scale space-time convolution and high and low frequency coupling; s4, calculating a risk score, estimating tail distribution by using mixed Pareto when the risk score exceeds a threshold, and giving a confidence upper bound; s5, constructing an objective function based on the tail distribution and the event coupling probability; s6, the EMS executes reference power according to SOC amplitude limiting, slope detection triggers compensation or calls a plan, and derating feedback is carried out during border crossing; s7, collecting pressure, residual error and loss, and adaptively updating convolution and scheduling parameters; according to the invention, real-time high-precision prediction of the power peak generated by superposition of minute-level photovoltaic fluctuation and millisecond-level load step is realized.

The invention discloses a distribution line broadband waveform sampling method, and belongs to the technical field of power system monitoring. By collecting quantum vacuum fluctuation noise, temperature, humidity and electromagnetic parameters, a quantum environment coupling entropy model is constructed, an optimal sparse base dictionary is dynamically generated, and a noise entropy break variable is calculated. And when the entropy break variable exceeds a threshold value, triggering sub-Nyquist compressed sampling, otherwise, adopting Nyquist rate sampling to realize dynamic matching of sampling resources and signal complexity. A high-frequency component of a compressed signal is reconstructed by combining an adversarial compressed sensing network with a residual network, a low-frequency component is generated based on an original signal, the distribution consistency of high and low frequency entropies is optimized by using a Wasserstein distance, and finally a three-dimensional waveform matrix containing a timestamp, a frequency bin and quantum entropy confidence is output. The problems of low sampling efficiency, poor sparse representation adaptability, slow noise response, inconsistent reconstruction and the like in a non-stationary signal and complex noise environment are solved, and the precision and energy efficiency of broadband waveform sampling are remarkably improved.

The invention relates to the technical field of treatment scheme optimization, and provides an emergency treatment critical patient first-aid path optimization method and system based on big data, and the method comprises the steps: building a standardized event sequence through extracting historical diagnosis and treatment data of a hospital information system, and analyzing the time sequence correlation between events through a graph structure mining technology, key diagnosis and treatment nodes and high and low frequency paths are identified, and a layered first-aid path state transition network is constructed based on the key diagnosis and treatment nodes and high and low frequency paths. When a real-time patient is processed, the system matches an electronic health record of the patient with a network core state, dynamically plans a subsequent standard transfer path, and avoids a low-frequency risk path marked as a taboo, so that an emergency path optimization scheme of risk avoidance and path dynamic optimization is realized, and the treatment efficiency of emergency critical patients is improved. The treatment effect is improved, and the bad prognosis risk is reduced.

The invention discloses an ocean-ionized layer ultralow-frequency electromagnetic target detection simulation method based on ADI-FDTD and SIBC, and belongs to the field of computational electromagnetics. The ocean-ionized layer ultralow-frequency electromagnetic target detection simulation method comprises the following steps: introducing a frequency domain surface impedance boundary condition SIBC into an ADI-FDTD model; the ocean-ionosphere simulation model is used for carrying out ocean-ionosphere ultra-low frequency electromagnetic wave attenuation analysis on the ocean-ionosphere ultra-low frequency electromagnetic wave attenuation. According to the method, the frequency domain surface impedance boundary condition (SIBC) is combined with the ADI-FDTD method for the first time, so that the calculation space for analyzing the influence of the seabed on electromagnetic wave propagation is reduced. The calculation requirement is obviously reduced, and the efficiency is improved. In addition, the SIBC model based on the ADI-FDTD method can be used for simulating the scattering effect of electromagnetic waves on a high-conductivity and low-conductivity area interface.

The invention provides a leather defect identification method based on double-branch frequency domain feature decoupling and a dynamic channel enhancement network. For the problem that the characteristics of a leather image are difficult to correctly extract due to the characteristics of complexity and non-uniformity of the leather image, a high and low frequency domain decoupling thought is introduced in the invention to extract main body and detail information respectively; a DCT (Discrete Cosine Transformation) guided high / low-frequency double-branch network is designed to respectively capture a local structure (high-frequency branch) of a microdefect and a continuity feature (low-frequency branch) of a macroscopic texture, a structure in which an SE (Square-and-Excitation) module is embedded in a feature fusion stage is designed, and channel weight and spatial position response are jointly optimized, so that the feature fusion is realized. And the significance expression of the key defect area is enhanced. In some public data sets, the method has better classification performance and higher diagnosis speed.

The invention relates to the technical field of intelligent power grid operation and maintenance, and discloses a power grid node health index assessment method suitable for remote operation and maintenance, and the method comprises the steps: S1, carrying out the fusion collection of multi-source data; s2, dynamic weight self-adaptive distribution is carried out; s3, dynamically correcting the life reference; step S4, layered health index calculation; s5, health state decision making and visualization are carried out; the data weight is automatically adjusted according to the timeliness difference of high and low frequency data through a dynamic weight adaptive distribution mechanism, so that the problem of health score long-term distortion possibly caused by low frequency data updating lag is solved, key parameters can be monitored in real time through a real-time anomaly detection and response mechanism, a parameter overrun early warning threshold value is set, and the early warning accuracy is improved. When the key parameters continuously exceed the limit, an online diagnosis mode is triggered, the data sampling frequency is increased, it is ensured that abnormal conditions can be quickly found and processed, the ability of the system to deal with sudden faults is improved, and the fault risk is reduced.

The invention relates to the technical field of network monitoring, in particular to a radio spectrum measurement system based on mobile crowdsourcing, which comprises a crowdsourcing acquisition module, a time sequence processing module, a space optimization module and a dynamic fusion module. According to the method, periodic scanning is combined with a multi-source heterogeneous data encapsulation protocol, timestamps, geographic coordinates, a signal-to-noise ratio and equipment voltage are integrated, multi-dimensional data standardized acquisition and dynamic association are achieved, time sequence weight parameters are calculated through a time sequence alignment algorithm, high and low frequency event distinction degree is enhanced, time noise is restrained, space weight coefficients are calculated through a DBSCAN algorithm, and multi-dimensional data acquisition and dynamic association are achieved. The method has the advantages that comprehensive credibility is generated through linear weighting, space-time coupling precision and evidence theory sorting credibility are improved, correlation between the credibility and the signal-to-noise ratio is analyzed, Kalman filtering is used for compensating voltage fluctuation, multi-source data fusion and error suppression are achieved, spectrum feature anti-interference capacity is enhanced, positioning deviation is reduced, and spectrum allocation efficiency and monitoring system stability are improved.

The invention discloses a high-precision frequency synchronization method based on cross-spectrum phase slope. The method comprises the following steps: a reference node sends a frequency information transmission signal; the common node receives the frequency information transmission signal; the common node performs mutual correlation with a reference signal generated by a local clock through matched filtering to obtain time delay information; the common node calculates a phase difference according to the time delay information and calibrates a local frequency according to the phase difference; wherein the time delay information is measured through a cross-spectrum phase slope method, and the cross-spectrum phase slope method calculates time delay according to the slope of a phase-frequency response curve of a matched filtering result. According to the high-precision frequency synchronization method based on the cross-spectrum phase slope, the problems of low phase discrimination resolution and low frequency offset resolution during frequency synchronization caused by low sampling frequency of a distributed system are solved.

The invention belongs to the technical field of noise reduction devices, and discloses an acoustic black hole combined noise reduction structure embedded with a micro-perforated structure. The device comprises an acoustic black hole cavity, a side cavity and a micro-perforated hole for communicating the acoustic black hole cavity with the side cavity, the acoustic black hole cavity comprises a thin plate array and an acoustic black hole cavity shell; the thin plate array is composed of a plurality of thin plates with round holes in the centers, the outer sides of the thin plates are fixed to the inner side of the acoustic black hole cavity shell, the thin plates are arranged at equal intervals, the centers of the thin plates are coaxial, the inner diameters of the thin plates are arranged in a monotonically decreasing or increasing mode, and an acoustic black hole structure is formed. The side cavity is located outside the acoustic black hole cavity and wraps the acoustic black hole cavity to form a closed space. The low-frequency, medium-frequency and high-frequency full-band noise reduction device is compact and simple in structure, convenient to use, diversified in form, capable of solving the noise reduction problem of low frequency, medium frequency and high frequency of 150 Hz or above, capable of improving the sound energy dissipation efficiency of system noise and wide in application prospect.

An electronic device performs sub-band decomposition on a to-be-coded audio to obtain a to-be-coded low frequency signal corresponding to a low frequency band and a to-be-coded high frequency signal corresponding to a high frequency band. The device performs compression coding on the to-be-coded low frequency signal to obtain low frequency coded data of the to-be-coded low frequency signal. The device determines high frequency prediction information according to the to-be-coded low frequency signal. The device performs feature extraction on the to-be-coded high frequency signal to obtain high frequency feature information. The device determines high frequency compensation information of the to-be-coded high frequency signal according to a difference between the high frequency feature information and the high frequency prediction information. The device also performs encapsulation processing on the low frequency coded data and the high frequency compensation information to obtain audio coded data of the to-be-coded audio.

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention provides a control method of a four-level hybrid clamping converter, and the core principle is to select a switching state which minimizes a cost function through a model predictive control (MPC) strategy. The topological structure and the working principle of the 4L-HCC converter are deeply researched, a traditional MPC control strategy is adopted, the charging and discharging process of the capacitors is accurately controlled, voltage fluctuation of the direct-current bus capacitor and the flying capacitor at low frequency and zero frequency is effectively restrained, safe operation of the capacitors, switches and other elements is guaranteed, and the stability and reliability of the converter are improved. By reasonably designing the cost function, the converter can output high-quality electric energy under different working conditions, the distortion of load voltage and current is reduced, the requirements of application scenes with high requirements on electric energy quality, such as motor driving and renewable energy power generation grid connection, are met, and the performance of the system is comprehensively improved.

The invention discloses an optical remote sensing satellite high and low frequency relative radiation error correction method and system, and the method comprises the steps: S1, carrying out the systematic high frequency radiation error calibration of a remote sensing image, so as to eliminate stripe noise; s2, performing frequency domain decomposition on the image after high-frequency correction, and separating high and low frequency components; s3, calculating a global statistical moment of the low-frequency component as a correction reference; s4, calculating a local statistical moment of the low-frequency component column by column; s5, calculating an independent correction coefficient for each column according to the global and local statistical moments; s6, correcting the low-frequency component by using the correction coefficient; and S7, recombining the corrected low-frequency component with the original high-frequency component to generate a final image. The method is suitable for improving the quality of the remote sensing image with multiple composite radiation errors, high-frequency stripes and low-frequency brightness gradient can be effectively eliminated, original texture details of ground features are reserved to the maximum extent in the correction process, and the radiation fidelity is guaranteed.

A system having the low frequency RF generator is described. The low frequency RF has an operating frequency range between 10 kilohertz (kHz) and 330 kHz. The low frequency RF generator generates an RF signal. The system further includes an impedance matching circuit coupled to the low frequency RF generator for receiving the RF signal. The impedance matching circuit modifies an impedance of the RF signal to output a modified RF signal. The system includes a plasma chamber coupled to the RF generator for receiving the modified RF signal. The plasma chamber includes a chuck having a dielectric layer and a base metal layer. The dielectric layer is located on top of the base metal layer. The dielectric layer has a bottom surface, and the base metal layer has a top surface. The base metal layer has a porous plug and the bottom surface of the dielectric layer has a portion that is in contact with the porous plug.

The invention discloses a lightweight single-image super-resolution reconstruction method based on local and global feature collaborative enhancement perception, which is used for solving the problems of insufficient utilization of high and low frequency clues in single-image super-resolution, low feature fusion efficiency and difficulty in consideration of structural consistency and visual fidelity of reconstructed images. The network adopts a double-flow heterogeneous architecture; a local branch uses multi-type differential convolution explicit coding image edge and texture prior to enhance detail characterization capability; the global branch effectively models long-range dependency and low-frequency semantic information by integrating local, cross-regional and global multi-level spatial self-attention mechanisms. The frequency sensing fusion module provided by the invention generates a channel specific space weight based on frequency characteristics, self-adaptively fuses double-branch characteristics, accurately balances structure maintenance and detail enhancement requirements in a characteristic fusion process, and effectively reduces characteristic redundancy. According to the super-resolution network, the model complexity is remarkably reduced, high-resolution images which are consistent in structure, vivid in vision and rich in details can be generated while lightweight design is kept, and an effective scheme is provided for efficient and high-performance lightweight single-image super-resolution reconstruction model design.

The invention discloses a combined denoising and demosaicing method for a low-exposure original image under low light, and the method is divided into two stages: in the first stage, introducing a high and low frequency fusion module based on wavelet transform, and generating relatively clean pseudo long exposure data to reduce noise; and in the second stage, through a color space attention subnet, image reconstruction is carried out by using the pseudo long exposure data generated in the first stage, the color is recovered, and texture details are enhanced. In addition, the invention also provides a wavelet loss function to balance detail recovery and noise suppression, and discusses the influence of three different training strategies on the performance of the model. Experimental results show that the method provided by the invention is superior to most advanced algorithms in the aspects of quantitative indexes, color accuracy, detail retention and the like.

The invention discloses an OCT (Optical Coherence Tomography) retina image denoising method based on a high-frequency enhanced diffusion model, which is characterized in that a double-branch diffusion model network constructed based on frequency perception Fourier transform attention (FFTA) is used for separating different frequency domains, enhancing specific frequency domain features and fusing the specific frequency domain features into a spatial domain feature map to realize stronger frequency domain perception and processing capability; the invention relates to a time step T coding method for a module of frequency domain attention. The time step T of the diffusion model is used as a parameter to be coded into a frequency domain attention module to participate in self-attention matrix calculation, so that frequency domain feature processing is aligned with the iteration step number of the diffusion model, and different frequency domain information is pointedly processed at different time steps T; the frequency selective hopping mechanism uses pooling operation to obtain a high / low frequency characteristic pattern, so that the network has adaptive frequency domain retention capability for different local parts.

The embodiment of the invention discloses a load transient enhancement circuit of a voltage mode DCDC converter, which is characterized in that a traditional 3-type compensation network is improved, a zero-point adjustable compensation network is generated by a compensation transconductance amplifier to carry out loop compensation, and then zero-point adjustment is carried out by detecting output voltage. When the output voltage exceeds the threshold due to the overshoot current, the transconductance can be increased through the change of the tail current, so that the zero point moves to the low frequency, the bandwidth can be increased, and the transient response capability is improved. According to the embodiment of the invention, a zero point adjustable compensation network is generated by adding a feedback path to compensate the loop, and the adjustable zero point is adjusted by detecting the output voltage. And when the load jumps, the bandwidth is increased to low frequency through zero point shift, so that the load response capability is enhanced, and the overshoot of the output voltage is reduced.

The invention belongs to the technical field of metal crack detection, and discloses a metal microcrack ultrasonic detection method, which comprises the following modules: an ultrahigh frequency composite array probe with a center frequency dynamic adjustable range of 5-50 MHz; the dual-mode excitation and receiving module is used for independently generating a pulse reflection mode excitation signal and a diffraction mode chirp frequency modulation signal; the phase-controlled synthetic aperture dynamic focusing module is used for realizing + / -60-degree deflection and sub-wavelength level focusing of acoustic beams; the nonlinear harmonic enhancement module is used for exciting a crack nonlinear effect by superposing 1-2MHz fundamental waves; and a time-space-frequency three-domain data fusion module. According to the method, signal-to-noise ratio collapse caused by the diffraction effect in a traditional method is overcome, more importantly, the system has low-frequency penetrability and high-frequency resolution at the same time due to 80%-120% of broadband coverage of chirp frequency modulation signals, the diffraction signal gain is increased by 20 dB or above, a near-surface blind area is compressed to be within 0.2 mm, and the system can be applied to the field of radar imaging. And the core pain point of missing detection of the near-surface microcracks of the thin-wall component is solved.

The invention discloses a video crowd counting method based on a cascaded cross-domain feature interaction network. The method comprises the following steps: carrying out data enhancement processing of random cutting and horizontal flipping on a current frame and front and back frames of the current frame; and constructing a cross-domain feature interaction network composed of a spatial domain branch and a frequency domain branch. The frequency domain branch extracts frequency domain feature output of different stages through a high and low frequency signal aggregation module and a feature encoder based on adjacent frames; the spatial domain branch is based on a single-frame image, and static spatial semantic features are extracted through a feature encoder. Cascade fusion is carried out on the double-branch features on multiple scales, two-way channel cross attention is utilized to reconstruct time sequence correlation frequency domain features of a current frame, and fusion and reconstruction of the two domain features are achieved through a cross-domain feature mutual modulation module. And after the reconstructed double-branch features are processed by the fusion network, outputting a crowd density map of the current frame by a density regression head. And after training is completed, storing the optimal model for video crowd counting. According to the invention, through cross-domain feature cascade and bidirectional time sequence modeling, the accuracy and robustness of crowd counting in a video scene are effectively improved.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) phase filtering method based on a plurality of interferograms, which comprises the following steps of: preprocessing acquired main and auxiliary SAR images to obtain the plurality of InSAR interferograms; dividing the plurality of InSAR interferograms into square interferogram patches along the azimuth direction and the distance direction; converting each interferogram patch from a spatial domain into a frequency domain signal, and constructing a corresponding frequency spectrum amplitude diagram; using a K-means clustering method to cluster and extract an initial low-pass filter in the spectrum amplitude diagram, and constructing a phase filter together with a Kaiser window function; the frequency domain signals of the interferogram plaques pass through a phase filter, the signal frequency band power is reserved, the noise at the high-frequency position is filtered out, meanwhile, the noise at the low-frequency position is filtered out through smoothing operation, and the filtered frequency domain signals are obtained; and the filtered frequency domain signals are converted to a spatial domain, filtered interferogram patches are obtained and spliced, and a filtered InSAR interferogram is obtained. The method is beneficial to popularization and promotion of the development of the current time sequence InSAR interferometry technology and the application in engineering.

The invention belongs to the technical field of artificial intelligence and data analysis, and discloses a time sequence prediction method and device based on wavelet transform and a large language model. The time sequence prediction method comprises the following steps: obtaining known historical time sequence data; based on the obtained known historical time sequence data, performing time sequence prediction in a future time period by using the trained time sequence prediction model to obtain predicted values of three frequency domain components of low frequency, intermediate frequency and high frequency; adding the obtained predicted values of the low frequency domain component, the intermediate frequency domain component and the high frequency domain component, and carrying out inverse normalization processing on the added result to obtain a final time sequence prediction result; the time sequence prediction model comprises a wavelet decomposition module, a backbone network of a pre-training large language model and a prediction head. According to the technical scheme disclosed by the invention, the accuracy of time sequence prediction can be remarkably improved.

The invention relates to the technical field of data compression, in particular to a ZSTD compression algorithm optimization method based on joint coding and ternary decomposition, and the method comprises the following steps: S1, building a joint frequency table based on the joint distribution of the literal length and the matching length of a previous section, setting a frequency deviation criterion, and dynamically adjusting a low-frequency combination proportion; s2, decomposing the offset value structure into a leading segment, a trailing segment and a middle segment, performing bit width-entropy ratio analysis on the middle segment, and selecting a symbol index or bit stream coding; and S3, entropy discriminant coding is performed on the joint symbol and the offset symbol respectively, and a structure position is mapped synchronously to generate a structured compressed output stream. According to the method, collaborative optimization of frequency self-adaption, redundancy minimization and structured mapping is realized through joint symbol dynamic modeling and offset ternary structure entropy discriminant coding, so that the ZSTD compression efficiency and the coding stability are remarkably improved.

The invention discloses a blurred image segmentation method based on a mixed diffusion model, and relates to the technical field of artificial intelligence image processing and computer vision. The invention provides a novel fast, efficient and accurate hybrid denoising segmentation model HAD-Net. The HAD-Net is an end-to-end architecture composed of a diffusion model and a segmentation model; wherein underlying distribution of data is learned by using a diffusion model, and noise distribution is fully captured through a Markov chain, so that an image is denoised in a reasoning stage; a clear denoised image is used as prior input to a segmentation model, a feature encoder containing wavelet convolution is designed, and high and low frequency information is fully extracted through wavelet convolution to enable a prior image to realize guide segmentation; reasonable construction of the overall architecture greatly reduces training cost, and reasonable network depth and internal module design solve the problem of low segmentation precision caused by too large difference between morphological distribution and size of segmentation targets.

The invention discloses a current source type LCCL double-frequency resonance system and a parameter matching method thereof, which are suitable for synchronous double-frequency induction quenching of complex workpieces such as gears. The system is composed of two current type resonant inverters working at a low frequency and a high frequency respectively and a dual-frequency LCCL resonant cavity, and dual-frequency current superposition is realized through parallel output. The double-frequency LCCL resonant cavity has two parallel resonance points, a mode based on simplified topology is adopted to simplify parameter design, and meanwhile design errors are maintained at a low level. A power matching coefficient is introduced, the ratio of high-frequency output power to low-frequency output power is adjusted by adjusting the value of the power matching coefficient, and the problem of double-frequency power unbalance caused by frequency variation characteristics of load equivalent resistance and the like is solved. The superposed double-frequency current forms a low-frequency envelope embedded high-frequency oscillation composite waveform on the induction heating coil, cooperative control of tooth root deep quenching (low frequency) and tooth surface hard quenching (high frequency) is achieved, and the method has the advantages of being simple in parameter design, high in power decoupling performance, high in operation stability and the like.

The invention discloses a high and steep slope rock mass stability intelligent monitoring system and monitoring method, and relates to the technical field of intelligent monitoring, and the method comprises the steps: 1, collecting images, vibration and inclinometer data in real time, and carrying out comprehensive analysis to generate a landslide coefficient; 2, if the coefficient exceeds a threshold value, triggering a first-level alarm, otherwise, entering trend analysis; and step 3, judging the trend speed through the ratio of the low frequency to the high frequency of the vibration signal and the time slope, triggering secondary early warning when the trend speed exceeds a threshold value, or else, circularly monitoring. Vector conversion and included angle cosine are matched with a landslide direction threshold value, effective deformation is quantified by combining bending radius and stress analysis, multidirectional bending interference of an inclinometer is eliminated, displacement direction measurement precision is improved, early warning errors are reduced, a trend variable is constructed by utilizing a ratio of low-frequency amplitude to high-frequency amplitude, trend speed is analyzed by combining time sequence slope, and the method has the advantages of being high in accuracy and high in accuracy. The rock body loosening sign is comprehensively pre-judged, early warning is achieved, and the sudden landslide risk is reduced.